Applied Sciences

20 pages, 6830 KiB

Open AccessArticle

Analysis of Inundation Flow Characteristics and Risk Assessment in a Subway Model Using Flow Simulations

by Jaehyun Shin, Dong Sop Rhee and Inhwan Park

Appl. Sci. 2024, 14(17), 8096; https://doi.org/10.3390/app14178096 - 9 Sep 2024

Viewed by 612

Subway station platforms are vulnerable to flood damage. Thus, an investigation of inundation in subway platforms is required to ensure the safety of citizens against flooding. This study analyzed and validated the inundation characteristics and safety areas in a subway station model using [...] Read more.

Subway station platforms are vulnerable to flood damage. Thus, an investigation of inundation in subway platforms is required to ensure the safety of citizens against flooding. This study analyzed and validated the inundation characteristics and safety areas in a subway station model using experimental inundation depth measurements and numerical simulations. Then by using the simulation, the effects of increased inflow to water velocity and depth were analyzed, and its impact on human models was found by using risk assessments which included specific force (

M_{0}

), Flood Hazard Degree (FD), Flood Intensity Factors (FIF), toppling velocity, and sliding velocity. The flood risk assessment analysis results show that assessments using

M_{0}

could increase uncertainty by broadening the evaluation of risky areas compared to other indices. Also, the drag force applied to the human models was calculated using the simulations, which provided inundation risk values to people in subway stations. Overall, the risk assessments would provide a criterion for flood situations in subway stations. Full article

(This article belongs to the Section Civil Engineering)

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22 pages, 61878 KiB

Open AccessArticle

Three-Stage Non-Surgical Treatment of Skeletal Class III Malocclusion in Adolescents—A Report of Four Cases with Long-Term Follow-Up

by Małgorzata Kuc-Michalska, Magdalena Pokucińska, Katarzyna Grocholewicz and Joanna Janiszewska-Olszowska

Appl. Sci. 2024, 14(17), 8095; https://doi.org/10.3390/app14178095 - 9 Sep 2024

Viewed by 732

Abstract

(1) Background: Postponing orthodontic treatment in Class III malocclusion until deterioration and growth cessation to perform orthognathic surgery does not seem to be an optimal solution for every patient. This report describes short- and long-term outcomes for nonsurgical treatment of four adolescents with [...] Read more.

(1) Background: Postponing orthodontic treatment in Class III malocclusion until deterioration and growth cessation to perform orthognathic surgery does not seem to be an optimal solution for every patient. This report describes short- and long-term outcomes for nonsurgical treatment of four adolescents with severe Class III malocclusion. (2) Methods: Four patients (aged 13–15 y) with skeletal Class III (Wits appraisal below 7.5 mm) started a three-stage treatment, consisting of a six-month-long phase I and involving orthopedic treatment with an individual chin-cup. Phase II involved orthopedic treatment with a bonded Haas-type expander on acrylic splints, a face-mask, a lower fixed appliance and Class III elastics; phase III involved full fixed appliance, elastics and reuse of the individual chin-cup. Pre and posttreatment cephalograms were analyzed and superimposed. (3) Results: Improved skeletal and dental relationships and facial appearance was achieved in all patients. Wits appraisal, angles ANB, ANPg and lower face height increased; an improvement of overbite was noted. (4) Conclusions: Severe skeletal Class III in adolescents may be successfully treated with combined orthopedic/camouflage treatment with a Haas-type expander on acrylic splints, Class III elastics, fixed appliance and orthopedic devices (individual chin-cup and facemask) with a very good compliance. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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21 pages, 3597 KiB

Open AccessArticle

A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing

by Xinghao Zhao, Yanzhu Hu, Tingxin Qin, Wang Wan and Yudi Wang

Appl. Sci. 2024, 14(17), 8094; https://doi.org/10.3390/app14178094 - 9 Sep 2024

Viewed by 567

Abstract

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research [...] Read more.

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks. Full article

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12 pages, 729 KiB

Open AccessArticle

Structural Equation Modeling of Musculoskeletal Pains, Work–Family Conflict, and Sleep-Related Problems on Well-Being of Food Manufacturing Workers

by Jun Won Kim and Byung Yong Jeong

Appl. Sci. 2024, 14(17), 8093; https://doi.org/10.3390/app14178093 - 9 Sep 2024

Viewed by 539

Abstract

The objective of this study is to investigate the causal relationships between musculoskeletal pains, work–family conflict, sleep-related problems, and the well-being of food manufacturing workers using structural equation modeling. This study analyzed 523 food manufacturing workers extracted from the Sixth Korea Working Conditions [...] Read more.

The objective of this study is to investigate the causal relationships between musculoskeletal pains, work–family conflict, sleep-related problems, and the well-being of food manufacturing workers using structural equation modeling. This study analyzed 523 food manufacturing workers extracted from the Sixth Korea Working Conditions Survey. We formulated six hypotheses based on literature reviews and examined the structural causal relationship between work–family conflict, musculoskeletal pains, sleep-related problems, and well-being. According to the results of structural equation modeling, work–family conflict has a significant impact on musculoskeletal pains (standardized path coefficient of 0.113). Furthermore, both musculoskeletal pains (standardized path coefficient of 0.350) and work–family conflict (standardized path coefficient of 0.212) have been found to affect sleep-related problems. It has also been established that musculoskeletal pains have a direct influence on well-being (standardized path coefficient of 0.115). The association and structural causality between musculoskeletal pain and psychological factors in food manufacturing workers can be used for customized measures to improve the well-being of food manufacturing workers. This study is also meaningful in that musculoskeletal pain and psychological factors should be managed in an integrated manner. Full article

