Applied Sciences

20 pages, 6902 KB

Open AccessArticle

Characterization of Cyclic Peptides for Antituberculosis Drug Development Targeting ClpC1

by Robel Demissie, Tasneem M. Vaid, Youngjin Kwon, Gauri Shetye, Thao Tran, Fatema Nomani, Shengnan Jin, Joo-Won Suh, Hanki Lee, Yern-Hyerk Shin, Jinsheng Cui, Dong-Chan Oh, Scott G. Franzblau, Sanghyun Cho and Hyun Lee

Appl. Sci. 2025, 15(21), 11425; https://doi.org/10.3390/app152111425 (registering DOI) - 25 Oct 2025

Abstract

Drug-resistant Mycobacterium tuberculosis (Mtb) remains a major global health challenge, prompting the need for new therapeutics targeting essential bacterial proteins. The caseinolytic protein C1 (ClpC1) is a promising drug target, and accurate measurement of its ATPase activity is critical for understanding [...] Read more.

Drug-resistant Mycobacterium tuberculosis (Mtb) remains a major global health challenge, prompting the need for new therapeutics targeting essential bacterial proteins. The caseinolytic protein C1 (ClpC1) is a promising drug target, and accurate measurement of its ATPase activity is critical for understanding drug mechanisms. We optimized a sensitive luminescence-based ATPase assay and evaluated ClpC1 constructs with various tag positions and truncations. N-terminal tagging significantly impaired enzymatic activity, whereas C-terminal tagging had no effect; truncated domains showed reduced activity compared to native full-length (FL) ClpC1. Using the native FL-ClpC1, we assessed ecumicin (ECU) and five analogs via ATPase activity and surface plasmon resonance (SPR), using rufomycin (RUF) and cyclomarin A (CYMA) as controls. RUF and CYMA bound tightly (K_D = 0.006–0.023 µM) and inhibited Mtb growth (MIC₉₀ = 0.02–0.094 µM) but modestly stimulated ATPase activity (≤2-fold). In contrast, ECU and its analogs strongly enhanced ATPase activity (4–9-fold) despite slightly weaker binding (K_D = 0.042–0.80 µM) and growth inhibition (MIC₉₀ = 0.19 µM). The partial correlation among AC₅₀, K_D, and MIC values highlights the complementary value of enzymatic, biophysical, and cellular assays. Our assay platform enables mechanistic characterization of ClpC1-targeting compounds and supports rational antitubercular drug development. Full article

(This article belongs to the Special Issue Tuberculosis—a Millennial Disease in the Age of New Technologies)

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22 pages, 1512 KB

Open AccessArticle

A Data-Driven Multi-Granularity Attention Framework for Sentiment Recognition in News and User Reviews

by Wenjie Hong, Shaozu Ling, Siyuan Zhang, Yinke Huang, Yiyan Wang, Zhengyang Li, Xiangjun Dong and Yan Zhan

Appl. Sci. 2025, 15(21), 11424; https://doi.org/10.3390/app152111424 (registering DOI) - 25 Oct 2025

Abstract

Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient context modeling, and dispersed emotional signals. To address [...] Read more.

Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient context modeling, and dispersed emotional signals. To address these issues, a multi-granularity attention-based sentiment analysis model built on a transformer backbone is proposed. The framework integrates sentence-level and document-level hierarchical modeling, a different-dimensional embedding strategy, and a cross-granularity contrastive fusion mechanism, thereby achieving unified representation and dynamic alignment of local and global emotional features. Static word embeddings combined with dynamic contextual embeddings enhance both semantic stability and context sensitivity, while the cross-granularity fusion module alleviates sparsity and dispersion of emotional cues in long texts, improving robustness and discriminability. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed model. On the Financial Forum Reviews dataset, it achieves an accuracy of 0.932, precision of 0.928, recall of 0.925, F1-score of 0.926, and AUC of 0.951, surpassing state-of-the-art baselines such as BERT and RoBERTa. On the Financial Product User Reviews dataset, the model obtains an accuracy of 0.902, precision of 0.898, recall of 0.894, and AUC of 0.921, showing significant improvements for short-text sentiment tasks. On the Financial News dataset, it achieves an accuracy of 0.874, precision of 0.869, recall of 0.864, and AUC of 0.895, highlighting its strong adaptability to professional and domain-specific texts. Ablation studies further confirm that the multi-granularity transformer structure, the different-dimensional embedding strategy, and the cross-granularity fusion module each contribute critically to overall performance improvements. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Data Mining: Emerging Trends and Applications)

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19 pages, 5713 KB

Open AccessArticle

Integration of Theoretical and Experimental Torsional Vibration Analysis in a Marine Propulsion System with Component Degradation

by Quang Dao Vuong, Jiwoong Lee and Jae-Ung Lee

Appl. Sci. 2025, 15(21), 11423; https://doi.org/10.3390/app152111423 (registering DOI) - 25 Oct 2025

Abstract

This study investigates torsional vibration characteristics in an aged coastal car ferry propulsion system using theoretical calculations based on the Matrix method alongside experimental measurements. While the measured torsional vibration at the propeller shaft remained within the limits, it was significantly higher than [...] Read more.

