Eng | Special Issue : Feature Papers in Eng 2022

Editorial

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11 pages, 244 KiB

Open AccessEditorial

Special Issue: Feature Papers in Eng 2022

by Antonio Gil Bravo

Eng 2023, 4(2), 1156-1166; https://doi.org/10.3390/eng4020067 - 14 Apr 2023

Viewed by 1081

Abstract

The aim of this second Eng Special Issue is to collect experimental and theoretical re-search relating to engineering science and technology [...] Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

Research

Jump to: Editorial, Review

7 pages, 1451 KiB

Open AccessCommunication

Measuring the Adoption of Drones: A Case Study of the United States Agricultural Aircraft Sector

by Roberto Rodriguez III

Eng 2023, 4(1), 977-983; https://doi.org/10.3390/eng4010058 - 17 Mar 2023

Cited by 2 | Viewed by 2479

Abstract

Unmanned aircraft systems (UAS), commonly referred to as drones, are an emerging technology that has changed the way many industries conduct business. Precision agriculture is one industry that has consistently been predicted to be a major locus of innovation for UAS. However, this [...] Read more.

Unmanned aircraft systems (UAS), commonly referred to as drones, are an emerging technology that has changed the way many industries conduct business. Precision agriculture is one industry that has consistently been predicted to be a major locus of innovation for UAS. However, this has not been the case globally. The agricultural aircraft sector in the United States is used as a case study here to consider different metrics to evaluate UAS adoption, including a proposed metric, the normalized UAS adoption index. In aggregate, UAS operators only make up 5% of the number of agricultural aircraft operators. However, the annual number of new UAS operators exceeded that of manned aircraft operators in 2022. When used on a state-by-state basis, the normalized UAS adoption index shows that there are regional differences in UAS adoption with western and eastern states having higher UAS adoption rates while central states have significantly lower UAS adoption rates. This has implications for UAS operators, manufacturers, and regulators as this industry continues to develop at a rapid pace. Full article

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13 pages, 1366 KiB

Open AccessArticle

A Review of Coastal Protection Using Artificial and Natural Countermeasures—Mangrove Vegetation and Polymers

by Deborah Amos and Shatirah Akib

Eng 2023, 4(1), 941-953; https://doi.org/10.3390/eng4010055 - 8 Mar 2023

Cited by 4 | Viewed by 5249

Abstract

Any stretch of coastline requires protection when the rate of erosion exceeds a certain threshold and seasonal coastal drift fluctuations fail to restore balance. Coast erosion can be caused by natural, synthetic, or a combination of the two. Severe storm occurrences, onshore interventions [...] Read more.

Any stretch of coastline requires protection when the rate of erosion exceeds a certain threshold and seasonal coastal drift fluctuations fail to restore balance. Coast erosion can be caused by natural, synthetic, or a combination of the two. Severe storm occurrences, onshore interventions liable for sedimentation, wave action on the coastlines, and rising sea levels caused by climate change are instances of natural factors. The protective methods used to counteract or prevent coastal flooding are categorized as hard and soft engineering techniques. This review paper is based on extensive reviews and analyses of scientific publications. In order to establish a foundation for the selection of appropriate adaptation measures for coastal protection, this research compiles literature on a combination of both natural and artificial models using mangrove trees and polymer-based models’ configurations and their efficiency in coastal flooding. Mangrove roots occur naturally and cannot be manipulated unlike artificial model configuration which can be structurally configured with different hydrodynamic properties. Artificial models may lack the real structural features and hydrodynamic resistance of the mangrove root it depicts, and this can reduce its real-life application and accuracy. Further research is required on the integration of hybrid configuration to fully optimize the functionality of mangrove trees for coastal protection. Full article

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19 pages, 3189 KiB

Open AccessFeature PaperArticle

On Long-Range Characteristic Length Scales of Shell Structures

by Harri Hakula

Eng 2023, 4(1), 884-902; https://doi.org/10.3390/eng4010053 - 6 Mar 2023

Cited by 1 | Viewed by 1507

Abstract

Shell structures have a rich family of boundary layers including internal layers. Each layer has its own characteristic length scale, which depends on the thickness of the shell. Some of these length scales are long, something that is not commonly considered in the [...] Read more.

Shell structures have a rich family of boundary layers including internal layers. Each layer has its own characteristic length scale, which depends on the thickness of the shell. Some of these length scales are long, something that is not commonly considered in the literature. In this work, three types of long-range layers are demonstrated over an extensive set of simulations. The observed asymptotic behavior is consistent with theoretical predictions. These layers are shown to also appear on perforated structures underlying the fact these features are properties of the elasticity equations and not dependent on effective material parameters. The simulations are performed using a high-order finite element method implementation of the Naghdi-type dimensionally reduced shell model. Additionally, the effect of the perforations on the first eigenmodes is discussed. One possible model for buckling analysis is outlined. Full article

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14 pages, 8513 KiB

Open AccessArticle

Bending and Torsional Stress Factors in Hypotrochoidal H-Profiled Shafts Standardised According to DIN 3689-1

by Masoud Ziaei

Eng 2023, 4(1), 829-842; https://doi.org/10.3390/eng4010050 - 6 Mar 2023

Cited by 4 | Viewed by 2261

Abstract

Hypotrochoidal profile contours have been produced in industrial applications in recent years using two-spindle processes, and they are considered effective high-quality solutions for form-fit shaft and hub connections. This study mainly concerns analytical approaches to determine the stresses and deformations in hypotrochoidal profile [...] Read more.

