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Volume 15
Issue 9
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Appl. Sci., Volume 15, Issue 9 (May-1 2025) – 627 articles

Cover Story (view full-size image): The CHA2DS2-VASc score is the most widely used and recognized method for stroke risk stratification in atrial fibrillation (AF) patients. However, some patients with low scores still experience strokes. Given that 90% of cardiogenic strokes are caused by thrombus in the left atrial appendage (LAA), it is essential to incorporate hemodynamic and geometric features of the LAA into existing risk stratification models. This review first evaluates current stroke and bleeding risk stratification strategies, then analyzes the geometric and hemodynamic parameters within the left atrium and LAA, and finally compares the methods and techniques available for acquiring these parameters. Through these retrospective analyses, insights and recommendations for the management of AF patients and stroke prevention are provided. View this paper
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24 pages, 838 KiB  
Article
Cost-Effective and Reliable Sidechain Approach for Managing Small-Scale Digital Asset Trading Platforms
by Nam-Yong Lee
Appl. Sci. 2025, 15(9), 5221; https://doi.org/10.3390/app15095221 - 7 May 2025
Viewed by 139
Abstract
This study proposes a cost-effective and reliable blockchain-based approach for managing small-scale digital asset trading platforms. Instead of developing an independent blockchain, the proposed method constructs a sidechain anchored to established blockchains known for their stability and reliability, such as Bitcoin and Ethereum. [...] Read more.
This study proposes a cost-effective and reliable blockchain-based approach for managing small-scale digital asset trading platforms. Instead of developing an independent blockchain, the proposed method constructs a sidechain anchored to established blockchains known for their stability and reliability, such as Bitcoin and Ethereum. In this study, we refer to these established blockchains serving as the mainchain for our sidechain as the reference chain. The proposed sidechain, termed the platform chain in this paper, inherits the security and trust of the reference chain, while reducing operational costs and requiring no modifications to it. To enhance efficiency and privacy, the proposed method introduces a dual-sidechain architecture. The platform chain can be constructed either as a private blockchain or a consortium blockchain, depending on the specific operational requirements. In this architecture, only the hash values of transactions are recorded on the platform chain by default, while complete transaction content is disclosed through a dual platform chain under controlled conditions. This enables strong privacy guarantees, alongside auditable transparency when needed. To evaluate the security and feasibility of our approach, we perform a comprehensive threat assessment, addressing critical threats such as operator-level manipulation, invalid or harmful user actions, collusion among system entities, and dishonest behavior by the auditor. Our results confirm that the proposed sidechain framework provides a secure, scalable foundation for digital asset trading platforms, effectively enhancing privacy and ensuring robust protection under various adversarial conditions. Full article
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28 pages, 5255 KiB  
Article
Sustainable Cultivation of Galdieria phlegrea in an IoT-Integrated Twin-Layer Photobioreactor: System Design, Growth Dynamics, and Isotopic Perspective
by Maria Rosa di Cicco, Simona Altieri, Antonio Spagnuolo, Claudia Ciniglia, Chiara Germinario, Silvio Bove, Antonio Masiello, Carmela Vetromile, Iolanda Galante and Carmine Lubritto
Appl. Sci. 2025, 15(9), 5220; https://doi.org/10.3390/app15095220 - 7 May 2025
Viewed by 121
Abstract
This study showcases an attached-biomass system based on twin-layer technology for cultivating Galdieria phlegrea using municipal wastewater, equipped with a smart sensor system for the remote monitoring of operational parameters. From an industrial scale-up perspective, the system offers high scalability, with low impact [...] Read more.
This study showcases an attached-biomass system based on twin-layer technology for cultivating Galdieria phlegrea using municipal wastewater, equipped with a smart sensor system for the remote monitoring of operational parameters. From an industrial scale-up perspective, the system offers high scalability, with low impact and operating costs. Mathematical approximation modelling identified the optimal growth conditions across five experiments. The theoretical yield was estimated to reach 1 kgDW/m2 of biomass within two months. Integrated use of isotopic mass spectrometry and spectrophotometric methods allowed us to study the metabolic strategies implemented by the algal community during the best growth condition at different resolutions, showing an increase in the nitrogen concentration over time and a favourable affinity of the organism for nitrogen species that are commonly present in the urban effluent. SEM studies showed a clean algal biofilm (free of foreign organisms), which could guarantee usage in the high economic potential market of biorefineries. Full article
(This article belongs to the Special Issue Novel Technologies for Wastewater Treatment and Reuse)
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27 pages, 1034 KiB  
Review
Microbiome-Based Interventions for Food Safety and Environmental Health
by Blessing Oteta Simon, Nnabueze Darlington Nnaji, Christian Kosisochukwu Anumudu, Job Chinagorom Aleke, Chiemerie Theresa Ekwueme, Chijioke Christopher Uhegwu, Francis Chukwuebuka Ihenetu, Promiselynda Obioha, Onyinye Victoria Ifedinezi, Precious Somtochukwu Ezechukwu and Helen Onyeaka
Appl. Sci. 2025, 15(9), 5219; https://doi.org/10.3390/app15095219 - 7 May 2025
Viewed by 229
Abstract
The human microbiome plays a critical role in health and disease, with recent innovations in microbiome research offering groundbreaking insights that could reshape the future of healthcare. This study explored emerging methodologies, such as long-read sequencing, culturomics, synthetic biology, machine learning, and AI-driven [...] Read more.
