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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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12 pages, 2091 KiB  
Article
Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization
by Sanghyeop Lee, Junyeob Kim, Hyeon Kang, Do-Young Kang and Jangsik Park
Appl. Sci. 2021, 11(2), 744; https://doi.org/10.3390/app11020744 - 14 Jan 2021
Cited by 59 | Viewed by 8172
Abstract
Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of [...] Read more.
Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of these technologies is very difficult. The main reason for this problem is that the performance of the Convolutional Neural Networks (CNN) differ greatly depending on the statistical distribution of the input dataset. Different hyperparameters also greatly affect the convergence of the CNN models. With this amount of information, selecting appropriate parameters for the network structure has became a large research area. Genetic Algorithm (GA), is a very popular technique to automatically select a high-performance network architecture. In this paper, we show the possibility of optimising the network architecture using GA, where its search space includes both network structure configuration and hyperparameters. To verify the performance of our Algorithm, we used an amyloid brain image dataset that is used for Alzheimer’s disease diagnosis. As a result, our algorithm outperforms Genetic CNN by 11.73% on a given classification task. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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14 pages, 2303 KiB  
Article
The Effect of Chairside Verbal Instructions Matched with Instagram Social Media on Oral Hygiene of Young Orthodontic Patients: A Randomized Clinical Trial
by Andrea Scribante, Simone Gallo, Karin Bertino, Stefania Meles, Paola Gandini and Maria Francesca Sfondrini
Appl. Sci. 2021, 11(2), 706; https://doi.org/10.3390/app11020706 - 13 Jan 2021
Cited by 30 | Viewed by 4216
Abstract
Objective: To investigate the effectiveness of Instagram in improving oral hygiene compliance and knowledge in young orthodontic patients compared to traditional chairside verbal instructions. Design: Single-center, parallel, randomized controlled trial. Setting: Section of Dentistry of University of Pavia. Participants: 40 patients having fixed [...] Read more.
Objective: To investigate the effectiveness of Instagram in improving oral hygiene compliance and knowledge in young orthodontic patients compared to traditional chairside verbal instructions. Design: Single-center, parallel, randomized controlled trial. Setting: Section of Dentistry of University of Pavia. Participants: 40 patients having fixed appliances in both arches were recruited and randomly divided into an intervention (n = 20) and a control group (n = 20). Intervention: At a first appointment, both groups were given verbal instructions and motivated to oral hygiene. In addition, multimedia contents on Instagram were sent weekly to trial participants for six months. Main outcome measures: For all participants, the bleeding index (BI), modified gingival index (MGI), and plaque index (PI) were assessed at baseline (T0), after one (T1), three (T2), and six months (T3). A questionnaire was administered at the beginning (T0) and at the end of the study (T3) to assess participants’ knowledge. Results: In both groups, BI, MGI, and PI significantly decreased (p < 0.05) at T1 (means control group: BI 0.26 ± 0.22, MGI 0.77 ± 0.36, PI 0.53 ± 0.20; means test group: BI 0.24 ± 0.22, MGI 0.65 ± 0.46, PI 0.49 ± 0.21) compared to baseline (means control group: BI 0.56 ± 0.27, MGI 1.23 ± 0.41, PI 0.87 ± 0.23; means test group: BI 0.54 ± 0.26, MGI 1.18 ± 0.39, PI 0.93 ± 0.20) but no significant differences in clinical measures were showed between T1, T2, and T3 (p > 0.05) (intragroup differences). Trial patients demonstrated significant improvements in knowledge with respect to controls comparing scores at T0 and T3 (p < 0.05) but despite this result in the test group clinical outcomes did not report significant intergroup differences at any time (p > 0.05). Conclusions: Presenting multimedia information through Instagram resulted in a significant improvement in knowledge. Therefore, this social media represents an aid to the standard verbal motivation performed by orthodontists towards young patients under an orthodontic treatment. Full article
(This article belongs to the Special Issue Clinical Applications for Dentistry and Oral Health)
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14 pages, 1238 KiB  
Article
Evaluation of the Reaction Time and Accuracy Rate in Normal Subjects, MCI, and Dementia Using Serious Games
by Yen-Ting Chen, Chun-Ju Hou, Natan Derek, Shuo-Bin Huang, Min-Wei Huang and You-Yu Wang
Appl. Sci. 2021, 11(2), 628; https://doi.org/10.3390/app11020628 - 11 Jan 2021
Cited by 19 | Viewed by 4601
Abstract
The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n1 = 16), MCI (Mild Cognitive [...] Read more.
The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n1 = 16), MCI (Mild Cognitive Impairment) (n2 = 10), and dementia—moderate-to-severe (n3 = 11) groups based on the MMSE (Mini Mental State Examination). Two serious games were designed: (1) whack-a-mole and (2) hit-the-ball. Two dependent variables, reaction time and accuracy rate, were statistically analyzed to compare elders’ performances in the games among the three groups for three levels of speed: slow, medium, and fast. There were significance differences between the normal group, the MCI group, and the moderate-to-severe dementia group in both the reaction-time and accuracy-rate analyses. We determined that the reaction times of the MCI and dementia groups were shorter compared to those of the normal group, with poorer results also observed in accuracy rate. Therefore, we conclude that our serious games have the feasibility to evaluate reaction performance and could be used in the daily lives of elders followed by clinical treatment in the future. Full article
(This article belongs to the Special Issue Serious Games and Mixed Reality Applications for Healthcare)
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21 pages, 1523 KiB  
Review
A Review on Recent Advancements of Graphene and Graphene-Related Materials in Biological Applications
by Federica Catania, Elena Marras, Mauro Giorcelli, Pravin Jagdale, Luca Lavagna, Alberto Tagliaferro and Mattia Bartoli
Appl. Sci. 2021, 11(2), 614; https://doi.org/10.3390/app11020614 - 10 Jan 2021
Cited by 87 | Viewed by 8643
Abstract
Graphene is the most outstanding material among the new nanostructured carbonaceous species discovered and produced. Graphene’s astonishing properties (i.e., electronic conductivity, mechanical robustness, large surface area) have led to a deep change in the material science field. In this review, after a brief [...] Read more.
Graphene is the most outstanding material among the new nanostructured carbonaceous species discovered and produced. Graphene’s astonishing properties (i.e., electronic conductivity, mechanical robustness, large surface area) have led to a deep change in the material science field. In this review, after a brief overview of the main characteristics of graphene and related materials, we present an extensive overview of the most recent achievements in biological uses of graphene and related materials. Full article
(This article belongs to the Special Issue Graphene Growth and Its Nanostructuring)
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22 pages, 6553 KiB  
Article
Nonlinear Dynamic Response of a Precast Concrete Building to Sudden Column Removal
by Simone Ravasini, Beatrice Belletti, Emanuele Brunesi, Roberto Nascimbene and Fulvio Parisi
Appl. Sci. 2021, 11(2), 599; https://doi.org/10.3390/app11020599 - 9 Jan 2021
Cited by 26 | Viewed by 3927
Abstract
Robustness of reinforced concrete (RC) structures is an ongoing challenging research topic in the engineering community. During an extreme event, the loss of vertical load-bearing elements can activate large-deformation resisting mechanisms such as membrane and catenary actions in beams and floor slabs of [...] Read more.