Hypotrochoidal profile contours have been produced in industrial applications in recent years using two-spindle processes, and they are considered effective high-quality solutions for form-fit shaft and hub connections. This study mainly concerns analytical approaches to determine the stresses and deformations in hypotrochoidal profile shafts due to pure bending loads. The formulation was developed according to bending principles using the mathematical theory of elasticity and conformal mappings. The loading was further used to investigate the rotating bending behaviour. The stress factors for the classical calculation of maximum bending stresses were also determined for all those profiles presented and compiled in the German standard DIN3689-1 for practical applications. The results were also compared with the corresponding numerical and experimental results, and very good agreement was observed. Additionally, based on previous work, the stress factor was determined for the case of torsional loading to calculate the maximum torsional stresses in the standardised profiles, and the results are listed in a table. This study contributes to the further refinement of the current DIN3689 standard. Full article

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17 pages, 392 KiB

Open AccessArticle

Formalising Autonomous Construction Sites with the Help of Abstract Mathematics

by Dmitrii Legatiuk and Daniel Luckey

Eng 2023, 4(1), 799-815; https://doi.org/10.3390/eng4010048 - 1 Mar 2023

Cited by 1 | Viewed by 1690

Abstract

With the rapid development of modern technologies, autonomous or robotic construction sites are becoming a new reality in civil engineering. Despite various potential benefits of the automation of construction sites, there is still a lack of understanding of their complex nature combining physical [...] Read more.

With the rapid development of modern technologies, autonomous or robotic construction sites are becoming a new reality in civil engineering. Despite various potential benefits of the automation of construction sites, there is still a lack of understanding of their complex nature combining physical and cyber components in one system. A typical approach to describing complex system structures is to use tools of abstract mathematics, which provide a high level of abstraction, allowing a formal description of the entire system while omitting non-essential details. Therefore, in this paper, autonomous construction is formalised using categorical ontology logs enhanced by abstract definitions of individual components of an autonomous construction system. In this context, followed by a brief introduction to category theory and ologs, exemplary algebraic definitions are given as a basis for the olog-based conceptual modelling of autonomous construction systems. As a result, any automated construction system can be described without providing exhausting detailed definitions of the system components. Existing ologs can be extended, contracted or revised to fit the given system or situation. To illustrate the descriptive capacity of ologs, a lattice of representations is presented. The main advantage of using the conceptual modelling approach presented in this paper is that any given real-world or engineering problem could be modelled with a mathematically sound background. Full article

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13 pages, 1224 KiB

Open AccessArticle

Use of the Analytic Hierarchy Process Method in the Variety Selection Process for Sugarcane Planting

by Luiza L. P. Schiavon, Pedro A. B. Lima, Antonio F. Crepaldi and Enzo B. Mariano

Eng 2023, 4(1), 602-614; https://doi.org/10.3390/eng4010036 - 15 Feb 2023

Cited by 4 | Viewed by 1958

Abstract

The sugar and alcohol sectors are dynamic as a result of climate alterations, the introduction of sugarcane varieties, and new technologies. Despite these factors, Brazil stands out as the main producer of sugarcane worldwide, being responsible for 45% of the production of fuel [...] Read more.

The sugar and alcohol sectors are dynamic as a result of climate alterations, the introduction of sugarcane varieties, and new technologies. Despite these factors, Brazil stands out as the main producer of sugarcane worldwide, being responsible for 45% of the production of fuel ethanol. Several varieties of sugarcane have been developed in the past few years to improve features of the plant. This, however, led to the challenge of which variety producers should choose to plant on their property. In order to support this process, this research aims to test the application of the analytic hierarchy process (AHP) method to support producers to select which sugarcane variety to plant on their property. To achieve this goal, the research relied on a single case study performed on a rural property located inland of São Paulo state, the main producer state in Brazil. The results demonstrate the feasibility of the approach used, specifically owing to the adaptability capacity of the AHP method. Full article

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12 pages, 2354 KiB

Open AccessArticle

Tensor CSRMT System with Horizontal Electrical Dipole Sources and Prospects of Its Application in Arctic Permafrost Regions

by Alexander K. Saraev, Arseny A. Shlykov and Nikita Yu. Bobrov

Eng 2023, 4(1), 569-580; https://doi.org/10.3390/eng4010034 - 9 Feb 2023

Cited by 2 | Viewed by 1708

Abstract

When studying horizontally-inhomogeneous media, it is necessary to apply tensor modifications of electromagnetic soundings. Use of tensor measurements is of particular relevance in near-surface electrical prospecting because the upper part of the geological section is usually more heterogeneous than the deep strata. In [...] Read more.

When studying horizontally-inhomogeneous media, it is necessary to apply tensor modifications of electromagnetic soundings. Use of tensor measurements is of particular relevance in near-surface electrical prospecting because the upper part of the geological section is usually more heterogeneous than the deep strata. In the Enviro-MT system designed for the controlled-source radiomagnetotelluric (CSRMT) sounding method, two mutually perpendicular horizontal magnetic dipoles (two vertical loops) are used for tensor measurements. We propose a variant of the CSRMT method with two horizontal electrical dipole sources (two transmitter lines). The advantage of such sources is an extended frequency range of 1–1000 kHz in comparison with 1–12 kHz of the Enviro-MT system, greater operational distance (up to 3–4 km compared to 600–800 m), and the ability to measure the signal at the fundamental frequency and its subharmonics. To implement tensor measurements with the equipment of the CSRMT method described in the paper, a technique of creating a time-varying polarization of the electromagnetic field (rotating field) has been developed based on the use of two transmitters with slightly different current frequencies and two mutually-perpendicular transmitter lines grounded at the ends. In this way, we made it possible to change the direction of the electrical and magnetic field polarization continuously. This approach allows realization of the technique of tensor measurements using the new modification of the CSRMT method. In permafrost areas, the hydrogenic taliks are widespread. These local objects are important in the context of study of environmental changes in the Arctic and can be successfully explored by the tensor CSRMT method. For the numerical modeling, a 2D model of the talik was used. Results of the interpretation of synthetic data showed the advantage of bimodal inversion using CSRMT curves of both TM and TE modes compared to separate inversion of TM and TE curves. These new data demonstrate the prospects of the tensor CSRMT method in the study of permafrost regions. The problems that can be solved using the CSRMT method in the Arctic permafrost regions are discussed. Full article

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14 pages, 3414 KiB

Open AccessArticle

by Yubiry Gonzalez and Ronaldo C. Prati

Eng 2023, 4(1), 555-568; https://doi.org/10.3390/eng4010033 - 9 Feb 2023

Cited by 8 | Viewed by 2670

Abstract

Musical timbre is a phenomenon of auditory perception that allows the recognition of musical sounds. The recognition of musical timbre is a challenging task because the timbre of a musical instrument or sound source is a complex and multifaceted phenomenon that is influenced [...] Read more.