The human microbiome plays a critical role in health and disease, with recent innovations in microbiome research offering groundbreaking insights that could reshape the future of healthcare. This study explored emerging methodologies, such as long-read sequencing, culturomics, synthetic biology, machine learning, and AI-driven diagnostics, that are transforming the field of microbiome–host interactions. Unlike traditional broad-spectrum approaches, these tools enable precise interventions, such as detecting foodborne pathogens and remediating polluted soils for safer agriculture. This work highlights the integration of interdisciplinary approaches and non-animal models, such as 3D cultures and organ-on-a-chip technologies, which address the limitations of current research and present ethical, scalable alternatives for microbiome studies. Focusing on food safety and environmental health, we examine how microbial variability impacts pathogen control in food chains and ecosystem resilience, integrating socioeconomic and environmental factors. The study also emphasizes the need to expand beyond bacterial-focused microbiome research, advocating for the inclusion of fungi, viruses, and helminths to deepen our understanding of therapeutic microbial consortia. The combination of high-throughput sequencing, biosensors, bioinformatics, and machine learning drives precision strategies, such as reducing food spoilage and enhancing soil fertility, paving the way for sustainable food systems and environmental management. Hence, this work offers a comprehensive framework for advancing microbiome interventions, providing valuable insights for researchers and professionals navigating this rapidly evolving field. Full article
(This article belongs to the Special Issue Advanced Food Processing Technologies and Approaches)
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21 pages, 1961 KiB  
Article
Design and Simulation Verification of Model Predictive Attitude Control Based on Feedback Linearization for Quadrotor UAV
by Xingyu Yuan, Jinfa Xu and Shengwei Li
Appl. Sci. 2025, 15(9), 5218; https://doi.org/10.3390/app15095218 - 7 May 2025
Viewed by 98
Abstract
The flight dynamics of quadrotor UAVs are characterized by significant nonlinearity and inter-axis coupling, posing challenges for the direct application of linear control theory in flight control system design. This paper proposes a feedback linearization transformation method to reduce the computational burden of [...] Read more.
The flight dynamics of quadrotor UAVs are characterized by significant nonlinearity and inter-axis coupling, posing challenges for the direct application of linear control theory in flight control system design. This paper proposes a feedback linearization transformation method to reduce the computational burden of MPC while addressing the nonlinear coupled flight dynamics in attitude control. The quadrotor UAV’s attitude motion is transformed via feedback linearization into a linear decoupled model, consisting of three independent second-order systems. MPC theory is then employed to design the attitude control system. By solving the constrained optimal control problem within the MPC framework, control inputs for each second-order system are derived, enabling precise attitude tracking through receding horizon control theory. Simulation verifications were subsequently conducted, with the experiment comparing FLMPC, LMPC and AMPC. The results indicate that FLMPC outperforms both the other controllers in terms of control precision, computational efficiency and robustness to disturbances, suggesting its effectiveness for real-time UAV operations where both performance and computational resource constraints are critical. Full article
(This article belongs to the Special Issue Intelligent Optimization for Flight Control Systems)
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23 pages, 7269 KiB  
Article
The Data-Driven Optimization of Parcel Locker Locations in a Transit Co-Modal System with Ride-Pooling Last-Mile Delivery
by Zhanxuan Li and Baicheng Li
Appl. Sci. 2025, 15(9), 5217; https://doi.org/10.3390/app15095217 - 7 May 2025
Viewed by 124
Abstract
Integrating passenger and parcel transportation via transit (also known as transit co-modality) has been regarded as a potential solution to sustainable transportation, in which well-planned locations for parcel lockers are crucial for transferring parcels from transit to last-mile delivery vehicles. This paper proposes [...] Read more.
Integrating passenger and parcel transportation via transit (also known as transit co-modality) has been regarded as a potential solution to sustainable transportation, in which well-planned locations for parcel lockers are crucial for transferring parcels from transit to last-mile delivery vehicles. This paper proposes a data-driven optimization framework on parcel locker locations in a transit co-modal system, where last-mile delivery is realized via a ride-pooling service that pools passengers and parcels using the same fleet of vehicles. A p-median model is proposed to solve the problem of optimal parcel locker locations and matching between passengers and parcel lockers. We use the taxi trip data and the candidate parcel locker location data from Shenzhen, China, as inputs to the proposed p-median model. Given the size of the dataset, an optimization framework based on random sampling is then developed to determine the optimal parcel locker locations according to each candidate’s frequency of being selected in the sample. The numerical results are given to show the effectiveness of the proposed optimization framework, explore its properties, and perform sensitivity analyses on the key model parameters. Notably, we identify five types of optimal parcel location based on their ranking changes according to the maximum number of planned parcel locker locations, which suggests that planners should carefully determine the optimal number of candidate locations for parcel locker deployment. Moreover, the results of sensitivity analyses reveal that the average passenger detour distance is positively related to the density of passenger demand and is negatively impacted by the number of selected locations. We also identify the minimum distance between any pair of selected locations as an important factor in location planning, as it may significantly affect the candidates’ rankings. Full article
(This article belongs to the Section Transportation and Future Mobility)
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35 pages, 9814 KiB  
Article
A Joint Metro Train Demand Model Accounting for Disaggregate Consideration Probability and Aggregate Footfall
by Ganesh Ambi Ramakrishnan, Payel Roy, Harshit Kumar Varshney and Karthik K. Srinivasan
Appl. Sci. 2025, 15(9), 5216; https://doi.org/10.3390/app15095216 - 7 May 2025
Viewed by 138
Abstract
This study introduces a new metro train demand model that simultaneously captures both aggregate ridership from automated fare collection (AFC) data and disaggregate consideration propensities, using individual survey data from Chennai, India. This joint framework produces more accurate aggregate demand estimates than traditional [...] Read more.
This study introduces a new metro train demand model that simultaneously captures both aggregate ridership from automated fare collection (AFC) data and disaggregate consideration propensities, using individual survey data from Chennai, India. This joint framework produces more accurate aggregate demand estimates than traditional OLS (R2 improves from 0.67 to 0.75), as it is able to capture the complex and non-linear relationship between disaggregate consideration probability, reflecting potential demand, and aggregate footfall, reflecting realized demand. It is observed that increasing the consideration probability enhances the footfall overall. However, some locations exhibit an opposing trend between consideration and footfall (low consideration but high footfall, or vice versa). Also, the sets of influential factors vary across these two dimensions. For instance, individual-level variables (income and out-of-vehicle travel time) and multi-modal connectivity features (presence of an airport and multimodal hubs near the metro) play a key role in footfall. In contrast, consideration probability is primarily influenced by access time, cost, and egress distance. Furthermore, factors influencing consideration probability (walkability, train service quality, and first–last–mile connectivity) vary across segments (based on vehicle unavailability, exclusive vehicle availability, and limited vehicle availability). Evidence of selection bias among metro riders, non-normality, and intra-person variability effects in footfall is observed. From a policy perspective, neglecting the disaggregate consideration effects on realized aggregate demand, i.e., footfall models, can overestimate the role of metro costs and out-of-vehicle travel time. In addition, the ridership levels of the metro are overestimated at higher metro fare levels. The new model illustrates that applying location-specific and dimension-specific policy interventions can be more effective than uniform area-wide policies for enhancing the user base and realized ridership. Full article
(This article belongs to the Special Issue Recent Advances and Emerging Technologies in Transportation and Urban Planning)
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26 pages, 6397 KiB  
Review
Evaluation of the Service Performance of Soil–Bentonite Vertical Cut-Off Walls at Heavy Metal Contaminated Sites: A Review
by Ke Wang and Yan Zhang
Appl. Sci. 2025, 15(9), 5215; https://doi.org/10.3390/app15095215 - 7 May 2025
Viewed by 98
Abstract
Soil–bentonite (SB) vertical cut-off walls are widely utilized to mitigate the transport of soil contaminants in groundwater. Evaluating their long-term service performance is crucial for ensuring environmental safety and effective pollution control. The evaluation model for the long-term service performance of contaminant cut-off [...] Read more.
