Applied Sciences

30 pages, 9700 KiB

Open AccessArticle

Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II

by Hamza Reffad and Adel Alti

Appl. Sci. 2023, 13(8), 5218; https://doi.org/10.3390/app13085218 - 21 Apr 2023

Cited by 3 | Viewed by 2736

Abstract

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, [...] Read more.

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

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19 pages, 2329 KiB

Open AccessArticle

A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model

by Yumei Cao, Peng Li, Tianmian Liang, Xiaojun Wu, Xiaoming Wang and Yuanru Cui

Appl. Sci. 2023, 13(8), 5217; https://doi.org/10.3390/app13085217 - 21 Apr 2023

Cited by 2 | Viewed by 1512

Abstract

Opportunities networks’ message transmission is significantly impacted by routing prediction, which has been a focus of opportunity network research. The network of student nodes with smart devices is a particular type of opportunity network in the campus setting, and the predictability of campus [...] Read more.

Opportunities networks’ message transmission is significantly impacted by routing prediction, which has been a focus of opportunity network research. The network of student nodes with smart devices is a particular type of opportunity network in the campus setting, and the predictability of campus node movement trajectories is also influenced by the regularity of students’ social mobility. In this research, a novel Markov route prediction method is proposed under the campus background. When two nodes meet, they share the movement track data of other nodes stored in each other’s cache in order to predict the probability of two nodes meeting in the future. The impact of the node within the group is indicated by the node centrality. The utility value of the message is defined to describe the spread degree of the message and the energy consumption of the current node, then the cache is managed according to the utility value. By creating a concurrent hash mapping table of delivered messages, the remaining nodes are notified to delete the delivered messages and release the cache space in time after the messages are delivered to their destinations. The method suggested in this research can successfully lower the packet loss rate, minimize transmission latency and network overhead, and further increase the success rate of message delivery, according to experimental analysis and algorithm comparison. Full article

(This article belongs to the Special Issue Advancements in Wireless Communications, Networks and Signal Processing)

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18 pages, 4175 KiB

Open AccessArticle

Human Factors Analysis of the Improved FRAM Method for Take-Off Quality Lateral Shift

by Wenjian Ouyang, Xusheng Gan, Yarong Wu, Kai Qu and Jiabo Wang

Appl. Sci. 2023, 13(8), 5216; https://doi.org/10.3390/app13085216 - 21 Apr 2023

Cited by 2 | Viewed by 1506

Abstract

This article proposes an improved FRAM method based on the traditional FRAM method, using the study of aircraft take-off quality as an example to illustrate the operation method of the improved FRAM. To address the impact of pilot operations on take-off instructions during [...] Read more.

This article proposes an improved FRAM method based on the traditional FRAM method, using the study of aircraft take-off quality as an example to illustrate the operation method of the improved FRAM. To address the impact of pilot operations on take-off instructions during aircraft take-off, a functional network model was constructed based on the improved FRAM (functional resonance analysis method) method for the take-off and roll stages of the aircraft. On the basis of the aircraft take-off taxiing model, a simulation was used to sample the take-off data from the pilot many times under different conditions, and the data were put into the safety envelope for comparative analysis to find functional modules with abnormal changes. Using the functional network model, the resonance relationship between the abnormal module and other related functional modules was determined. According to the resonance relationship, setting up a safety barrier can reduce the risk of accidents. Finally, the safety barrier was substituted back into the improved FRAM method to verify the effectiveness of the safety barrier. Compared with the traditional FRAM method, the improved FRAM method can make full use of historical data, loop iteration, repeated verification, and continuous improvement until the final result reaches the user’s expected goal. The improved FRAM method reduces the dependence on expert evaluation and experience, so its conclusions have higher objectivity and reference value. Full article

(This article belongs to the Section Aerospace Science and Engineering)

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23 pages, 3773 KiB

Open AccessArticle

A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation

by Che Han Lim and Seung Ki Moon

Appl. Sci. 2023, 13(8), 5215; https://doi.org/10.3390/app13085215 - 21 Apr 2023

Cited by 2 | Viewed by 1936

Abstract

In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry [...] Read more.

In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the solution space while serving as a heuristic in locating good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these assignments and solved for the makespan. Compared to prior JSPT implementations, the proposed JSPT model considers job pre-emption, which is instrumental in enabling this FJSPT implementation to outperform certain established benchmarks, confirming the importance of considering job pre-emption. Results indicate that the proposed approach is effective, robust, and competitive. Full article

(This article belongs to the Special Issue Recent Advances in Smart Design and Manufacturing)

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16 pages, 14845 KiB

Open AccessArticle

Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion

by Chenhui Wang and Yijiu Zhao

Appl. Sci. 2023, 13(8), 5214; https://doi.org/10.3390/app13085214 - 21 Apr 2023

Cited by 5 | Viewed by 1360

Abstract

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing [...] Read more.

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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12 pages, 1273 KiB

Open AccessArticle

Metal Levels in Serranus atricauda and Sparisoma cretense from the North-Eastern Atlantic Ocean—Contribution to Risk Assessment

by Alberto Gutiérrez, Enrique Lozano-Bilbao, Ángel J. Gutiérrez-Fernández, Soraya Paz-Montelongo, Dailos González-Weller, Carmen Rubio-Armendáriz, Daniel Niebla-Canelo, Samuel Alejandro-Vega and Arturo Hardisson

Appl. Sci. 2023, 13(8), 5213; https://doi.org/10.3390/app13085213 - 21 Apr 2023

Cited by 2 | Viewed by 1234

Abstract

The objective of this study was to study whether the metal concentrations in Sparisoma cretense and Serranus atricauda differ between different coastal areas around the island of Tenerife, Canary Islands and to study whether these species are good bioindicators of pollution. Thirty samples [...] Read more.

