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Appl. Sci., Volume 14, Issue 13 (July-1 2024) – 348 articles

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20 pages, 2138 KiB  
Article
Effect of Hyperparameter Tuning on the Performance of YOLOv8 for Multi Crop Classification on UAV images
by Oluibukun Gbenga Ajayi, Pius Onoja Ibrahim and Oluwadamilare Samuel Adegboyega
Appl. Sci. 2024, 14(13), 5708; https://doi.org/10.3390/app14135708 (registering DOI) - 29 Jun 2024
Abstract
This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study’s primary objective is to refine the model’s batch size [...] Read more.
This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study’s primary objective is to refine the model’s batch size for improved accuracy and efficiency in crop detection and classification. Using the Google Colaboratory platform, the YOLOv8 model was trained over various batch sizes (10, 20, 30, 40, 50, 60, 70, 80, and 90) to automatically identify the five different classes (sugarcane, banana trees, spinach, pepper, and weeds) present on the UAV images. The performance of the model was assessed using classification accuracy, precision, and recall with the aim of identifying the optimal batch size. The results indicate a substantial improvement in classifier performance from batch sizes of 10 up to 60, while significant dips and peaks were recorded at batch sizes 70 to 90. Based on the analysis of the obtained results, Batch size 60 emerged with the best overall performance for automatic crop detection and classification. Although the F1 score was moderate, the combination of high accuracy, precision, and recall makes it the most balanced option. However, Batch Size 80 also shows very high precision (98%) and balanced recall (84%), which is suitable if the primary focus is on achieving high precision. The findings demonstrate the robustness of YOLOv8 for automatic crop identification and classification in a mixed crop farm while highlighting the significant impact of tuning to the appropriate batch size on the model’s overall performance. Full article
14 pages, 2186 KiB  
Article
Antiplatelet Activity of Phenolic Compounds-Fortified Merlot Wine and Pure Phenolic Compounds
by Lyanne Rodriguez, Óscar A. Muñoz-Bernal, Eduardo Fuentes, Emilio Alvarez-Parrilla and Iván Palomo
Appl. Sci. 2024, 14(13), 5707; https://doi.org/10.3390/app14135707 (registering DOI) - 29 Jun 2024
Abstract
Red wines and their pomace are valuable sources of phenolic compounds (PCs), which have been proposed as potential contributors to their cardioprotective effect through the inhibition of platelet aggregation. The antiplatelet activity of an extract depends on its chemical composition, specifically the presence [...] Read more.
Red wines and their pomace are valuable sources of phenolic compounds (PCs), which have been proposed as potential contributors to their cardioprotective effect through the inhibition of platelet aggregation. The antiplatelet activity of an extract depends on its chemical composition, specifically the presence of certain phenolic compounds, as well as the interactions between them affecting biological activity. In order to assess the effect on platelet aggregation, we investigated the effect of the grape pomace PC enrichment of a Merlot wine, as well as the effect of the five major phenolic compounds present in wine extracts: caffeic acid, gallic acid, quercetin, epicatechin, and catechin. We analyzed how their combination influenced platelet aggregation. We found that the fortified wine sample with the highest PC content (W8) exhibited a potent antiplatelet effect in aggregation and platelet activation assays induced by the agonists TRAP-6, collagen, and ADP, with its activity being most potent against the latter agonist (78 ± 4%). Among the evaluated phenolic compounds, quercetin showed the highest antiplatelet potential against all three agonists studied, while gallic acid showed minimal antiplatelet effect. These findings suggest that the cardioprotective effect of wines is related to their chemical composition and the synergy among phenolic compounds. However, further research is required to fully understand the underlying mechanisms and clinical relevance of this activity. Full article
(This article belongs to the Section Food Science and Technology)
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21 pages, 1079 KiB  
Article
Planning-Level Optimisation of Headway Regularity
by Pavle Pitka, Milan Simeunović, Milica Miličić, Tatjana Kovačević, Milja Simeunović, Dragan Marinković and Žarko Ćojbašić
Appl. Sci. 2024, 14(13), 5706; https://doi.org/10.3390/app14135706 (registering DOI) - 29 Jun 2024
Abstract
Headway variability has a negative impact on the public transport passengers’ perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them [...] Read more.
Headway variability has a negative impact on the public transport passengers’ perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator’s perspective, the passengers’ perspective tends to be overlooked, motivating the current work. In this article, an optimisation procedure for evaluating the viability of diametrical line splitting in terms of passenger travel time and headway regularity is proposed. The aim is to increase the robustness/resistance of the PPT system to the propagation of headway disturbances without reducing the service quality. The developed optimisation procedure was validated by applying it to real data pertaining to an urban PPT line. The results show that there is a positive correlation between the transport demand and the effects of the optimisation procedure, whereby an increase in the primary headway disturbance increases the sensitivity of the optimisation procedure to the transport demand. Full article
(This article belongs to the Special Issue New Technologies in Public Transport and Logistics)
14 pages, 3296 KiB  
Article
The Influence of the Strain-Hardening Model in the Axial Force Prediction of Single Point Incremental Forming
by Rogelio Perez-Santiago, Nicolas J. Hendrichs, Gustavo Capilla-González, Elisa Vázquez-Lepe and Enrique Cuan-Urquizo
Appl. Sci. 2024, 14(13), 5705; https://doi.org/10.3390/app14135705 (registering DOI) - 29 Jun 2024
Abstract
Estimation of the loading conditions during incremental sheet forming is important for designing dedicated equipment, safely utilizing adapted machinery, and developing online process control strategies or trajectory compensation for robot compliance. In this study, we investigate the forming force of pyramidal components of [...] Read more.