Soil–bentonite (SB) vertical cut-off walls are widely utilized to mitigate the transport of soil contaminants in groundwater. Evaluating their long-term service performance is crucial for ensuring environmental safety and effective pollution control. The evaluation model for the long-term service performance of contaminant cut-off walls considers key processes such as convection, diffusion, dispersion, and adsorption. These processes are closely linked to the physicochemical properties of the cut-off walls, which are influenced by the surrounding complex environment, ultimately impacting their long-term performance. This study delves into the long-term service performance of SB vertical cut-off walls. It focuses on the key factors that influence this performance and the measures that can enhance it. Moreover, it offers a detailed analysis of how the performance of seepage cut-off walls in soil–bentonite materials evolves under various environmental influences. These influences include chemical exposure, freeze–thaw cycles, and dry–wet cycles. Additionally, it outlines existing service performance evaluation methods and identifies their shortcomings. By leveraging the advantages of in situ testing methods, this paper proposes the establishment of a comprehensive evaluation system for the service performance of vertical cut-off walls based on in situ test parameters. The proposed evaluation system aims to provide a scientific assessment of the long-term service performance of SB vertical cut-off walls. Full article
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22 pages, 15168 KiB  
Article
Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM
by Xiaofeng Huang, Xuan Zhou, Junwei Yan and Xiaofei Huang
Appl. Sci. 2025, 15(9), 5214; https://doi.org/10.3390/app15095214 - 7 May 2025
Viewed by 111
Abstract
Cooling load forecasting is a crucial aspect of optimizing energy efficiency and efficient operation in central air conditioning systems for manufacturing plants. Due to the influence of multiple factors, the cooling load in manufacturing plants exhibits complex characteristics, including multi-peak patterns, periodic fluctuations, [...] Read more.
Cooling load forecasting is a crucial aspect of optimizing energy efficiency and efficient operation in central air conditioning systems for manufacturing plants. Due to the influence of multiple factors, the cooling load in manufacturing plants exhibits complex characteristics, including multi-peak patterns, periodic fluctuations, and short-term disturbances during meal periods. Existing methods struggle to accurately capture the relationships among variables and temporal dependencies, leading to limited forecasting accuracy. To address these challenges, this paper proposes a hybrid forecasting method based on the iTransformer-BiLSTM. First, the Pearson correlation coefficient is employed to select time-series variables that have a significant impact on cooling load. Then, iTransformer is utilized for feature extraction to capture nonlinear dependencies among multivariate inputs and global temporal patterns. Finally, BiLSTM is applied for temporal modeling, leveraging its bidirectional recurrent structure to capture both forward and backward dependencies in time series, thereby improving forecasting accuracy. Experimental validation on a cooling load dataset from a welding workshop in a manufacturing plant, including ablation studies and comparative analyses with other algorithms, demonstrates that the proposed method achieves superior performance compared to traditional approaches in forecasting accuracy. Meanwhile, by integrating the SHAP sensitivity analysis method, the contributions of input variables to the cooling load prediction results are systematically evaluated, thereby enhancing the interpretability of the model. This research provides a reliable technical foundation for energy-efficient control of central air conditioning systems in manufacturing plants. Full article
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22 pages, 2990 KiB  
Article
Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method
by Lihong Rong, Yuexin Pan and Zhimin Tong
Appl. Sci. 2025, 15(9), 5213; https://doi.org/10.3390/app15095213 - 7 May 2025
Viewed by 74
Abstract
This paper addresses fault estimation in discrete-time semi-Markov jump neural networks (s-MJNNs) under the Round-Robin protocol and proposes an innovative extended state observer-based approach. Unlike studies considering only constant transition rates, this work investigates s-MJNNs with time-varying transition probabilities, which more closely reflect [...] Read more.
This paper addresses fault estimation in discrete-time semi-Markov jump neural networks (s-MJNNs) under the Round-Robin protocol and proposes an innovative extended state observer-based approach. Unlike studies considering only constant transition rates, this work investigates s-MJNNs with time-varying transition probabilities, which more closely reflect practical situations. By incorporating actuator and sensor faults as augmented state variables, an extended state observer is proposed to estimate system states and faults simultaneously. To alleviate network congestion and optimize communication resources, the Round-Robin protocol is adopted to schedule data transmission efficiently. By constructing a Lyapunov–Krasovskii functional and applying the discrete Wirtinger inequality, sufficient conditions are derived to ensure the mean square exponential stability and dissipative performance of the system. The observer gain parameters are computed using the linear matrix inequality (LMI) method. Numerical simulations validate the effectiveness and performance of the proposed fault estimation method. Full article
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17 pages, 1744 KiB  
Article
Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
by Sabina Umirzakova, Shakhnoza Muksimova, Abrayeva Mahliyo Olimjon Qizi and Young Im Cho
Appl. Sci. 2025, 15(9), 5212; https://doi.org/10.3390/app15095212 - 7 May 2025
Viewed by 110
Abstract
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve high-precision detection under fully [...] Read more.
