Applied Sciences

20 pages, 2908 KiB

Open AccessArticle

LSTM with Short-Term Bias Compensation to Determine Trading Strategy under Black Swan Events of Taiwan ETF50 Stock

by Ray-I Chang, Chia-Hui Wang, Lien-Chen Wei and Ya-Fang Lu

Appl. Sci. 2024, 14(18), 8576; https://doi.org/10.3390/app14188576 - 23 Sep 2024

Viewed by 741

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model’s predictions, a calibration procedure called “Short-Term Bias Compensation” (STBC) is proposed to adjust the predicted [...] Read more.

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model’s predictions, a calibration procedure called “Short-Term Bias Compensation” (STBC) is proposed to adjust the predicted stock prices. In STBC, the daily prediction error is calculated to estimate the short-term bias (STB) in prediction. Then, the predicted price of its next day will be corrected if this STB has exceeded a certain threshold. In this paper, we apply Genetic Algorithms (GAs) to optimize the parameters used in STBC for providing more confidence in its estimation. Based on these predicted stock prices, we propose a Genetic Fuzzy System (GFS) to determine the trading strategy, with trading points for buying and selling stocks. In GFS, various technical indicators are used to establish the fuzzy rules of the trading strategy, and GAs are used to evolve the best parameters for these fuzzy rules. Our experiments cover over 17 years of data (from 2003 to 2020) for ETF50 to consider black swan events such as the 2020 COVID-19 pandemic, the 2018 US–China trade war, and the 2011 US debt crisis. The first 90% of the data is used as training data, and the last 10% is used as testing data. We use 12 technical indicators of these data as the input of LSTM. The predicted values of LSTM are corrected using STBC and compared to the uncorrected prices. We use Mean Square Error (MSE) to evaluate the prediction accuracy. The results show that STBC can nearly reduce 90% of the prediction error (where MSE drops from 11.5758 to 1.2687). By using GFS with STBC to determine trading points, we achieve a return rate of 32.0%. Full article

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

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14 pages, 4136 KiB

Open AccessArticle

Accurate and Reliable Food Nutrition Estimation Based on Uncertainty-Driven Deep Learning Model

by DaeHan Ahn

Appl. Sci. 2024, 14(18), 8575; https://doi.org/10.3390/app14188575 - 23 Sep 2024

Viewed by 1154

Abstract

Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can [...] Read more.

Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can endanger individuals sensitive to specific foods, as it may lead to significant errors in nutrient estimation. To address these issues, we propose an accurate and reliable food nutrient prediction model. Our model introduces a loss function designed to minimize prediction errors by leveraging the relationships among food nutrients. Additionally, we developed a method that enables the model to autonomously estimate its own uncertainty based on the loss, reducing the risk to users. Comparative experiments demonstrate that our model achieves superior performance, with an

R^{2}

value of 0.98 and an RMSE of 0.40, reflecting a 5–15% improvement over other models. The autonomous result rejection mechanism showing a 40.6% improvement further enhances robustness, particularly in handling uncertain predictions. These findings highlight the potential of our approach for precise and trustworthy nutritional assessments in real-world applications. Full article

(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)

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17 pages, 70466 KiB

Open AccessArticle

Experimental Research on the Influence of Repeated Overheating on the Thermal Diffusivity of the Inconel 718 Alloy

by Elisabeta Roxana Ungureanu Arva, Marioara Abrudeanu, Denis Aurelian Negrea, Andrei Galatanu, Magdalena Galatanu, Alin-Daniel Rizea, Daniel-Constantin Anghel, Mihai Branzei, Alexandra Ion Jinga and Mircea Ionut Petrescu

Appl. Sci. 2024, 14(18), 8574; https://doi.org/10.3390/app14188574 - 23 Sep 2024

Viewed by 939

Abstract

The Inconel 718 superalloy, a precipitation-hardenable material, is of particular interest for applications involving components operating under extreme conditions due to its excellent mechanical properties, high corrosion resistance at temperatures up to 700 °C, and good workability. At high temperatures, thermal transfer processes [...] Read more.

The Inconel 718 superalloy, a precipitation-hardenable material, is of particular interest for applications involving components operating under extreme conditions due to its excellent mechanical properties, high corrosion resistance at temperatures up to 700 °C, and good workability. At high temperatures, thermal transfer processes are crucial for temperature distribution across the component’s section, structural transformations, and variations in the alloy’s properties. The history of accidental overheating events is critical for the microstructure and properties of the alloy. Studies on thermal transfer in the Inconel 718 alloy available in the literature typically focus on the alloy in its as-delivered state. The experimental research presented in this paper examines the influence of repeated overheating history on the thermal diffusivity of the alloy. Full article

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24 pages, 8410 KiB

Open AccessArticle

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

by Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park and Kihyeon Kwon

Appl. Sci. 2024, 14(18), 8573; https://doi.org/10.3390/app14188573 - 23 Sep 2024

Viewed by 1324

Abstract

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the [...] Read more.