Musical timbre is a phenomenon of auditory perception that allows the recognition of musical sounds. The recognition of musical timbre is a challenging task because the timbre of a musical instrument or sound source is a complex and multifaceted phenomenon that is influenced by a variety of factors, including the physical properties of the instrument or sound source, the way it is played or produced, and the recording and processing techniques used. In this paper, we explore an abstract space with 7 dimensions formed by the fundamental frequency and FFT-Acoustic Descriptors in 240 monophonic sounds from the Tinysol and Good-Sounds databases, corresponding to the fourth octave of the transverse flute and clarinet. This approach allows us to unequivocally define a collection of points and, therefore, a timbral space (Category Theory) that allows different sounds of any type of musical instrument with its respective dynamics to be represented as a single characteristic vector. The geometric distance would allow studying the timbral similarity between audios of different sounds and instruments or between different musical dynamics and datasets. Additionally, a Machine-Learning algorithm that evaluates timbral similarities through Euclidean distances in the abstract space of 7 dimensions was proposed. We conclude that the study of timbral similarity through geometric distances allowed us to distinguish between audio categories of different sounds and musical instruments, between the same type of sound and an instrument with different relative dynamics, and between different datasets. Full article

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12 pages, 737 KiB

Open AccessArticle

Covering Arrays ML HPO for Static Malware Detection

by Fahad T. ALGorain and John A. Clark

Eng 2023, 4(1), 543-554; https://doi.org/10.3390/eng4010032 - 9 Feb 2023

Cited by 3 | Viewed by 1902

Abstract

Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with [...] Read more.

Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection. Full article

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19 pages, 477 KiB

Open AccessArticle

High-Performance Computation of the Number of Nested RNA Structures with 3D Parallel Tiled Code

by Piotr Błaszyński and Włodzimierz Bielecki

Eng 2023, 4(1), 507-525; https://doi.org/10.3390/eng4010030 - 3 Feb 2023

Cited by 1 | Viewed by 1258

Abstract

Many current bioinformatics algorithms have been implemented in parallel programming code. Some of them have already reached the limits imposed by Amdahl’s law, but many can still be improved. In our paper, we present an approach allowing us to generate a high-performance code [...] Read more.

Many current bioinformatics algorithms have been implemented in parallel programming code. Some of them have already reached the limits imposed by Amdahl’s law, but many can still be improved. In our paper, we present an approach allowing us to generate a high-performance code for calculating the number of RNA pairs. The approach allows us to generate parallel tiled code of the maximal dimension of tiles, which for the discussed algorithm is 3D. Experiments carried out by us on two modern multi-core computers, an Intel(R) Xeon(R) Gold 6326 (2.90 GHz, 2 physical units, 32 cores, 64 threads, 24 MB Cache) and Intel(R) i7(11700KF (3.6 GHz, 8 cores, 16 threads, 16 MB Cache), demonstrate a significant increase in performance and scalability of the generated parallel tiled code. For the Intel(R) Xeon(R) Gold 6326 and Intel(R) i7, target code speedup increases linearly with an increase in the number of threads. An approach presented in the paper to generate target code can be used by programmers to generate target parallel tiled code for other bioinformatics codes whose dependence patterns are similar to those of the code implementing the counting algorithm. Full article

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13 pages, 3147 KiB

Open AccessArticle

Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

by Shihan Ma and Jidong J. Yang

Eng 2023, 4(1), 444-456; https://doi.org/10.3390/eng4010027 - 1 Feb 2023

Cited by 4 | Viewed by 1809

Abstract

This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning methods, DINO and data2vec, were evaluated and compared for their representation learning of vehicle [...] Read more.

This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning methods, DINO and data2vec, were evaluated and compared for their representation learning of vehicle images. The former contrasts local and global views while the latter uses masked prediction on multiple layered representations. In the latter case, supervised learning is employed to finetune a pretrained YOLOR object detector for detecting vehicle wheels, from which definitive wheel positional features are retrieved. The representations learned from these self-supervised learning methods were combined with the wheel positional features for the vehicle classification task. Particularly, a random wheel masking strategy was utilized to finetune the previously learned representations in harmony with the wheel positional features during the training of the classifier. Our experiments show that the data2vec-distilled representations, which are consistent with our wheel masking strategy, outperformed the DINO counterpart, resulting in a celebrated Top-1 classification accuracy of 97.2% for classifying the 13 vehicle classes defined by the Federal Highway Administration. Full article

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18 pages, 19823 KiB

Open AccessArticle

Drone Detection Using YOLOv5

by Burchan Aydin and Subroto Singha

Eng 2023, 4(1), 416-433; https://doi.org/10.3390/eng4010025 - 1 Feb 2023

Cited by 29 | Viewed by 11554

Abstract

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating [...] Read more.

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). The present study sought to overcome these challenges by proposing a one-shot detector called You Only Look Once version 5 (YOLOv5), which can train the proposed model using pre-trained weights and data augmentation. The trained model was evaluated using mean average precision (mAP) and recall measures. The model achieved a 90.40% mAP, a 21.57% improvement over our previous model that used You Only Look Once version 4 (YOLOv4) and was tested on the same dataset. Full article

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22 pages, 8155 KiB

Open AccessArticle

An Approach to Quantifying the Influence of Particle Size Distribution on Buried Blast Loading

by Ross Waddoups, Sam Clarke, Andrew Tyas, Sam Rigby, Matt Gant and Ian Elgy

Eng 2023, 4(1), 319-340; https://doi.org/10.3390/eng4010020 - 28 Jan 2023

Cited by 2 | Viewed by 1589

Abstract

Buried charges pose a serious threat to both civilians and military personnel. It is well established that soil properties have a large influence on the magnitude and variability of loading from explosive blasts in buried conditions. In this study, work has been undertaken [...] Read more.