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve high-precision detection under fully and semi-supervised conditions. RASST integrates a hybrid Vision Transformer architecture augmented with rotationally aware patch embeddings, adaptive rotational convolutions, and a multi-scale feature fusion (MSFF) module that employs cross-scale attention to enhance detection across object sizes. To address the scarcity of labeled data, we introduce a novel Pseudo-Label Guided Learning (PGL) framework, which refines pseudo-labels through Rotation-Aware Adaptive Weighting (RAW) and Global Consistency (GC) losses, thereby improving generalization and robustness against noisy supervision. Despite its lightweight design, RASST achieves superior performance on the DOTA-v1.5 benchmark, outperforming existing state-of-the-art methods in supervised and semi-supervised settings. The proposed framework demonstrates high scalability, precise orientation sensitivity, and effective utilization of unlabeled data, establishing a new benchmark for efficient oriented object detection in remote sensing imagery. Full article
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34 pages, 17965 KiB  
Article
Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency
by Helena M. Ramos, João S. T. Coelho, Eyup Bekci, Toni X. Adrover, Oscar E. Coronado-Hernández, Modesto Perez-Sanchez, Kemal Koca, Aonghus McNabola and R. Espina-Valdés
Appl. Sci. 2025, 15(9), 5211; https://doi.org/10.3390/app15095211 - 7 May 2025
Viewed by 167
Abstract
This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution [...] Read more.
This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R2: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R2 (0.9289), and Scenario 5 the highest R2 (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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13 pages, 1227 KiB  
Article
The Link Between Physical Function, β-Amyloid, and Cognitive Aging in Women
by Raquel Pedrero-Chamizo and Cassandra Szoeke
Appl. Sci. 2025, 15(9), 5210; https://doi.org/10.3390/app15095210 - 7 May 2025
Viewed by 95
Abstract
This study aimed to examine associations between functional capacity (FC), brain β-amyloid (Aβ) burden, and longitudinal cognitive performance. Data from 89 cognitively normal women (70.0 ± 2.7 years) in the Women’s Healthy Ageing Project cohort were analyzed. FC was assessed using the timed [...] Read more.
This study aimed to examine associations between functional capacity (FC), brain β-amyloid (Aβ) burden, and longitudinal cognitive performance. Data from 89 cognitively normal women (70.0 ± 2.7 years) in the Women’s Healthy Ageing Project cohort were analyzed. FC was assessed using the timed up and go (TUG) test and the Aβ burden was quantified via a F-18 Florbetaben PET scan with Standardized Uptake Value Ratio (SUVR). Cognition was evaluated longitudinally using the Preclinical Alzheimer Cognitive Composite (PACC) over 3.9 ± 2.6 years. Multiple linear regression, mediation analysis, and linear mixed-effects models were applied. Baseline Aβ burden and years of education were associated with cognitive performance two to six years later, while the TUG performance was associated with cognitive outcomes at two years. Aβ burden was found to mediate the relationship between FC and cognition over time. A significant three-way interaction (TUG × SUVR × time) was observed, indicating that declines in the TUG performance over time were exclusively associated with steeper cognitive decline among women with elevated Aβ burden (SUVR ≥ 1.42). These findings suggest that maintaining functional mobility may be particularly relevant for women with increased Aβ burden and support future research targeting early motor-cognitive markers. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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20 pages, 1690 KiB  
Article
Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
by Qinglan Wei, Xinyi Ling and Jiqiu Hu
Appl. Sci. 2025, 15(9), 5209; https://doi.org/10.3390/app15095209 - 7 May 2025
Viewed by 99
Abstract
This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This [...] Read more.
This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development. Full article
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34 pages, 708 KiB  
Review
A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
by Lukas Nolte and Sven Tomforde
Appl. Sci. 2025, 15(9), 5208; https://doi.org/10.3390/app15095208 - 7 May 2025
Viewed by 261
Abstract
Designing and conducting experiments is a fundamental process across various scientific disciplines, such as materials science, biology, medicine, and chemistry. However, experimental research still predominantly relies on traditional, time-consuming, resource-intensive, and costly trial-and-error experimentation approaches that hinder rapid discovery, reproducibility, and scalability. Recent [...] Read more.
Designing and conducting experiments is a fundamental process across various scientific disciplines, such as materials science, biology, medicine, and chemistry. However, experimental research still predominantly relies on traditional, time-consuming, resource-intensive, and costly trial-and-error experimentation approaches that hinder rapid discovery, reproducibility, and scalability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer promising alternatives, but a comprehensive overview of their implementations in experimental design is lacking. This research fills this gap by providing a structured overview and analysis of existing frameworks for AI-driven experimental design, supporting researchers in selecting and developing suitable AI-driven approaches to automate and accelerate their experimental research. Moreover, it discusses the current limitations and challenges of AI techniques and ethical issues related to AI-driven experimental design frameworks. A search and filter strategy is developed and applied to appropriate databases with the objective of identifying the relevant literature. Here, active learning, particularly Bayesian optimization, stands out as the predominantly used methodology. The majority of frameworks are partially autonomous, while fully autonomous frameworks are underrepresented. However, more research is needed in the field of AI-driven experimental design due to the low number of relevant papers obtained. Full article
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18 pages, 12708 KiB  
Review
Ultra-High Spatial Resolution Clinical Positron Emission Tomography (PET) Systems
by Myungheon Chin, Muhammad Nasir Ullah, Derek Innes and Craig S. Levin
Appl. Sci. 2025, 15(9), 5207; https://doi.org/10.3390/app15095207 - 7 May 2025
Viewed by 123
Abstract
Positron emission tomography (PET) is an imaging modality for non-invasive visualization and quantification of molecular pathways in human diseases, with applications spanning clinical practice and biomedical research. Recent advances in PET system technology target ultra-high spatial resolution (<2 mm) to enhance diagnostic precision [...] Read more.