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

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23 pages, 589 KiB

Open AccessReview

Advancements in Analytical Strategies for Authentication and Quality Control of Grappa and Wine Brandy with Geographical Indications

by Silvia Arduini and Fabio Chinnici

Appl. Sci. 2024, 14(17), 8092; https://doi.org/10.3390/app14178092 - 9 Sep 2024

Viewed by 678

Abstract

In recent years, food authentication has acquired significant importance due to the increase in the incidence of fraud and counterfeiting. Alcoholic beverages are among the food products most susceptible to these kinds of illicit practices due to their high commercial value. In the [...] Read more.

In recent years, food authentication has acquired significant importance due to the increase in the incidence of fraud and counterfeiting. Alcoholic beverages are among the food products most susceptible to these kinds of illicit practices due to their high commercial value. In the EU alone, there are 47 categories of spirit drinks and approximately 250 geographical indications (GIs). The production and labeling of GIs are strictly regulated, and developing analytical procedures that can ensure compliance with the legislation is essential to guarantee the typicality of these products. The aim of this review is to summarize the most relevant analytical techniques used for the authentication and quality control of two well-renowned GIs: “Grappa” and wine brandy. It considers the last decade of advancements for both conventional targeted chromatographic techniques and less common methods mainly based on spectrometry coupled with chemometrics for quick and non-destructive discrimination of samples. Novel approaches and future perspectives are also highlighted. Full article

(This article belongs to the Special Issue Novel Research on Safety Detection and Quality Control of Food)

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35 pages, 12036 KiB

Open AccessArticle

Transfer Learning and Deep Neural Networks for Robust Intersubject Hand Movement Detection from EEG Signals

by Chiang Liang Kok, Chee Kit Ho, Thein Htet Aung, Yit Yan Koh and Tee Hui Teo

Appl. Sci. 2024, 14(17), 8091; https://doi.org/10.3390/app14178091 - 9 Sep 2024

Viewed by 526

Abstract

In this research, five systems were developed to classify four distinct motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering [...] Read more.

In this research, five systems were developed to classify four distinct motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The initial system, a preliminary study model, explored the overall framework of EEG signal processing and classification, utilizing time-domain features such as variance and frequency-domain features such as alpha and beta power, with a KNN model for classification. Insights from this study informed the development of a baseline system, which innovatively combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. This system classified six unique pairs of events derived from the four motor functions, achieving remarkable accuracy, with the highest being 99.73% for the GP–RV pair and the lowest 80.87% for the FW–GP pair in intersubject classification. Building on this success, three additional systems were developed for four-way classification. The final model, ML-CSP-OVR, demonstrated the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, featuring a novel combination of CSP-OVR, CWT, and GoogLeNet, represents a significant advancement in the field, showcasing strong potential as a general system for motor imagery (MI) tasks that is not dependent on the subject. This work highlights the prominence of the research contribution by demonstrating the effectiveness and robustness of the proposed approach in achieving high classification accuracy across different motor functions and subjects. Full article

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

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21 pages, 4725 KiB

Open AccessArticle

A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability

by Jordan Abarca-Albores, Danna Cristina Gutiérrez Cabrera, Luis Antonio Salazar-Licea, Dante Ruiz-Robles, Jesus Alejandro Franco, Alberto-Jesus Perea-Moreno, David Muñoz-Rodríguez and Quetzalcoatl Hernandez-Escobedo

Appl. Sci. 2024, 14(17), 8090; https://doi.org/10.3390/app14178090 - 9 Sep 2024

Viewed by 780

Abstract

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying [...] Read more.

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure. Full article

(This article belongs to the Special Issue Offshore Wind Turbines and Wave Energy: Modeling, Simulation and Applications)

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17 pages, 953 KiB

Open AccessArticle

Evaluation Methodology for Circular and Resilient Information Systems

by Stavros Lounis, Anastasios Koukopoulos, Timoleon Farmakis and Maria Aryblia

Appl. Sci. 2024, 14(17), 8089; https://doi.org/10.3390/app14178089 - 9 Sep 2024

Viewed by 485

Abstract

Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely [...] Read more.

Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely circular and resilient information systems (CRISs). These systems integrate with traditional ones to optimise key performance indicators (KPIs) related to circularity and resiliency. Despite extensive methodologies for evaluating traditional information systems, systems designed for circularity and resiliency need to be assessed in parallel and in depth. Existing evaluations focus on efficiency and user satisfaction but often neglect the unique demands of circularity and resiliency. This study introduces a novel evaluation methodology for CRISs. Through a case study of an innovative system and the established literature, we address real-life needs and challenges in manufacturing. In particular, the system serves the needs of three distinct case studies: Carbon Fibre-Reinforced Polymer (CFRP) waste utilisation in drone manufacturing, recovery of magnets from Waste Electrical and Electronic Equipment (WEEE), and the repurposing of citrus processing waste into juice by-products. Our methodology is built on the 5W1H method to make our approach context-specific and aligned with each case’s unique requirements, making it also replicable for other industries. Our findings offer insights and a tool for practitioners and researchers to evaluate CRIS performance. The research highlights the importance of a two-fold evaluation approach for CRISs, evaluating both pilot-specific KPIs and the system’s technical performance. Policy implications suggest the need for regulatory frameworks and incentives to support the adoption, as well as evaluation, of CRISs and promote sustainable and resilient industrial practices. Full article