Buried charges pose a serious threat to both civilians and military personnel. It is well established that soil properties have a large influence on the magnitude and variability of loading from explosive blasts in buried conditions. In this study, work has been undertaken to improve techniques for processing pressure data from discrete measurement apparatus; this is performed through the testing of truncation methodologies and the area integration of impulses, accounting for the particle size distribution (PSD) of the soils used in testing. Two experimental techniques have been investigated to allow for a comparison between a global impulse capture method and an area-integration procedure from a Hopkinson Pressure Bar array. This paper explores an area-limiting approach, based on particle size distribution, as a possible approach to derive a better representation of the loading on the plate, thus demonstrating that the spatial distribution of loading over a target can be related to the PSD of the confining material. Full article

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17 pages, 1694 KiB

Open AccessArticle

Application of Connected Vehicle Data to Assess Safety on Roadways

by Mandar Khanal and Nathaniel Edelmann

Eng 2023, 4(1), 259-275; https://doi.org/10.3390/eng4010015 - 14 Jan 2023

Cited by 5 | Viewed by 1632

Abstract

Using surrogate safety measures is a common method to assess safety on roadways. Surrogate safety measures allow for proactive safety analysis; the analysis is performed prior to crashes occurring. This allows for safety improvements to be implemented proactively to prevent crashes and the [...] Read more.

Using surrogate safety measures is a common method to assess safety on roadways. Surrogate safety measures allow for proactive safety analysis; the analysis is performed prior to crashes occurring. This allows for safety improvements to be implemented proactively to prevent crashes and the associated injuries and property damage. Existing surrogate safety measures primarily rely on data generated by microsimulations, but the advent of connected vehicles has allowed for the incorporation of data from actual cars into safety analysis with surrogate safety measures. In this study, commercially available connected vehicle data are used to develop crash prediction models for crashes at intersections and segments in Salt Lake City, Utah. Harsh braking events are identified and counted within the influence areas of sixty study intersections and thirty segments and then used to develop crash prediction models. Other intersection characteristics are considered as regressor variables in the models, such as intersection geometric characteristics, connected vehicle volumes, and the presence of schools and bus stops in the vicinity. Statistically significant models are developed, and these models may be used as a surrogate safety measure to analyze intersection safety proactively. The findings are applicable to Salt Lake City, but similar research methods may be employed by researchers to determine whether these models are applicable in other cities and to determine how the effectiveness of this method endures through time. Full article

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23 pages, 655 KiB

Open AccessArticle

Safety Occurrence Reporting amongst New Zealand Uncrewed Aircraft Users

by Claire Natalie Walton and Isaac Levi Henderson

Eng 2023, 4(1), 236-258; https://doi.org/10.3390/eng4010014 - 12 Jan 2023

Cited by 2 | Viewed by 2608

Abstract

Safety reporting has long been recognised as critical to reducing safety occurrences by identifying issues early enough that they can be remedied before an adverse outcome. This study examines safety occurrence reporting amongst a sample of 92 New Zealand civilian uncrewed aircraft users. [...] Read more.

Safety reporting has long been recognised as critical to reducing safety occurrences by identifying issues early enough that they can be remedied before an adverse outcome. This study examines safety occurrence reporting amongst a sample of 92 New Zealand civilian uncrewed aircraft users. An online survey was created to obtain the types of occurrences that these users have had, how (if at all) these are reported, and why participants did or did not report using particular systems. This study focussed on seven types of occurrences that have been highlighted by the Civil Aviation Authority of New Zealand as being reportable using a CA005RPAS form, the template for reporting to the authority for uncrewed aircraft occurrences. The number of each type of occurrence was recorded, as well as what percentage of occurrences were reported using a CA005RPAS form, an internal reporting system, or were non-reported. Qualitative questions were used to understand why participants did or did not report using particular systems. Categorical and numerical data were analysed using Chi-Squared Tests of Independence, Kruskal–Wallis H Tests, and Mann–Whitney U Tests. Qualitative data were analysed using thematic analysis. The findings reveal that 85.72% of reportable safety occurrences went unreported by pilots, with only 2.74% of occurrences being self-reported by pilots using a CA005RPAS form. The biggest reason for non-reporting was that the user did not perceive the occurrence as serious enough, with not being aware of reporting systems and not being legally required to report also being major themes. Significant differences were observed between user groups, providing policy implications to improve safety occurrence reporting, such as making reporting compulsory, setting minimum training standards, having an anonymous and non-punitive reporting system, and through working with member-based organisations. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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8 pages, 4117 KiB

Open AccessArticle

Network Pathway Extraction Focusing on Object Level

by Ali Alqahtani

Eng 2023, 4(1), 151-158; https://doi.org/10.3390/eng4010009 - 3 Jan 2023

Cited by 1 | Viewed by 1340

Abstract

In this paper, I propose an efficient method of identifying important neurons that are related to an object’s concepts by mainly considering the relationship between these neurons and their object concept or class. I first quantify the activation values among neurons, based on [...] Read more.