Positron emission tomography (PET) is an imaging modality for non-invasive visualization and quantification of molecular pathways in human diseases, with applications spanning clinical practice and biomedical research. Recent advances in PET system technology target ultra-high spatial resolution (<2 mm) to enhance diagnostic precision for early-stage disease detection and longitudinal monitoring. A key strategy involves organ-specific, or loco-regional, scanner configurations that optimize photon detection efficiency (PDE) while balancing the trade-off between spatial resolution and image signal-to-noise ratio (SNR). This study reviews innovations driving the development of next-generation clinical PET systems, including the following: (1) novel geometries tailored for anatomical regions such as the head/neck and breast, (2) high-performance detector materials and readout electronics, and (3) advanced image reconstruction algorithms. This paper emphasizes progress toward achieving ≤2 mm isotropic spatial resolution in clinical PET systems, and in particular focuses on describing a 1 mm3 resolution system dedicated to head-and-neck or breast cancer imaging that was developed in our laboratory. Full article
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17 pages, 6441 KiB  
Article
Experimental Investigation of Motion Control of a Closed-Kinematic Chain Robot Manipulator Using Synchronization Sliding Mode Method with Time Delay Estimation
by Tu T. C. Duong, Charles C. Nguyen and Thien Duc Tran
Appl. Sci. 2025, 15(9), 5206; https://doi.org/10.3390/app15095206 - 7 May 2025
Viewed by 90
Abstract
Closed-Kinematic Chain Manipulators (CKCM) have gained attention due to their precise Cartesian motion capability through coordinated active joint movements. Furthermore, ensuring synchronization among the joints of CKCMs is critical for reliable operation. An advanced control scheme for CKCMs that combines Nonsingular Fast Terminal [...] Read more.
Closed-Kinematic Chain Manipulators (CKCM) have gained attention due to their precise Cartesian motion capability through coordinated active joint movements. Furthermore, ensuring synchronization among the joints of CKCMs is critical for reliable operation. An advanced control scheme for CKCMs that combines Nonsingular Fast Terminal Sliding Mode Control (NFTSMC) with Time Delay Estimation (TDE) while utilizing synchronization errors, namely Syn-TDE-NFTSMC, to effectively address joint errors in CKCMs was developed. NFTSMC enables fast convergence through nonlinear terminal sliding while TDE eliminates the need for prior knowledge of the robot’s dynamics, thereby simplifying its implementation and reducing its computational requirements. It is known that the inclusion of TDE reduces about 98% of the computational requirement of control schemes without TDE. The newly developed control scheme was rigorously evaluated using computer simulation and its control performance was compared with that of existing control methods. This paper presents an experimental study where the newly developed control scheme and other existing control schemes were applied to a real CKCM with 2 degrees of freedom (DOF). The experimental results confirm that the control scheme performed much better than other existing control schemes in terms of synchronization and control performance, achieving a reduction in maximum tracking errors of up to 81% as compared to other existing control schemes. The results confirm the efficacy of the newly developed control scheme in enhancing control precision and system stability, making it a promising solution for improving CKCM control strategies in real-world applications. Full article
(This article belongs to the Section Robotics and Automation)
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15 pages, 5186 KiB  
Article
Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
by Chao Zhang, Ye Zhang, Yifan Liu and Xing Guo
Appl. Sci. 2025, 15(9), 5205; https://doi.org/10.3390/app15095205 - 7 May 2025
Viewed by 80
Abstract
The aim of this study was to investigate the influence of operational and design parameters on the conveying efficiency and material layer stability of air slides and to optimize the parameters of the XZ200 air slide. A gas–solid coupled simulation of the conveying [...] Read more.
The aim of this study was to investigate the influence of operational and design parameters on the conveying efficiency and material layer stability of air slides and to optimize the parameters of the XZ200 air slide. A gas–solid coupled simulation of the conveying process was conducted using ANSYS v2023 and Rocky v23R1 software. Three key variables—inclination angle, input air velocity, and permeable layer porosity—were analyzed to evaluate their effects on wheat flour conveying efficiency and layer stability. Orthogonal experiments and matrix analysis were applied to comprehensively assess the numerical simulation results. The findings reveal that the conveying ratio is positively correlated with input air velocity and inclination angle but negatively correlated with permeable layer porosity. Meanwhile, material layer fluctuation and stability increase with inclination angle but decrease with higher porosity. Through orthogonal testing and matrix analysis, the optimal parameter combination was determined as follows: input air velocity of 1.8 m/s, porosity of 37.84%, inclination angle of 6°, conveying ratio of 96.52%, and material layer fluctuation of 4.39 mm. This study provides a reference methodology for gas–solid coupled simulation in air slide design and offers practical guidance for parameter optimization in air slide systems. Full article
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16 pages, 5018 KiB  
Article
Detection of Welding Defects Using the YOLOv8-ELA Algorithm
by Yunxia Chen, Yangkai He and Lei Wu
Appl. Sci. 2025, 15(9), 5204; https://doi.org/10.3390/app15095204 - 7 May 2025
Viewed by 88
Abstract
To address the issue of the low precision in detecting defects in aluminum alloy weld seam digital radiography (DR) images using the current target detection algorithms, a modified algorithm named YOLOv8-ELA based on YOLOv8 is proposed. The model integrates a novel HS-FPN feature [...] Read more.
To address the issue of the low precision in detecting defects in aluminum alloy weld seam digital radiography (DR) images using the current target detection algorithms, a modified algorithm named YOLOv8-ELA based on YOLOv8 is proposed. The model integrates a novel HS-FPN feature fusion module, which optimizes the parameter efficiency and enhances the detection performance. For better identification of small defect features, the CA attention mechanism within HS-FPN is substituted with the ELA attention mechanism. Additionally, the first output layer is enhanced with a SimAM attention mechanism to improve the small target recognition. The experimental findings indicate that, at a 0.5 threshold, the YOLOv8-ELA model achieves mean average precision (mAP@0.5) values of 93.3%, 96.4%, and 96.5% for detecting pores, inclusions, and incomplete welds, respectively. These values surpass those of the original YOLOv8 model by 1.4, 2.3, and 0.1 percentage points. Overall, the model attains an average mAP of 95.4%, marking a 1.3% improvement over its predecessor, confirming its superior defect detection capabilities. Full article
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23 pages, 59897 KiB  
Article
Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets
by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo and Adolfo Cobo
Appl. Sci. 2025, 15(9), 5203; https://doi.org/10.3390/app15095203 - 7 May 2025
Viewed by 111
Abstract
This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant–Boussinesq approximation to simulate [...] Read more.