Estimation of the loading conditions during incremental sheet forming is important for designing dedicated equipment, safely utilizing adapted machinery, and developing online process control strategies or trajectory compensation for robot compliance. In this study, we investigate the forming force of pyramidal components of uniform wall angles through analytical, experimental, and numerical approaches. After reviewing the existing research, the accuracy of the estimations from two analytical models and finite element simulations was assessed. Experimental results revealed that the maximum force occurs at 45° and 60° for AA1050-H24 and AISI-SAE 304 materials, respectively. FEA simulations leveraging tensile test data and refined isotropic hardening laws provided conservative estimations for the two materials. In the case of the AA1050-H24, numerical models accurately reproduce the experimental trend of components formed with Single Point Incremental Forming (SPIF) at different wall angles. On the other hand, alternative hardening models may be required to improve the force predictions for the AISI-SAE 304 material. Full article
(This article belongs to the Section Mechanical Engineering)
18 pages, 6416 KiB  
Article
The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
by Joan Lambert Cause, Ángel Solé Morillo, Juan C. García-Naranjo, Johan Stiens and Bruno da Silva
Appl. Sci. 2024, 14(13), 5704; https://doi.org/10.3390/app14135704 (registering DOI) - 29 Jun 2024
Abstract
Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips [...] Read more.
Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments. Full article
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13 pages, 2039 KiB  
Article
A Comparative Study on Soil-Crop Selenium Characteristics in High-Incidence Areas of Keshan Disease in Chinese Loess and Black Soil
by Jun Zhao, Zhu Rao, Siwen Liu, Lei Wang, Peng Wang, Tao Yang and Jin Bai
Appl. Sci. 2024, 14(13), 5703; https://doi.org/10.3390/app14135703 (registering DOI) - 29 Jun 2024
Abstract
With the gradual emphasis on health by people, the research on the pathogenesis of endemic diseases has become increasingly in-depth. Through analyzing the environmental selenium characteristics and conducting a comparative study in typical areas of Chinese loess and black soil in this paper, [...] Read more.
With the gradual emphasis on health by people, the research on the pathogenesis of endemic diseases has become increasingly in-depth. Through analyzing the environmental selenium characteristics and conducting a comparative study in typical areas of Chinese loess and black soil in this paper, it is concluded that the environmental selenium in the two regions has different characteristics. The soil in the loess area has the characteristics of high alkalinity, low selenium, and relatively high selenium availability, and the crops are selenium-deficient, while the soil in the black soil area has the characteristics of high organic matter, low selenium availability, and relatively high selenium in crops. The research concluded that the environmental occurrence mechanism of Keshan disease in the loess area and the black soil area is different. Keshan disease can be induced in both low-selenium and sufficient-selenium environments, and environmental selenium should be one of the inducing factors of Keshan disease. This research provides a reference for predicting the areas where Keshan disease occurs and for disease prevention, and it can also serve in the prevention and control of endemic diseases. Full article
(This article belongs to the Special Issue The Mobilization, Speciation and Transformation of Organic and Inorganic Contaminants in Soil-Groundwater Ecosystems)
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20 pages, 9785 KiB  
Article
Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis
by Laurentiu Cristea and Marius Deaconu
Appl. Sci. 2024, 14(13), 5702; https://doi.org/10.3390/app14135702 (registering DOI) - 29 Jun 2024
Abstract
Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, this analysis covers theoretical and practical aspects of prediction methods used for the development of sound attenuators for gas-turbine testing stands. This paper presents a preliminary [...] Read more.
Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, this analysis covers theoretical and practical aspects of prediction methods used for the development of sound attenuators for gas-turbine testing stands. This paper presents a preliminary analysis and evaluation of the improvement of the Embleton method for projecting a noise attenuator for industrial applications, especially for gas-turbine test stands. While primarily focusing on the static acoustic behavior of the attenuator, certain considerations were also made regarding flow conditions, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective on applying a suitable evaluation method. The study investigates different calculation methods for the assessment of noise reduction for linear and staggered baffles applied on a scaled reduced model of an attenuator. Thus, the critical parameters and development requirements necessary for effective noise reduction in high-performance gas-turbine testing environments will be evaluated in a downscaled model. Key factors examined include the selection of design parameters and configurations from various topological options (single, double, and triple parallel baffles vs. double and triple staggered baffles). Advanced computational methods, like analytic and finite-element analysis (FEM), are used to predict acoustic performance and evaluate the prediction method. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The results indicate that an improved prediction method led to a better design for a sound-attenuator module, which can significantly reduce noise levels without compromising the operational performance of the gas turbine inside a test cell. Full article
(This article belongs to the Special Issue Noise Measurement, Acoustic Signal Processing and Noise Control)
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16 pages, 2979 KiB  
Article
The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
by Pavol Michal, Milan Vaško, Milan Sapieta, Jaroslav Majko and Jakub Fiačan
Appl. Sci. 2024, 14(13), 5701; https://doi.org/10.3390/app14135701 (registering DOI) - 29 Jun 2024
Abstract
This article investigates the impact of changes in the internal structure of composite materials on their dynamic properties. The present research focuses on 3D-printed specimens with different reinforcement fiber arrangements. The specimens are printed on a Markforged Mark Two 3D printer. The base [...] Read more.
This article investigates the impact of changes in the internal structure of composite materials on their dynamic properties. The present research focuses on 3D-printed specimens with different reinforcement fiber arrangements. The specimens are printed on a Markforged Mark Two 3D printer. The base material is nylon filled with chopped carbon fibers (Onyx) and the reinforcement is in the form of long carbon, glass and Kevlar fibers. The experiment is carried out by the impact method. The principle of this method is to expose the specimen to a short impulse of force while monitoring its frequency response. The obtained results determine the natural frequencies and internal damping of the individual structures. We found that the highest damping is achieved by specimens with glass and Kevlar fibers in 45°, 90° and ±45° configurations. On the other hand, the lowest damping is achieved by specimens with carbon fibers and 0° and 0°,90° configurations. Also, the specimens with circumferential reinforcement show lower damping coefficient values. The knowledge and results of this work can be used in the development of new components; for example, in the transport industry, where the low weight and sufficient strength of components are important factors. These components have to absorb vibrations from various sources, such as motors and external influences. Full article
(This article belongs to the Section Additive Manufacturing Technologies)
13 pages, 1123 KiB  
Article
Possibilities of Quality Management of Chicken Meat Produced in Polish Industrial Conditions Using an Own-Construction Device for Poultry Electric Stunning in a Water Bath
by Joanna Katarzyna Banach, Ryszard Żywica and Małgorzata Grzywińska-Rąpca
Appl. Sci. 2024, 14(13), 5700; https://doi.org/10.3390/app14135700 (registering DOI) - 29 Jun 2024
Abstract
The aim of the research was to determine the possibilities for managing chicken meat (fillet) quality by applying the own-construction (OC) device for electrical stunning. We determined the effects based on selected technological and visual features of fillets, providing a measurable basis for [...] Read more.
The aim of the research was to determine the possibilities for managing chicken meat (fillet) quality by applying the own-construction (OC) device for electrical stunning. We determined the effects based on selected technological and visual features of fillets, providing a measurable basis for producers to take preventive actions to improve meat quality management practices. The experimental material consisted of fillets from broiler chickens. The process of electrically stunning chickens in a water bath was carried out in Polish industrial conditions using two devices: own-construction (OC) and commercial from a Polish company (PLC). We determined the quality of fresh and stored meat (14 days/4 °C) based on technological characteristics (pH, color, tenderness) and visual assessment (number of small and large hemorrhages, defects). As a result, the use of the own-construction (OC) device compared to the commercial (PLC) one has a beneficial effect on: (1) reducing the number of hemorrhages, (2) increasing the share of high-quality fillet production by approx. 50%, (3) brightening and uniforming the color of fillets, and (4) improving the tenderness of fresh meat and maintaining it during 14 days of storage. The effects of using the OC device are beneficial for meat producers on the market. Full article
(This article belongs to the Special Issue Novel Food Technologies and Applications)
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16 pages, 5584 KiB  
Article
Wind Turbine Performance Evaluation Method Based on Dual Optimization of Power Curves and Health Regions
by Qixue Guan, Jiarui Han, Keying Geng and Yueqiu Jiang
Appl. Sci. 2024, 14(13), 5699; https://doi.org/10.3390/app14135699 (registering DOI) - 29 Jun 2024
Abstract
The wind power curve serves as a critical metric for assessing wind turbine performance. Developing a model based on this curve and evaluating turbine efficiency within a defined health region, derived from the statically optimized power curve, holds significant value for wind farm [...] Read more.