The objective of this study was to study whether the metal concentrations in Sparisoma cretense and Serranus atricauda differ between different coastal areas around the island of Tenerife, Canary Islands and to study whether these species are good bioindicators of pollution. Thirty samples of each species were collected from three parts of the coastline around the island, and samples of muscle and liver tissue were taken from the collected specimens. The determination of the metal content (Al, Cd, Pb, Ca, K, Mg, Na, B, Ba, Cr, Cu, Fe, Li, Mn, Mo, Ni, Zn) was performed by inductively coupled plasma optical emission spectrometry (ICP-OES) before conducting a PERMANOVA analysis. The mean metal concentration was significantly higher in the liver tissue than in the muscle tissue of the two species studied. S. atricauda specimens had a larger number of metals with a higher concentration, and the samples from the northern and eastern zones were found to have a higher concentration of elements than those from the southern zone. The northern and eastern zones were found to have a higher concentration of metals and trace elements than the southern zone, which could be explained by the fact that these zones are more polluted due to their higher population density. Full article

(This article belongs to the Special Issue Toxicants and Contaminants in Food)

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16 pages, 1709 KiB

Open AccessReview

The New Role of the Dental Assistant and Nurse in the Age of Advanced Artificial Intelligence in Telehealth Orthodontic Care with Dental Monitoring: Preliminary Report

by Jana Surovková, Sára Haluzová, Martin Strunga, Renáta Urban, Michaela Lifková and Andrej Thurzo

Appl. Sci. 2023, 13(8), 5212; https://doi.org/10.3390/app13085212 - 21 Apr 2023

Cited by 11 | Viewed by 4484

Abstract

This paper explores the impact of Artificial Intelligence (AI) on the role of dental assistants and nurses in orthodontic practices, as there is a gap in understanding the currently evolving impact on orthodontic treatment workflows. The introduction of AI-language models such as ChatGPT [...] Read more.

This paper explores the impact of Artificial Intelligence (AI) on the role of dental assistants and nurses in orthodontic practices, as there is a gap in understanding the currently evolving impact on orthodontic treatment workflows. The introduction of AI-language models such as ChatGPT 4 is changing patient-office communication and transforming the role of orthodontic nurses. Teledentistry is now heavily reliant on AI implementation in orthodontics. This paper presents the proof of a novel concept: an AI-powered orthodontic workflow that provides new responsibilities for an orthodontic nurse. It also provides a report of an assessment of such a workflow in an orthodontic practice that uses an AI solution called Dental Monitoring over a period of three years. The paper evaluates the benefits and drawbacks of daily automated assessments of orthodontic treatment progress, the impact of AI on personalized care, and the new role of a dental assistant. The paper concludes that AI will improve dental practice through more precise and personalized treatment, bringing new roles and responsibilities for trained medical professionals but raising new ethical and legal issues for dental practices. Full article

(This article belongs to the Special Issue Current Advances in Orthodontic Treatment: From Biomaterials to Instrumental Techniques Volume II)

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18 pages, 3408 KiB

Open AccessArticle

A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury

by Maria Roldan and Panicos A. Kyriacou

Appl. Sci. 2023, 13(8), 5211; https://doi.org/10.3390/app13085211 - 21 Apr 2023

Cited by 5 | Viewed by 2530

Abstract

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed [...] Read more.

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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14 pages, 2941 KiB

Open AccessArticle

Switch-Off Policies in Job Shop Controlled by Workload Control Concept

by Paolo Renna

Appl. Sci. 2023, 13(8), 5210; https://doi.org/10.3390/app13085210 - 21 Apr 2023

Cited by 3 | Viewed by 1223

Abstract

The reduction in emissions and the increase in energy costs push companies to identify solutions to reduce energy consumption in production systems. One of the approaches proposed in the literature is the shutdown of machines to reduce energy consumption in the idle state. [...] Read more.

The reduction in emissions and the increase in energy costs push companies to identify solutions to reduce energy consumption in production systems. One of the approaches proposed in the literature is the shutdown of machines to reduce energy consumption in the idle state. This solution does not affect production processes and can be applied in various manufacturing fields. This paper proposes switch-off policies in manufacturing systems under a workload control system. The shutdown policies developed consider the number of items in the queue and the calculation derived from the workload control mechanism. Simulation models have been developed to test the proposed policies using the case always on as a benchmark, considering different levels of absorbed power in the inactivity and warm-up states and different warm-up times. The results highlight how the switch policies that include the workload evaluation drastically reduce the number of on/off activities, assuring lower energy consumption. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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16 pages, 20970 KiB

Open AccessArticle

Thermal Analysis and Junction Temperature Estimation under Different Ambient Temperatures Considering Convection Thermal Coupling between Power Devices

by Kaixin Wei, Peiji Shi, Pili Bao, Xianping Gao, Yang Du and Yanzhou Qin

Appl. Sci. 2023, 13(8), 5209; https://doi.org/10.3390/app13085209 - 21 Apr 2023

Cited by 4 | Viewed by 2191

Abstract

The convection thermal coupling between adjacent power devices in power converters is dependent on the ambient temperature. When the ambient temperature changes, the convection thermal coupling also changes. This results in an inaccurate thermal model that causes errors in the prediction of the [...] Read more.