In this paper, I propose an efficient method of identifying important neurons that are related to an object’s concepts by mainly considering the relationship between these neurons and their object concept or class. I first quantify the activation values among neurons, based on which histograms of each neuron are generated. Then, the obtained histograms are clustered to identify the neurons’ importance. A network-wide holistic approach is also introduced to efficiently identify important neurons and their influential connections to reveal the pathway of a given class. The influential connections as well as their important neurons are carefully evaluated to reveal the sub-network of each object’s concepts. The experimental results on the MNIST and Fashion MNIST datasets show the effectiveness of the proposed method. Full article

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15 pages, 13800 KiB

Open AccessArticle

Investigating Metals and Metalloids in Soil at Micrometric Scale Using µ-XRF Spectroscopy—A Case Study

by Sofia Barbosa, António Dias, Marta Pacheco, Sofia Pessanha and J. António Almeida

Eng 2023, 4(1), 136-150; https://doi.org/10.3390/eng4010008 - 2 Jan 2023

Cited by 2 | Viewed by 1969

Abstract

Micrometric 2D mapping of distinct elements was performed in distinct soil grain-size fractions of a sample using the micro-X-ray Fluorescence (µ-XRF) technique. The sample was collected in the vicinity of São Domingos, an old mine of massive sulphide minerals located in the Portuguese [...] Read more.

Micrometric 2D mapping of distinct elements was performed in distinct soil grain-size fractions of a sample using the micro-X-ray Fluorescence (µ-XRF) technique. The sample was collected in the vicinity of São Domingos, an old mine of massive sulphide minerals located in the Portuguese Iberian Pyrite Belt. As expected, elemental high-grade concentrations of distinct metals and metalloids in the dependence of the existent natural geochemical anomaly were detected. Clustering and k-means statistical analysis were developed considering Red–Green–Blue (RGB) pixel proportions in the produced 2D micrometric image maps, allowing for the identification of elemental spatial distributions at 2D. The results evidence how elemental composition varies significantly at the micrometric scale per grain-size class, and how chemical elements present irregular spatial distributions in the direct dependence of distinct mineral spatial distributions. Due to this fact, elemental composition is more differentiated in coarser grain-size classes, whereas griding-milled fraction does not always represent the average of all partial grain-size fractions. Despite the complexity of the performed analysis, the achieved results evidence the suitability of µ-XRF to characterize natural, heterogeneous, granular soils samples at the micrometric scale, being a very promising investigation technique of high resolution. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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29 pages, 4713 KiB

Open AccessArticle

Using ARIMA to Predict the Growth in the Subscriber Data Usage

by Mike Nkongolo

Eng 2023, 4(1), 92-120; https://doi.org/10.3390/eng4010006 - 1 Jan 2023

Cited by 9 | Viewed by 3694

Abstract

Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. Exploratory analysis of this type of data will facilitate the prediction of varied information that can be geographical, demographic, financial, or any other. Prediction can therefore be an asset in the [...] Read more.

Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. Exploratory analysis of this type of data will facilitate the prediction of varied information that can be geographical, demographic, financial, or any other. Prediction can therefore be an asset in the decision-making process of telecommunications companies, but only if the information retrieved follows a plan with strategic actions. The exploratory analysis of subscriber data was implemented in this research to predict subscriber usage trends based on historical time-stamped data. The predictive outcome was unknown but approximated using the data at hand. We have used 730 data points selected from the Insights Data Storage (IDS). These data points were collected from the hourly statistic traffic table and subjected to exploratory data analysis to predict the growth in subscriber data usage. The Auto-Regressive Integrated Moving Average (ARIMA) model was used to forecast. In addition, we used the normal Q-Q, correlogram, and standardized residual metrics to evaluate the model. This model showed a p-value of 0.007. This result supports our hypothesis predicting an increase in subscriber data growth. The ARIMA model predicted a growth of 3 Mbps with a maximum data usage growth of 14 Gbps. In the experimentation, ARIMA was compared to the Convolutional Neural Network (CNN) and achieved the best results with the UGRansome data. The ARIMA model performed better with execution speed by a factor of 43 for more than 80,000 rows. On average, it takes 0.0016 s for the ARIMA model to execute one row, and 0.069 s for the CNN to execute the same row, thus making the ARIMA 43× (

\frac{0.069}{0.0016}

) faster than the CNN model. These results provide a road map for predicting subscriber data usage so that telecommunication companies can be more productive in improving their Quality of Experience (QoE). This study provides a better understanding of the seasonality and stationarity involved in subscriber data usage’s growth, exposing new network concerns and facilitating the development of novel predictive models. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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12 pages, 5815 KiB

Open AccessFeature PaperArticle

Angle of the Perforation Line to Optimize Partitioning Efficiency on Toilet Papers

by Joana Costa Vieira, André Costa Vieira, Marcelo L. Ribeiro, Paulo T. Fiadeiro and Ana Paula Costa

Eng 2023, 4(1), 80-91; https://doi.org/10.3390/eng4010005 - 1 Jan 2023

Cited by 2 | Viewed by 1871

Abstract

Currently, tissue product producers try to meet consumers’ requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the [...] Read more.

Currently, tissue product producers try to meet consumers’ requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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16 pages, 3704 KiB

Open AccessArticle

Experimental Design for the Propagation of Smoldering Fires in Corn Powder and Cornflour

by Ana C. Rosa, Ivenio Teixeira, Ana M. Lacasta, Laia Haurie, Carlos A. P. Soares, Vivian W. Y. Tam and Assed Haddad

Eng 2023, 4(1), 15-30; https://doi.org/10.3390/eng4010002 - 24 Dec 2022

Cited by 4 | Viewed by 1883

Abstract

Corn is an example of an agricultural grain with a specific combustibility level and can promote smoldering fires during storage. This paper conducts an experimental design to numerically evaluate how three parameters, namely particle size, moisture, and air ventilation, influence the smoldering velocity. [...] Read more.