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings. Full article

(This article belongs to the Special Issue Hydrogen Energy and Hydrogen Safety)

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22 pages, 10776 KiB

Open AccessArticle

Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks

by Zheng Liu, Yuhao Zhang, Haodong Liu, Xin Liu, Jinlong Liang and Zhenjiang Shao

Appl. Sci. 2024, 14(18), 8572; https://doi.org/10.3390/app14188572 - 23 Sep 2024

Viewed by 953

Abstract

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and [...] Read more.

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning-Based Fault Detection and Diagnosis)

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23 pages, 5066 KiB

Open AccessArticle

Promotion Effects of High-Speed Rail on Urban Development: Evidence from Three Lines in China

by Chen Chen

Appl. Sci. 2024, 14(18), 8571; https://doi.org/10.3390/app14188571 - 23 Sep 2024

Viewed by 625

Abstract

Amid the vigorous development of the high-speed rail (HSR) network, local governments in China generally consider the construction of HSR as a crucial task in their regional development strategies. Currently, most provincial capitals and prefecture-level cities in the eastern and central regions of [...] Read more.

Amid the vigorous development of the high-speed rail (HSR) network, local governments in China generally consider the construction of HSR as a crucial task in their regional development strategies. Currently, most provincial capitals and prefecture-level cities in the eastern and central regions of China already have operational HSR services. This study aims to examine a key question: has the objective of local governments to promote urban development through the construction of HSR been effective? The research selects cities along the Beijing–Shanghai, Beijing–Guangzhou, and Harbin–Dalian HSR lines as the study subjects. Based on the principles of proximity and similarity, cities with operational HSR and those without are chosen as the experimental group and the control group, respectively. Following the double difference (difference-in-differences) approach, an advantage index is proposed to systematically evaluate the impact of HSR operation on urban development from three dimensions: population aggregation, economic development, and expansion of construction land. Furthermore, the evaluation results are systematically clustered to identify city types that exhibit different promotional effects in various dimensions. The research findings indicate the following: (1) The promotion effect of HSR on the development of small to medium-sized cities is more reflected in economic growth and construction land growth. (2) The promotion effect of HSR on the development of large cities is more reflected in the growth of the employment population. (3) For smaller or economically less-developed cities, HSR may be detrimental to the aggregation of resident and employment populations. (4) Cities with moderate size and good economic development have the opportunity to use HSR to promote population aggregation. On this basis, combined with the findings above, strategies to promote the coordinated development of high-speed rail construction and urban systems are discussed. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility)

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13 pages, 2338 KiB

Open AccessArticle

Investigating the Effect of Motion Capture Suits on the Test–Retest Reliability of Gait Parameters

by Matt C. Smith, Phaedra Leveridge, Garry Massey, Jessica Tyrrell, Malcolm Hilton and Genevieve K. R. Williams

Appl. Sci. 2024, 14(18), 8570; https://doi.org/10.3390/app14188570 - 23 Sep 2024

Viewed by 640

Abstract

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the [...] Read more.

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model. Full article

(This article belongs to the Special Issue Advances in Sport and Exercise Biomechanics)

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17 pages, 1568 KiB

Open AccessArticle

New Functionality for Moodle E-Learning Platform: Files Communication by Chat Window

by Vasile Baneș, Cristian Ravariu and Avireni Srinivasulu

Appl. Sci. 2024, 14(18), 8569; https://doi.org/10.3390/app14188569 - 23 Sep 2024

Viewed by 560

Abstract

Moodle allows communication between students through the chat window, where you can send text messages and emoticons. A study carried out on 45 students identified which method they prefer to use to send attachments—which seems to them to be the most effective and [...] Read more.

Moodle allows communication between students through the chat window, where you can send text messages and emoticons. A study carried out on 45 students identified which method they prefer to use to send attachments—which seems to them to be the most effective and easy to use. The challenges we started with in this implementation of the solution were the non-existence of this way of transmitting files within the Moodle platform and the need to introduce this new method, which has an impact on the communication process that is beneficial to users. When a requirement arises from users such as sending files through the chat window, a feature that does not exist now, the IT administrator has the possibility to create a new method by implementing a plugin that may be imported into the Moodle platform settings. By writing the necessary parameters, arguments, and command lines in the developed plugin, it was possible to create a new way to send files. This paper presents a new solution that contributes the possibility of transmitting files through the chat window, with various extensions such as .pdf, .zip, .docx, .jpg, .xls, .mp4, and other types and sizes of files that can be sent at any time and as many as desired, not limited by the number of uploads related to the transmission. Full article

(This article belongs to the Special Issue Advancements in Intelligent Systems: The Confluence of AI, Machine Learning, and Robotics)