Robustness of reinforced concrete (RC) structures is an ongoing challenging research topic in the engineering community. During an extreme event, the loss of vertical load-bearing elements can activate large-deformation resisting mechanisms such as membrane and catenary actions in beams and floor slabs of cast-in-situ RC buildings to resist gravity loads. However, few studies have been conducted for precast concrete (PC) buildings, especially focused on the capacity of such structures to withstand column loss scenarios, which mainly relies on connection strength. Additional resistance resource and alternate load paths could be reached via tying systems. In this paper, the progressive collapse resistance of a PC frame building is analyzed by means of nonlinear dynamic finite element analyses focusing on the fundamental roles played by beam-to-column connection strength and tying reinforcement. A simplified modelling approach is illustrated in order to investigate the response of such a structural typology to a number of sudden column-removal scenarios. The relative simplicity of the modelling technique is considered useful for engineering practice, providing new input for further research in this field. Full article
(This article belongs to the Special Issue Structural Reliability of RC Frame Buildings)
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19 pages, 989 KiB  
Article
Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms
by Amirreza Kandiri, Farid Sartipi and Mahdi Kioumarsi
Appl. Sci. 2021, 11(2), 485; https://doi.org/10.3390/app11020485 - 6 Jan 2021
Cited by 72 | Viewed by 4357
Abstract
Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject [...] Read more.
Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other. Full article
(This article belongs to the Section Civil Engineering)
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12 pages, 3543 KiB  
Article
Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning
by Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ryu Matsuoka, Suguru Yasutomi, Kanto Shozu, Ai Dozen, Hidenori Machino, Hirokazu Hidaka, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Akihiko Sekizawa and Ryuji Hamamoto
Appl. Sci. 2021, 11(1), 371; https://doi.org/10.3390/app11010371 - 2 Jan 2021
Cited by 81 | Viewed by 11325
Abstract
Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences [...] Read more.
Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of “explainable AI.” Full article
(This article belongs to the Special Issue Machine Learning in Medical Applications)
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18 pages, 1173 KiB  
Article
A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety
by Juan Jesús Roldán-Gómez, Eduardo González-Gironda and Antonio Barrientos
Appl. Sci. 2021, 11(1), 363; https://doi.org/10.3390/app11010363 - 1 Jan 2021
Cited by 80 | Viewed by 14615
Abstract
Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry [...] Read more.
Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm. Full article
(This article belongs to the Special Issue Multi-Robot Systems: Challenges, Trends and Applications)
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15 pages, 6685 KiB  
Article
Development of 340-GHz Transceiver Front End Based on GaAs Monolithic Integration Technology for THz Active Imaging Array
by Yang Liu, Bo Zhang, Yinian Feng, Xiaolin Lv, Dongfeng Ji, Zhongqian Niu, Yilin Yang, Xiangyang Zhao and Yong Fan
Appl. Sci. 2020, 10(21), 7924; https://doi.org/10.3390/app10217924 - 9 Nov 2020
Cited by 71 | Viewed by 4686
Abstract
Frequency multipliers and mixers based on Schottky barrier diodes (SBDs) are widely used in terahertz (THz) imaging applications. However, they still face obstacles, such as poor performance consistency caused by discrete flip-chip diodes, as well as low efficiency and large receiving noise temperature. [...] Read more.
Frequency multipliers and mixers based on Schottky barrier diodes (SBDs) are widely used in terahertz (THz) imaging applications. However, they still face obstacles, such as poor performance consistency caused by discrete flip-chip diodes, as well as low efficiency and large receiving noise temperature. It is very hard to meet the requirement of multiple channels in THz imaging array. In order to solve this problem, 12-μm-thick gallium arsenide (GaAs) monolithic integrated technology was adopted. In the process, the diode chip shared the same GaAs substrate with the transmission line, and the diode’s pads were seamlessly connected to the transmission line without using silver glue. A three-dimensional (3D) electromagnetic (EM) model of the diode chip was established in Ansys High Frequency Structure Simulator (HFSS) to accurately characterize the parasitic parameters. Based on the model, by quantitatively analyzing the influence of the surface channel width and the diode anode junction area on the best efficiency, the final parameters and dimensions of the diode were further optimized and determined. Finally, three 0.34 THz triplers and subharmonic mixers (SHMs) were manufactured, assembled, and measured for demonstration, all of which comprised a waveguide housing, a GaAs circuit integrated with diodes, and other external connectors. Experimental results show that all the triplers and SHMs had great performance consistency. Typically, when the input power was 100 mW, the output power of the THz tripler was greater than 1 mW in the frequency range of 324 GHz to 352 GHz, and a peak efficiency of 6.8% was achieved at 338 GHz. The THz SHM exhibited quite a low double sideband (DSB) noise temperature of 900~1500 K and a DSB conversion loss of 6.9~9 dB over the frequency range of 325~352 GHz. It is indicated that the GaAs monolithic integrated process, diodes modeling, and circuits simulation method in this paper provide an effective way to design THz frequency multiplier and mixer circuits. Full article
(This article belongs to the Special Issue Terahertz Sensing and Imaging)
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19 pages, 1993 KiB  
Review
The Impact of Drought in Plant Metabolism: How to Exploit Tolerance Mechanisms to Increase Crop Production
by Dhriti Kapoor, Savita Bhardwaj, Marco Landi, Arti Sharma, Muthusamy Ramakrishnan and Anket Sharma
Appl. Sci. 2020, 10(16), 5692; https://doi.org/10.3390/app10165692 - 17 Aug 2020
Cited by 366 | Viewed by 20117
Abstract
Plants are often exposed to unfavorable environmental conditions, for instance abiotic stresses, which dramatically alter distribution of plant species among ecological niches and limit the yields of crop species. Among these, drought stress is one of the most impacting factors which alter seriously [...] Read more.
Plants are often exposed to unfavorable environmental conditions, for instance abiotic stresses, which dramatically alter distribution of plant species among ecological niches and limit the yields of crop species. Among these, drought stress is one of the most impacting factors which alter seriously the plant physiology, finally leading to the decline of the crop productivity. Drought stress causes in plants a set of morpho-anatomical, physiological and biochemical changes, mainly addressed to limit the loss of water by transpiration with the attempt to increase the plant water use efficiency. The stomata closure, one of the first consistent reactions observed under drought, results in a series of consequent physiological/biochemical adjustments aimed at balancing the photosynthetic process as well as at enhancing the plant defense barriers against drought-promoted stress (e.g., stimulation of antioxidant systems, accumulation of osmolytes and stimulation of aquaporin synthesis), all representing an attempt by the plant to overcome the unfavorable period of limited water availability. In view of the severe changes in water availability imposed by climate change factors and considering the increasing human population, it is therefore of outmost importance to highlight: (i) how plants react to drought; (ii) the mechanisms of tolerance exhibited by some species/cultivars; and (iii) the techniques aimed at increasing the tolerance of crop species against limited water availability. All these aspects are necessary to respond to the continuously increasing demand for food, which unfortunately parallels the loss of arable land due to changes in rainfall dynamics and prolonged period of drought provoked by climate change factors. This review summarizes the most updated findings on the impact of drought stress on plant morphological, biochemical and physiological features and highlights plant mechanisms of tolerance which could be exploited to increase the plant capability to survive under limited water availability. In addition, possible applicative strategies to help the plant in counteracting unfavorable drought periods are also discussed. Full article
(This article belongs to the Special Issue Plant Response to Arid Environment)
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18 pages, 7008 KiB  
Article
Incorporation of Bioactive Glasses Containing Mg, Sr, and Zn in Electrospun PCL Fibers by Using Benign Solvents
by Rachele Sergi, Valeria Cannillo, Aldo R. Boccaccini and Liliana Liverani
Appl. Sci. 2020, 10(16), 5530; https://doi.org/10.3390/app10165530 - 10 Aug 2020
Cited by 27 | Viewed by 3858
Abstract
Poly(ε-caprolactone) (PCL) and PCL/bioactive glass composite fiber mats were produced by electrospinning technique. To improve cell adhesion and proliferation (i) 45S5, (ii) a bioactive glass containing strontium and magnesium oxides, and (iii) a bioactive glass containing zinc oxide were separately added to the [...] Read more.