Corn is an example of an agricultural grain with a specific combustibility level and can promote smoldering fires during storage. This paper conducts an experimental design to numerically evaluate how three parameters, namely particle size, moisture, and air ventilation, influence the smoldering velocity. The work methodology is based on Minitab’s experimental design, which defined the number of experiments. First, a pile of corn is heated by a hot plate and a set of thermocouples registers all temperature variations. Then, a full-factorial experiment is implemented in Minitab to analyze the smoldering, which provides a mathematical equation to represent the smoldering velocity. The results indicate that particle size is the most influential factor in the reaction, with 35% and 45% variation between the dried and wet samples. Moreover, comparing the influence of moisture between corn flour and corn powder samples, a variation of 19% and 31% is observed; additionally, analyzing the ventilation as the only variant, we noticed variations of 15% and 17% for dried and wet corn flour, and 27% and 10% for dried and wet corn powder. Future studies may use the experimental design of this work to standardize the evaluation methodology and more effectively evaluate the relevant influencing factors. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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14 pages, 327 KiB

Open AccessArticle

Strategic Participation of Active Citizen Energy Communities in Spot Electricity Markets Using Hybrid Forecast Methodologies

by Hugo Algarvio

Eng 2023, 4(1), 1-14; https://doi.org/10.3390/eng4010001 - 21 Dec 2022

Cited by 6 | Viewed by 1622

Abstract

The increasing penetrations of distributed renewable generation lead to the need for Citizen Energy Communities. Citizen Energy Communities may be able to be active market players and solve local imbalances. The liberalization of the electricity sector brought wholesale and retail competition as a [...] Read more.

The increasing penetrations of distributed renewable generation lead to the need for Citizen Energy Communities. Citizen Energy Communities may be able to be active market players and solve local imbalances. The liberalization of the electricity sector brought wholesale and retail competition as a natural evolution of electricity markets. In retail competition, retailers and communities compete to sign bilateral contracts with consumers. In wholesale competition, producers, retailers and communities can submit bids to spot markets, where the prices are volatile or sign bilateral contracts, to hedge against spot price volatility. To participate in those markets, communities have to rely on risky consumption forecasts, hours ahead of real-time operation. So, as Balance Responsible Parties they may pay penalties for their real-time imbalances. This paper proposes and tests a new strategic bidding process in spot markets for communities of consumers. The strategic bidding process is composed of a forced forecast methodology for day-ahead and short-run trends for intraday forecasts of consumption. This paper also presents a case study where energy communities submit bids to spot markets to satisfy their members using the strategic bidding process. The results show that bidding at short-term markets leads to lower forecast errors than to long and medium-term markets. Better forecast accuracy leads to higher fulfillment of the community programmed dispatch, resulting in lower imbalances and control reserve needs for the power system balance. Furthermore, by being active market players, energy communities may save around 35% in their electrical energy costs when comparing with retail tariffs. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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16 pages, 8127 KiB

Open AccessArticle

Real-Time Human Authentication System Based on Iris Recognition

by Huma Hafeez, Muhammad Naeem Zafar, Ch Asad Abbas, Hassan Elahi and Muhammad Osama Ali

Eng 2022, 3(4), 693-708; https://doi.org/10.3390/eng3040047 - 15 Dec 2022

Cited by 6 | Viewed by 4607

Abstract

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique [...] Read more.

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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16 pages, 4635 KiB

Open AccessArticle

On the “Thixotropic” Behavior of Fresh Cement Pastes

by Youssef El Bitouri and Nathalie Azéma

Eng 2022, 3(4), 677-692; https://doi.org/10.3390/eng3040046 - 14 Dec 2022

Cited by 5 | Viewed by 2423

Abstract

Thixotropic behavior describes a time-dependent rheological behavior characterized by reversible changes. Fresh cementitious materials often require thixotropic behavior to ensure sufficient workability and proper casting without vibration. Non-thixotropic behavior induces a workability loss. Cementitious materials cannot be considered as an ideal thixotropic material [...] Read more.

Thixotropic behavior describes a time-dependent rheological behavior characterized by reversible changes. Fresh cementitious materials often require thixotropic behavior to ensure sufficient workability and proper casting without vibration. Non-thixotropic behavior induces a workability loss. Cementitious materials cannot be considered as an ideal thixotropic material due to cement hydration, which leads to irreversible changes. However, in some cases, cement paste may demonstrate thixotropic behavior during the dormant period of cement hydration. The aim of this work is to propose an approach able to quantify the contribution of cement hydration during the dormant period and to examine the conditions under which the cement paste may display thixotropic behavior. The proposed approach consists of a succession of stress growth procedures that allow the static yield stress to be measured. For an inert material, such as a calcite suspension, the structural build-up is due to the flocculation induced by attractive Van der Waals forces. This structural build-up is reversible. For cement paste, there is a significant increase in the static yield stress due to cement hydration. The addition of superplasticizer allows the thixotropic behavior to be maintained during the first hours due to its retarding effect. However, an increase in the superplasticizer dosage leads to a decrease in the magnitude of the Van der Waals forces, which can erase the thixotropic behavior. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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24 pages, 7097 KiB

Open AccessArticle

Infrared Spectroscopy for the Quality Control of a Granular Tebuthiuron Formulation

by Joel B. Johnson, Hugh Farquhar, Mansel Ismay and Mani Naiker

Eng 2022, 3(4), 596-619; https://doi.org/10.3390/eng3040041 - 2 Dec 2022

Cited by 2 | Viewed by 1717

Abstract

Tebuthiuron is a selective herbicide for woody species and is commonly manufactured and sold as a granular formulation. This project investigated the use of infrared spectroscopy for the quality analysis of tebuthiuron granules, specifically the prediction of moisture content and tebuthiuron content. A [...] Read more.

Tebuthiuron is a selective herbicide for woody species and is commonly manufactured and sold as a granular formulation. This project investigated the use of infrared spectroscopy for the quality analysis of tebuthiuron granules, specifically the prediction of moisture content and tebuthiuron content. A comparison of different methods showed that near-infrared spectroscopy showed better results than mid-infrared spectroscopy, while a handheld NIR instrument (MicroNIR) showed slightly improved results over a benchtop NIR instrument (Antaris II FT-NIR Analyzer). The best-performing models gave an R²_CV of 0.92 and RMSECV of 0.83% w/w for moisture content, and R²_CV of 0.50 and RMSECV of 7.5 mg/g for tebuthiuron content. This analytical technique could be used to optimise the manufacturing process and reduce the costs of post-manufacturing quality assurance. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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13 pages, 6235 KiB

Open AccessArticle

Evaluation of Color Anomaly Detection in Multispectral Images for Synthetic Aperture Sensing

by Francis Seits, Indrajit Kurmi and Oliver Bimber

Eng 2022, 3(4), 541-553; https://doi.org/10.3390/eng3040038 - 29 Nov 2022

Cited by 5 | Viewed by 2485

Abstract

In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in [...] Read more.