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11 pages, 3535 KiB

Open AccessArticle

Influence of Dentin Sealing Technique on the Shear Bond Strength between Lithium Disilicate Ceramics and Try-In-Paste-Contaminated Dentin

by Gozde Ciftci, Orhun Ekren, Koray Soygun and Volkan Ciftci

Appl. Sci. 2024, 14(18), 8568; https://doi.org/10.3390/app14188568 - 23 Sep 2024

Viewed by 617

Abstract

This study aimed to evaluate the effect of try-in paste contamination on the bond strength of lithium disilicate glass–ceramic to dentin treated with immediate (IDS) or delayed (DDS) dentin sealing techniques. Occlusal halves of 33 molars were decapitated and divided into three groups [...] Read more.

This study aimed to evaluate the effect of try-in paste contamination on the bond strength of lithium disilicate glass–ceramic to dentin treated with immediate (IDS) or delayed (DDS) dentin sealing techniques. Occlusal halves of 33 molars were decapitated and divided into three groups (n = 10). Lithium disilicate discs (3 × 5 mm) were prepared. For Group A, the provisional crown was applied over dentin and was soaked in distilled water. Lithium disilicate discs were cemented following dentin conditioning with a three-step etch and rinse adhesive. In Group B (IDS), the three-step adhesive was applied to dentin. The dentin surfaces were conditioned only in the final cementation for Group C (DDS). The intaglio surfaces of test groups were contaminated with try-in paste. All specimens were thermally cycled 3000 times at 5–55 °C and were subjected to shear tests. An additional three specimens for each group were contaminated with try-in paste and subjected to the same surface cleaning as the test specimens were examined with SEM/EDS. The adhesive surfaces were also examined with SEM/EDS for try-in paste remnants. Group C showed a significant decrease in bond strength values compared to Group B and Group A (5.84 ±1.4 MPa, 11.45 ±2.4 MPa, and 10.29 ±2.5 MPa, respectively). No statistically significant difference was detected between Group B and Group A (p ≥ 0.05). The SEM-EDS analyses revealed obstructions of the dentinal tubules in the try-in-paste-contaminated specimens. Immediate dentin sealing application enhanced the bonding strength of lithium disilicate to the try-in-paste-contaminated dentin. Try-in paste contamination over dentin negatively influenced the bonding process. Full article

(This article belongs to the Special Issue Biotechnology Applied to Dentistry)

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10 pages, 5446 KiB

Open AccessArticle

Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization

by Tae-Jin Park, Kwang-Yeon Kim and Dong-Wook Oh

Appl. Sci. 2024, 14(18), 8567; https://doi.org/10.3390/app14188567 - 23 Sep 2024

Viewed by 781

Abstract

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe [...] Read more.

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 °C, while the ambient temperature was maintained at 14 °C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 °C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection. Full article

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17 pages, 2720 KiB

Open AccessArticle

Comprehensive Characterization and Metamorphic Control Analysis of Full Apertures in Different Coal Ranks within Deep Coal Seams

by Qi Li, Yong Wu and Lei Qiao

Appl. Sci. 2024, 14(18), 8566; https://doi.org/10.3390/app14188566 - 23 Sep 2024

Viewed by 514

Abstract

The pore fracture structure of deep coal reservoirs is crucial for evaluating the potential of deep coalbed methane resources, conducting exploration and development, and controlling coal mine gas disasters. Mercury intrusion porosimetry, the liquid nitrogen method, and the low-temperature carbon dioxide adsorption method [...] Read more.

The pore fracture structure of deep coal reservoirs is crucial for evaluating the potential of deep coalbed methane resources, conducting exploration and development, and controlling coal mine gas disasters. Mercury intrusion porosimetry, the liquid nitrogen method, and the low-temperature carbon dioxide adsorption method were used to study the full pore size structure and pore fractal characteristics of different coal grades in deep coal and comprehensively characterize the pore structure of kilometer-level coal mining. The sponge, Frenkel–Halsey–Hill (FHH), and density function models were applied to comprehensively analyze the pore complexity of coal, and the influence of metamorphic degree on pore size structure was evaluated. The distribution relationship of pore volume in different stages of coal samples was macropore→mesopore→micropore, and macropores had the best connectivity. Micropores and mesopores had the largest specific surface area, and the development of micropores and microcracks controlled the deep gas adsorption performance. The micropore volume and specific surface area both revealed a nonlinear decreasing trend with the increase in volatile matter, and coal metamorphism promoted the development of micropores. The pore volume and specific surface area of mesopores and macropores decreased first and then increased in a “U” shape with increasing volatile matter. In contrast, the fractal dimension D₁ revealed an inverted U shape with increasing volatile matter, followed by a decrease. The D₂ value decreased nonlinearly with increasing volatile matter, whereas the D₃ value increased nonlinearly with increasing volatile matter. The degree of metamorphism increased, and the microporous structure became more regular. Full article

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17 pages, 3343 KiB

Open AccessArticle

Anti-Vibration Method for the Near-Bit Measurement While Drilling of Pneumatic Down-the-Hole Hammer Drilling

by Lu Wang, Wenchao Gou, Jun Wang and Zheng Zhou

Appl. Sci. 2024, 14(18), 8565; https://doi.org/10.3390/app14188565 - 23 Sep 2024

Viewed by 3228

Abstract

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH [...] Read more.