The convection thermal coupling between adjacent power devices in power converters is dependent on the ambient temperature. When the ambient temperature changes, the convection thermal coupling also changes. This results in an inaccurate thermal model that causes errors in the prediction of the thermal distribution and junction temperature based on a fixed ambient temperature for power devices in converters application. To solve this variable-ambient-temperature-related issue, a thermal coupling experiment for semiconductor power devices (the MOSFET and diode) was performed to discuss the influence of the thermal coupling effect between adjacent devices and the FEM (Finite Element Method) thermal models for the power devices considering the convection thermal coupling are established. Through these simulations, the junction temperatures of devices under different ambient temperatures were obtained, and the relationships between the junction temperature and ambient temperatures were established. Moreover, the junction temperatures of power devices under different ambient temperatures were calculated and temperature distributions are analyzed in this paper. This method shows a strong significance and has potential applications for high-efficiency and high-power density converter designs. Full article

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9 pages, 1215 KiB

Open AccessArticle

Relationship between HLB Number and Predominant Destabilization Process in Microfluidized Nanoemulsions Formulated with Lemon Essential Oil

by Jenifer Santos, Maria-Carmen Alfaro-Rodríguez, Lili Vega and José Muñoz

Appl. Sci. 2023, 13(8), 5208; https://doi.org/10.3390/app13085208 - 21 Apr 2023

Cited by 3 | Viewed by 1729

Abstract

Lemon essential oil (LEO) is associated with a multitude of health benefits due to its anticancer, antioxidant, antiviral, anti-inflammatory and bactericidal properties. Its drawback is that it is very sensitive to oxidation by heat. For this reason, researchers are increasingly investigating the use [...] Read more.

Lemon essential oil (LEO) is associated with a multitude of health benefits due to its anticancer, antioxidant, antiviral, anti-inflammatory and bactericidal properties. Its drawback is that it is very sensitive to oxidation by heat. For this reason, researchers are increasingly investigating the use of LEO in nanoemulsions. In this work, we used laser diffraction, rheology and multiple light scattering techniques to study the effects of different HLB numbers (indicating different mixtures of Tween 80 and Span 20) on the physical stability of nanoemulsions formulated with LEO. We found that different HLB numbers induced different destabilization mechanisms in these emulsions. An HLB number lower than 12 resulted in an Ostwald ripening effect; an HLB number higher than 12 resulted in coalescence. In addition, all the developed nanoemulsions exhibited Newtonian behavior, which could favor the mechanism of creaming. All emulsions exhibited not only a growth in droplet size, but also a creaming with aging time. These findings highlight the importance of selecting the right surfactant to stabilize nanoemulsions, with potential applications in the food industry. Full article

(This article belongs to the Special Issue Microfluidic Technology in Food Processing)

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13 pages, 6479 KiB

Open AccessCommunication

Infrared All-Dielectric Metasurface Beam Splitter Based on Transflective Structures

by Yingzheng Ren, Zhongzhu Liang, Xiaoyan Shi, Fuming Yang, Xiqing Zhang, Rui Dai, Shoutao Zhang and Weizhen Liu

Appl. Sci. 2023, 13(8), 5207; https://doi.org/10.3390/app13085207 - 21 Apr 2023

Cited by 4 | Viewed by 2012

Abstract

Beam splitters are widely applied in various optical systems as a common beam-splitting device. The conventional stereoscopic and flat-type beam splitters greatly limit the packaging and integration of optical systems due to their large size and restricted emitting direction. Recently, beam-splitting devices made [...] Read more.

Beam splitters are widely applied in various optical systems as a common beam-splitting device. The conventional stereoscopic and flat-type beam splitters greatly limit the packaging and integration of optical systems due to their large size and restricted emitting direction. Recently, beam-splitting devices made of various transmissive or reflective metasurfaces have shown the potential to overcome these challenges. However, in optical systems such as machine vision, these single-ended beam splitters increase the design complexity of the signal feedback link due to the limitation of the beam-splitting path direction. Here, we proposed and numerically simulated a transflective all-dielectric metasurface beam splitter by applying incompletely transmissive structural designs to the metasurface and using the transmission phase modulation mechanism. It can realize the beam separation for arbitrarily polarized incident light on the same side of the normal at both transmissive and reflective ends with a single-layer unit cell arrangement structure and has a similar emergence angle. The results reveal that at 1550 nm, the angular tolerance bandwidth is about 32°, the total splitting efficiency is over 90%, and the splitting ratio is approximately 1:1. After comparison and verification of simulation results, this transflective metasurface beam splitter is hopeful to be applied in new compact optical systems that require real-time signal feedback, such as coaxial light sources and photoelectric sensing. Full article

(This article belongs to the Special Issue Design and Applications of Plasmon-Based Nanodevices)

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39 pages, 32732 KiB

Open AccessArticle

Design and Assessment of an Interactive Role-Play System for Learning and Sustaining Traditional Glove Puppetry by Digital Technology

by Chao-Ming Wang and Shih-Mo Tseng

Appl. Sci. 2023, 13(8), 5206; https://doi.org/10.3390/app13085206 - 21 Apr 2023

Cited by 2 | Viewed by 2614

Abstract

As an ancient performing art, the puppet show was popular entertainment for early civilians. However, with the advance of media technology, traditional puppetry declined gradually, and old puppets became relics displayed in museums. In this study, an interactive role-play system for learning and [...] Read more.