(This article belongs to the Special Issue Digital Twins: Technologies and Applications)

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11 pages, 3502 KiB

Open AccessArticle

3D Modeling Simulation and Innovative Design of a Saw Cutting Mechanism for a Movable Buckling Machine

by Xuechun Yang, Xiangjie Wu and Huadong Xu

Appl. Sci. 2024, 14(17), 8088; https://doi.org/10.3390/app14178088 - 9 Sep 2024

Viewed by 451

Abstract

In this research, a sawing mechanism of a mobile buckling machine was innovatively designed for the forest resources and environmental characteristics of the northeast region of China. The northeast region of China is rich in forest resources, but the climate is cold, which [...] Read more.

In this research, a sawing mechanism of a mobile buckling machine was innovatively designed for the forest resources and environmental characteristics of the northeast region of China. The northeast region of China is rich in forest resources, but the climate is cold, which puts high demands on the performance and adaptability of mechanical equipment. In this study, the three-dimensional modeling of the saw-cutting mechanism was completed by Pro/Engineer 5.0 modeling software, and an in-depth dynamics simulation analysis was carried out using dynamics simulation software. The study aims to assess the applicability of this saw-cutting mechanism in the northeast region of China, analyze its design characteristics, and explore possible directions for performance optimization. Through this study, we expect to provide valuable references for future technological innovations, as well as to promote the practical application and development of mobile buckling machines in cold regions. This research not only has regional characteristics, but also reflects the innovative idea of combining mechanical equipment design with environmental adaptability. Full article

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4 pages, 182 KiB

Open AccessEditorial

Advances in Protective Clothing Research Meeting the Challenges in the Changing World

by Kalev Kuklane and Anna Dąbrowska

Appl. Sci. 2024, 14(17), 8087; https://doi.org/10.3390/app14178087 - 9 Sep 2024

Viewed by 505

Abstract

The world keeps changing, and the changes are becoming quicker with technological development [...] Full article

(This article belongs to the Special Issue Advances in Protective Clothing Research Meeting the Challenges in the Changing World)

16 pages, 1841 KiB

Open AccessArticle

Synergism Interactions of Plant-Based Proteins: Their Effect on Emulsifying Properties in Oil/Water-Type Model Emulsions

by Raquel Reis Lima, Maria Eduarda Martins Vieira, Nathalia da Silva Campos, Ítalo Tuler Perrone, Rodrigo Stephani, Federico Casanova and Antônio Fernandes de Carvalho

Appl. Sci. 2024, 14(17), 8086; https://doi.org/10.3390/app14178086 - 9 Sep 2024

Viewed by 523

Abstract

This study investigated the synergistic effects of three protein concentrates from legumes (pea, lentil, and lima bean) as emulsifiers and stabilizers of oil-in-water (O/W) emulsions using a simplex-centroid mixture design. The aim was to check whether proteins combined in different proportions have better [...] Read more.

This study investigated the synergistic effects of three protein concentrates from legumes (pea, lentil, and lima bean) as emulsifiers and stabilizers of oil-in-water (O/W) emulsions using a simplex-centroid mixture design. The aim was to check whether proteins combined in different proportions have better emulsifying properties than isolated proteins. During this study, each protein concentrate was characterized by different evaluated parameters: emulsifying activity, emulsion stability, accelerated stability test, thermal coagulation time, stability to coalescence, and others. After statistical analysis mixture optimization, it was found that the best formulation for stabilizing O/W emulsion under the tested conditions (2% total protein; 3% sunflower oil) was the protein blend containing 21.21% pea, 32.78% lentil, and 46.01% fava bean. This blend exhibited better emulsification properties compared to the individual proteins. Full article

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

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22 pages, 5874 KiB

Open AccessArticle

A Method for Optimizing Terminal Sliding Mode Controller Parameters Based on a Multi-Strategy Improved Crayfish Algorithm

by Zhenghao Wei, Zhibin He, Fumiao Yang and Bin Sun

Appl. Sci. 2024, 14(17), 8085; https://doi.org/10.3390/app14178085 - 9 Sep 2024

Viewed by 444

Abstract

This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning systems. The TSMC is designed for the “Xinhongzhuan” vessel of Dalian Maritime University. [...] Read more.

This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning systems. The TSMC is designed for the “Xinhongzhuan” vessel of Dalian Maritime University. JLSCOA integrates subtractive averaging, Levy Flight, and sparrow search strategies to overcome the limitations of traditional crayfish algorithms. Compared to COA, WOA, and SSA algorithms, JLSCOA demonstrates superior optimization accuracy, convergence performance, and stability across 12 benchmark test functions. It achieves the optimal value in 83% of cases, outperforms the average in 83% of cases, and exhibits stronger robustness in 75% of cases. Simulations show that applying JLSCOA to TSMC parameter optimization significantly outperforms traditional non-optimized controllers, reducing the average time for three degrees of freedom position changes by over 300 s and nearly eliminating control force and velocity oscillations. Full article

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9 pages, 792 KiB

Open AccessArticle

Seven-Year Retrospective Study on Conometric Retention for Complete Fixed Prosthesis

by Eriberto Bressan, Riccardo Guazzo, Riccardo Favero, Luca Sbricoli and Lucia Schiavon

Appl. Sci. 2024, 14(17), 8084; https://doi.org/10.3390/app14178084 - 9 Sep 2024

Viewed by 407

Abstract

The aim of the present work was to evaluate retrospectively, after seven years of function, the efficacy of a conometric retention to stabilize complete prostheses (CPs) on four implants. Data from twenty-three patients with CPs supported by four implants, with at least seven [...] Read more.