This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant–Boussinesq approximation to simulate the resulting ground deformation detected by virtual fiber-optic cables. These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. This framework uniquely integrates detailed traffic modeling with DAS physics, providing a novel tool to develop and evaluate DAS signal processing techniques, optimize cable layout deployments, and advance DAS applications in complex transportation monitoring scenarios. Creating such a procedure offers significant potential for advancing the application of DAS in transportation monitoring and smart city initiatives. Full article
(This article belongs to the Special Issue Recent Research on Intelligent Sensors)
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26 pages, 46466 KiB  
Article
Experimental Investigation of Mechanical Properties and Pore Characteristics of Hipparion Laterite Under Freeze–Thaw Cycles
by Tengfei Pan, Zhou Zhao, Jianquan Ma and Fei Liu
Appl. Sci. 2025, 15(9), 5202; https://doi.org/10.3390/app15095202 - 7 May 2025
Viewed by 122
Abstract
The Loess Plateau region of China has an anomalous climate and frequent geological disasters. Hipparion laterite in seasonally frozen regions exhibits heightened susceptibility to freeze–thaw (F-T) cycling, which induces progressive structural weakening and significantly elevates the risk of slope instability through mechanisms including [...] Read more.
The Loess Plateau region of China has an anomalous climate and frequent geological disasters. Hipparion laterite in seasonally frozen regions exhibits heightened susceptibility to freeze–thaw (F-T) cycling, which induces progressive structural weakening and significantly elevates the risk of slope instability through mechanisms including pore water phase transitions, aggregate disintegration, and shear strength degradation. This study focuses on the slip zone Hipparion laterite from the Nao panliang landslide in Fugu County, Shaanxi Province. We innovatively integrated F-T cycling tests with ring-shear experiments to establish a hydro-thermal–mechanical coupled multi-scale evaluation framework for assessing F-T damage in the slip zone material. The microstructural evolution of soil architecture and pore characteristics was systematically analyzed through scanning electron microscopy (SEM) tests. Quantitative characterization of mechanical degradation mechanisms was achieved using advanced microstructural parameters including orientation frequency, probabilistic entropy, and fractal dimensions, revealing the intrinsic relationship between pore network anisotropy and macroscopic strength deterioration. The experimental results demonstrate that Hipparion laterite specimens undergo progressive deterioration with increasing F-T cycles and initial moisture content, predominantly exhibiting brittle deformation patterns. The soil exhibited substantial strength degradation, with total reduction rates of 51.54% and 43.67% for peak and residual strengths, respectively. The shear stress–displacement curves transitioned from strain-softening to strain-hardening behavior, indicating plastic deformation-dominated shear damage. Moisture content critically regulates pore microstructure evolution, reducing micropore proportion to 23.57–28.62% while promoting transformation to mesopores and macropores. At 24% moisture content, the areal porosity, probabilistic entropy, and fractal dimension increased by 0.2263, 0.0401, and 0.0589, respectively. Temperature-induced pore water phase transitions significantly amplified mechanical strength variability through cyclic damage accumulation. These findings advance the theoretical understanding of Hipparion laterite’s engineering geological behavior while providing critical insights for slope stability assessment and landslide risk mitigation strategies in loess plateau regions. Full article
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15 pages, 9882 KiB  
Article
The Development and Challenge of the CHSN01 Jacket for the CS Magnet in China’s Future Fusion Device
by Yongsheng Wu, Weijun Wang, Jing Jin, Jinhao Shi, Ming Deng and Jinggang Qin
Appl. Sci. 2025, 15(9), 5201; https://doi.org/10.3390/app15095201 - 7 May 2025
Viewed by 90
Abstract
The Institute of Plasma Physics Chinese Academy of Sciences (ASIPP) is currently engaged in the design of a compact fusion device with a fusion power gain (Q) exceeding one. Due to space limitation for the device, the conductor jacket of the central solenoid [...] Read more.
The Institute of Plasma Physics Chinese Academy of Sciences (ASIPP) is currently engaged in the design of a compact fusion device with a fusion power gain (Q) exceeding one. Due to space limitation for the device, the conductor jacket of the central solenoid (CS) magnet experiences significant electromagnetic stress. Therefore, a higher strength stainless steel known as modified N50 (CHSN01) is utilized for manufacturing the jacket. To effectively heat the plasma, the CS magnet within the device requires operation with alternating current. It is crucial to monitor fatigue crack growth caused by stress cycles in the CS jacket and assess its severity in order to ensure the safety and reliability of the fusion device. In this study, a finite element method is applied to establish a functional relationship between the stress intensity factor range ∆K and the jacket defect depth a precisely based on actual cyclic loads experienced by CS magnet operation. Experimental investigations are conducted to determine fatigue crack growth rates (FCGRs) at 4.2 Kelvin (K) for the CHSN01 jacket. The maximum likelihood estimation method is employed to calculate the probability equations of FCGRs with a random variable description. Consequently, it is possible to determine the maximum allowable initial defect size for a jacket to withstand 60,000 plasma pulses, which will serve as an input parameter for non-destructive testing of jackets. Full article
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19 pages, 5898 KiB  
Article
Multi-Module Combination for Underwater Image Enhancement
by Zhe Jiang, Huanhuan Wang, Gang He, Jiawang Chen, Wei Feng and Gaosheng Luo
Appl. Sci. 2025, 15(9), 5200; https://doi.org/10.3390/app15095200 - 7 May 2025
Viewed by 79
Abstract
Underwater observation and operation for divers and underwater robots still largely depend on optic methods, such as cameras videos, etc. However, due to the poor quality of images captured in murky waters, underwater operations in such areas are greatly hindered. In order to [...] Read more.
Underwater observation and operation for divers and underwater robots still largely depend on optic methods, such as cameras videos, etc. However, due to the poor quality of images captured in murky waters, underwater operations in such areas are greatly hindered. In order to solve the issue of degraded images, this paper proposes a multi-module combination method (UMMC) for underwater image enhancement. This is a new solution for processing a single image. Specifically, the process consists of five modules. With five separate modules working in tandem, UMMC provides the flexibility to address key challenges such as color distortion, haze, and low contrast. The UMMC framework starts with a color deviation detection module that intelligently separates images with and without color deviation, followed by a color and white balance correction module to restore accurate color. Effective defogging is then performed using a rank-one prior matrix-based approach, while a reference curve transformation adaptively enhances the contrast. Finally, the fusion module combines the visibility and contrast functions with reference to two weights to produce clear and natural results. A large number of experimental results demonstrate the effectiveness of the method proposed in this paper, which shows good performance compared to existing algorithms, both on real and synthetic data. Full article
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20 pages, 2316 KiB  
Article
Antimicrobial Activity and Phytochemical Profiling of Natural Plant Extracts for Biological Control of Wash Water in the Agri-Food Industry
by Piotr Kanarek, Barbara Breza-Boruta and Marcin Stocki
Appl. Sci. 2025, 15(9), 5199; https://doi.org/10.3390/app15095199 - 7 May 2025
Viewed by 119
Abstract
Water used in cleaning processes within the agri-food industry can be a vector for post-harvest contaminants, thus contributing to cross-contamination. The contamination risk is increased when water is not replaced between batches or when disinfection protocols are insufficient. Given the increasing focus in [...] Read more.