This study investigates torsional vibration characteristics in an aged coastal car ferry propulsion system using theoretical calculations based on the Matrix method alongside experimental measurements. While the measured torsional vibration at the propeller shaft remained within the limits, it was significantly higher than the calculated values, particularly at the 5th harmonic order excited by engine combustion. Negative torque peaks observed during transient clutch engagement caused gear hammering. Structural vibration analysis identified potential gearbox defects, such as wear or misalignment. Multiple torsional vibration calculation models were developed considering various degrees of degradation of the aged rubber blocks and viscous torsional damper. A model assuming that the damping capacity of damper drops to about 1%, corresponding to the specified values at 125 °C, produced results that closely reproduced the measured vibration characteristics. The finding, confirmed by an actual inspection, identifies viscous oil leakage and deterioration of the damper as the primary cause of excessive vibration. Prompt replacement of the viscous oil is recommended to improve torsional vibration behavior. Full article

(This article belongs to the Special Issue Structural Dynamics and Vibration)

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20 pages, 3076 KB

Open AccessSystematic Review

Influence of Local and Systemic Antibiotics in Non-Surgical Peri-Implantitis Treatment: A Systematic Review and Meta-Analysis Update

by Madalena Meném, Catarina Estácio, Paulo Mascarenhas and Alexandre Santos

Appl. Sci. 2025, 15(21), 11422; https://doi.org/10.3390/app152111422 (registering DOI) - 25 Oct 2025

Abstract

Background: Adjunctive antibiotics are frequently used alongside mechanical debridement (MD) for peri-implantitis, yet their additional clinical benefit remains uncertain. Objective: To systematically assess whether adding local or systemic antibiotics to non-surgical MD improves clinical outcomes in peri-implantitis. Methods: The review protocol was registered [...] Read more.

Background: Adjunctive antibiotics are frequently used alongside mechanical debridement (MD) for peri-implantitis, yet their additional clinical benefit remains uncertain. Objective: To systematically assess whether adding local or systemic antibiotics to non-surgical MD improves clinical outcomes in peri-implantitis. Methods: The review protocol was registered in PROSPERO (CRD42022380401). We included randomised controlled trials (RCTs) involving peri-implantitis patients treated with MD plus local or systemic antibiotics, compared to MD alone, with at least 3 months of follow-up. Searches were conducted in PubMed, Cochrane Library, LILACS, Web of Science, and Embase up to 9 April 2025. Eleven RCTs (634 patients) were included in the qualitative synthesis. The Cochrane RoB 2.0 tool evaluated the risk of bias. Random-effects meta-analyses of data from 10 studies, adjusting results to an equivalent 6-month follow-up time-frame, assessed treatment efficacy based on changes in probing pocket depth (PPD) and bleeding on probing (BoP), the primary outcomes. Meta-regressions examined the influence of mean patient age and implant-to-patient ratio on adjusted outcomes. Results: Systemic antibiotics resulted in generally greater PPD reduction and BoP reduction over MD alone or plus chlorhexidine, with the greatest benefits observed in amoxicillin-based multi-agent regimens and longer follow-up duration. Comparatively, local antimicrobial adjuncts performed less effectively on PPD reduction. No implant losses were reported, and adverse events were rare. Limitations: Some included trials had a high risk of bias and considerable heterogeneity. Follow-up was limited to the short term, and definitions of clinical “success” varied across studies. Conclusions: Adjunctive systemic antibiotics, particularly amoxicillin-based combinations, substantially improve short-term clinical outcomes of non-surgical peri-implantitis treatment compared to MD alone. Nevertheless, given the variability in study quality and potential risks associated with antibiotic use, their application should be judicious. Further long-term RCTs are warranted to confirm sustained efficacy and safety. Full article

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

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25 pages, 9379 KB

Open AccessArticle

Effectiveness of High-Performance Concrete Jacketing in Improving the Performance of RC Structures

by Marijana Hadzima-Nyarko, Ercan Işık, Dorin Radu, Borko Bulajić, Silva Lozančić, Josip Radić and Antonija Ereš

Appl. Sci. 2025, 15(21), 11421; https://doi.org/10.3390/app152111421 (registering DOI) - 25 Oct 2025

Abstract

The seismic vulnerability of existing reinforced concrete (RC) buildings that constitute a large portion of the urban building stock has become a growing concern for urban safety. This situation was once again revealed by the massive destruction that occurred in RC structures following [...] Read more.