The aim of the present work was to evaluate retrospectively, after seven years of function, the efficacy of a conometric retention to stabilize complete prostheses (CPs) on four implants. Data from twenty-three patients with CPs supported by four implants, with at least seven years of follow up were retrieved. All the CPs were immediately fixed to the implants using a conometric retention. Outcome measures were prosthesis and implant success, biological and prosthetic complications, probing pocket depth changes, marginal bleeding, and plaque index changes. A total of 92 implants were evaluated. No fixture or abutment fractures were reported. No abutment unscrewing was reported. Four framework fractures occurred after three, four, six, and seven years of function. The overall success rate of the rehabilitation was 82.6%. Mucositis was observed in eight patients and 13 implants. No peri-implantitis was recorded. A 0.55 mm difference of PPD and 0.74 mm of MBL was recorded after seven years (p < 0.001). The present implant-supported conometric retention system proved to be effective in giving fixed retention to a CP supported by four implants. Biological complications were easily detected and treated. An adequate metal framework should be provided to the definitive restoration to avoid fractures in the long term. Full article

(This article belongs to the Special Issue Advances in Dental Implants)

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23 pages, 5184 KiB

Open AccessArticle

Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm

by Liuyan Wang, Lin Liu, Dong Dai, Bo Liu and Zhenya Cheng

Appl. Sci. 2024, 14(17), 8083; https://doi.org/10.3390/app14178083 - 9 Sep 2024

Viewed by 369

Abstract

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high-performance concrete (UHPC), this study examined the impact of both single factorsand combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel [...] Read more.

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high-performance concrete (UHPC), this study examined the impact of both single factorsand combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA–SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine (SVM), and other single models, the AOA–SVR model achieves the highest R² value and the lowest error rates. The results demonstrate that the optimized AOA–SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC. Full article

(This article belongs to the Topic Artificial Intelligence (AI) Applied in Civil Engineering, 2nd Volume)

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17 pages, 7480 KiB

Open AccessArticle

Evaluating the Plastic Anisotropic Effect on the Forming Limit Curve of 2024-T3 Aluminum Alloy Sheets Using Marciniak Tests and Digital Image Correlation

by Roberto Iquilio, Kurt Fehrmann, Sergio Núñez Sepúlveda, Enzo Tesser, Meyli Valín and José Luis Valín

Appl. Sci. 2024, 14(17), 8082; https://doi.org/10.3390/app14178082 - 9 Sep 2024

Viewed by 490

Abstract

This study thoroughly investigates the influence of anisotropy on the formability of 2024-T3 aluminum alloy sheets using advanced techniques such as digital image correlation (DIC) and Marciniak tests. A key finding is the relatively small variation in anisotropy values across different strain paths [...] Read more.

This study thoroughly investigates the influence of anisotropy on the formability of 2024-T3 aluminum alloy sheets using advanced techniques such as digital image correlation (DIC) and Marciniak tests. A key finding is the relatively small variation in anisotropy values across different strain paths and orientations, contrasting with more significant variations reported in other studies. Tests were conducted on nine samples with various geometries to induce specific strain paths, including uniaxial, plane, and balanced biaxial strains, oriented in different directions relative to the rolling direction. The study also provides a detailed analysis of microstructural and mechanical characteristics, such as precipitate distribution and anisotropy behavior, which are crucial for understanding the relationship between microstructure and material formability. The results show that while anisotropy impacts deformation capacity, the differences in formability among the directions were minimal, with slightly greater formability observed in the diagonal direction. These findings are compared with forming limit curves (FLCs), offering an integrated view of how relatively uniform anisotropic properties influence formability. These insights are essential for optimizing the processing and application of 2024-T3 alloy in industrial contexts, emphasizing the importance of understanding anisotropy in the design of metal components. Full article

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22 pages, 6794 KiB

Open AccessArticle

3D X-ray Tomography Analysis of Mg–Si–Zn Alloys for Biomedical Applications: Elucidating the Morphology of the MgZn Phase

by Guilherme Lisboa de Gouveia, Eshan Ganju, Danusa Moura, Swapnil K. Morankar, José Eduardo Spinelli and Nikhilesh Chawla

Appl. Sci. 2024, 14(17), 8081; https://doi.org/10.3390/app14178081 - 9 Sep 2024

Viewed by 438

Abstract

Temporary metal implants, made from materials like titanium (Ti) or stainless steel, can cause metabolic issues, raise toxicity levels within the body, and negatively impact the patient’s long-term health. This necessitates a subsequent operation to extract these implants once the healing process is [...] Read more.