As an ancient performing art, the puppet show was popular entertainment for early civilians. However, with the advance of media technology, traditional puppetry declined gradually, and old puppets became relics displayed in museums. In this study, an interactive role-play system for learning and sustaining traditional glove puppetry is proposed. Constructed with RFID and multimedia techniques to replace the traditional static displays of puppetry, the proposed system allows in-person experiencing of operating real puppets of famous roles. Statistical analyses of the comments collected from expert interviews and the users’ answers to a questionnaire survey lead to the following findings: (1) it is easy to understand and operate the puppets as physical interfacing with the system; (2) the interactive system design conforms to the 3E indicators of easiness, effectiveness, and enjoyableness; (3) the users’ experiences of role-plays emulating experts’ puppet shows help learn the knowledge and skills of the traditional puppetry; (4) in-person operations of real puppets and experiences of RFID-based interactive interfacing bring the users feelings of pleasure and senses of achievement as puppet performers; and (5) the content designs and operations of the puppet characters can turn into a fine material for learning the traditional puppetry. Full article

(This article belongs to the Special Issue Human‑Computer Interaction: Designing for All)

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27 pages, 4538 KiB

Open AccessArticle

Multi-Class Transfer Learning and Domain Selection for Cross-Subject EEG Classification

by Rito Clifford Maswanganyi, Chungling Tu, Pius Adewale Owolawi and Shengzhi Du

Appl. Sci. 2023, 13(8), 5205; https://doi.org/10.3390/app13085205 - 21 Apr 2023

Cited by 3 | Viewed by 1823

Abstract

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In [...] Read more.

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT. Full article

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9 pages, 1002 KiB

Open AccessCommunication

The Modification of Titanium Surface by Decomposition of Tannic Acid Coating

by Beata Kaczmarek-Szczepańska, Lidia Zasada, Marta Michalska-Sionkowska, Jithin Vishnu and Geetha Manivasagam

Appl. Sci. 2023, 13(8), 5204; https://doi.org/10.3390/app13085204 - 21 Apr 2023

Cited by 3 | Viewed by 1836

Abstract

Titanium is one of the most widely used metals in implantology owing to its reduced modulus, improved corrosion resistance and good biocompatibility. In spite of its excellent biocompatibility, it does not exhibit inherent antibacterial and antioxidant activity. Tannic acid is a naturally occurring [...] Read more.

Titanium is one of the most widely used metals in implantology owing to its reduced modulus, improved corrosion resistance and good biocompatibility. In spite of its excellent biocompatibility, it does not exhibit inherent antibacterial and antioxidant activity. Tannic acid is a naturally occurring polyphenol compound which exhibits excellent antibacterial, antioxidant and antimutagenic activity. The development of tannic acid-based coatings on the titanium surface holds great potential to reduce the risks associated with implant applications, thereby increasing the longevity of implants. In the present study, tannic acid was deposited on the titanium surface and the surface displayed a slightly improved hydrophilic character with an increase in surface energy. The release kinetics of tannic acid from titanium surface was analyzed and it showed an initial burst effect followed by a gradual decrease over time. Hemolysis tests revealed the erythrocyte compatibility of the developed surfaces. The improved hydrophilicity observed the release kinetics of tannic acid and reduced hemolysis rates revealed the potential of this facile technique for implant surface engineering applications. Full article

(This article belongs to the Special Issue Advances in Surface Science and Thin Films)

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24 pages, 3280 KiB

Open AccessReview

Electromyographic Activity of the Pectoralis Major Muscle during Traditional Bench Press and Other Variants of Pectoral Exercises: A Systematic Review and Meta-Analysis

by Abraham López-Vivancos, Noelia González-Gálvez, Francisco Javier Orquín-Castrillón, Rodrigo Gomes de Souza Vale and Pablo Jorge Marcos-Pardo

Appl. Sci. 2023, 13(8), 5203; https://doi.org/10.3390/app13085203 - 21 Apr 2023

Cited by 2 | Viewed by 11409

Abstract

The popularity of the bench press (BP) is justified by being one of the most effective exercises to improve strength and power in the upper body. The primary aim of this systematic review and meta-analysis was to compare the electromyography activity (EMG) of [...] Read more.

The popularity of the bench press (BP) is justified by being one of the most effective exercises to improve strength and power in the upper body. The primary aim of this systematic review and meta-analysis was to compare the electromyography activity (EMG) of pectoralis muscle between BP and other variants of pectoral exercises (OP). Methods: This study was conducted according to the PRISMA. Original research articles published by March 2023, were located using an electronic search of four databases and yielded 951 original publications. This review included studies that compared the EMG activity of pectoralis muscle between BP and OP. Data were extracted and independently coded by three researchers. Finally, 23 studies were included for systematic review and meta-analysis. Meta-analysis with fixed or random effect model was performed to infer the pooled estimated standardized mean difference, depending on the heterogeneity. The studies were grouped according to the type of the comparison: grip widths, type of grip, inclination of the bench, stability, or exercise type. Results: The original option of BP activates the sternal portion significantly more than the variant with the inclined bench (SMD = 1.80; 95%CI 0.40 to 3.19; p = 0.017). Performing the exercise in an unstable situation produced significantly more activation during the concentric phase than performing the exercise in a stable situation (SMD = −0.18; 95%CI −0.33 to 3.74; p = 0.029). When comparing by type of exercise, greater activations are also seen in the original bench press vs. the comparisons (p = 0.023 to 0.001). Conclusions: The results suggest that the traditional bench press performed with the bench in a horizontal position, with a bar and a grip width between 150% and 200% of the biacromial distance (BAD) results from a greater EMG involvement of the pectoralis major in most variations with the same relative load. However, the sternal portion of pectoralis major showed greater activation with the declined variant of bench press. Full article

(This article belongs to the Special Issue Deep Networks for Biosignals)

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15 pages, 31510 KiB

Open AccessArticle

A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes

by Pinyang Zhang and Changzheng Chen

Appl. Sci. 2023, 13(8), 5202; https://doi.org/10.3390/app13085202 - 21 Apr 2023

Cited by 3 | Viewed by 1314

Abstract

In the operation and maintenance of planetary gearboxes, the growth of monitoring data is often faster than its analysis and classification. Careful data analysis is generally considered to require more expertise. Rendering the machine learning algorithm able to provide more information, not just [...] Read more.