Poly(ε-caprolactone) (PCL) and PCL/bioactive glass composite fiber mats were produced by electrospinning technique. To improve cell adhesion and proliferation (i) 45S5, (ii) a bioactive glass containing strontium and magnesium oxides, and (iii) a bioactive glass containing zinc oxide were separately added to the starting PCL solution before electrospinning. A good incorporation of bioactive glass particles in PCL electrospun mats was confirmed by SEM and FTIR analyses. Bioactivity was evaluated by immersion of PCL mats and PCL/bioactive glass electrospun fiber mats in simulated body fluid (SBF). Bone murine stromal cells (ST-2) were employed in WST-8 assay to assess cell viability, cell morphology, and proliferation. The results showed that the presence of bioactive glass particles in the fibers enhances cell adhesion and proliferation compared to neat PCL mats. Furthermore, PCL/bioactive glass electrospun mats showed higher wound-healing rate (measured as cell migration rate) in vitro compared to neat PCL electrospun mats. Therefore, the characteristics of the PCL matrix combined with biological properties of bioactive glasses make PCL/bioactive glass composite ideal candidate for biomedical application. Full article
(This article belongs to the Special Issue Novel Insights in Fabrication of Scaffolds Using Electrospinning and Electrochemical / Electrophoretic Depositions)
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31 pages, 3546 KiB  
Article
A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future
by Adrien Bécue, Eva Maia, Linda Feeken, Philipp Borchers and Isabel Praça
Appl. Sci. 2020, 10(13), 4482; https://doi.org/10.3390/app10134482 - 28 Jun 2020
Cited by 109 | Viewed by 18768
Abstract
In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on [...] Read more.
In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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14 pages, 970 KiB  
Article
Levels and Changes of Physical Activity in Adolescents during the COVID-19 Pandemic: Contextualizing Urban vs. Rural Living Environment
by Natasa Zenic, Redha Taiar, Barbara Gilic, Mateo Blazevic, Dora Maric, Haris Pojskic and Damir Sekulic
Appl. Sci. 2020, 10(11), 3997; https://doi.org/10.3390/app10113997 - 9 Jun 2020
Cited by 139 | Viewed by 15213
Abstract
The COVID-19 pandemic and the social distancing implemented shortly after influence physical activity levels (PALs). The purpose of this investigation was to evaluate the changes in PAL and factors associated with PALs among Croatian adolescents while considering the impact of community (urban vs. [...] Read more.
The COVID-19 pandemic and the social distancing implemented shortly after influence physical activity levels (PALs). The purpose of this investigation was to evaluate the changes in PAL and factors associated with PALs among Croatian adolescents while considering the impact of community (urban vs. rural living environment). The sample included 823 adolescents (mean age: 16.5 ± 2.1 years) who were tested on baseline (from October 2019 to March 2020; before COVID-19 pandemic in Croatia) and follow-up (in April 2020; during the COVID-19 pandemic and imposed rules of social distancing). Baseline testing included anthropometrics, physical fitness status, and evaluation of PALs, while follow-up included only PALs (evaluated by a standardized questionnaire through an internet application). The results showed a significant influence of the living environment on the decrease of PAL, with a larger decrease in urban adolescents. Logistic regression showed a higher likelihood for normal PALs at baseline in adolescents who had better fitness status, with no strong confounding effect of the urban/rural environment. The fitness status of urban adolescents predicted their PALs at follow-up. The differences between urban and rural adolescents with regard to the established changes in PALs and relationships between the predictors and PALs are explained by the characteristics of the living communities (lack of organized sports in rural areas), and the level of social distancing in the studied period and region/country. Full article
(This article belongs to the Special Issue COVID-19: Impact on Human Health and Behavior)
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15 pages, 688 KiB  
Article
COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
by Vasilis Papastefanopoulos, Pantelis Linardatos and Sotiris Kotsiantis
Appl. Sci. 2020, 10(11), 3880; https://doi.org/10.3390/app10113880 - 3 Jun 2020
Cited by 120 | Viewed by 17068
Abstract
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow [...] Read more.
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future. Full article
(This article belongs to the Special Issue 10th Anniversary of Applied Sciences: Invited Papers in Computing and Artificial Intelligence Section)
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12 pages, 3068 KiB  
Article
Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings
by Arash Moradzadeh, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo and Amjad Anvari-Moghaddam
Appl. Sci. 2020, 10(11), 3829; https://doi.org/10.3390/app10113829 - 31 May 2020
Cited by 109 | Viewed by 4817
Abstract
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, [...] Read more.
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies. Full article
(This article belongs to the Special Issue Renewable Energy Systems 2020)
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17 pages, 499 KiB  
Review
Trends in Biodiesel Production from Animal Fat Waste
by Fidel Toldrá-Reig, Leticia Mora and Fidel Toldrá
Appl. Sci. 2020, 10(10), 3644; https://doi.org/10.3390/app10103644 - 25 May 2020
Cited by 127 | Viewed by 15784
Abstract
The agro-food industry generates large amounts of waste that contribute to environmental contamination. Animal fat waste constitutes some of the most relevant waste and the treatment of such waste is quite costly because environmental regulations are quite strict. Part of such costs might [...] Read more.
The agro-food industry generates large amounts of waste that contribute to environmental contamination. Animal fat waste constitutes some of the most relevant waste and the treatment of such waste is quite costly because environmental regulations are quite strict. Part of such costs might be reduced through the generation of bioenergy. Biodiesel constitutes a valid renewable source of energy because it is biodegradable, non-toxic and has a good combustion emission profile and can be blended up to 20% with fossil diesel for its use in many countries. Furthermore, up to 70% of the total cost of biodiesel majorly depends on the cost of the raw materials used, which can be reduced using animal fat waste because they are cheaper than vegetable oil waste. In fact, 6% of total feedstock corresponded to animal fat in 2019. Transesterification with alkaline catalysis is still preferred at industrial plants producing biodiesel. Recent developments in heterogeneous catalysts that can be easily recovered, regenerated and reused, as well as immobilized lipases with increased stability and resistance to alcohol denaturation, are promising for future industrial use. This manuscript reviews the available processes and recent advances for biodiesel generation from animal fat waste. Full article
(This article belongs to the Special Issue New Trends in Environmental Engineering, Agriculture, Food Production, and Analysis)
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29 pages, 2044 KiB  
Review
Biochar for Wastewater Treatment—Conversion Technologies and Applications
by Ghizlane Enaime, Abdelaziz Baçaoui, Abdelrani Yaacoubi and Manfred Lübken
Appl. Sci. 2020, 10(10), 3492; https://doi.org/10.3390/app10103492 - 18 May 2020
Cited by 278 | Viewed by 18096
Abstract
Biochar as a stable carbon-rich material shows incredible potential to handle water/wastewater contaminants. Its application is gaining increasing interest due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. The efficacy of biochar to remove organic [...] Read more.