Temporary metal implants, made from materials like titanium (Ti) or stainless steel, can cause metabolic issues, raise toxicity levels within the body, and negatively impact the patient’s long-term health. This necessitates a subsequent operation to extract these implants once the healing process is complete or when they are outgrown by the patient. In contrast, medical devices fabricated from absorbable alloys have the advantage of being biodegradable, allowing them to be naturally absorbed by the body once they have fulfilled their role in facilitating tissue healing. Among the various absorbable alloy systems studied, magnesium (Mg) alloys stand out due to their biocompatibility, mechanical properties, and corrosion behavior. The existing literature on absorbable Mg alloys highlights the effectiveness of silicon (Si) and zinc (Zn) additions in improving mechanical properties and controlling corrosion susceptibility; however, there is a lack of comprehensive quantitative morphological analysis of the intermetallic phases within these alloy systems. The quantification of the complex morphology of intermetallic particles is a challenging task and has significant implications for the micromechanical properties of the alloys. This study, therefore, aims to introduce a robust set of morphometric parameters for evaluating the morphology of intermetallic phases within two as-cast Mg alloys with Si and Zn additions. X-ray Computed Tomography (XCT) was used to capture the 3D tomographic data of the alloys, and a novel pair of morphological parameters (ratio of convex hull to particle volume and convex hull sphericity) was applied to the 3D tomographic data to assess the MgZn phase formed in the two alloys. In addition to the impact of composition, the effect of solidification rate on the morphological parameters was also studied. Furthermore, Scanning Electron Microscopy (SEM) and Energy-Dispersive Spectroscopy (EDS) were employed to gather detailed 2D microstructural and compositional information on the intermetallics. The comprehensive characterization reveals that the morphological complexity and size distribution of the MgZn phase are influenced by both compositional changes and the solidification rate. However, the change in MgZn intermetallic particle morphology with size was found to follow a predictable trend, which was relatively agnostic of the chosen casting conditions. Full article

(This article belongs to the Special Issue Computed Tomography: Technological Developments, Methods and Applications)

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27 pages, 3219 KiB

Open AccessArticle

Analyzing the Digital Infrastructure Enabling Project Management Success: A Hybrid FAHP-FTOPSIS Approach

by Mohammad Awni Khasawneh and Fikri Dweiri

Appl. Sci. 2024, 14(17), 8080; https://doi.org/10.3390/app14178080 - 9 Sep 2024

Viewed by 476

Abstract

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these [...] Read more.

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CC_i) of 0.8525, followed by those related to digital fitness (CC_i = 0.6481) and digital readiness (CC_i = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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18 pages, 1508 KiB

Open AccessReview

Metal–Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods

by Emilly Karoline Tonini Silva Volante, Vinícius Bonifácio Volante, Manuel José Lis, Siddanth Saxena, Meritxell Martí, Murilo Pereira Moisés, Marc Pallares, Guilherme Andreoli Gil and Fabricio Maestá Bezerra

Appl. Sci. 2024, 14(17), 8079; https://doi.org/10.3390/app14178079 - 9 Sep 2024

Viewed by 909

Abstract

We present a review of current research on promising flame retardants using specific methods of applying metal–organic frameworks (MOFs) to the highly flammable fibrous surface of cotton fabric. In this review, we initially address the reasons why the search for new flame retardants [...] Read more.

We present a review of current research on promising flame retardants using specific methods of applying metal–organic frameworks (MOFs) to the highly flammable fibrous surface of cotton fabric. In this review, we initially address the reasons why the search for new flame retardants has becomes critically important in textile finishing, the area responsible for adhering new functionalities to substrates. This addition of characteristics is closely linked to the nature of the fibers, so the reason for the improvement in cotton fabric in relation to flame retardancy is discussed. Furthermore, the development of highly porous nanomaterials that can generate composites with specific functions is described, as well as their application and methods of integration into textile surfaces. Finally, the main candidates for flame retardant functionality in cellulosic materials are identified. It is also hoped that this work will facilitate researchers to develop and formulate new methods of applying nanomaterials to textile substrates, with a view to becoming a reference for new research into the development of adhesion of emerging materials to traditional materials. Full article

(This article belongs to the Topic Modern Material Technologies Intended for Industrial Applications)

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18 pages, 1057 KiB

Open AccessReview

Advancing in RGB-D Salient Object Detection: A Survey

by Ai Chen, Xin Li, Tianxiang He, Junlin Zhou and Duanbing Chen

Appl. Sci. 2024, 14(17), 8078; https://doi.org/10.3390/app14178078 - 9 Sep 2024

Viewed by 418

Abstract

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces [...] Read more.

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computer Vision and Object Detection)

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15 pages, 2868 KiB

Open AccessArticle

Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries

by Sang-Bum Kim and Sang-Hyun Lee

Appl. Sci. 2024, 14(17), 8077; https://doi.org/10.3390/app14178077 - 9 Sep 2024

Viewed by 495

Abstract

This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of [...] Read more.

This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of batteries to predict the SOC. The LSTM model was trained using battery data collected under various temperature and load conditions. To evaluate the performance of the artificial intelligence model, measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering was utilized. The LSTM model excels in learning long-term dependencies from sequence data, effectively modeling temporal patterns in battery data. The study trained the LSTM model based on battery data collected from various charge and discharge cycles and evaluated the model’s performance by epoch to determine the optimal configuration. The proposed model demonstrated high SOC estimation accuracy for various charging and discharging profiles. As training progressed, the model’s predictive performance improved, with the predicted SOC moving from 14.8400% at epoch 10 to 12.4968% at epoch 60, approaching the actual SOC value of 13.5441%. Simultaneously, the mean absolute error (MAE) and root mean squared error (RMSE) decreased from 0.9185% and 1.3009% at epoch 10 to 0.2333% and 0.5682% at epoch 60, respectively, indicating continuous improvement in predictive performance. In conclusion, this study demonstrates the effectiveness of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance the performance of battery management systems. Full article