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5–20 Hz and vibration level of 10–40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects. Full article

(This article belongs to the Special Issue Drilling Theory Research and Its Engineering Applications)

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21 pages, 3308 KiB

Open AccessArticle

Method of Multi-Label Visual Emotion Recognition Fusing Fore-Background Features

by Yuehua Feng and Ruoyan Wei

Appl. Sci. 2024, 14(18), 8564; https://doi.org/10.3390/app14188564 - 23 Sep 2024

Viewed by 582

Abstract

This paper proposes a method for multi-label visual emotion recognition that fuses fore-background features to address the following issues that visual-based multi-label emotion recognition often overlooks: the impacts of the background that the person is placed in and the foreground, such as social [...] Read more.

This paper proposes a method for multi-label visual emotion recognition that fuses fore-background features to address the following issues that visual-based multi-label emotion recognition often overlooks: the impacts of the background that the person is placed in and the foreground, such as social interactions between different individuals on emotion recognition; the simplification of multi-label recognition tasks into multiple binary classification tasks; and it ignores the global correlations between different emotion labels. First, a fore-background-aware emotion recognition model (FB-ER) is proposed, which is a three-branch multi-feature hybrid fusion network. It efficiently extracts body features by designing a core region unit (CR-Unit) that represents background features as background keywords and extracts depth map information to model social interactions between different individuals as foreground features. These three features are fused at both the feature and decision levels. Second, a multi-label emotion recognition classifier (ML-ERC) is proposed, which captures the relationship between different emotion labels by designing a label co-occurrence probability matrix and cosine similarity matrix, and uses graph convolutional networks to learn correlations between different emotion labels to generate a classifier that considers emotion correlations. Finally, the visual features are combined with the object classifier to enable the multi-label recognition of 26 different emotions. The proposed method was evaluated on the Emotic dataset, and the results show an improvement of 0.732% in the mAP and 0.007 in the Jaccard’s coefficient compared with the state-of-the-art method. Full article

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

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29 pages, 2459 KiB

Open AccessReview

Substituting Sugar in Pastry and Bakery Products with Functional Ingredients

by Huțu Dana and Amariei Sonia

Appl. Sci. 2024, 14(18), 8563; https://doi.org/10.3390/app14188563 - 23 Sep 2024

Viewed by 3677

Abstract

Replacing the amount of sugar in pastries with functional ingredients may be a strategy of interest to food manufacturers. Reducing the content of sugar in pastries and bakery products could be a measure to reduce diseases such as obesity, diabetes, cardiovascular disease, tooth [...] Read more.

Replacing the amount of sugar in pastries with functional ingredients may be a strategy of interest to food manufacturers. Reducing the content of sugar in pastries and bakery products could be a measure to reduce diseases such as obesity, diabetes, cardiovascular disease, tooth decay, and cognitive impairment. Additionally, energy consumption, greenhouse gas emissions, and global warming potential are the main issues in sugar beet agricultural production systems. Due to the multiple roles that sugar has in the dough (i.e., provide energy, sweeten, improve the structural characteristics, extend shelf life, limit the swelling of the starch, give color and flavor to ripe products, and ensure the preservation of products), there have been attempts at substituting sugar in percentages of up to 100% in different products such as cakes, muffins, pies, biscuits, cookies, and bread. From the points of view of technology and consumer perception, the best substitutes are apple puree, inulin, oligofructose, stevia, apple pomace, polydextrose, dried apples, Nypa fruticans sap, grape juice/syrup, and date powder/syrup. Depending on the substituent, when substituting sugar in percentages from 10 to 100%, improvements were obtained in terms of texture, rheological properties, sensory properties, consumer acceptability, and physicochemical and nutritional properties. Full article

(This article belongs to the Special Issue Advances in Bioactive Compounds from Plants and Their Applications)

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18 pages, 33106 KiB

Open AccessArticle

Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change

by Yanqian Pei, Haijun Qiu and Yaru Zhu

Appl. Sci. 2024, 14(18), 8562; https://doi.org/10.3390/app14188562 - 23 Sep 2024

Viewed by 890

Abstract

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development [...] Read more.