In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate the runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces such as HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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18 pages, 1227 KiB

Open AccessFeature PaperArticle

Dispersive Optical Solitons for Stochastic Fokas-Lenells Equation With Multiplicative White Noise

by Elsayed M. E. Zayed, Mahmoud El-Horbaty, Mohamed E. M. Alngar and Mona El-Shater

Eng 2022, 3(4), 523-540; https://doi.org/10.3390/eng3040037 - 28 Nov 2022

Cited by 14 | Viewed by 1661

Abstract

For the first time, we study the Fokas–Lenells equation in polarization preserving fibers with multiplicative white noise in Itô sense. Four integration algorithms are applied, namely, the method of modified simple equation (MMSE), the method of sine-cosine (MSC), the method of Jacobi elliptic [...] Read more.

For the first time, we study the Fokas–Lenells equation in polarization preserving fibers with multiplicative white noise in Itô sense. Four integration algorithms are applied, namely, the method of modified simple equation (MMSE), the method of sine-cosine (MSC), the method of Jacobi elliptic equation (MJEE) and ansatze involving hyperbolic functions. Jacobi-elliptic function solutions, bright, dark, singular, combo dark-bright and combo bright-dark solitons are presented. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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19 pages, 1295 KiB

Open AccessArticle

A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models

by Brian Andersen, Jason Hou, Andrew Godfrey and Dave Kropaczek

Eng 2022, 3(4), 504-522; https://doi.org/10.3390/eng3040036 - 23 Nov 2022

Cited by 2 | Viewed by 1821

Abstract

This work presents the use of a high-fidelity neural network surrogate model within a Modular Optimization Framework for treatment of crud deposition as a constraint within light-water reactor core loading pattern optimization. The neural network was utilized for the treatment of crud constraints [...] Read more.

This work presents the use of a high-fidelity neural network surrogate model within a Modular Optimization Framework for treatment of crud deposition as a constraint within light-water reactor core loading pattern optimization. The neural network was utilized for the treatment of crud constraints within the context of an advanced genetic algorithm applied to the core design problem. This proof-of-concept study shows that loading pattern optimization aided by a neural network surrogate model can optimize the manner in which crud distributes within a nuclear reactor without impacting operational parameters such as enrichment or cycle length. Several analysis methods were investigated. Analysis found that the surrogate model and genetic algorithm successfully minimized the deviation from a uniform crud distribution against a population of solutions from a reference optimization in which the crud distribution was not optimized. Strong evidence is presented that shows boron deposition in crud can be optimized through the loading pattern. This proof-of-concept study shows that the methods employed provide a powerful tool for mitigating the effects of crud deposition in nuclear reactors. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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12 pages, 440 KiB

Open AccessArticle

Investigating The Impact of Roadway Characteristics on Intersection Crash Severity

by Mostafa Sharafeldin, Ahmed Farid and Khaled Ksaibati

Eng 2022, 3(4), 412-423; https://doi.org/10.3390/eng3040030 - 8 Oct 2022

Cited by 8 | Viewed by 2548

Abstract

Intersections are commonly recognized as crash hot spots on roadway networks. Therefore, intersection safety is a major concern for transportation professionals. Identifying and quantifying the impact of crash contributing factors is crucial to planning and implementing the appropriate countermeasures. This study covered the [...] Read more.

Intersections are commonly recognized as crash hot spots on roadway networks. Therefore, intersection safety is a major concern for transportation professionals. Identifying and quantifying the impact of crash contributing factors is crucial to planning and implementing the appropriate countermeasures. This study covered the analysis of nine years of intersection crash records in the State of Wyoming to identify the contributing factors to crash injury severity at intersections. The study involved the investigation of the influence of roadway (intersection) and environmental characteristics on crash injury severity. The results demonstrated that several parameters related to intersection attributes (pavement friction; urban location; roadway functional classification; guardrails; right shoulder width) and two environmental conditions (road surface condition and lighting) influence the injury severity of intersection crashes. This study identified the significant roadway characteristics influencing crash severity and explored the key role of pavement friction, which is a commonly omitted variable. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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14 pages, 1825 KiB

Open AccessArticle

Preliminary Siting, Operations, and Transportation Considerations for Licensing Fission Batteries in the United States

by DaeHo Lee and Mihai A. Diaconeasa

Eng 2022, 3(3), 373-386; https://doi.org/10.3390/eng3030027 - 4 Sep 2022

Cited by 4 | Viewed by 1901

Abstract

Nuclear energy is currently in the spotlight as a future energy source all over the world amid the global warming crisis. In the current state of miniaturization, through the development of advanced reactors, such as small modular reactors (SMRs) and micro-reactors, a fission [...] Read more.