(This article belongs to the Special Issue Integrating Artificial Intelligence in Renewable Energy Systems)

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19 pages, 7597 KiB

Open AccessArticle

Ergonomic Assessment of Key Biomechanical Factors in Patient Lifting: Results from a Cross-Sectional Study

by Karolis Senvaitis, Aušra Adomavičienė, Alina Tomaševič, Radvilė Kernagytė, Ada Petrauskaitė and Kristina Daunoravičienė

Appl. Sci. 2024, 14(17), 8076; https://doi.org/10.3390/app14178076 - 9 Sep 2024

Viewed by 486

Abstract

This study includes an ergonomic evaluation of patient lifting motion performed by healthcare specialists. This analysis focuses on the neck, shoulder, and elbow, as these are statistically significant areas with insufficient research data. Data collection was conducted using the Movella Xsens system as [...] Read more.

This study includes an ergonomic evaluation of patient lifting motion performed by healthcare specialists. This analysis focuses on the neck, shoulder, and elbow, as these are statistically significant areas with insufficient research data. Data collection was conducted using the Movella Xsens system as a standard 17 IMU (inertia measurement unit) marker set. A total of 44 test subjects participated, resulting in 396 measurements. A mathematical model was presented, including the main expressions and a three-dimensional moment arm of the shoulder calculation determining both the moment and accumulated moment. The patient load profile was measured in the experiment and parametrically integrated into the mathematical model. Ergonomic limits were calculated and presented, showing that during the lifting motion, the neck exceeds its ergonomic limit by 66%, the shoulders by 49%, and the elbow by 76%. The accumulated moment can vary by up to 23% depending on different evaluated techniques or data cross-sections. The model was verified by comparing it with data from other experiments, and recommendations were presented based on the findings, along with suggestions for future research development in the area. Full article

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

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36 pages, 5430 KiB

Open AccessReview

Advancements in Copper-Based Catalysts for Efficient Generation of Reactive Oxygen Species from Peroxymonosulfate

by Bakhta Bouzayani, Bárbara Lomba-Fernández, Antía Fdez-Sanromán, Sourour Chaâbane Elaoud and Maria Ángeles Sanromán

Appl. Sci. 2024, 14(17), 8075; https://doi.org/10.3390/app14178075 - 9 Sep 2024

Viewed by 394

Abstract

Over the past few decades, peroxymonosulfate (PMS)-driven advanced oxidation processes (AOPs) have garnered substantial interest in the field of organic decontamination. The copper (Cu)/PMS system is intriguing due to its diverse activation pathways and has been extensively employed for the clearance of refractory [...] Read more.

Over the past few decades, peroxymonosulfate (PMS)-driven advanced oxidation processes (AOPs) have garnered substantial interest in the field of organic decontamination. The copper (Cu)/PMS system is intriguing due to its diverse activation pathways and has been extensively employed for the clearance of refractory organic pollutants in water. This article is designed to offer a comprehensive overview of the latest trends in Cu-based catalysts such as single-metal and mixed-metal catalysts aimed at treating recalcitrant pollutants, highlighting PMS activation. Subsequently, investigative methodologies for assessing PMS activation with copper-based catalysts are reviewed and summarized. Then, the implications of pH, PMS and catalytic agent concentrations, anions, and natural organic matter are also addressed. The combination of Cu-based catalyst/PMS systems with other advanced oxidation technologies is also discussed. Following that, the degradation mechanisms in the Cu-based catalyst-activated PMS system are considered and synopsized. Lastly, potential future research avenues are proposed to enhance the technology and offer support for developing of economically viable materials based on copper for activating PMS. Full article

(This article belongs to the Section Environmental Sciences)

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30 pages, 17683 KiB

Open AccessArticle

Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits

by Cristhian Manuel Durán Acevedo, Dayan Diomedes Cárdenas Niño and Jeniffer Katerine Carrillo Gómez

Appl. Sci. 2024, 14(17), 8074; https://doi.org/10.3390/app14178074 - 9 Sep 2024

Viewed by 535

Abstract

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages [...] Read more.

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products. Full article

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13 pages, 1616 KiB

Open AccessReview

Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing

by Mengjie Liang, Ji Fang, Dunkui Chen, Lang Chen, Lingling Peng, Chi Zhang, Yingchun Chen and Xiang Lu

Appl. Sci. 2024, 14(17), 8073; https://doi.org/10.3390/app14178073 - 9 Sep 2024

Viewed by 453

Abstract

Optoelectronic integrated circuit (OEIC) technology has attracted considerable research attention. Studies have achieved numerous breakthroughs in the basic scientific problems, key technologies, demonstration applications, and industrial promotions of OEIC. This study details the technical process, development status, existing problems, and future research trends [...] Read more.

Optoelectronic integrated circuit (OEIC) technology has attracted considerable research attention. Studies have achieved numerous breakthroughs in the basic scientific problems, key technologies, demonstration applications, and industrial promotions of OEIC. This study details the technical process, development status, existing problems, and future research trends of the design, manufacturing, and packaging of OEIC to provide a systematic summary of OEIC technology. Full article

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18 pages, 6499 KiB

Open AccessArticle

Permeability Characteristics of Improved Loess and Prediction Method for Permeability Coefficient

by Guoliang Ran, Yanpeng Zhu, Xiaohui Yang, Anping Huang and Dong Chen

Appl. Sci. 2024, 14(17), 8072; https://doi.org/10.3390/app14178072 - 9 Sep 2024

Viewed by 369

Abstract

Due to its unique geotechnical properties, loess presents itself as a cost-effective and energy-efficient material for engineering construction, aiding in cost reduction and environmental sustainability. However, to meet engineering specifications, loess often requires enhancement. Evaluating its permeability properties holds significant importance for employing [...] Read more.