In the operation and maintenance of planetary gearboxes, the growth of monitoring data is often faster than its analysis and classification. Careful data analysis is generally considered to require more expertise. Rendering the machine learning algorithm able to provide more information, not just the diagnosis conclusion, is promising work. This paper proposes an analysis and diagnosis two-stage framework based on time-frequency information analysis. In the first stage, a U-net model is used for the semantic segmentation of vibration time-frequency spectrum to highlight faulty feature regions. Shape features are then calculated to extract useful information from the segmented image. In the second stage, the decision tree algorithm completes the health state classification of the planetary gearboxes using the input of shape features. The real data of wind turbine planetary gearboxes and augmented data are utilized to verify the proposed framework’s effectiveness and superiority. The F1-score of segmentation and the classification accuracy reach 0.942 and 97.4%, respectively, while in the environmental robustness experiment, they reached 0.747 and 83.1%. Equipping the two-stage framework with different analytical methods and diagnostic algorithms can construct flexible diagnostic systems for similar problems in the community. Full article

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12 pages, 4320 KiB

Open AccessArticle

T* Revise Attenuation Tomography for Q Estimation

by Ziqi Jin, Ruoteng Wang and Ying Shi

Appl. Sci. 2023, 13(8), 5201; https://doi.org/10.3390/app13085201 - 21 Apr 2023

Viewed by 1823

Abstract

Seismic attenuation is often calculated by attenuated travel time tomography. The accuracy of this method is controlled by the precision of attenuated travel time. In this paper, a novel T* revise in attenuated travel time tomography method for Q inversion was developed. The [...] Read more.

Seismic attenuation is often calculated by attenuated travel time tomography. The accuracy of this method is controlled by the precision of attenuated travel time. In this paper, a novel T* revise in attenuated travel time tomography method for Q inversion was developed. The attenuated travel time was calculated from seismic data by using a logarithmic spectral ratio inversion strategy. In the inversion process, multiple offset traces were used for multiple attenuated travel time calculations. The proposed method produced more accurate results compared to those of the conventional approach without the requirement of choosing an optimistic frequency band. The accuracy of the proposed method was improved by avoiding the effect of overburden. Both synthetic and real data examples prove the viability and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Geotechnics and Engineering Geology)

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18 pages, 7390 KiB

Open AccessArticle

A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning

by Chun-Myoung Noh, Jun-Gyo Jang, Sung-Soo Kim, Soon-Sup Lee, Sung-Chul Shin and Jae-Chul Lee

Appl. Sci. 2023, 13(8), 5200; https://doi.org/10.3390/app13085200 - 21 Apr 2023

Cited by 6 | Viewed by 1665

Abstract

With increasing interest in smart factories, considerable attention has been paid to the development of deep-learning-based quality inspection systems. Deep-learning-based quality inspection helps productivity improvements by solving the limitations of existing quality inspection methods (e.g., an inspector’s human errors, various defects, and so [...] Read more.

With increasing interest in smart factories, considerable attention has been paid to the development of deep-learning-based quality inspection systems. Deep-learning-based quality inspection helps productivity improvements by solving the limitations of existing quality inspection methods (e.g., an inspector’s human errors, various defects, and so on). In this study, we propose an optimized YOLO (You Only Look Once) v5-based model for inspecting small coils. Performance improvement techniques (model structure modification, model scaling, pruning) are applied for model optimization. Furthermore, the model is prepared by adding data applied with histogram equalization to improve model performance. Compared with the base model, the proposed YOLOv5 model takes nearly half the time for coil inspection and improves the accuracy of inspection by up to approximately 1.6%, thereby enhancing the reliability and productivity of the final products. Full article

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15 pages, 3177 KiB

Open AccessArticle

Signals Intelligence System with Software-Defined Radio

by Florin Radu, Petru A. Cotfas, Marian Alexandru, Titus C. Bălan, Vlad Popescu and Daniel T. Cotfas

Appl. Sci. 2023, 13(8), 5199; https://doi.org/10.3390/app13085199 - 21 Apr 2023

Viewed by 3225

Abstract

In this paper, we present the implementation of a system that identifies the modulation of complex radio signals. This is realized using an artificial intelligence model developed, trained, and integrated with Microsoft Azure cloud. We consider that cloud-based platforms offer enough flexibility and [...] Read more.