Biochar as a stable carbon-rich material shows incredible potential to handle water/wastewater contaminants. Its application is gaining increasing interest due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. The efficacy of biochar to remove organic and inorganic pollutants depends on its surface area, pore size distribution, surface functional groups, and the size of the molecules to be removed, while the physical architecture and surface properties of biochar depend on the nature of feedstock and the preparation method/conditions. For instance, pyrolysis at high temperatures generally produces hydrophobic biochars with higher surface area and micropore volume, allowing it to be more suitable for organic contaminants sorption, whereas biochars produced at low temperatures own smaller pore size, lower surface area, and higher oxygen-containing functional groups and are more suitable to remove inorganic contaminants. In the field of water/wastewater treatment, biochar can have extensive application prospects. Biochar have been widely used as an additive/support media during anaerobic digestion and as filter media for the removal of suspended matter, heavy metals and pathogens. Biochar was also tested for its efficiency as a support-based catalyst for the degradation of dyes and recalcitrant contaminants. The current review discusses on the different methods for biochar production and provides an overview of current applications of biochar in wastewater treatment. Full article
(This article belongs to the Special Issue Biochar for the Environmental Wastewater Treatment)
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22 pages, 4406 KiB  
Review
Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review
by Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Adele Parmentola
Appl. Sci. 2020, 10(8), 2897; https://doi.org/10.3390/app10082897 - 22 Apr 2020
Cited by 78 | Viewed by 10793
Abstract
Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable [...] Read more.
Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable principles that are based on the concept of circular economy. Digital innovation enables a circular economy model, promoting the use of solutions like digital platforms, smart devices, and artificial intelligence that help to optimize resources. Thus, the purpose of the research is to present a systematic literature review on what enabling technologies can promote new circular business models. A total of 31 articles were included in the study. Our results showed that realization of the circular economy involved two main changes: (i) managerial changes and (ii) legislative changes. Furthermore, the creation of the circular economy can certainly be facilitated by innovation, especially through the introduction of new technologies and through the introduction of digital innovations. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)
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19 pages, 5683 KiB  
Article
Automatically Processing IFC Clipping Representation for BIM and GIS Integration at the Process Level
by Junxiang Zhu, Peng Wu, Mengcheng Chen, Mi Jeong Kim, Xiangyu Wang and Tingchen Fang
Appl. Sci. 2020, 10(6), 2009; https://doi.org/10.3390/app10062009 - 15 Mar 2020
Cited by 90 | Viewed by 9035
Abstract
The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS [...] Read more.
The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS integrated application comes from the fundamental data conversion, especially for the geometric information. BIM and GIS use different modeling paradigms to represent objects. The BIM dataset takes, for example, Industry Foundation Classes (IFC) that use solid models, such as boundary representation (B-Rep), swept solid, constructive solid geometry (CSG), and clipping, while the GIS dataset mainly uses surface models or B-Rep. The fundamental data conversion between BIM and GIS is the foundation of BIM and GIS integrated application. However, the efficiency of data conversion has been greatly impaired by the human intervention needed, especially for the conversion of the clipping geometry. The goal of this study is to automate the conversion of IFC clipping representation into the shapefile format. A process-level approach was developed with an algorithm for instantiating unbounded half spaces using B-Rep. Four IFC models were used to validate the proposed method. The results show that (1) the proposed approach can successfully automate the conversion of IFC clipping representation into the shapefile format; and (2) increasing boundary size has no effect on the file size of unbounded half spaces, but slightly increases the producing time of half spaces and processing time of building components. The efficiency of this study can be further improved by using an open-source package, instead of using the low-efficiency packages provided by ArcGIS. Full article
(This article belongs to the Special Issue BIM and GIS Integration for Driving Smarter Decisions)
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16 pages, 4404 KiB  
Article
Real-Time Remote Maintenance Support Based on Augmented Reality (AR)
by Dimitris Mourtzis, Vasileios Siatras and John Angelopoulos
Appl. Sci. 2020, 10(5), 1855; https://doi.org/10.3390/app10051855 - 8 Mar 2020
Cited by 114 | Viewed by 11295
Abstract
In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended [...] Read more.
In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended Reality (XR) have become mainstream including Augmented Reality (AR). Furthermore, Cloud Computing has enabled the provision of high-quality services, especially in the controversial field of maintenance. However, since modern machines are becoming more complex, maintenance must be carried out from experienced and well-trained personnel, while overseas support is timely and financially costly. Although AR is a back-bone technology facilitating the development of robust maintenance support tools, they are limited to the provision of predefined scenarios, covering only a limited number of scenarios. This research work aims to address this emerging challenge with the design and development of a framework, for the support of remote maintenance and repair operation based on AR, by creating suitable communication channels between the shop-floor technicians and the expert engineers who are utilizing real-time feedback from the operator’s field of view. The applicability of the developed framework is tested in vitro in a lab-based machine shop and in a real-life industrial scenario. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
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14 pages, 3183 KiB  
Review
Artifactual Lung Ultrasonography: It Is a Matter of Traps, Order, and Disorder
by Gino Soldati, Andrea Smargiassi, Libertario Demi and Riccardo Inchingolo
Appl. Sci. 2020, 10(5), 1570; https://doi.org/10.3390/app10051570 - 25 Feb 2020
Cited by 59 | Viewed by 7469
Abstract
When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic [...] Read more.
When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic study of the interstitial pathology of the lung. In fact, all lung pathologies which increase the density of the tissue, and do not consolidate the organ, are characterized by the presence of ultrasound artifacts. Many studies of the past have only assessed the number of vertical artifacts (generally known as B-Lines) as a sign of disease severity. However, recent observations suggest that the appearance of the individual artifacts, their variability, and their internal structure, may play a role for a non-invasive characterization of the surface of the lungs, directing the diagnoses and identifying groups of diseases. In this review, we discuss the meaning of lung ultrasound artifacts, and introduce hypothesis on the correlation between their presence and the structural variation of the sub-pleural tissue in light of current knowledge of the acoustic properties of the pleural plane. Full article
(This article belongs to the Special Issue Signal Processing and Image Analysis Techniques for Lung Ultrasound Imaging)
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29 pages, 4614 KiB  
Review
Edge Couplers in Silicon Photonic Integrated Circuits: A Review
by Xin Mu, Sailong Wu, Lirong Cheng and H.Y. Fu
Appl. Sci. 2020, 10(4), 1538; https://doi.org/10.3390/app10041538 - 24 Feb 2020
Cited by 154 | Viewed by 32035
Abstract
Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with [...] Read more.
Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with current semiconductor fabrication process. Optical interconnects is an important issue in silicon photonic integrated circuits for transmitting light, and fiber-to-chip optical interconnects is vital in application scenarios such as data centers and optical transmission systems. There are mainly two categories of fiber-to-chip optical coupling: off-plane coupling and in-plane coupling. Grating couplers work under the former category, while edge couplers function as in-plane coupling. In this paper, we mainly focus on edge couplers in silicon photonic integrated circuits. We deliver an introduction to the research background, operation mechanisms, and design principles of silicon photonic edge couplers. The state-of-the-art of edge couplers is reviewed according to the different structural configurations of the device, while identifying the performance, fabrication feasibility, and applications. In addition, a brief comparison between edge couplers and grating couplers is conducted. Packaging issues are also discussed, and several prospective techniques for further improvements of edge couplers are proposed. Full article
(This article belongs to the Special Issue Advances on New Silicon-Integrating Devices: From the Technology of Silicon to the Technology with Silicon)
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29 pages, 6865 KiB  
Article
Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning
by Shoayee Alotaibi, Rashid Mehmood, Iyad Katib, Omer Rana and Aiiad Albeshri
Appl. Sci. 2020, 10(4), 1398; https://doi.org/10.3390/app10041398 - 19 Feb 2020
Cited by 84 | Viewed by 12144
Abstract
Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable [...] Read more.
Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Smart City and Smart Enterprise)
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37 pages, 3851 KiB  
Review
Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions
by Josué González-García, Alfonso Gómez-Espinosa, Enrique Cuan-Urquizo, Luis Govinda García-Valdovinos, Tomás Salgado-Jiménez and Jesús Arturo Escobedo Cabello
Appl. Sci. 2020, 10(4), 1256; https://doi.org/10.3390/app10041256 - 13 Feb 2020
Cited by 181 | Viewed by 27204
Abstract
Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater [...] Read more.
Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations. Full article
(This article belongs to the Special Issue Underwater Robots in Ocean and Coastal Applications)
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18 pages, 7178 KiB  
Article
Numerical Study on Hysteretic Behaviour of Horizontal-Connection and Energy-Dissipation Structures Developed for Prefabricated Shear Walls
by Limeng Zhu, Lingmao Kong and Chunwei Zhang
Appl. Sci. 2020, 10(4), 1240; https://doi.org/10.3390/app10041240 - 12 Feb 2020
Cited by 83 | Viewed by 7917
Abstract
This study proposed a developed horizontal-connection and energy-dissipation structure (HES), which could be employed for horizontal connection of prefabricated shear wall structural system. The HES consists of an external replaceable energy dissipation (ED) zone mainly for energy dissipation and an internal stiffness lifting [...] Read more.
This study proposed a developed horizontal-connection and energy-dissipation structure (HES), which could be employed for horizontal connection of prefabricated shear wall structural system. The HES consists of an external replaceable energy dissipation (ED) zone mainly for energy dissipation and an internal stiffness lifting (SL) zone for enhancing the load-bearing capacity. By the predicted displacement threshold control device, the ED zone made in bolted low-yielding steel plates could firstly dissipate the energy and can be replaced after damage, the SL zone could delay the load-bearing and the load-displacement curves of the HES would exhibit “double-step” characteristics. Detailed finite element models are established and validated in software ABAQUS. parametric analysis including aspect ratio, the shape of the steel plate in the ED zone and the displacement threshold in the SL zone, is conducted. It is found that the HES depicts high energy dissipation ability and its bearing capacity could be obtained again after the yielding of the ED zone. The optimized X-shaped steel plate in the ED zone exhibit better performance. The “double-step” design of the HES is a potential way of improving the seismic and anti-collapsing performance of prefabricated shear wall structures against large and super-large earthquakes. Full article
(This article belongs to the Special Issue Recent Advances in the Design of Structures with Passive Energy Dissipation Systems)
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17 pages, 4597 KiB  
Article
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
by Vikash Chouhan, Sanjay Kumar Singh, Aditya Khamparia, Deepak Gupta, Prayag Tiwari, Catarina Moreira, Robertas Damaševičius and Victor Hugo C. de Albuquerque
Appl. Sci. 2020, 10(2), 559; https://doi.org/10.3390/app10020559 - 12 Jan 2020
Cited by 532 | Viewed by 35061
Abstract
Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study [...] Read more.
Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Biomedical Data)
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22 pages, 931 KiB  
Review
Advanced Methods for Photovoltaic Output Power Forecasting: A Review
by Adel Mellit, Alessandro Massi Pavan, Emanuele Ogliari, Sonia Leva and Vanni Lughi
Appl. Sci. 2020, 10(2), 487; https://doi.org/10.3390/app10020487 - 9 Jan 2020
Cited by 199 | Viewed by 11029
Abstract
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such [...] Read more.
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems - Volume II)
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15 pages, 581 KiB  
Article
Evaluation of the Ecotoxicological Potential of Fly Ash and Recycled Concrete Aggregates Use in Concrete
by Patrícia Rodrigues, José D. Silvestre, Inês Flores-Colen, Cristina A. Viegas, Hawreen H. Ahmed, Rawaz Kurda and Jorge de Brito
Appl. Sci. 2020, 10(1), 351; https://doi.org/10.3390/app10010351 - 3 Jan 2020
Cited by 36 | Viewed by 4217
Abstract
This study applies a methodology to evaluate the ecotoxicological potential of raw materials and cement-based construction materials. In this study, natural aggregates and Portland cement were replaced with non-conventional recycled concrete aggregates (RA) and fly ash (FA), respectively, in the production of two [...] Read more.
This study applies a methodology to evaluate the ecotoxicological potential of raw materials and cement-based construction materials. In this study, natural aggregates and Portland cement were replaced with non-conventional recycled concrete aggregates (RA) and fly ash (FA), respectively, in the production of two concrete products alternative to conventional concrete (used as reference). The experimental program involved assessing both the chemical properties (non-metallic and metallic parameters) and ecotoxicity data (battery of tests with the luminescent bacterium Vibrio fischeri, the freshwater crustacean Daphnia magna, and the yeast Saccharomyces cerevisiae) of eluates obtained from leaching tests of RA, FA, and the three concrete mixes. Even though the results indicated that RA and FA have the ability to release some chemicals into the water and induce its alkalinisation, the respective eluate samples presented no or low levels of potential ecotoxicity. However, eluates from concrete mixes produced with a replacement ratio of Portland cement with 60% of FA and 100% of natural aggregates and produced with 60% of FA and 100% of RA were classified as clearly ecotoxic mainly towards Daphnia magna mobility. Therefore, raw materials with weak evidences of ecotoxicity could lead to the production of concrete products with high ecotoxicological potential. Overall, the results obtained highlight the importance of integrating data from the chemical and ecotoxicological characterization of materials’ eluate samples aiming to assess the possible environmental risk of the construction materials, namely of incorporating non-conventional raw materials in concrete, and contributing to achieve construction sustainability. Full article
(This article belongs to the Special Issue Low Binder Concrete and Mortars)
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22 pages, 7677 KiB  
Article
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
by Mahdi Shariati, Mohammad Saeed Mafipour, Peyman Mehrabi, Alireza Bahadori, Yousef Zandi, Musab N A Salih, Hoang Nguyen, Jie Dou, Xuan Song and Shek Poi-Ngian
Appl. Sci. 2019, 9(24), 5534; https://doi.org/10.3390/app9245534 - 16 Dec 2019
Cited by 284 | Viewed by 7923
Abstract
Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly [...] Read more.
Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices. Full article
(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)
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21 pages, 960 KiB  
Article
Finite Element Analysis of Thermo-Diffusion and Multi-Slip Effects on MHD Unsteady Flow of Casson Nano-Fluid over a Shrinking/Stretching Sheet with Radiation and Heat Source
by Liaqat Ali, Xiaomin Liu, Bagh Ali, Saima Mujeed and Sohaib Abdal
Appl. Sci. 2019, 9(23), 5217; https://doi.org/10.3390/app9235217 - 30 Nov 2019
Cited by 85 | Viewed by 2946
Abstract
In this article, we probe the multiple-slip effects on magnetohydrodynamic unsteady Casson nano-fluid flow over a penetrable stretching sheet, sheet entrenched in a porous medium with thermo-diffusion effect, and injection/suction in the presence of heat source. The flow is engendered due to the [...] Read more.
In this article, we probe the multiple-slip effects on magnetohydrodynamic unsteady Casson nano-fluid flow over a penetrable stretching sheet, sheet entrenched in a porous medium with thermo-diffusion effect, and injection/suction in the presence of heat source. The flow is engendered due to the unsteady time-dependent stretching sheet retained inside the porous medium. The leading non-linear partial differential equations are transmuted in the system of coupled nonlinear ordinary differential equations by using appropriate transformations, then the transformed equations are solved by using the variational finite element method numerically. The velocity, temperature, solutal concentration, and nano-particles concentration, as well as the rate of heat transfer, the skin friction coefficient, and Sherwood number for solutal concentration, are presented for several physical parameters. Next, the effects of these various physical parameters are conferred with graphs and tables. The exact values of flow velocity, skin friction, and Nusselt number are compared with a numerical solution acquired with the finite element method (FEM), and also with numerical results accessible in literature. In the end, we rationalize the convergence of the finite element numerical solution, and the calculations are carried out by reducing the mesh size. Full article
(This article belongs to the Section Nanotechnology and Applied Nanosciences)
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30 pages, 2734 KiB  
Review
A Review of Hydrogen Direct Injection for Internal Combustion Engines: Towards Carbon-Free Combustion
by Ho Lung Yip, Aleš Srna, Anthony Chun Yin Yuen, Sanghoon Kook, Robert A. Taylor, Guan Heng Yeoh, Paul R. Medwell and Qing Nian Chan
Appl. Sci. 2019, 9(22), 4842; https://doi.org/10.3390/app9224842 - 12 Nov 2019
Cited by 275 | Viewed by 36282
Abstract
A paradigm shift towards the utilization of carbon-neutral and low emission fuels is necessary in the internal combustion engine industry to fulfil the carbon emission goals and future legislation requirements in many countries. Hydrogen as an energy carrier and main fuel is a [...] Read more.