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale. Full article

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18 pages, 2376 KiB

Open AccessArticle

Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic

by Ahmad Hani El Fawal, Ali Mansour and Abbass Nasser

Appl. Sci. 2024, 14(18), 8561; https://doi.org/10.3390/app14188561 - 23 Sep 2024

Viewed by 733

Abstract

Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself [...] Read more.

Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself to be an invaluable tool for decision-making systems, natural language processing, and predictive modeling. In an Internet of Things (IoT), Machine-to-Machine (M2M) traffic necessitates new traffic models due to its unique pattern and different goals. In this context, we have two types of modeling: (1) source traffic modeling, used to design stochastic processes so that they match the behavior of physical quantities of measured data traffic (e.g., video, data, voice), and (2) aggregated traffic modeling, which refers to the process of combining multiple small packets into a single packet in order to reduce the header overhead in the network. In IoT studies, balancing the accuracy of the model while managing a large number of M2M devices is a heavy challenge for academia. One the one hand, source traffic models are more competitive than aggregated traffic models because of their dependability. However, their complexity is expected to make managing the exponential growth of M2M devices difficult. In this paper, we propose to use a Markov-Modulated Poisson Process (MMPP) framework to explore Human-to-Human (H2H) traffic and M2M heterogeneous traffic effects. As a tool for stochastic processes, we employ Markov chains to characterize the coexistence of H2H and M2M traffic. Using the traditional evolved Node B (eNodeB), our simulation results show that the network’s service completion rate will suffer significantly. In the worst-case scenario, when an accumulative storm of M2M requests attempts to access the network simultaneously, the degradation reaches 8% as a completion task rate. However, using our “Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long term evolution” (CHANAL) solution, we can achieve a service completion rate of 96%. Full article

(This article belongs to the Special Issue AI, Machine Learning and Deep Learning in Signal Processing, 2nd Edition)

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17 pages, 13756 KiB

Open AccessCommunication

Sign Language Interpreting System Using Recursive Neural Networks

by Erick A. Borges-Galindo, Nayely Morales-Ramírez, Mario González-Lee, José R. García-Martínez, Mariko Nakano-Miyatake and Hector Perez-Meana

Appl. Sci. 2024, 14(18), 8560; https://doi.org/10.3390/app14188560 - 23 Sep 2024

Viewed by 938

Abstract

According to the World Health Organization (WHO), 5% of people around the world have hearing disabilities, which limits their capacity to communicate with others. Recently, scientists have proposed systems based on deep learning techniques to create a sign language-to-text translator, expecting this to [...] Read more.

According to the World Health Organization (WHO), 5% of people around the world have hearing disabilities, which limits their capacity to communicate with others. Recently, scientists have proposed systems based on deep learning techniques to create a sign language-to-text translator, expecting this to help deaf people communicate; however, the performance of such systems is still low for practical scenarios. Furthermore, the proposed systems are language-oriented, which leads to particular problems related to the signs for each language. For this reason, to address this problem, in this paper, we propose a system based on a Recursive Neural Network (RNN) focused on Mexican Sign Language (MSL) that uses the spatial tracking of hands and facial expressions to predict the word that a person intends to communicate. To achieve this, we trained four RNN-based models using a dataset of 600 clips that were 30 s long; each word included 30 clips. We conducted two experiments; we tailored the first experiment to determine the most well-suited model for the target application and measure the accuracy of the resulting system in offline mode; in the second experiment, we measured the accuracy of the system in online mode. We assessed the system’s performance using the following metrics: the precision, recall, F1-score, and the number of errors during online scenarios, and the results computed indicate an accuracy of 0.93 in the offline mode and a higher performance for the online operating mode compared to previously proposed approaches. These results underscore the potential of the proposed scheme in scenarios such as teaching, learning, commercial transactions, and daily communications among deaf and non-deaf people. Full article

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

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9 pages, 412 KiB

Open AccessArticle

Health and TMJ Function in Adult Patients Treated for Dentoskeletal Open Bite with Orthognathic Surgery—A Retrospective Cohort Study

by Mariachiara Benetti, Luca Montresor, Lorenzo Trevisiol, Antonio D’Agostino, Nicoletta Zerman, Alessio Verdecchia and Enrico Spinas

Appl. Sci. 2024, 14(18), 8559; https://doi.org/10.3390/app14188559 - 23 Sep 2024

Viewed by 676

Abstract

This study aims to assess the presence and progression of TMD in adult patients undergoing orthognathic surgery to correct dentoskeletal open bite and evaluate whether these changes can be attributed to the intervention. A retrospective cohort study was conducted on 44 adult patients [...] Read more.