Water used in cleaning processes within the agri-food industry can be a vector for post-harvest contaminants, thus contributing to cross-contamination. The contamination risk is increased when water is not replaced between batches or when disinfection protocols are insufficient. Given the increasing focus in recent years on the potential of natural, non-invasive plant extracts to combat a variety of pathogens, including multidrug-resistant bacteria, environmental strains, and clinical isolates, this study aimed to evaluate the antibacterial activity of selected water-ethanol plant extracts against six opportunistic pathogens isolated from wash water in the agri-food industry, along with chromatographic analyses of the selected extracts. Plant extracts were obtained from the fruits, leaves, shoots, roots, and bark of 13 species. Antibacterial activity was assessed using the well diffusion method. The results indicated that antimicrobial activity was exhibited by six extracts: Tilia cordata Mill., Camellia sinensis, Quercus robur L., Betula pendula Roth, Rubus idaeus L., and Salix alba L. The extracts showed strain-dependent antimicrobial activity, with C. sinensis and R. idaeus up to 4.0 mm and 8.0 mm inhibition zones, respectively. P. aeruginosa and E. faecalis were the most susceptible strains, demonstrating the largest inhibition zones. In contrast, P. vulgaris and K. oxytoca were more resistant. The efficacy of the most active extracts can be linked to the presence of phytochemicals identified via GC-MS, including epicatechin, shikimic acid, quinic acid, gallic acid, and caffeine. These metabolites are known to interfere with bacterial cell structures and metabolic pathways. These studies may serve as a preliminary step toward the development of non-invasive water treatment methods for wash water. Full article
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20 pages, 1966 KiB  
Article
Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
by Jaekeol Choi
Appl. Sci. 2025, 15(9), 5198; https://doi.org/10.3390/app15095198 - 7 May 2025
Viewed by 196
Abstract
Evaluating query-passage relevance is a crucial task in information retrieval (IR), where the performance of large language models (LLMs) greatly depends on the quality of prompts. Current prompt optimization methods typically require multiple candidate generations or iterative refinements, resulting in significant computational overhead [...] Read more.
Evaluating query-passage relevance is a crucial task in information retrieval (IR), where the performance of large language models (LLMs) greatly depends on the quality of prompts. Current prompt optimization methods typically require multiple candidate generations or iterative refinements, resulting in significant computational overhead and limited practical applicability. In this paper, we propose a novel prompt optimization method that leverages LLM-based confusion matrix feedback to efficiently optimize prompts for the relevance evaluation task. Unlike previous approaches, our method systematically analyzes LLM predictions—both correct and incorrect—using a confusion matrix, enabling prompt refinement through a single-step update. Our experiments in realistic IR scenarios demonstrate that our method achieves competitive or superior performance compared to existing methods while drastically reducing computational costs, highlighting its potential as a practical and scalable solution. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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27 pages, 8377 KiB  
Article
An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans
by Yanzhao Gong, Richard Amankwa Adjei, Guocheng Tao, Yitao Zeng and Chengwei Fan
Appl. Sci. 2025, 15(9), 5197; https://doi.org/10.3390/app15095197 - 7 May 2025
Viewed by 89
Abstract
This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the [...] Read more.
This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems. Full article
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18 pages, 3542 KiB  
Article
Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data Settings
by Kenta Kubota, Ren Togo, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama
Appl. Sci. 2025, 15(9), 5196; https://doi.org/10.3390/app15095196 - 7 May 2025
Viewed by 77
Abstract
Foundation models have achieved remarkable success across various domains, but still face critical challenges such as limited data availability, high computational requirements, and rapid knowledge obsolescence. To address these issues, we propose a novel framework that integrates model merging with federated learning to [...] Read more.
Foundation models have achieved remarkable success across various domains, but still face critical challenges such as limited data availability, high computational requirements, and rapid knowledge obsolescence. To address these issues, we propose a novel framework that integrates model merging with federated learning to enable continual foundation model updates without centralizing sensitive data. In this framework, each client fine-tunes a local model, and the server merges these models using multiple merging strategies. We experimentally evaluate the effectiveness of these methods using the CLIP model for image classification tasks across diverse datasets. The results demonstrate that advanced merging methods can surpass simple averaging in terms of accuracy, although they introduce challenges such as catastrophic forgetting and sensitivity to hyperparameters. This study defines a realistic and practical problem setting for decentralized foundation model updates, and provides a comparative analysis of merging techniques, offering valuable insights for scalable and privacy-preserving model evolution in dynamic environments. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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17 pages, 6708 KiB  
Article
Diesel Engine Urea Injection Optimization Based on the Crested Porcupine Optimizer and Genetic Algorithm
by Xu Chen, Changhai Ma, Quanli Dou, Shuzhan Bai, Ke Sun and Zhenguo Li
Appl. Sci. 2025, 15(9), 5195; https://doi.org/10.3390/app15095195 - 7 May 2025
Viewed by 79
Abstract
As a major emission pollutant from diesel engines, NOx is extremely harmful to the environment and human health. In order to reduce NOx emissions, countries around the world have been implementing increasingly stringent emissions regulations. The urea injection strategies of the Selective Catalytic [...] Read more.