A paradigm shift towards the utilization of carbon-neutral and low emission fuels is necessary in the internal combustion engine industry to fulfil the carbon emission goals and future legislation requirements in many countries. Hydrogen as an energy carrier and main fuel is a promising option due to its carbon-free content, wide flammability limits and fast flame speeds. For spark-ignited internal combustion engines, utilizing hydrogen direct injection has been proven to achieve high engine power output and efficiency with low emissions. This review provides an overview of the current development and understanding of hydrogen use in internal combustion engines that are usually spark ignited, under various engine operation modes and strategies. This paper then proceeds to outline the gaps in current knowledge, along with better potential strategies and technologies that could be adopted for hydrogen direct injection in the context of compression-ignition engine applications—topics that have not yet been extensively explored to date with hydrogen but have shown advantages with compressed natural gas. Full article
(This article belongs to the Special Issue Progress in Combustion Diagnostics, Science and Technology)
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18 pages, 5451 KiB  
Article
Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
by Tatiana F. Vieira and Sérgio F. Sousa
Appl. Sci. 2019, 9(21), 4538; https://doi.org/10.3390/app9214538 - 25 Oct 2019
Cited by 93 | Viewed by 16697
Abstract
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point [...] Read more.
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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13 pages, 939 KiB  
Review
What is still Limiting the Deployment of Cellulosic Ethanol? Analysis of the Current Status of the Sector
by Monica Padella, Adrian O’Connell and Matteo Prussi
Appl. Sci. 2019, 9(21), 4523; https://doi.org/10.3390/app9214523 - 24 Oct 2019
Cited by 96 | Viewed by 7814
Abstract
Ethanol production from cellulosic material is considered one of the most promising options for future biofuel production contributing to both the energy diversification and decarbonization of the transport sector, especially where electricity is not a viable option (e.g., aviation). Compared to conventional (or [...] Read more.
Ethanol production from cellulosic material is considered one of the most promising options for future biofuel production contributing to both the energy diversification and decarbonization of the transport sector, especially where electricity is not a viable option (e.g., aviation). Compared to conventional (or first generation) ethanol production from food and feed crops (mainly sugar and starch based crops), cellulosic (or second generation) ethanol provides better performance in terms of greenhouse gas (GHG) emissions savings and low risk of direct and indirect land-use change. However, despite the policy support (in terms of targets) and significant R&D funding in the last decade (both in EU and outside the EU), cellulosic ethanol production appears to be still limited. The paper provides a comprehensive overview of the status of cellulosic ethanol production in EU and outside EU, reviewing available literature and highlighting technical and non-technical barriers that still limit its production at commercial scale. The review shows that the cellulosic ethanol sector appears to be still stagnating, characterized by technical difficulties as well as high production costs. Competitiveness issues, against standard starch based ethanol, are evident considering many commercial scale cellulosic ethanol plants appear to be currently in idle or on-hold states. Full article
(This article belongs to the Special Issue Cutting-Edge Technologies for Renewable Energy Production and Storage)
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28 pages, 2771 KiB  
Review
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
by Hongyu Liu and Bo Lang
Appl. Sci. 2019, 9(20), 4396; https://doi.org/10.3390/app9204396 - 17 Oct 2019
Cited by 646 | Viewed by 48982
Abstract
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, [...] Read more.
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies. Full article
(This article belongs to the Special Issue Machine Learning for Cybersecurity Threats, Challenges, and Opportunities)
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12 pages, 1587 KiB  
Article
Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
by Tuong Le, Minh Thanh Vo, Bay Vo, Eenjun Hwang, Seungmin Rho and Sung Wook Baik
Appl. Sci. 2019, 9(20), 4237; https://doi.org/10.3390/app9204237 - 10 Oct 2019
Cited by 196 | Viewed by 12817
Abstract
The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination [...] Read more.
The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans. Full article
(This article belongs to the Special Issue Actionable Pattern-Driven Analytics and Prediction)
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15 pages, 3636 KiB  
Article
Swing Vibration Control of Suspended Structure Using Active Rotary Inertia Driver System: Parametric Analysis and Experimental Verification
by Chunwei Zhang and Hao Wang
Appl. Sci. 2019, 9(15), 3144; https://doi.org/10.3390/app9153144 - 2 Aug 2019
Cited by 85 | Viewed by 4671
Abstract
The Active Rotary Inertia Driver (ARID) system is a novel vibration control system that can effectively mitigate the swing vibration of suspended structures. Parametric analysis is carried out using Simulink based on the mathematical model and the effectiveness is further validated by a [...] Read more.
The Active Rotary Inertia Driver (ARID) system is a novel vibration control system that can effectively mitigate the swing vibration of suspended structures. Parametric analysis is carried out using Simulink based on the mathematical model and the effectiveness is further validated by a series of experiments. Firstly, the active controller is designed based on the system mathematical model and the LQR (linear quadratic regulator) algorithm. Next, the parametric analysis is carried out using Simulink to study the key parameters such as the coefficient of the control algorithm, the rotary inertia ratio. Lastly, the ARID system control effectiveness and the parametric analysis results are further validated by the shaking table experiments. The effectiveness and robustness of the ARID system are well verified. The dynamic characteristics of this system are further studied, and the conclusions of this paper provide a theoretical basis for further development of such unique control system. Full article
(This article belongs to the Special Issue Energy Dissipation and Vibration Control: Materials, Modeling, Algorithm and Devices)
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27 pages, 1970 KiB  
Review
Flocculation Harvesting Techniques for Microalgae: A Review
by Ibrahim A. Matter, Vu Khac Hoang Bui, Mikyoung Jung, Jung Yoon Seo, Young-Eun Kim, Young-Chul Lee and You-Kwan Oh
Appl. Sci. 2019, 9(15), 3069; https://doi.org/10.3390/app9153069 - 29 Jul 2019
Cited by 123 | Viewed by 21747
Abstract
Microalgae have been considered as one of the most promising biomass feedstocks for various industrial applications such as biofuels, animal/aquaculture feeds, food supplements, nutraceuticals, and pharmaceuticals. Several biotechnological challenges associated with algae cultivation, including the small size and negative surface charge of algal [...] Read more.