In this paper, we present the implementation of a system that identifies the modulation of complex radio signals. This is realized using an artificial intelligence model developed, trained, and integrated with Microsoft Azure cloud. We consider that cloud-based platforms offer enough flexibility and processing power to use them instead of conventional computers for signal processing based on artificial intelligence. We tested the implementation using a software-defined radio platform developed in GNU Radio that generates and receives real modulated signals. This process ensures that the solution proposed is viable to be used in real signal processing systems. The results obtained show that for certain modulation types, the identification is performed with a high degree of success. The use of a cloud-based platform allows quick access to the system. The user is able to identify the signal modulation using only a laptop that has access to the internet. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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20 pages, 1938 KiB

Open AccessArticle

Leveraging Causal Reasoning in Educational Data Mining: An Analysis of Brazilian Secondary Education

by Rogério Luiz Cardoso Silva Filho, Kellyton Brito and Paulo Jorge Leitão Adeodato

Appl. Sci. 2023, 13(8), 5198; https://doi.org/10.3390/app13085198 - 21 Apr 2023

Cited by 2 | Viewed by 1804

Abstract

This study presents an approach to investigating the main interventions related to gains on performance using a combination of educational data mining (EDM) techniques and traditional theory-driven models. The goal is to overcome the limitation of previous EDM studies that lack of causal [...] Read more.

This study presents an approach to investigating the main interventions related to gains on performance using a combination of educational data mining (EDM) techniques and traditional theory-driven models. The goal is to overcome the limitation of previous EDM studies that lack of causal reasoning, which is a critical concern for educational specialists. We use large-scale assessment data from Brazil and map the main sources of unobserved confounders using causal graphs. We then use a two-way logistic regression fixed effects to account for these confounding factors. The model is evaluated for its predictive ability and further investigated through classification rules and decision trees, resulting in the proposition of new insights into the data. The findings of the study underline the importance of socio-economic factors and showcase the significant impact of faculty education policies as well as the vital role of Brazilian states in these policies. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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21 pages, 2626 KiB

Open AccessArticle

Biogas Production and Metagenomic Analysis in a New Hybrid Anaerobic Labyrinth-Flow Bioreactor Treating Dairy Wastewater

by Marcin Zieliński, Marta Kisielewska, Marcin Dębowski, Paulina Rusanowska, Anna Nowicka and Magda Dudek

Appl. Sci. 2023, 13(8), 5197; https://doi.org/10.3390/app13085197 - 21 Apr 2023

Viewed by 1481

Abstract

Increasing worldwide milk manufacturing and dairy processing resulted in producing more effluents, and thus effective management of wastewater is now the most important issue. This study used a new design of a pilot plant-scale hybrid anaerobic labyrinth-flow bioreactor (AL-FB) to increase the efficiency [...] Read more.

Increasing worldwide milk manufacturing and dairy processing resulted in producing more effluents, and thus effective management of wastewater is now the most important issue. This study used a new design of a pilot plant-scale hybrid anaerobic labyrinth-flow bioreactor (AL-FB) to increase the efficiency of anaerobic biodegradation and biogas productivity and improve anaerobic microflora performance. In addition, effluent recirculation was used to boost the treatment of dairy wastewater. Metagenomic analyses of the anaerobic microbial community were performed. It was found that an organic loading rate (OLR) of 4.0–8.0 g COD/L·d contributed to the highest CH₄ yield of 0.18 ± 0.01–0.23 ± 0.02 L CH₄/g COD removed, which corresponded to a high COD removal of 87.5 ± 2.8–94.1 ± 1.3%. The evenest distribution of the microorganisms’ phyla determined the highest biogas production. In all tested samples, Bacteroidetes and Firmicutes abundance was the highest, and Archaea accounted for about 4%. Metagenomic studies showed that methane was mainly produced in acetoclastic methanogenesis; however, higher OLRs were more favorable for enhanced hydrogenotrophic methanogenesis. Effluent recirculation enhanced the overall treatment. Thus, at OLR of 10.0 g COD/L·d, the highest COD removal was 89.2 ± 0.4%, and methane production yield achieved 0.20 ± 0.01 L CH₄/g COD removed, which was higher by 25% compared to the achievements without recirculation. Full article

(This article belongs to the Special Issue Production, Treatment, Utilization and Future Opportunities of Biogas)

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21 pages, 6226 KiB

Open AccessArticle

Effects of the Excavation of a Hydraulic Tunnel on Groundwater at the Wuyue Pumped Storage Power Station

by Tong Jiang, Xun Pei, Wenxue Wang, Longfei Li and Shihao Guo

Appl. Sci. 2023, 13(8), 5196; https://doi.org/10.3390/app13085196 - 21 Apr 2023

Cited by 3 | Viewed by 1840

Abstract

The tailwater tunnel of the Wuyue pumped storage power station is located in bedrock and extends to depths between tens and hundreds of meters. It is impossible to analyze and evaluate the whole engineering area from geological exploration data, and the hydrogeological conditions [...] Read more.

The tailwater tunnel of the Wuyue pumped storage power station is located in bedrock and extends to depths between tens and hundreds of meters. It is impossible to analyze and evaluate the whole engineering area from geological exploration data, and the hydrogeological conditions are complicated. In the early stages of the tailwater tunnel’s construction, the drinking water wells in four villages dried up. This paper reports the results from a field investigation, in situ tests, laboratory tests, and numerical simulation carried out to determine how the groundwater was affected when the tunnel was excavated. A hydrogeological model of the region was established from the inverted regional natural flow field parameters. The model was validated, and an analysis of the errors showed that there was an average error of 1.98% between the natural flow field and the hydrogeological survey flow field. The model was then used to simulate the three-dimensional transient seepage fields under normal seepage conditions and limited seepage conditions, as far as was practical. The results showed that, as the excavation of the tailwater tunnel advanced, the water inflow to the tunnel also increased. When the water inflow increased from 1000 to 5000 m³/d, the water level at a distance of 100 m from the axis of the tunnel dropped from −0.956 to −1.604 m. We then analyzed how the water level changed as the water inflow varied and proposed a formula for calculating the extent of the influence on the groundwater. We studied how the water level changed at different well points to ascertain how a groundwater well became depleted and determined the factors that influenced seepage in the regional flow field. The water level in different areas of the project area was simulated and analyzed, and the extent of the groundwater area affected by the tunnel construction was clarified. We then studied how the groundwater in different areas of, and distances from, the project area was influenced by normal seepage conditions and limited seepage conditions and proposed a formula for calculating the extent of the influence on groundwater for different water inflows. We constructed a ‘smart site’ for visualizing data, sharing information, and managing the project. Time–frequency domain analysis was applied to explore the extent of the impacts and range of the vibration effects on residential housing at different distances from the project area caused by the different methods for excavating the tailwater tunnel. The results from this analysis will provide useful insights into how the excavation of this tailwater tunnel will impact the local residents and living areas. Full article