This study aims to assess the presence and progression of TMD in adult patients undergoing orthognathic surgery to correct dentoskeletal open bite and evaluate whether these changes can be attributed to the intervention. A retrospective cohort study was conducted on 44 adult patients (14 males and 30 females) aged 18 to 43 years. Articular assessments were performed to evaluate temporomandibular joint (TMJ) health and functionality before (T0) and after (T1) combined orthodontic-surgical treatment. TMJ health was assessed by maximum mouth opening, joint noises, parafunctions (bruxism or clenching), joint locking, TMJ pain, masticatory muscle pain, and headaches. Statistical analyses used McNemar’s Exact Test and paired T-tests. The study shows a significant reduction (p < 0.05) in symptoms, except for locking, with the most substantial decrease in headaches (p = 0.0001). Overall, articular symptoms markedly decreased post-surgery, with sustained joint functionality. Restoring physiological occlusion in patients with anterior open bite is crucial for maintaining the stomatognathic system’s balance. Orthognathic surgery, when indicated, appears beneficial in alleviating articular symptoms while preserving TMJ function. Full article

(This article belongs to the Special Issue Orthodontics and Maxillofacial Surgery)

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26 pages, 3533 KiB

Open AccessSystematic Review

Energy-Efficient Industrial Internet of Things in Green 6G Networks

by Xavier Fernando and George Lăzăroiu

Appl. Sci. 2024, 14(18), 8558; https://doi.org/10.3390/app14188558 - 23 Sep 2024

Viewed by 2363

Abstract

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data [...] Read more.

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. Full article

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33 pages, 3404 KiB

Open AccessReview

Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis

by Muhammad Nadeem, Ersin Elbasi, Aymen I. Zreikat and Mohammad Sharsheer

Appl. Sci. 2024, 14(18), 8557; https://doi.org/10.3390/app14188557 - 23 Sep 2024

Viewed by 2595

Abstract

Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant [...] Read more.

Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study. Full article

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23 pages, 7552 KiB

Open AccessArticle

A Novel Data Fusion Method to Estimate Bridge Acceleration with Surrogate Inclination Mode Shapes through Independent Component Analysis

by Xuzhao Lu, Chenxi Wei, Limin Sun, Ye Xia and Wei Zhang

Appl. Sci. 2024, 14(18), 8556; https://doi.org/10.3390/app14188556 - 23 Sep 2024

Cited by 1 | Viewed by 720

Abstract

Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, [...] Read more.

Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method’s practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. Full article

(This article belongs to the Special Issue Advances in Intelligent Bridge: Maintenance and Monitoring)

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16 pages, 7653 KiB

Open AccessArticle

People Detection Using Artificial Intelligence with Panchromatic Satellite Images

by Peter Golej, Pavel Kukuliač, Jiří Horák, Lucie Orlíková and Pavol Partila

Appl. Sci. 2024, 14(18), 8555; https://doi.org/10.3390/app14188555 - 23 Sep 2024

Viewed by 607

Abstract

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, [...] Read more.

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R–CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm. Full article

(This article belongs to the Special Issue Application of Deep Learning in Geomatics and Satellite Image Processing)

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18 pages, 5022 KiB

Open AccessArticle

Seismic Design and Ductility Evaluation of Thin-Walled Stiffened Steel Square Box Columns

by Mwaura Njiru and Iraj H. P. Mamaghani

Appl. Sci. 2024, 14(18), 8554; https://doi.org/10.3390/app14188554 - 23 Sep 2024

Viewed by 589

Abstract

This paper investigates the seismic performance of thin-walled stiffened steel square box columns, modeling bridge piers subjected to unidirectional cyclic lateral loading with a constant axial load, focusing on local, global, and local-global interactive buckling phenomena. Initially, the finite element model was validated [...] Read more.

This paper investigates the seismic performance of thin-walled stiffened steel square box columns, modeling bridge piers subjected to unidirectional cyclic lateral loading with a constant axial load, focusing on local, global, and local-global interactive buckling phenomena. Initially, the finite element model was validated against existing experimental results. The study further explored the degradation in strength and ductility of both thin-walled and compact columns under cyclic loading. Thin-walled, stiffened steel square box columns exhibited buckling near the base, forming a half-sine wave shape. The research also addresses discrepancies from different material models used to analyze steel tubular bridge piers. Analysis using a modified two-surface plasticity model (2SM) yielded results closer to experimental data than a multi-linear kinematic hardening model, particularly for compact sections. The 2SM, which accounts for cycling within the yield plateau and strain hardening regime, demonstrated enhanced accuracy over the multi-linear kinematic hardening model. Additionally, a parametric study was conducted to assess the impact of key design parameters—such as width-to-thickness ratio (R_f), column slenderness ratio (λ), and magnitude of axial load (P/P_y)—on the performance of thin-walled stiffened steel square box columns. Design equations were then developed to predict the strength and ductility of bridge piers. These equations closely matched experimental results, achieving an accuracy of 95% for ultimate strength and 97% for ductility. Full article

(This article belongs to the Special Issue Advances in Bridge Design and Construction: Technologies and Applications)

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12 pages, 8256 KiB

Open AccessArticle

A New Criterion for the Splashing of a Droplet on Dry Surface from High-Fidelity Simulations

by Shijie Jiang, Hongbing Xiong, Baolin Tian and Zhaosheng Yu

Appl. Sci. 2024, 14(18), 8553; https://doi.org/10.3390/app14188553 - 23 Sep 2024

Viewed by 653

Abstract

In this study, a new criterion for the splashing of a droplet on a dry smooth surface is established from high-fidelity numerical simulations. The new criterion involves the Weber number, Reynolds number and contact angle. A new splashing mode, termed spreading splashing, is [...] Read more.