Microalgae have been considered as one of the most promising biomass feedstocks for various industrial applications such as biofuels, animal/aquaculture feeds, food supplements, nutraceuticals, and pharmaceuticals. Several biotechnological challenges associated with algae cultivation, including the small size and negative surface charge of algal cells as well as the dilution of its cultures, need to be circumvented, which increases the cost and labor. Therefore, efficient biomass recovery or harvesting of diverse algal species represents a critical bottleneck for large-scale algal biorefinery process. Among different algae harvesting techniques (e.g., centrifugation, gravity sedimentation, screening, filtration, and air flotation), the flocculation-based processes have acquired much attention due to their promising efficiency and scalability. This review covers the basics and recent research trends of various flocculation techniques, such as auto-flocculation, bio-flocculation, chemical flocculation, particle-based flocculation, and electrochemical flocculation, and also discusses their advantages and disadvantages. The challenges and prospects for the development of eco-friendly and economical algae harvesting processes have also been outlined here. Full article
(This article belongs to the Special Issue Algal Biorefinery and Microbial Fuel Cells)
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17 pages, 1907 KiB  
Review
Morphology and Properties of Electrospun PCL and Its Composites for Medical Applications: A Mini Review
by Mokgaotsa Jonas Mochane, Teboho Simon Motsoeneng, Emmanuel Rotimi Sadiku, Teboho Clement Mokhena and Jeremia Shale Sefadi
Appl. Sci. 2019, 9(11), 2205; https://doi.org/10.3390/app9112205 - 29 May 2019
Cited by 162 | Viewed by 11698
Abstract
Polycaprolactone (PCL) is one of the most used synthetic polymers for medical applications due to its biocompatibility and slow biodegradation character. Combining the inherent properties of the PCL matrix with the characteristic of nanofibrous particles, result into promising materials that can be suitable [...] Read more.
Polycaprolactone (PCL) is one of the most used synthetic polymers for medical applications due to its biocompatibility and slow biodegradation character. Combining the inherent properties of the PCL matrix with the characteristic of nanofibrous particles, result into promising materials that can be suitable for different applications, including the biomedical applications. The advantages of nanofibrous structures include large surface area, a small diameter of pores and a high porosity, which make them of great interest in different applications. Electrospinning, as technique, has been heavily used for the preparation of nano- and micro-sized fibers. This review discusses the different methods for the electrospinning of PCL and its composites for advanced applications. Furthermore, the steady state conditions as well as the effect of the electrospinning parameters on the resultant morphology of the electrospun fiber are also reported. Full article
(This article belongs to the Special Issue Electrospinning Technology: Control of Morphology for Nanostructure)
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28 pages, 15721 KiB  
Review
Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
by Seyednima Khezr, Md Moniruzzaman, Abdulsalam Yassine and Rachid Benlamri
Appl. Sci. 2019, 9(9), 1736; https://doi.org/10.3390/app9091736 - 26 Apr 2019
Cited by 375 | Viewed by 59068
Abstract
One of the most important discoveries and creative developments that is playing a vital role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information [...] Read more.
One of the most important discoveries and creative developments that is playing a vital role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter how far they are. In the last couple of years, the upsurge in blockchain technology has obliged scholars and specialists to scrutinize new ways to apply blockchain technology with a wide range of domains. The dramatic increase in blockchain technology has provided many new application opportunities, including healthcare applications. This survey provides a comprehensive review of emerging blockchain-based healthcare technologies and related applications. In this inquiry, we call attention to the open research matters in this fast-growing field, explaining them in some details. We also show the potential of blockchain technology in revolutionizing healthcare industry. Full article
(This article belongs to the Special Issue Advances in Blockchain Technology and Applications)
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23 pages, 786 KiB  
Review
Current Biomedical Applications of 3D Printing and Additive Manufacturing
by Pouyan Ahangar, Megan E Cooke, Michael H Weber and Derek H Rosenzweig
Appl. Sci. 2019, 9(8), 1713; https://doi.org/10.3390/app9081713 - 25 Apr 2019
Cited by 211 | Viewed by 20412
Abstract
Additive manufacturing (AM) has emerged over the past four decades as a cost-effective, on-demand modality for fabrication of geometrically complex objects. The ability to design and print virtually any object shape using a diverse array of materials, such as metals, polymers, ceramics and [...] Read more.
Additive manufacturing (AM) has emerged over the past four decades as a cost-effective, on-demand modality for fabrication of geometrically complex objects. The ability to design and print virtually any object shape using a diverse array of materials, such as metals, polymers, ceramics and bioinks, has allowed for the adoption of this technology for biomedical applications in both research and clinical settings. Current advancements in tissue engineering and regeneration, therapeutic delivery, medical device fabrication and operative management planning ensure that AM will continue to play an increasingly important role in the future of healthcare. In this review, we outline current biomedical applications of common AM techniques and materials. Full article
(This article belongs to the Special Issue Biocompatible Materials)
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31 pages, 478 KiB  
Article
When Energy Trading Meets Blockchain in Electrical Power System: The State of the Art
by Naiyu Wang, Xiao Zhou, Xin Lu, Zhitao Guan, Longfei Wu, Xiaojiang Du and Mohsen Guizani
Appl. Sci. 2019, 9(8), 1561; https://doi.org/10.3390/app9081561 - 15 Apr 2019
Cited by 160 | Viewed by 12950
Abstract
With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many [...] Read more.
With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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13 pages, 5867 KiB  
Article
Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting
by Amal Charles, Ahmed Elkaseer, Lore Thijs, Veit Hagenmeyer and Steffen Scholz
Appl. Sci. 2019, 9(6), 1256; https://doi.org/10.3390/app9061256 - 26 Mar 2019
Cited by 124 | Viewed by 8777
Abstract
Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind [...] Read more.
Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind conventional manufacturing in terms of surface quality. This proves a hindrance for many companies considering investment in AM. The aim of this work is to investigate the effect of varying process parameters on the resultant roughness of the down-facing surfaces in selective laser melting (SLM). A systematic experimental study was carried out and the effects of the interaction of the different parameters and their effect on the surface roughness (Sa) were analyzed. It was found that the interaction and interdependency between parameters were of greatest significance to the obtainable surface roughness, though their effects vary greatly depending on the applied levels. This behavior was mainly attributed to the difference in energy absorbed by the powder. Predictive process models for optimization of process parameters for minimizing the obtained Sa in 45° and 35° down-facing surface, individually, were achieved with average error percentages of 5% and 6.3%, respectively, however further investigation is still warranted. Full article
(This article belongs to the Special Issue Micro/Nano Manufacturing)
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15 pages, 3450 KiB  
Review
Micro-LEDs, a Manufacturability Perspective
by Kai Ding, Vitaliy Avrutin, Natalia Izyumskaya, Ümit Özgür and Hadis Morkoç
Appl. Sci. 2019, 9(6), 1206; https://doi.org/10.3390/app9061206 - 22 Mar 2019
Cited by 227 | Viewed by 18455
Abstract
Compared with conventional display technologies, liquid crystal display (LCD), and organic light emitting diode (OLED), micro-LED displays possess potential advantages such as high contrast, fast response, and relatively wide color gamut, low power consumption, and long lifetime. Therefore, micro-LED displays are deemed as [...] Read more.
Compared with conventional display technologies, liquid crystal display (LCD), and organic light emitting diode (OLED), micro-LED displays possess potential advantages such as high contrast, fast response, and relatively wide color gamut, low power consumption, and long lifetime. Therefore, micro-LED displays are deemed as a promising technology that could replace LCD and OLED at least in some applications. While the prospects are bright, there are still some technological challenges that have not yet been fully resolved in order to realize the high volume commercialization, which include efficient and reliable assembly of individual LED dies into addressable arrays, full-color schemes, defect and yield management, repair technology and cost control. In this article, we review the recent technological developments of micro-LEDs from various aspects. Full article
(This article belongs to the Special Issue Group III-V Nitride Semiconductor Microcavities and Microemitters)
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20 pages, 1734 KiB  
Review
Biochar as a Multifunctional Component of the Environment—A Review
by Bogdan Saletnik, Grzegorz Zaguła, Marcin Bajcar, Maria Tarapatskyy, Gabriel Bobula and Czesław Puchalski
Appl. Sci. 2019, 9(6), 1139; https://doi.org/10.3390/app9061139 - 18 Mar 2019
Cited by 90 | Viewed by 8952
Abstract
The growing demand for electricity, caused by dynamic economic growth, leads to a decrease in the available non-renewable energy resources constituting the foundation of global power generation. A search for alternative sources of energy that can support conventional energy technologies utilizing fossil fuels [...] Read more.