Due to its unique geotechnical properties, loess presents itself as a cost-effective and energy-efficient material for engineering construction, aiding in cost reduction and environmental sustainability. However, to meet engineering specifications, loess often requires enhancement. Evaluating its permeability properties holds significant importance for employing improved loess for construction materials in landfills and artificial water bodies. This study investigates the influence of dry densities, grain size characteristics, grain size distribution, and admixture contents and types on the permeability of improved loess, focusing on the Malan and Lishi loess. The falling head permeability test was conducted to analyze how each factor affects the permeability of the improved loess. The findings indicate that the permeability coefficient decreases with increased dry density and admixture content. Conversely, it demonstrates a linear increase with the average grain size (d₅₀), restricted grain size (d₆₀), and the product of the coefficient of uniformity and coefficient of curvature (C_u × C_c). The primary influencing factor is the type of admixture, followed by C_c and d₆₀. Furthermore, this study developed a predictive model for permeability using a support vector machine (SVM), surpassing the predictive accuracy of linear regression and neural network models. The model provides a robust prediction for the permeability of superior loess material. Full article

(This article belongs to the Topic Artificial Intelligence (AI) Applied in Civil Engineering, 2nd Volume)

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44 pages, 1051 KiB

Open AccessReview

Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review

by Gustave Udahemuka, Karim Djouani and Anish M. Kurien

Appl. Sci. 2024, 14(17), 8071; https://doi.org/10.3390/app14178071 - 9 Sep 2024

Viewed by 1425

Abstract

The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as [...] Read more.

The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the current state of multimodal emotion recognition methods that integrate visual, vocal or physiological modalities for practical emotion computing. Recent empirical evidence on deep learning methods used for fine-grained recognition is reviewed, with discussions on the robustness issues of such methods. This review elaborates on the profound learning challenges and solutions required for a high-quality emotion recognition system, emphasizing the benefits of dynamic expression analysis, which aids in detecting subtle micro-expressions, and the importance of multimodal fusion for improving emotion recognition accuracy. The literature was comprehensively searched via databases with records covering the topic of affective computing, followed by rigorous screening and selection of relevant studies. The results show that the effectiveness of current multimodal emotion recognition methods is affected by the limited availability of training data, insufficient context awareness, and challenges posed by real-world cases of noisy or missing modalities. The findings suggest that improving emotion recognition requires better representation of input data, refined feature extraction, and optimized aggregation of modalities within a multimodal framework, along with incorporating state-of-the-art methods for recognizing dynamic expressions. Full article

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

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20 pages, 11516 KiB

Open AccessArticle

Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model

by Hefeng Dong, Ran Wan, Changan Huang, Shoulie Liu, Shamiao Luo, Liangbin Chen, Shaobin Li and Xizhen Song

Appl. Sci. 2024, 14(17), 8070; https://doi.org/10.3390/app14178070 - 9 Sep 2024

Viewed by 368

Abstract

Disc stack centrifuge belongs to one kind of sedimentation centrifuge, widely used in the environmental protection, pharmacy, and chemical industries, etc. The flow process inside the disc stack centrifuge seriously affects the separation efficiency. However, the flow process inside the disc stack centrifuge [...] Read more.

Disc stack centrifuge belongs to one kind of sedimentation centrifuge, widely used in the environmental protection, pharmacy, and chemical industries, etc. The flow process inside the disc stack centrifuge seriously affects the separation efficiency. However, the flow process inside the disc stack centrifuge and its influence on the separation efficiency have not yet been detailed. We plan to study the flow process of oil and water phases inside the disc stack centrifuge and to explore the process of fragmentation and accumulation of water droplets. In this study, the Mixture-PBM (Population Balance Model) model is used to numerically simulate the two-phase flow of oil and water in the disc stack centrifuge and compare it with the tests. The research found that with the increase in rotational speed, the separation efficiency rises in both the test and numerical simulation results, and the difference between the test and simulation results is below 1%. The effect of ribs on the flow is considered, and the results show that the hysteresis of the liquid flow in the disc stack centrifuge is significantly reduced after considering the ribs, and the numerical simulation results can reach 98% of the theoretical results. Fluid entering the separation channel from the neutral pore creates a vortex, and as the dimensionless number λ increases, the degree of deviation of the fluid’s trajectory from the generatrix increases. The circumferential and radial velocities in the separation channel appear large in the center and small near the wall. The water content in the rising channel gradually decreases, and 90% of the water finishes settling in the distributor. The processing volume of the separation channel in each layer shows a small bottom layer, a large top layer, and a uniform law in the middle. The coalescence of water droplets occurs mainly in the separation channel, as found by analyzing the laws of the internal flow of the disc stack centrifuge, which provides the basis for improving the structure of the disc stack centrifuge, increasing the separation efficiency and reducing the floor space. Full article

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24 pages, 4942 KiB

Open AccessArticle

Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller

by Jiyuan Sun, Gengyun Tian, Pin Li, Chunlin Tian and Zhenxiong Zhou

Appl. Sci. 2024, 14(17), 8069; https://doi.org/10.3390/app14178069 - 9 Sep 2024

Viewed by 319

Abstract

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The magnetic levitation ruler can serve as a viable alternative to the linear measurement system of a coordinate measuring machine. The stability of the four magnetic [...] Read more.