The wind power curve serves as a critical metric for assessing wind turbine performance. Developing a model based on this curve and evaluating turbine efficiency within a defined health region, derived from the statically optimized power curve, holds significant value for wind farm operations. This paper proposes an optimized wind power curve segmentation modeling method based on an improved PCF algorithm to address the inconsistency between the function curve and the wind power curve, as well as the issues of prolonged curve modeling training time and susceptibility to local optima. A health region optimization method based on data increment inflection points is developed, which enables the delineation of the health performance evaluation region for wind turbines. Through the aforementioned optimization, the performance evaluation method for wind turbines is significantly improved. The effectiveness of the performance evaluation method is validated through experimental case studies, combining the wind power curve with the rotational speed stability, power characteristic consistency coefficient, and power generation efficiency indicators. The proposed modeling technique achieves a precision level of 0.998, confirming its applicability and effectiveness in practical engineering scenarios. Full article
(This article belongs to the Special Issue Advances and Challenges in Wind Turbine Mechanics)
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20 pages, 1905 KiB  
Article
Study on the Distribution Law of External Water Pressure with Limited Discharge during Shield Construction of Soft Rock Tunnel in Western Henan Province
by Haining Liu, Wenjia Ma, Minglei Kang, Yunyou Fu, Tingsong Yan, Handong Liu and Benchao Zhao
Appl. Sci. 2024, 14(13), 5698; https://doi.org/10.3390/app14135698 (registering DOI) - 29 Jun 2024
Abstract
When shield tunneling is carried out in poor geological areas with high water head and low strength, there is a great construction risk, and the external water pressure is one of the key factors affecting the stability and safety of the tunnel during [...] Read more.
When shield tunneling is carried out in poor geological areas with high water head and low strength, there is a great construction risk, and the external water pressure is one of the key factors affecting the stability and safety of the tunnel during shield tunneling. Taking the Yinguruxin tunnel of Xin’an County as the engineering background, the geological conditions and water-bearing characteristics of the water-rich area in the tunnel excavation path are analyzed by means of drilling and high-density electrical method, and the pumping test is used to evaluate the groundwater linkage in the water-rich area. The 2022 version of Midas GTS NX is used to study the distribution characteristics of external water pressure during tunnel excavation in fault zones, and the influence of different drainage rates on the external water pressure of the tunnel is analyzed. The results show that the rock mass in the unfavorable geological section of the tunnel excavation is broken and rich in water, but the complexity of the stratum leads to uneven water richness in the axis direction of the tunnel. The drainage rate is the key to affecting the external water pressure of the lining. The drainage rate is the key to affecting the external water pressure of the tunnel. The correct drainage rate is an effective measure to reduce the external water pressure of the tunnel. The internal and external water pressure of the tunnel decreases with the increase of the drainage rate. When the drainage rate reaches 66.67% of the water inflow, the external water pressure of the tunnel can be reduced to 0.3 MPa to ensure the safety of the earth pressure balance shield machine in the tunneling process. The conclusion provides a useful reference for the high water pressure control of the tunnel shield tunneling process. Full article
(This article belongs to the Section Civil Engineering)
22 pages, 1530 KiB  
Article
Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness
by Goran Ferenc, Dragoje Timotijević, Ivana Tanasijević and Danijela Simić
Appl. Sci. 2024, 14(13), 5697; https://doi.org/10.3390/app14135697 (registering DOI) - 29 Jun 2024
Abstract
This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively [...] Read more.
This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios. Full article
(This article belongs to the Special Issue Human-Centered Approaches to Automated Vehicles)
17 pages, 1395 KiB  
Article
A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
by Faisal Qarah and Tawfeeq Alsanoosy
Appl. Sci. 2024, 14(13), 5696; https://doi.org/10.3390/app14135696 (registering DOI) - 29 Jun 2024
Abstract
Pretrained language models have achieved great success in various natural language understanding (NLU) tasks due to their capacity to capture deep contextualized information in text using pretraining on large-scale corpora. Tokenization plays a significant role in the process of lexical analysis. Tokens become [...] Read more.
Pretrained language models have achieved great success in various natural language understanding (NLU) tasks due to their capacity to capture deep contextualized information in text using pretraining on large-scale corpora. Tokenization plays a significant role in the process of lexical analysis. Tokens become the input for other natural language processing (NLP) tasks, like semantic parsing and language modeling. However, there is a lack of research on the evaluation of the impact of tokenization on the Arabic language model. Therefore, this study aims to address this gap in the literature by evaluating the performance of various tokenizers on Arabic large language models (LLMs). In this paper, we analyze the differences between WordPiece, SentencePiece, and BBPE tokenizers by pretraining three BERT models using each tokenizer while measuring the performance of each model on seven different NLP tasks using 29 different datasets. Overall, the model pretrained with text tokenized using the SentencePiece tokenizer significantly outperforms the other two models that utilize WordPiece and BBPE tokenizers. The results of this paper will assist researchers in developing better models, making better decisions in selecting the best tokenizers, improving feature engineering, and making models more efficient, thus ultimately leading to advancements in various NLP applications. Full article
21 pages, 2183 KiB  
Article
A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
by Yifan Zhang, Ye Tian and Qi Xia
Appl. Sci. 2024, 14(13), 5695; https://doi.org/10.3390/app14135695 (registering DOI) - 29 Jun 2024
Abstract
Curved grid stiffeners, compared to straight stiffeners, offer greater flexibility in adjusting the force transmission paths and give better structural performance. In this paper, a level-set-based density method is employed to generate layouts of curved grid stiffeners so that the critical buckling load [...] Read more.