The growing demand for electricity, caused by dynamic economic growth, leads to a decrease in the available non-renewable energy resources constituting the foundation of global power generation. A search for alternative sources of energy that can support conventional energy technologies utilizing fossil fuels is not only of key significance for the power industry but is also important from the point of view of environmental conservation and sustainable development. Plant biomass, with its specific chemical structure and high calorific value, is a promising renewable source of energy which can be utilized in numerous conversion processes, enabling the production of solid, liquid, and gaseous fuels. Methods of thermal biomass conversion include pyrolysis, i.e., a process allowing one to obtain a multifunctional product known as biochar. The article presents a review of information related to the broad uses of carbonization products. It also discusses the legal aspects and quality standards applicable to these materials. The paper draws attention to the lack of uniform legal and quality conditions, which would allow for a much better use of biochar. The review also aims to highlight the high potential for a use of biochar in different environments. The presented text attempts to emphasize the importance of biochar as an alternative to classic products used for energy, environmental and agricultural purposes. Full article
(This article belongs to the Special Issue New Carbon Materials from Biomass and Their Applications)
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14 pages, 3312 KiB  
Article
Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models
by Hui Chen, Panagiotis G. Asteris, Danial Jahed Armaghani, Behrouz Gordan and Binh Thai Pham
Appl. Sci. 2019, 9(6), 1042; https://doi.org/10.3390/app9061042 - 13 Mar 2019
Cited by 128 | Viewed by 6817
Abstract
The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as [...] Read more.
The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions. Full article
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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22 pages, 4394 KiB  
Review
Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
by M. S. Aruna Gandhi, Suoda Chu, K. Senthilnathan, P. Ramesh Babu, K. Nakkeeran and Qian Li
Appl. Sci. 2019, 9(5), 949; https://doi.org/10.3390/app9050949 - 6 Mar 2019
Cited by 123 | Viewed by 14413
Abstract
The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high [...] Read more.
The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high resolution, and low cost, FOPS turn out to be potential alternatives to conventional biological fiber optic sensors. FOPS use optical transduction mechanisms to enhance sensitivity and resolution. The optical transduction mechanisms of FOPS with different geometrical structures and the photonic properties of the geometries are discussed in detail. The studies of optical properties with a combination of suitable materials for testing the biosamples allow for diagnosing diseases in the medical field. Full article
(This article belongs to the Special Issue Recent Progress in Fiber Optic Sensors: Bringing Light to Measurement)
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29 pages, 10285 KiB  
Review
Review of Artificial Intelligence Adversarial Attack and Defense Technologies
by Shilin Qiu, Qihe Liu, Shijie Zhou and Chunjiang Wu
Appl. Sci. 2019, 9(5), 909; https://doi.org/10.3390/app9050909 - 4 Mar 2019
Cited by 261 | Viewed by 29966
Abstract
In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. [...] Read more.
In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools. Full article
(This article belongs to the Special Issue Advances in Deep Learning)
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15 pages, 11368 KiB  
Article
Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests
by Yixian Wang, Panpan Guo, Xian Li, Hang Lin, Yan Liu and Haiping Yuan
Appl. Sci. 2019, 9(5), 900; https://doi.org/10.3390/app9050900 - 3 Mar 2019
Cited by 92 | Viewed by 7448
Abstract
The beneficial role of combining fiber reinforcement with lime stabilization in altering soil behavior has been established in the literature. However, the coupling effect of their combination still remains unclear in terms of its magnitude and microscopic mechanism, especially for natural fibers with [...] Read more.
The beneficial role of combining fiber reinforcement with lime stabilization in altering soil behavior has been established in the literature. However, the coupling effect of their combination still remains unclear in terms of its magnitude and microscopic mechanism, especially for natural fibers with special microstructures. The objective of this study was to investigate the coupling effect of wheat straw fiber reinforcement and lime stabilization on the mechanical behavior of Hefei clayey soil. To achieve this, an experimental program including unconsolidated–undrained (UU) triaxial tests and SEM analysis was implemented. Static compaction test samples were prepared on untreated soil, fiber-reinforced soil, lime-stabilized soil, and lime-stabilized/fiber-reinforced soil at optimum moisture content with determining of the maximum dry density of the untreated soil. The lime was added in three different contents of 2%, 4%, and 6%, and 13 mm long wheat straw fiber slices with a cross section one-quarter that of the intact ones were mixed in at 0.2%, 0.4%, and 0.6% by dry weight of soil. Analysis of the derived results indicated that the addition of a small amount of wheat straw fibers into lime-stabilized soil improved the intensity of the strain-softening behavior associated with mere lime stabilization. The observed evidence that the shear strength increase brought by a combination of 0.4% fiber reinforcement and 4% lime stabilization was smaller than the summation of the shear strength increases brought by their presence alone in a sample demonstrated a coupling effect between fiber reinforcement and lime stabilization. This coupling effect was also detected in the comparisons of the secant modulus and failure pattern between the combined treatment and the individual treatments. These manifestations of the coupling effect were explained by a microscopic mechanism wherein the fiber reinforcing effect was made more effective by the ways in which lime chemically stabilized the soil and lime stabilization development was quickened by the water channels passing through the surfaces and honeycomb pores of the wheat straw fibers. Full article
(This article belongs to the Section Civil Engineering)
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44 pages, 6655 KiB  
Review
Organic Crystals for THz Photonics
by Mojca Jazbinsek, Uros Puc, Andreja Abina and Aleksander Zidansek
Appl. Sci. 2019, 9(5), 882; https://doi.org/10.3390/app9050882 - 1 Mar 2019
Cited by 147 | Viewed by 10738
Abstract
Organic crystals with second-order optical nonlinearity feature very high and ultra-fast optical nonlinearities and are therefore attractive for various photonics applications. During the last decade, they have been found particularly attractive for terahertz (THz) photonics. This is mainly due to the very intense [...] Read more.
Organic crystals with second-order optical nonlinearity feature very high and ultra-fast optical nonlinearities and are therefore attractive for various photonics applications. During the last decade, they have been found particularly attractive for terahertz (THz) photonics. This is mainly due to the very intense and ultra-broadband THz-wave generation possible with these crystals. We review recent progress and challenges in the development of organic crystalline materials for THz-wave generation and detection applications. We discuss their structure, intrinsic properties, and advantages compared to inorganic alternatives. The characteristic properties of the most widely employed organic crystals at present, such as DAST, DSTMS, OH1, HMQ-TMS, and BNA are analyzed and compared. We summarize the most important principles for THz-wave generation and detection, as well as organic THz-system configurations based on either difference-frequency generation or optical rectification. In addition, we give state-of-the-art examples of very intense and ultra-broadband THz systems that rely on organic crystals. Finally, we present some recent breakthrough demonstrations in nonlinear THz photonics enabled by very intense organic crystalline THz sources, as well as examples of THz spectroscopy and THz imaging using organic crystals as THz sources for various scientific and technological applications. Full article
(This article belongs to the Special Issue Nonlinear Optical Materials and Phenomena)
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17 pages, 2852 KiB  
Article
Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm
by Khalid Elbaz, Shui-Long Shen, Annan Zhou, Da-Jun Yuan and Ye-Shuang Xu
Appl. Sci. 2019, 9(4), 780; https://doi.org/10.3390/app9040780 - 22 Feb 2019
Cited by 93 | Viewed by 5847
Abstract
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the [...] Read more.
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Smart City and Smart Enterprise)
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