Nuclear energy is currently in the spotlight as a future energy source all over the world amid the global warming crisis. In the current state of miniaturization, through the development of advanced reactors, such as small modular reactors (SMRs) and micro-reactors, a fission battery is inspired by the idea that nuclear energy can be used by ordinary people using the “plug-and-play” concept, such as chemical batteries. As for design requirements, fission batteries must be economical, standardized, installed, unattended, and reliable. Meanwhile, the commercialization of reactors is regulated by national bodies, such as the United States (U.S.) Nuclear Regulatory Commission (NRC). At an international level, the International Atomic Energy Agency (IAEA) oversees the safe and peaceful use of nuclear power. However, regulations currently face a significant gap in terms of their applicability to advanced non-light water reactors (non-LWRs). Therefore, this study investigates the regulatory gaps in the licensing of fission batteries concerning safety in terms of siting, autonomous operation, and transportation, and suggests response strategies to supplement them. To figure out the applicability of the current licensing framework to fission batteries, we reviewed the U.S. NRC Title 10, Code of Federal Regulations (CFR), and IAEA INSAG-12. To address siting issues, we explored the non-power reactor (NPR) approach for site restrictions and the permit-by-rule (PBR) approach for excessive time burdens. In addition, we discussed how the development of an advanced human-system interface augmented with artificial intelligence and monitored by personnel for fission batteries may enable successful exemptions from the current regulatory operation staffing requirements. Finally, we discovered that no transportation regulatory challenge exists. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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9 pages, 1878 KiB

Open AccessArticle

Efficient Identification of Jiles–Atherton Model Parameters Using Space-Filling Designs and Genetic Algorithms

by Varun Khemani, Michael H. Azarian and Michael G. Pecht

Eng 2022, 3(3), 364-372; https://doi.org/10.3390/eng3030026 - 18 Aug 2022

Cited by 6 | Viewed by 2122

Abstract

The Jiles–Atherton model is widespread in the hysteresis description of ferromagnetic, ferroelectric, magneto strictive, and piezoelectric materials. However, the determination of model parameters is not straightforward because the model involves numerical integration and the solving of ordinary differential equations, both of which are [...] Read more.

The Jiles–Atherton model is widespread in the hysteresis description of ferromagnetic, ferroelectric, magneto strictive, and piezoelectric materials. However, the determination of model parameters is not straightforward because the model involves numerical integration and the solving of ordinary differential equations, both of which are error prone. As a result, stochastic optimization techniques have been used to explore the vast ranges of these parameters in an effort to identify the parameter values that minimize the error differential between experimental and modelled hysteresis curves. Because of the time-consuming nature of these optimization techniques, this paper explores the design space of the parameters using a space-filling design. This design provides a narrower range of parameters to look at with optimization algorithms, thereby reducing the time required to identify the optimal Jiles–Atherton model parameters. This procedure can also be carried out without using expensive hysteresis measurement devices, provided the desired transformer’s secondary voltage is known. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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18 pages, 609 KiB

Open AccessArticle

Analysis of Grid Disturbances Caused by Massive Integration of Utility Level Solar Power Systems

by Esteban A. Soto, Lisa B. Bosman, Ebisa Wollega and Walter D. Leon-Salas

Eng 2022, 3(2), 236-253; https://doi.org/10.3390/eng3020018 - 29 Apr 2022

Cited by 10 | Viewed by 4637

Abstract

Solar generation has increased rapidly worldwide in recent years and it is projected to continue to grow exponentially. A problem exists in that the increase in solar energy generation will increase the probability of grid disturbances. This study focuses on analyzing the grid [...] Read more.

Solar generation has increased rapidly worldwide in recent years and it is projected to continue to grow exponentially. A problem exists in that the increase in solar energy generation will increase the probability of grid disturbances. This study focuses on analyzing the grid disturbances caused by the massive integration to the transmission line of utility-scale solar energy loaded to the balancing authority high-voltage transmission lines in four regions of the United States electrical system: (1) California, (2) Southwest, (3) New England, and (4) New York. Statistical analysis of equality of means was carried out to detect changes in the energy balance and peak power. Results show that when comparing the difference between hourly net generation and demand, energy imbalance occurs in the regions with the highest solar generation: California and Southwest. No significant difference was found in any of the four regions in relation to the energy peaks. The results imply that regions with greater utility-level solar energy adoption must conduct greater energy exchanges with other regions to reduce potential disturbances to the grid. It is essential to bear in mind that as the installed solar generation capacity increases, the potential energy imbalances created in the grid increase. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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33 pages, 7134 KiB

Open AccessFeature PaperReview

Acoustic-Based Machine Condition Monitoring—Methods and Challenges

by Gbanaibolou Jombo and Yu Zhang

Eng 2023, 4(1), 47-79; https://doi.org/10.3390/eng4010004 - 1 Jan 2023

Cited by 19 | Viewed by 6970

Abstract

The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor [...] Read more.

The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor its operational health parameters. However, these methods fall short when additional sensors cannot be installed on the machine due to cost, space constraint or sensor reliability concerns. On the other hand, the use of acoustic-based monitoring technique provides an improved alternative, as acoustic sensors (e.g., microphones) can be implemented quickly and cheaply in various scenarios and do not require physical contact with the machine. The collected acoustic signals contain relevant operating health information about the machine; yet they can be sensitive to background noise and changes in machine operating condition. These challenges are being addressed from the industrial applicability perspective for acoustic-based machine condition monitoring. This paper presents the development in methodology for acoustic-based fault diagnostic techniques and highlights the challenges encountered when analyzing sound for machine condition monitoring. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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19 pages, 5326 KiB

Open AccessReview

All-Purpose Nano- and Microcontainers: A Review of the New Engineering Possibilities

by George Kordas

Eng 2022, 3(4), 554-572; https://doi.org/10.3390/eng3040039 - 30 Nov 2022

Cited by 4 | Viewed by 1684

Abstract

Recently, a subcategory of nanotechnology—nano-, and microcontainers—has developed rapidly, with unexpected results. By nano- and microcontainers, we mean hollow spherical structures whose shells can be organic or inorganic. These containers can be filled with substances released when given an excitation, and fulfill their [...] Read more.

Recently, a subcategory of nanotechnology—nano-, and microcontainers—has developed rapidly, with unexpected results. By nano- and microcontainers, we mean hollow spherical structures whose shells can be organic or inorganic. These containers can be filled with substances released when given an excitation, and fulfill their missions of corrosion healing, cancer therapy, cement healing, antifouling, etc. This review summarizes the scattered innovative technology that has beneficial effects on improving people’s lives. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2022)

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