Curved grid stiffeners, compared to straight stiffeners, offer greater flexibility in adjusting the force transmission paths and give better structural performance. In this paper, a level-set-based density method is employed to generate layouts of curved grid stiffeners so that the critical buckling load factor (BLF) of the stiffened structures is improved. During the optimization process, volume constraint is incorporated to control material utilization, and gradient constraints are employed to maintain uniformity in the width of the stiffeners. Finally, the proposed method is demonstrated through several numerical examples. Full article
(This article belongs to the Special Issue Structural Optimization Methods and Applications, 2nd Edition)
22 pages, 2913 KiB  
Article
A Multi-Objective Evaluation Method for Smart Highway Operation and Management
by Li Li, Yixin Long and Chongmei Peng
Appl. Sci. 2024, 14(13), 5694; https://doi.org/10.3390/app14135694 (registering DOI) - 29 Jun 2024
Abstract
Smart highways represent a novel highway concept in the era of big data, emphasizing the synergy among people, vehicles, road facilities, and the environment. However, the operation and management of smart highways have become more intricate, surpassing the adaptability of traditional highway evaluation [...] Read more.
Smart highways represent a novel highway concept in the era of big data, emphasizing the synergy among people, vehicles, road facilities, and the environment. However, the operation and management of smart highways have become more intricate, surpassing the adaptability of traditional highway evaluation and management methods. This study integrates the distinctive characteristics of smart highway facilities and operational objectives to enhance and modernize the existing highway evaluation system. Drawing from research on smart highway construction projects, a smart highway evaluation system encompassing facility structure, electromechanical facilities, and operation services is formulated based on a hierarchical analysis method. The quantitative evaluation of each indicator is achieved by combining existing specifications and expert questionnaire solicitation. The group decision-making method is initially employed to optimize subjective weights, followed by the calculation of combined weights using both the entropy weight method and critic method in objective evaluation. Finally, a comprehensive evaluation model is established and validated through engineering projects. The results demonstrate that the evaluation system effectively highlights the advantages and disadvantages in the operation and management of smart highways, thereby fostering the advancement of smart highway iteration. Full article
(This article belongs to the Section Transportation and Future Mobility)
20 pages, 7509 KiB  
Article
How Does Digital Technology Inspire Global Fashion Design Trends? Big Data Analysis on Design Elements
by Nahyun Lee and Sungeun Suh
Appl. Sci. 2024, 14(13), 5693; https://doi.org/10.3390/app14135693 (registering DOI) - 29 Jun 2024
Abstract
Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of [...] Read more.
Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of digital technology on fashion design elements (e.g., color, print and graphic, textiles, and style and details) was evident through various keywords related to digital technology, humans, and nature, and the relationships between these keywords were confirmed. The analysis of the implicit meanings and directions of the derived keywords resulted in four clusters: (1) human- and nature-oriented design in the digital world as a new reality; (2) new textiles reflecting digital technology; (3) sustainable design technology; and (4) new utility fashion in the digital space. This study proposed a new design research methodology in which big data were incorporated and could be applied to educational curricula, allowing students to derive practical design elements through big data analysis and serving as a guide for planning and developing technology-inspired designs. Practically, it provided specific information on the direction of digital-technology-inspired fashion design trends, which could assist fashion designers and aspiring entrepreneurs in planning. Full article
(This article belongs to the Special Issue Text Mining and Data Mining)
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26 pages, 1009 KiB  
Article
Simplified Tunnel–Soil Model Based on Thin-Layer Method–Volume Method–Perfectly Matched Layer Method
by Yu Wang, Mengfan Zhou, Yanmei Cao, Xiaoxi Wang, Zhe Li and Meng Ma
Appl. Sci. 2024, 14(13), 5692; https://doi.org/10.3390/app14135692 (registering DOI) - 29 Jun 2024
Abstract
In order to analyze the ground vibration responses induced by the dynamic loads in a tunnel, this paper proposes a new simplified tunnel–soil model. Specifically, based on the basic theory of the thin-layer method (TLM), the basic solution of three-dimensional layered foundation soil [...] Read more.
In order to analyze the ground vibration responses induced by the dynamic loads in a tunnel, this paper proposes a new simplified tunnel–soil model. Specifically, based on the basic theory of the thin-layer method (TLM), the basic solution of three-dimensional layered foundation soil displacement was derived in the cylindrical coordinate system. The perfectly matched layer (PML) boundary condition was applied to the TLM. Subsequently, a tunnel–soil dynamic interaction analysis model was established using the volume method (VM) in conjunction with the TLM-PML method. The displacement frequency response function of the foundation soil around the tunnel foundation was derived. Finally, a ground vibration test under an impact load in a tunnel was carried out. The test and calculated results were compared. The comparison results show that the ground vibration acceleration response values within 25 m from the load are similar. Compared with the test results, the theoretical calculation results exhibit a decreasing trend in the range of 40–80 Hz between 25 and 60 m, with the maximum reduction being approximately one order of magnitude. In addition, the experimental comparison demonstrates that the model can be used to analyze the ground vibrations caused by underground loads. Full article
(This article belongs to the Special Issue Geotechnical Earthquake Engineering: Current Progress and Road Ahead)
19 pages, 3876 KiB  
Article
An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
by Zhaoxu Tian, Yongmei Cheng and Shun Yao
Appl. Sci. 2024, 14(13), 5691; https://doi.org/10.3390/app14135691 (registering DOI) - 29 Jun 2024
Abstract
In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO [...] Read more.