As a major emission pollutant from diesel engines, NOx is extremely harmful to the environment and human health. In order to reduce NOx emissions, countries around the world have been implementing increasingly stringent emissions regulations. The urea injection strategies of the Selective Catalytic Reduction (SCR) system are the main factors affecting NOx emissions and NH3 slips of diesel engines. In this study, test data were obtained from an engine test stand and a Support Vector Machine (SVM) was developed using the test data to predict NOx conversion efficiency and NH3 slip. The SVM model was optimized using the Crested Porcupine Optimizer (CPO) to improve its prediction accuracy and was made to replace the mathematical model to save computational time. Finally, the Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to optimize the urea injection volume for all conditions. The optimized urea injection volume maximizes the NOx conversion efficiency of the SCR system while controlling the NH3 slip within 10 ppm. In addition, based on this method, the urea injection pulse spectrum under full operating conditions was obtained, and the optimized urea injection amount can effectively reduce the NOx accumulation of the WHTC cycle by about 7.5%, as shown through bench testing. Full article
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20 pages, 9590 KiB  
Article
Monitoring Water Area Dynamics in Kashgar (2003–2023) Using Multi-Source Remote Sensing Data
by Cong Ding and Chao Ren
Appl. Sci. 2025, 15(9), 5194; https://doi.org/10.3390/app15095194 - 7 May 2025
Viewed by 67
Abstract
Understanding the dynamics of regional water area changes is crucial for effective water resource management and ecological conservation, particularly in arid regions. Located in northwestern China’s arid zone, changes in water area in Kashgar significantly impact local agricultural productivity, ecological integrity, and human [...] Read more.
Understanding the dynamics of regional water area changes is crucial for effective water resource management and ecological conservation, particularly in arid regions. Located in northwestern China’s arid zone, changes in water area in Kashgar significantly impact local agricultural productivity, ecological integrity, and human socioeconomic activities. However, long-term trends in water area changes and their driving factors in Kashgar remain poorly understood. This study leverages Landsat and Sentinel-2 imagery from 2003 to 2023, employing a random forest algorithm to extract water body information. Key findings are as follows: (1) both total and seasonal water area exhibit a fluctuating downward trend, while permanent water area displays a fluctuating upward trend; (2) precipitation and temperature emerged as primary drivers of water area changes, with precipitation in the surrounding regions of Kashgar exerting a particularly significant influence, while evaporation exhibited a lesser impact; (3) the influence of climate change and anthropogenic activities in surrounding areas on water area changes in Kashgar cannot be overlooked. Full article
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11 pages, 2568 KiB  
Article
Mechanical Resistance of Implant-Supported Crowns with Abutments Exhibiting Different Margin Designs
by Daniela Stoeva, Galena Mateeva, Danimir Jevremovic, Ana Jevremović, Branka Trifkovic and Dimitar Filtchev
Appl. Sci. 2025, 15(9), 5193; https://doi.org/10.3390/app15095193 - 7 May 2025
Viewed by 60
Abstract
Background: Modern dentistry demands accurate finish line designs for abutments. CAD/CAM systems enable the fabrication of thin prosthetic structures to fulfill this requirement. The aim of this study is to research the mechanical resistance of customized implant abutments with different types of marginal [...] Read more.
Background: Modern dentistry demands accurate finish line designs for abutments. CAD/CAM systems enable the fabrication of thin prosthetic structures to fulfill this requirement. The aim of this study is to research the mechanical resistance of customized implant abutments with different types of marginal design in laboratory environment. The null hypothesis is there is no difference in fatigue loading and compression strength in custom implant abutments with chamfer or vertical marginal design. Methods: The study model includes 60 specimens of implant suprastructures, organized into four test groups, by the margin design and used material: Group A—suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with vertical marginal design; Group B—suprastructures, monolithic lithium disilicate implant crown and titanium custom abutment with vertical marginal design; Group C—suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with chamfer marginal design; and Group D—suprastructures, made of monolithic lithium disilicate implant crown and titanium custom abutment with chamfer marginal design. All samples were subjected to fatigue loading test in chewing Simulator CS-4 (SD-Mechatronik, Westerham, Germany) for 1250,000 cycles, at a frequency of 2 Hz. The specimens, which survived, was conducted to compressive strength test in universal testing machine Instron M 1185 (Instron, Norwood, MA, USA). Results: The results analysis highlighted Group A as the most resistant to compressive forces (4411 MPa). Group D was with lowest values (1864 MPa)—twice than Group A. Group B (3314 MPa) had lower results than Group A, but higher than Groups C (3130 MPa) and D. Conclusion: Compression strength significantly depends on the choice of marginal design of implant abutments. Vertical margin design has better performance, that chamfer one. Full article
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25 pages, 4784 KiB  
Article
Dynamic Simulation and Characteristic Analysis on Freezing Process in Ballast Tanks of Polar LNG Carriers
by Xu Bai, Cao Xu and Daolei Wu
Appl. Sci. 2025, 15(9), 5192; https://doi.org/10.3390/app15095192 - 7 May 2025
Viewed by 56
Abstract
The ballast tank is a critical system for LNG carriers, ensuring structural safety and stability during navigation. When LNG carriers navigate in polar regions, the ballast tank is prone to freezing, which will reduce the efficiency of ballast water circulation. Furthermore, the freezing [...] Read more.
The ballast tank is a critical system for LNG carriers, ensuring structural safety and stability during navigation. When LNG carriers navigate in polar regions, the ballast tank is prone to freezing, which will reduce the efficiency of ballast water circulation. Furthermore, the freezing process generates frost heaving forces that may damage the walls of the ballast tank, shorten the structure’s service life, and disrupt the ship’s normal operations. Therefore, analyzing the freezing process of ballast tanks is essential. This paper focuses on the ballast tank of a polar LNG carrier as the research subject. It assumes that the ballast water is fresh water with unchanging physical properties and takes into account the environmental conditions in polar regions. A numerical simulation model of the freezing process within the ballast tank is established. This study investigates the influence of various environmental parameters on the freezing process and determines the evolution of ice shape in relation to temperature field changes under different environmental conditions. The results indicate that as the ambient temperature decreases, the rate of temperature reduction at the ballast water level accelerates, resulting in a thicker ice layer formed by freezing. Additionally, as the seawater temperature decreases, the rate of temperature decline in the ballast water at the bulkhead is significantly accelerated, leading to an increased rate of ice shape evolution. Furthermore, a reduction in the height of the ballast water level enhances the heat transfer rate of the ballast water, which markedly increases the degree of freezing in the ballast water. Full article
(This article belongs to the Section Marine Science and Engineering)
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