In this study, a new criterion for the splashing of a droplet on a dry smooth surface is established from high-fidelity numerical simulations. The new criterion involves the Weber number, Reynolds number and contact angle. A new splashing mode, termed spreading splashing, is proposed, which predominates for contact angles below 120 degrees. For contact angles above 120 degrees, prompt splashing dominates. For contact angles above 90 degrees, there exists a critical Weber number of around 60, below which splashing does not occur. Full article

(This article belongs to the Special Issue Recent Advances in Engineering Applications of Computational Fluid Dynamics)

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23 pages, 5391 KiB

Open AccessArticle

Applications and Prospects of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering

by Rong Fan, Tielin Chen, Man Li and Shunyu Wang

Appl. Sci. 2024, 14(18), 8552; https://doi.org/10.3390/app14188552 - 23 Sep 2024

Viewed by 818

Abstract

Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely [...] Read more.

Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely Lagrangian approach, offering significant advantages in fluid simulations. By leveraging a set of arbitrarily distributed particles to represent the continuous fluid medium, SPH enables the precise estimation of partial differential equations. This grid-free methodology effectively addresses many challenges associated with conventional methods, providing a more adaptable and efficient solution framework. SPH’s versatility is evident across a broad spectrum of applications, ranging from advanced computational fluid dynamics (CFD) to complex computational solid mechanics (CSM), and proves effective across various scales—from micro to macro and even astronomical phenomena. Although SPH excels in tackling problems involving multiple degrees of freedom, complex boundaries, and large discontinuous deformations, it is still in its developmental phase and has not yet been widely adopted. As such, a thorough understanding and systematic analysis of SPH’s foundational theories are critical. This paper offers a comprehensive review of the defining characteristics and theoretical foundations of the SPH method, supported by practical examples derived from the Navier–Stokes (N-S) equations. It also provides a critical examination of successful SPH applications across various fields. Additionally, the paper presents case studies of SPH’s application in tunnel and underground engineering based on practical engineering experiences and long-term on-site monitoring, highlighting SPH’s alignment with real-world conditions. The theory and application of SPH have thus emerged as highly dynamic and rapidly evolving research areas. The detailed theoretical analysis and case studies presented in this paper offer valuable insights and practical guidance for scholars and practitioners alike. Full article

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17 pages, 1962 KiB

Open AccessArticle

Molecular and Enzymatic Responses of Chlorococcum dorsiventrale to Heavy Metal Exposure: Implications for Their Removal

by Rihab Hmani, Jihen Elleuch, Fatma Elleuch, Marwa Drira, Philippe Michaud, Lotfi Aleya, Slim Abdelkafi and Imen Fendri

Appl. Sci. 2024, 14(18), 8551; https://doi.org/10.3390/app14188551 - 23 Sep 2024

Viewed by 814

Abstract

Heavy metals are one of the main threats to marine life and ecosystems and any remedial action in that regard is urgently required. The aim of this work is to study the bioremoval of cadmium, chromium and lead in a microalgae strain Chlorococcum [...] Read more.

Heavy metals are one of the main threats to marine life and ecosystems and any remedial action in that regard is urgently required. The aim of this work is to study the bioremoval of cadmium, chromium and lead in a microalgae strain Chlorococcum dorsiventrale isolated from Tunisian coastal waters along with assessing its enzymatic and molecular responses. The microalgae were tested in artificial seawater to evaluate their capacity for phycoremediation in an aquatic environment. This strain tolerated exposure to Cd (II), Cr (VI), and Pb (II) and was able to grow for 14 days. Cd and Cr exposures elicited a decrease in chlorophyll, lipid and polysaccharide contents, whereas no damages were detected following Pb treatment. For protein content, no significant changes were seen except after Pb exposure which induced a slight increase after treatment with 5 mg/L. The assessment of stress defense-related gene expression using qRT-PCR revealed that exposure to Pb and Cr induced an up-regulation of catalase, superoxide dismutase and photosystem II protein D1 encoding genes. Moreover, heat shock protein 70 was slightly overexpressed. Removal efficiencies for Cr and Pb attained 89% and 95%, respectively. The mechanisms by which C. dorsiventrale removed Cr involved both intracellular and extracellular biosorption, while Pb was predominantly removed through membrane adsorption. This study highlights the potential of C. dorsiventrale as an efficient agent for the bioremediation of heavy metal-contaminated water, including industrial wastewater, thus paving the way for practical and environmental applications in pollution control. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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17 pages, 2687 KiB

Open AccessArticle

An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming

by Tiantian Han, Yonghe Chu and Fangzheng Liu

Appl. Sci. 2024, 14(18), 8550; https://doi.org/10.3390/app14188550 - 23 Sep 2024

Viewed by 950

Abstract

Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in [...] Read more.

Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in search algorithms, but it also has some issues. The single-objective approach to validate and select offspring patches without considering other constraints can affect the efficiency of patch generation. To address this issue, this paper proposes an automatic software repair method based on Multi-objective Genetic Programming (MGPRepair). Firstly, the method adopts a lightweight context analysis strategy to find suitable repair materials. Secondly, it decouples the replacement statements and insertion statements in the repair materials, using a lower-granularity patch representation method to encode the patches in the search space. Then, the automatic software defect repair is treated as a multi-objective search problem, and the NSGA-II multi-objective optimization algorithm is used to find simpler repair patches. Finally, the test case filtering technique is used to accelerate the patch validation process and generate correct patches. MGPRepair was experimentally evaluated on 395 real Java software defects from the Defects4J dataset. The experimental results show that MGPRepair can generate test case-passing patches for 51 defects, of which 35 defect patches are equivalent to manually generated patches. Its repair the efficiency and success rate are higher to other excellent automatic software defect repair methods such as jGenProg, RSRepair, ARJA, Nopol, Capgen, and SequenceR. Full article

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25 pages, 8294 KiB

Open AccessArticle

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

by Wafaa Alsaggaf, Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen and Ahmed R. Ginidi

Appl. Sci. 2024, 14(18), 8549; https://doi.org/10.3390/app14188549 - 23 Sep 2024

Cited by 1 | Viewed by 686

Abstract

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation [...] Read more.

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%. Full article

(This article belongs to the Special Issue Materials and Optical Strategies for Solar Cells: Recent Advances and Challenges)

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23 pages, 18218 KiB

Open AccessArticle

Analysis of Granite Deformation and Rupture Law and Evolution of Grain-Based Model Force Chain Network under Anchor Reinforcement

by Jiangfeng Guo, Doudou Fan, Liyuan Yu, Meixia Shi, Haijian Su, Tao Zhang and Bowen Hu

Appl. Sci. 2024, 14(18), 8548; https://doi.org/10.3390/app14188548 - 23 Sep 2024

Viewed by 552

Abstract

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite [...] Read more.

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite rock mass is essential. It can provide significant reference and support for the design of underground engineering, engineering safety assessment, the theory of rock mechanics, and resource development. In this study, indoor experiments are combined with numerical simulations to explore the impact of fracture dip angles on the mechanical behavior of unanchored and anchored granite samples from both macroscopic and microscopic perspectives. It also investigates the evolution of the anchoring and crack-stopping effect of rock bolts on granite containing fractures with different dip angles. The results show that the load-displacement trends, displacement fields, and debris fields from indoor experiments and numerical simulations are highly similar. Additionally, it was discovered that, in comparison to the unanchored samples, the anchored samples with fractures at various angles all exhibited a higher degree of tensile failure rather than shear failure that propagates diagonally across the samples from the regions around the fracture tips. This finding verifies the effectiveness of the numerical model parameter calibration. At the same time, it was observed that the internal force chain value level in the anchored samples is higher than in the unanchored samples, indicating that the anchored samples possess greater load-bearing capacity. Furthermore, as the angle α_s increases, the reinforcing and crack-stopping effects of the rock bolts become increasingly less pronounced. Full article

(This article belongs to the Special Issue Advances in Numerical Computation and Mathematical Modelling for Geotechnical Engineering)

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14 pages, 2500 KiB

Open AccessArticle

A Study on the Spatial Layout of Newly Built Townhouses in Kaohsiung City

by Cheng-Chi Tseng, Long-Sheng Huang and Chung-Fah Huang

Appl. Sci. 2024, 14(18), 8547; https://doi.org/10.3390/app14188547 - 23 Sep 2024

Viewed by 536

Abstract

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It [...] Read more.

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including the facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing a reference for the future planning and design of such structures. The study results showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m², a facade width between 4.1 and 6.38 m, and a building depth from 7.67 to 12.68 m. In addition, they showed a low negative correlation between the facade width and total floor area, a high positive correlation between the building depth and total floor area, and a moderate negative correlation between the facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable rooms and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable rooms increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used. This means that planning of staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses. Full article

(This article belongs to the Topic Innovation, Communication and Engineering)

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