In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy. Full article
(This article belongs to the Collection Advances in Automation and Robotics)
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33 pages, 2758 KiB  
Article
Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms
by Daniel Osmundsen Dirdal, Danny Vo, Yuming Feng and Reggie Davidrajuh
Appl. Sci. 2024, 14(13), 5690; https://doi.org/10.3390/app14135690 (registering DOI) - 29 Jun 2024
Abstract
Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first [...] Read more.
Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of High-Performance Computing and Parallel Computing)
18 pages, 1768 KiB  
Article
Analysis of the Concentration of Selected Elements in Teeth Hard Tissues and Their Role in Biomineralization Processes
by Mirona Palczewska-Komsa, Renata Pilarczyk, Viktoriia Havryliak, Alicja Nowicka, Katarzyna Grocholewicz and Ewa Sobolewska
Appl. Sci. 2024, 14(13), 5689; https://doi.org/10.3390/app14135689 (registering DOI) - 29 Jun 2024
Abstract
The role of trace elements, heavy metals, and their effect on the development of hard tissue mineralization balance is poorly documented and the available results are often contradictory. The aim of the present study was the assessment of the concentration of the selected [...] Read more.
The role of trace elements, heavy metals, and their effect on the development of hard tissue mineralization balance is poorly documented and the available results are often contradictory. The aim of the present study was the assessment of the concentration of the selected elements in the teeth of deer (Capreolus capreolus) and red deer (Cervus elaphus) with respect to their potential role in maintaining biomineralization balance in teeth hard tissues. Moreover, the aim was to determine whether trace elements accumulate in teeth with age. This study was conducted on 22 molar teeth of red deer (Cervus elaphus) and 54 molar teeth of roe deer (Capreolus capreolus) from Poland. Samples were analyzed with the use of inductively coupled plasma optical emission spectrometry equipped with a concentric nebulizer and cyclonic spray chamber in order to determine their Al, Ba, Cd, Fe, K, Li, Mg, Na, Ni, Pb, Sr, and Zn contents. There were strong positive correlations between strontium and zinc with heavy metals, particularly in dentine, which may affect the neutralization of the negative effects of heavy metals on tooth tissue and, consequently, maintaining ionic balance. The accumulation of trace elements with age in serenades has not been confirmed. Full article
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14 pages, 7338 KiB  
Article
pH-Dependent Morphology of Copper (II) Oxide in Hydrothermal Process and Their Photoelectrochemical Application for Non-Enzymatic Glucose Biosensor
by Trung Tin Tran, Anh Hao Huynh Vo, Thien Trang Nguyen, Anh Duong Nguyen, My Hoa Huynh Tran, Viet Cuong Tran and Trung Nghia Tran
Appl. Sci. 2024, 14(13), 5688; https://doi.org/10.3390/app14135688 (registering DOI) - 29 Jun 2024
Abstract
In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies [...] Read more.
In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies including nanoparticles, nanorods, and nanosheets according to the pH levels of 4, 7, and 12, respectively. The observed variations in surface morphology are attributed to fluctuations in growth rates across different crystal facets, which are influenced by the presence of intermediate species within the reaction. This report also compared the structural and optical properties of the synthesized CuO nanostructures and explored their potential for photoelectrochemical glucose sensing. Notably, CuO nanoparticles and nanorods displayed exceptional performance with calculated limits of detection of 0.69 nM and 0.61 nM, respectively. Both of these morphologies exhibited a linear response to glucose within their corresponding concentration ranges (3–20 nM and 20–150 nM). As a result, CuO nanorods appear to be a more favorable photoelectrochemical sensing method because of the large surface area as well as the lowest solution resistance in electroimpedance analysis compared to CuO nanoparticles and nanosheets forms. These findings strongly suggest the promising application of hydrothermal-synthesized CuO nanostructures for ultrasensitive photoelectrochemical glucose biosensors. Full article
(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)
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17 pages, 15662 KiB  
Article
A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
by Rui Liu and Dong Li
Appl. Sci. 2024, 14(13), 5687; https://doi.org/10.3390/app14135687 (registering DOI) - 29 Jun 2024
Abstract
To investigate the development of cracks in the walls of reinforced concrete silos under feeding–discharge cycle loading, their causes, and their fatigue life during dynamic loading, a study was conducted using a combination of in situ monitoring and numerical simulation analysis. The following [...] Read more.