(This article belongs to the Special Issue Geo-Environmental Problems Caused by Underground Construction)

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12 pages, 2726 KiB

Open AccessFeature PaperArticle

Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer

by S. Ponseenivasan, Anish Kumar and K. V. Rajkumar

Appl. Sci. 2023, 13(8), 5195; https://doi.org/10.3390/app13085195 - 21 Apr 2023

Cited by 3 | Viewed by 1833

Abstract

For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is [...] Read more.

For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is required to be inspected from the buttering layer side. The buttering layer exhibits very high elastic anisotropy due to elongated columnar grains with preferred orientations. In this paper, the effect of elastic anisotropy on the phased array ultrasonic imaging of defects in the buttering layer is demonstrated for data acquired in full matrix capture (FMC) mode and reconstructed with the total focusing method (TFM). The anisotropy in the buttering layer leads to distorted flaw images, which limits the lateral resolution and defect detection sensitivity. Angle-dependent ultrasonic velocity measured in through-transmission FMC mode has been used for processing the FMC data to obtain high-resolution TFM images with improved sensitivity. The velocity values used are in line with the grain orientations observed by electron-backscatter diffraction (EBSD) studies. Further, an alternate approach is also proposed to obtain a TFM image with improved resolution using a suitable isotropic velocity. The approach can be implemented in any commercial phased array ultrasonic system having the FMC-TFM feature. Full article

(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)

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18 pages, 550 KiB

Open AccessArticle

Modeling Evacuees’ Intended Responses to a Phased Hurricane Evacuation Order

by Ruijie Bian, Pamela Murray-Tuite, Joseph Trainor, Praveen Edara and Konstantinos Triantis

Appl. Sci. 2023, 13(8), 5194; https://doi.org/10.3390/app13085194 - 21 Apr 2023

Cited by 3 | Viewed by 1933

Abstract

Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households’ responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would [...] Read more.

Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households’ responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes (“evacuation reluctant” and “evacuation keen”) by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. “Evacuation reluctant” individuals with a longer housing tenure are more likely to follow phased evacuation orders. “Evacuation keen” individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees’ compliance behavior to phased evacuation orders. Full article

(This article belongs to the Section Transportation and Future Mobility)

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11 pages, 2035 KiB

Open AccessArticle

Assessment of Photosynthetic Carbon Capture versus Carbon Footprint of an Industrial Microalgal Process

by Arthur Oliver, Cristobal Camarena-Bernard, Jules Lagirarde and Victor Pozzobon

Appl. Sci. 2023, 13(8), 5193; https://doi.org/10.3390/app13085193 - 21 Apr 2023

Cited by 5 | Viewed by 2040

Abstract

It is often read that industrial microalgal biotechnology could contribute to carbon capture through photosynthesis. While technically accurate, this claim is rarely supported by sound figures nor put in regard to the carbon emissions associated with said processes. In this view, this work [...] Read more.

It is often read that industrial microalgal biotechnology could contribute to carbon capture through photosynthesis. While technically accurate, this claim is rarely supported by sound figures nor put in regard to the carbon emissions associated with said processes. In this view, this work provides a quantitative assessment of the extent microalgal processes compensation for their carbon dioxide emissions. To do so, microalgae were cultivated under photolimited conditions. Their growth dynamic and photosynthetic apparatus status were monitored by daily cell density measurement and fluorescence assays. Ultimate analyses were used to determine microalgal carbon content. Simultaneously, the power consumption of the process was recorded, and the associated carbon dioxide emissions were computed using European electrical production carbon intensity. All in all, the recorded values confirmed microalgae growth under good physiological conditions and allowed computing the carbon capture rate, the energy storing rate, and the carbon dioxide emissions of the process. The process captured 0.72 ± 0.19 gCO₂/day while emitting 182 gCO₂/day, on average (over 15 days). The photoconversion efficiency was 4.34 ± 0.68%. Even if it were highly optimized (red/blue LED instead of white, for example), the process could only capture 1.02 ± 0.40% of its emissions. From these figures, the claim stating that a biotechnological microalgal production process could partly compensate for its emission seems rather bold. Authors should, therefore, emphasize other ecosystemic benefits of microalgal cultivation, such as phosphorous intake. Finally, we were also able to evaluate Chlorella vulgaris light and dark respiration (0.0377 ± 0.042 day⁻¹ and 7.42 × 10⁻³ ± 3.33 × 10⁻³ day⁻¹), which could help to assess carbon emission by biomass respiratory activity. Full article

(This article belongs to the Special Issue CCUS: Paving the Way to Net Zero Emissions Technologies)

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17 pages, 3458 KiB

Open AccessArticle

Strata Movement Characteristics in Underground Coal Gasification (UCG) under Thermal Coupling and Surface Subsidence Prediction Methods

by Xiaopeng Liu, Liangji Xu and Kun Zhang

Appl. Sci. 2023, 13(8), 5192; https://doi.org/10.3390/app13085192 - 21 Apr 2023

Cited by 5 | Viewed by 1399

Abstract

As a green, safe, and efficient method of coal development, underground coal gasification (UCG) technology has gradually moved from the experimental stage to the industrial production stage. This technology plays one of the key roles in the sustainable development of resources and energy. [...] Read more.