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The magnetic levitation ruler can serve as a viable alternative to the linear measurement system of a coordinate measuring machine. The stability of the four magnetic fields in air gaps and the levitation position of the maglev ruler is one of the key factors for the stability of the thrust force on the power core, and it is also one of the key factors for ensuring the precision of the maglev ruler. There is cross-coupling between the two ends of the mover core of the maglev ruler, resulting in a strongly coupled, nonlinear, multi-input and multi-output system for the levitating system of the maglev ruler. This paper establishes a mathematical model for the levitating system of the maglev ruler and designs a levitating control system for the maglev ruler based on an active disturbance rejection control algorithm to achieve decoupling and disturbance suppression. Through simulation analysis and experimental testing of the levitating system with starting and disturbance, it is proved that the levitating control system of the maglev ruler has good dynamic characteristics, static characteristics, and robustness. Full article

(This article belongs to the Special Issue Advanced Control Systems and Control Engineering)

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15 pages, 1174 KiB

Open AccessReview

Low-Level Laser Therapy for Tooth Sensitivity after Tooth Bleaching: A Systematic Review

by Paraskevi Giannakopoulou, Chariklia Neophytou, Panagiotis Karakostas, Konstantinos Papadimitriou, Dimitrios Dionysopoulos, Kosmas Tolidis and Sotiria Davidopoulou

Appl. Sci. 2024, 14(17), 8068; https://doi.org/10.3390/app14178068 - 9 Sep 2024

Viewed by 501

Abstract

Tooth bleaching is a popular cosmetic procedure known for its effective whitening results. However, it may cause tooth sensitivity. Various desensitizing therapies, including laser treatments, are used to alleviate pain and improve patient comfort. This study aims to conduct a systematic review to [...] Read more.

Tooth bleaching is a popular cosmetic procedure known for its effective whitening results. However, it may cause tooth sensitivity. Various desensitizing therapies, including laser treatments, are used to alleviate pain and improve patient comfort. This study aims to conduct a systematic review to evaluate the effectiveness of low-level laser therapy for treating tooth sensitivity following bleaching therapy. A comprehensive search was conducted across 13 electronic databases (PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science, Ovid, BMJ evidence-based medicine, proQuest, Greylit.org, Ethos, Livivo, Clinical trials gov, and Meta register of controlled trials) to identify relevant studies according to specific eligibility criteria, following the PRISMA guidelines. Two independent reviewers screened and selected the studies, performed data extraction, and assessed the risk of bias using the revised Cochrane risk-of-bias tool for randomized clinical trials (RCTs). The initial search yielded 2875 articles, which were subsequently screened to remove duplicates. After evaluating 1532 articles based on title and 136 by abstract, 21 studies remained for full-text assessment. Ultimately, only six RCTs met all of the eligibility criteria. The application of low-level laser therapy appears to reduce tooth sensitivity following tooth bleaching. Despite the positive reported effects, further research is necessary to determine the optimal use of low-level laser therapy for effective pain relief. Full article

(This article belongs to the Special Issue Photobiomodulation and Photodynamic Therapy in Dentistry and Oral Science)

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17 pages, 27624 KiB

Open AccessArticle

Large-Scale Physical Simulation Experiment of Water Invasion Law for Multi-Well Development in Sandstone Gas Reservoirs with Strong Water Drive

by Feifei Fang, Sijie He, Jian Zhuang, Jie Zhang and Yanan Bian

Appl. Sci. 2024, 14(17), 8067; https://doi.org/10.3390/app14178067 - 9 Sep 2024

Viewed by 329

Abstract

In order to clarify the water invasion law and residual gas distribution characteristics in edge and bottom water gas reservoirs with multi-well development, a large-scale three-dimensional physical simulation model was developed and a physical simulation experiment method for the water invasion law of [...] Read more.

In order to clarify the water invasion law and residual gas distribution characteristics in edge and bottom water gas reservoirs with multi-well development, a large-scale three-dimensional physical simulation model was developed and a physical simulation experiment method for the water invasion law of multi-well development in sandstone gas reservoirs with strong water drives was established. Water invasion physical simulation experiments of multi-well development under the conditions of different water body multiples and production systems were conducted. The results show the following: (1) Gas wells near fractures and high-permeability zones experience the earliest water breakthrough. The larger the water body multiple, the faster the rate of water invasion, the earlier the water breakthrough time of gas wells, the more severe the degree of water invasion in gas reservoirs, and the lower the ultimate recovery rate. (2) Shutting in low-position gas wells immediately after water breakthrough reduces the overall water production of the gas reservoir and extends the overall water-free gas production period. However, the ultimate recovery rate is lower than when the wells are not shut in. (3) The residual gas in the fracture model is mainly distributed around the fracture and the edge of the gas reservoir, with the ultimate recovery rate ranging from 38.5% to 58.2%. The residual gas in the fracture–high-permeability zone model is mainly distributed around the fracture–high-permeability zone and the edge of the gas reservoir, with the ultimate recovery rate ranging from 28.32% to 41.8%. The experimental results have important guiding significance for the economical and efficient development of similar gas reservoirs. Full article

(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)

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