To investigate the development of cracks in the walls of reinforced concrete silos under feeding–discharge cycle loading, their causes, and their fatigue life during dynamic loading, a study was conducted using a combination of in situ monitoring and numerical simulation analysis. The following conclusions were drawn: during the loading and unloading process of the silo, the time of occurrence of the minimum pressure points follows a 4:3 ratio; extreme points are approximately 15 min apart; the minimum pressure increases during material addition and decreases during material subtraction; and the load in the non-discharge area is 1.43 times that of the load in the discharge area. That is, at the same elevation, the load borne by the silo wall is uneven, with fluctuations and rotational effects occurring. Under such uneven load conditions, the silo wall experiences significant bending and torsional moments, causing excessive local tension and leading to cracking. Our analysis showed that the most unfavorable load condition occurs when discharge ports 5 and 7 are operating simultaneously, which causes the maximum tensile damage to the silo wall. For the first time, a fatigue life prediction model for reinforced concrete silos was proposed, and the accuracy of this prediction method was verified based on actual conditions. Full article
(This article belongs to the Section Civil Engineering)
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25 pages, 1198 KiB  
Article
Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis
by Keyue Yan, Simon Fong, Tengyue Li and Qun Song
Appl. Sci. 2024, 14(13), 5686; https://doi.org/10.3390/app14135686 (registering DOI) - 29 Jun 2024
Abstract
Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework [...] Read more.
Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication. Full article
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20 pages, 2404 KiB  
Review
E-Waste Management in Serbia, Focusing on the Possibility of Applying Automated Separation Using Robots
by Dragana Nišić, Branko Lukić, Zaviša Gordić, Uroš Pantelić and Arso Vukićević
Appl. Sci. 2024, 14(13), 5685; https://doi.org/10.3390/app14135685 (registering DOI) - 29 Jun 2024
Abstract
To encourage proper waste management for electrical and electronic devices (e-waste), it is necessary to invest heavily in the development of recycling technologies. One way to improve the process is to automate separating the shredded parts of e-waste using a robot. This paper’s [...] Read more.
To encourage proper waste management for electrical and electronic devices (e-waste), it is necessary to invest heavily in the development of recycling technologies. One way to improve the process is to automate separating the shredded parts of e-waste using a robot. This paper’s literature review, utilizing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, showcases potential robotic technologies for e-waste separation. However, the intricate design of these devices can pose significant challenges in their implementation. Various legal, organizational, and sociological obstacles have left Serbia’s e-waste management practice underdeveloped, resulting in an unsatisfactory recycling rate. In this paper, we examined the possibility of using robots in the precise example of recycling refrigerators in a recycling center in Eastern Serbia, concluding that such a solution would have multiple positive effects, both on the employees and the working environment, on the operations of the recycling center itself, and on increasing the e-waste recycling rate in the country. Full article
(This article belongs to the Special Issue Research Progress in Waste Resource Utilization)
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14 pages, 416 KiB  
Article
In Vitro Bioactivities of Cereals, Pseudocereals and Seeds: Assessment of Antiglycative and Carbonyl-Trapping Properties
by Marta Mesías, Francisca Holgado, Elena Olombrada and Francisco José Morales
Appl. Sci. 2024, 14(13), 5684; https://doi.org/10.3390/app14135684 (registering DOI) - 28 Jun 2024
Abstract
Advanced glycation endproducts (AGEs) are the final products resulting from non-enzymatic glycation, which plays a crucial role in diabetes and aging-related health issues. The aim of the present investigation was to examine the inhibitory effects on AGE formation of aqueous and methanolic extracts [...] Read more.
Advanced glycation endproducts (AGEs) are the final products resulting from non-enzymatic glycation, which plays a crucial role in diabetes and aging-related health issues. The aim of the present investigation was to examine the inhibitory effects on AGE formation of aqueous and methanolic extracts from cereals (rice, rye, and wheat), pseudocereals (amaranth, quinoa, and buckwheat) and chia seeds. Different in vitro models simulating AGEs induced by glucose (Glc) and methylglyoxal (MGO) were evaluated. The MGO-trapping capacity of extracts was evaluated, alongside their antioxidant capacity and phenolic compound composition, with the aim of exploring any potential correlation with AGEs’ inhibitory effects. Extracts (25 mg/mL) demonstrated inhibitory effects on AGEs in protein–Glc and protein–MGO assays, with inhibition levels ranging from below 10% (amaranth extracts) to over 90% (buckwheat extracts) compared with aminoguanidine. Buckwheat methanolic extract exhibited the highest anti-AGE activity (98.3% inhibition in the BSA–Glc and 89.5% inhibition in the BSA–MGO assay), followed by chia seed extracts (80–82% inhibition). Buckwheat aqueous extract showed the greatest capacity to directly trap MGO (IC50 = 0.3 mg/mL). Antioxidants and phenolic compounds likely contributed to their antiglycative activity. In conclusion, aqueous and methanolic extracts derived from different natural ingredients such as cereals, pseudocereals, and seeds can be valuable in preventing glycation-related complications. Full article
(This article belongs to the Special Issue Biological Activity, Chemical Characterization and Contaminants of Plants and Waste)
34 pages, 4900 KiB  
Review
A Survey on Visual Mamba
by Hanwei Zhang, Ying Zhu, Dan Wang, Lijun Zhang, Tianxiang Chen, Ziyang Wang and Zi Ye
Appl. Sci. 2024, 14(13), 5683; https://doi.org/10.3390/app14135683 (registering DOI) - 28 Jun 2024
Abstract
State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how [...] Read more.