As a green, safe, and efficient method of coal development, underground coal gasification (UCG) technology has gradually moved from the experimental stage to the industrial production stage. This technology plays one of the key roles in the sustainable development of resources and energy. However, underground mining will inevitably lead to strata movement and surface subsidence, which will have certain impacts on the surface environment and buildings. Currently, limited research results on strata movement and surface subsidence under high-temperature environments hardly support the further development of the UCG technology. Hence, this study aims at the key problems of UCG strata movement and surface subsidence prediction. The study established a numerical model to analyze the effects of thermal stress and coal–rock burnt on strata movement and surface subsidence. Results show that coal–rock burnt caused by high temperature has greatly changed the characteristics of UCG strata movement and surface subsidence and is the main controlling factor for aggravating the strata movement and surface subsidence of UCG. The coordinated deformation calculation method of the UCG cavity roof-coal pillar-floor is formed. Moreover, the cooperative subsidence space is regarded as the mining space. A prediction model of surface subsidence based on continuous-discrete medium theory is also established using the probability integral method. The reliability of the predicted model is proved by comparing the measured value with the predicted value. Full article

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15 pages, 795 KiB

Open AccessArticle

Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models

by Guochang Ye, Peshala Thibbotuwawa Gamage, Vignesh Balasubramanian, John K.-J. Li, Ersoy Subasi, Munevver Mine Subasi and Mehmet Kaya

Appl. Sci. 2023, 13(8), 5191; https://doi.org/10.3390/app13085191 - 21 Apr 2023

Cited by 1 | Viewed by 1855

Abstract

Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to predict subsequent adverse cardiovascular events/treatments for patients [...] Read more.

Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to predict subsequent adverse cardiovascular events/treatments for patients within 90 days after their first diagnostic catheterizations. Patients (6539) without previously diagnosed CVD were selected from the DukeCath dataset. Ten ML methods were used. Three medical outcomes, varied cardiovascular-related scenarios, percutaneous coronary intervention (PCI) treatments, and coronary artery bypass graft (CABG) treatments, were targeted individually. With patient medical history, vital measurements, laboratory results, and the number of diseased vessels, the random forest classifier (RFC) performed best in predicting combined cardiovascular scenarios, including CABG, PCI, valve surgery (VS), stroke, and myocardial infarction (MI), achieving accuracy: 88.17%, sensitivity: 89.72%, specificity: 86.98%, area under receiver operating characteristic (AUROC): 91.68%. The gradient boosting classifier (GBC) performed best in predicting the PCI and CABG treatments (PCI treatments: accuracy: 89.21%, sensitivity: 90.20%, specificity: 88.74%, AUROC: 94.16%; CABG treatments: accuracy: 93.86%, sensitivity: 77.57%, specificity: 96.23%, AUROC: 96.47%). Our results show that the ML applications effectively identify high-risk patients, can provide diagnostic assistance in cardiovascular treatment planning, and improve outcomes in cardiovascular medicine. Full article

(This article belongs to the Collection Machine Learning for Biomedical Application)

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22 pages, 6260 KiB

Open AccessArticle

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

by Guozhe Yang, Qingze Tan, Zhiqiang Tian, Xingyu Jiang, Keqiang Chen, Yitao Lu, Weijun Liu and Peisheng Yuan

Appl. Sci. 2023, 13(8), 5190; https://doi.org/10.3390/app13085190 - 21 Apr 2023

Cited by 2 | Viewed by 1560

Abstract

To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop [...] Read more.

To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data Analytics)

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16 pages, 3651 KiB

Open AccessArticle

Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics

by Yiran Liu, Boyi Chen, Jinbao Chen and Yanbin Liu

Appl. Sci. 2023, 13(8), 5189; https://doi.org/10.3390/app13085189 - 21 Apr 2023

Viewed by 1454

Abstract

This paper investigates a rapid modeling method and robust analysis of hypersonic vehicles using multidisciplinary integrated techniques. First, the geometrical configuration is described using parametric methods based on the class–shape technique. Aerodynamic forces and moments are estimated for the specific configuration using engineering [...] Read more.

This paper investigates a rapid modeling method and robust analysis of hypersonic vehicles using multidisciplinary integrated techniques. First, the geometrical configuration is described using parametric methods based on the class–shape technique. Aerodynamic forces and moments are estimated for the specific configuration using engineering methods. Moreover, the nonlinear model is simplified by the polynomial fitting expressions, and the linear variable parameter model is obtained for the tracking control design and dynamic characteristic analysis with the aid of the sensitivity analysis and gap metric methods. A velocity-driven trajectory design method is deduced for hypersonic ascent, and the tracking control law is developed to realize the flight process from the initial point to the cruise point. Furthermore, a robust analysis process based on gap margin is proposed for climb trajectory tracking. Simulation results are provided to verify the feasibility of the proposed modeling method and show that the flight control of a hypersonic vehicle is more sensitive to altitude variation. Full article

(This article belongs to the Special Issue Advanced Guidance and Control of Hypersonic Vehicles)

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