State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Processing)
20 pages, 651 KiB  
Article
The Development of a Named Entity Recognizer for Detecting Personal Information Using a Korean Pretrained Language Model
by Sungsoon Jang, Yeseul Cho, Hyeonmin Seong, Taejong Kim and Hosung Woo
Appl. Sci. 2024, 14(13), 5682; https://doi.org/10.3390/app14135682 (registering DOI) - 28 Jun 2024
Abstract
Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing [...] Read more.
Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing to errors of unrecognition and misrecognition. Research in named entity recognition predominantly focuses on English, which poses challenges for non-English languages. By specifying procedures for the development of Korean-based tag sets, data collection, and preprocessing, we formulated directions on the application of entity recognition research to non-English languages. Such research could significantly benefit artificial intelligence (AI)-based natural language processing globally. We developed a personal information tag set comprising 33 items and established guidelines for dataset creation, later converting it into JSON format for AI learning. State-of-the-art AI models, BERT and ELECTRA, were employed to implement and evaluate the named entity recognition (NER) model, which achieved an 0.943 F1-score and outperformed conventional recognizers in detecting personal information. This advancement suggests that the proposed NER model can effectively prevent personal information leakage in systems processing interactive text data, marking a significant stride in safeguarding privacy across digital platforms. Full article
(This article belongs to the Special Issue Natural Language Processing: Theory, Methods and Applications)
11 pages, 1120 KiB  
Article
Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
by Simiao Chen, Jie Huang, Fenshan Su, Xuelei Feng and Yong Shen
Appl. Sci. 2024, 14(13), 5681; https://doi.org/10.3390/app14135681 (registering DOI) - 28 Jun 2024
Abstract
Miniature loudspeakers exhibit pronounced nonlinear characteristics due to physical size limitations and the demand for sufficient sound pressure. The primary nonlinear properties, including the force factor, mechanical stiffness, and mechanical resistance, depend on the displacement or velocity of the voice coil. The creep [...] Read more.
Miniature loudspeakers exhibit pronounced nonlinear characteristics due to physical size limitations and the demand for sufficient sound pressure. The primary nonlinear properties, including the force factor, mechanical stiffness, and mechanical resistance, depend on the displacement or velocity of the voice coil. The creep effect in the loudspeaker’s suspension system, which significantly affects low-frequency displacement, is often neglected in existing studies on nonlinear dynamics. This study enhances the modeling of miniature loudspeakers by incorporating the creep effect into an extended nonlinear model. The voice coil displacement and total harmonic distortion (THD) predicted by the extended model were validated against experimental data. The displacement and THD values predicted by the nonlinear model in consideration of the creep effect corresponded closely to the actual measurement values, substantiating the effectiveness and precision of the model. Full article
17 pages, 2283 KiB  
Article
Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables
by Simin Xing, Zimu Lin, Xianglan Gao, Dehua Wang, Guohui Liu, Yi Cao and Yadi Liu
Appl. Sci. 2024, 14(13), 5680; https://doi.org/10.3390/app14135680 (registering DOI) - 28 Jun 2024
Abstract
Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study [...] Read more.
Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process. Full article
(This article belongs to the Special Issue Applications of Machine Learning Technology in Agricultural Data Mining)
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15 pages, 4273 KiB  
Article
Development of an Image Processing Application for Element Detection in a Printed Circuit Board Manufacturing Cell
by Antonio Trejo-Morales, Milton Bautista-Ortega, Leonardo Barriga-Rodríguez, Celso Eduardo Cruz-González and Edgar Adrián Franco-Urquiza
Appl. Sci. 2024, 14(13), 5679; https://doi.org/10.3390/app14135679 (registering DOI) - 28 Jun 2024
Abstract
Industrial automation in the manufacturing environment has revolutionized production and manufacturing in many industries, generating significant improvements in efficiency, quality, and process effectiveness. However, it has also posed challenges related to feedback in manufacturing environment monitoring systems, and increasing the effectiveness, productivity, and [...] Read more.
Industrial automation in the manufacturing environment has revolutionized production and manufacturing in many industries, generating significant improvements in efficiency, quality, and process effectiveness. However, it has also posed challenges related to feedback in manufacturing environment monitoring systems, and increasing the effectiveness, productivity, and quality in industrial production. Feedback systems in the manufacturing environment are fundamental to industrial automation, which is why an application has been developed for the detection of elements in a printed circuit board manufacturing cell. The solution presented in this article proposes implementing a continuous feedback system with the ability to provide real-time information to identify the location of elements in a manufacturing cell and potentially detect anomalies, with the goal of improving the manufacturing process appropriately. Full article
(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing, 2nd Volume)
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