The seismic vulnerability of existing reinforced concrete (RC) buildings that constitute a large portion of the urban building stock has become a growing concern for urban safety. This situation was once again revealed by the massive destruction that occurred in RC structures following the 2023 Kahramanmaraş earthquakes. Particularly in buildings constructed before 1990 and without adequate engineering services, destruction and damage were much greater. In this paper, structural models were created with inadequate transverse reinforcement, low-strength concrete, and inadequate concrete cover thickness, which all play a critical role in the seismic performance of the buildings. Structural analyses were updated for high-performance concrete jacketing models, considering the deformation status obtained for each inadequate parameter. It has been determined that the high-performance concrete can significantly increase structural performance, especially significant increases in shear strength capacities without the need for transverse reinforcement. Full article

(This article belongs to the Special Issue High-Performance Concrete: Preparation, Properties, Evaluation and Applications)

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13 pages, 1250 KB

Open AccessArticle

Ge⁴⁺ Stabilizes Cu¹⁺ Active Sites to Synergistically Regulate the Interfacial Microenvironment for Electrocatalytic CO₂ Reduction to Ethanol

by Xianlong Lu, Lili Wang, Hongtao Xie, Zhendong Li, Xiangfei Du and Bangwei Deng

Appl. Sci. 2025, 15(21), 11420; https://doi.org/10.3390/app152111420 (registering DOI) - 24 Oct 2025

Abstract

Electrocatalytic conversion of CO₂ to high-energy-density multicarbon products (C₂₊) offers a sustainable route for renewable energy storage and carbon neutrality. Precisely modulating Cu-based catalysts to enhance C₂₊ selectivity remains challenging due to uncontrollable reduction of Cu^δ+ active sites. [...] Read more.

Electrocatalytic conversion of CO₂ to high-energy-density multicarbon products (C₂₊) offers a sustainable route for renewable energy storage and carbon neutrality. Precisely modulating Cu-based catalysts to enhance C₂₊ selectivity remains challenging due to uncontrollable reduction of Cu^δ+ active sites. Here, an efficient and stable Ge/Cu catalyst was developed for CO₂ reduction to ethanol via Ge modification. A Cu₂O/GeO₂/Cu core–shell composite was constructed by controlling Ge doping. The structure–performance relationship was elucidated through in situ characterization and theoretical calculations. Ge⁴⁺ stabilized Cu¹⁺ active sites and regulated the surface microenvironment via electronic effects. Ge modification simultaneously altered CO intermediate adsorption to promote asymmetric CO–CHO coupling, optimized water structure at the electrode/electrolyte interface, and inhibited over-reduction of Cu^δ+. This multi-scale synergistic effect enabled a significant ethanol Faradaic efficiency enhancement (11–20%) over a wide potential range, demonstrating promising applicability for renewable energy conversion. This study provides a strategy for designing efficient ECR catalysts and offers mechanistic insights into interfacial engineering for C–C coupling in sustainable fuel production. Full article

(This article belongs to the Topic Nanomaterials for Energy and Environmental Applications, 2nd Edition)

22 pages, 6125 KB

Open AccessArticle

Deep Learning-Driven Parameter Identification for Rock Masses from Excavation-Induced Tunnel Deformations

by Zhenhao Yan, Qiang Li, Guogang Ying, Rongjun Zheng, Liuqi Ying and Huijuan Zhang

Appl. Sci. 2025, 15(21), 11419; https://doi.org/10.3390/app152111419 (registering DOI) - 24 Oct 2025

Abstract

Efficient acquisition of rock mass parameters is a critical step for conducting numerical simulations in tunneling and ensuring the safety of subsequent construction. This paper proposes an intelligent back-analysis method for key rock mass parameters (Young’s modulus, Poisson’s ratio, cohesion, and friction angle) [...] Read more.

Efficient acquisition of rock mass parameters is a critical step for conducting numerical simulations in tunneling and ensuring the safety of subsequent construction. This paper proposes an intelligent back-analysis method for key rock mass parameters (Young’s modulus, Poisson’s ratio, cohesion, and friction angle) based on excavation-induced deformation data, using a deformation database that incorporates multi-feature values from tunnel excavation. This study employs five machine learning algorithms with single-feature inputs and three deep neural networks (DNNs) with multi-feature inputs, with a particular focus on convolutional neural network (CNN) due to their superior performance in terms of both accuracy and efficiency. The results demonstrate that the CNN model incorporating excavation features achieves excellent performance in parameter back-analysis, with an R² of 0.99 and 97.8% of predictions having errors within 5%. Compared with machine learning models using single-feature inputs, the CNN-based approach improves predictive performance by an average of 13.9%. Furthermore, compared with other DNNs, the CNN consistently outperforms across various evaluation metrics. This study also investigates the CNN’s capability to predict rock mass parameters using deformation data from early-stage excavation. After ten excavation steps, 96.9% of test samples had prediction errors within 5%. Finally, the proposed method was validated using field-monitored deformation data from a real highway tunnel project, confirming the method’s effectiveness and practical applicability. Full article

(This article belongs to the Section Civil Engineering)

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25 pages, 1714 KB

Open AccessArticle

Microscopic Behavioral and Psychological Analysis of Road User Interactions in Shared Spaces

by Xinyu Liang, Rushdi Alsaleh, Tarek Sayed, Ghoncheh Moshiri and Abdulaziz Haider

Appl. Sci. 2025, 15(21), 11418; https://doi.org/10.3390/app152111418 (registering DOI) - 24 Oct 2025

Abstract

The concept of shared space is proposed to improve the safety and health of vulnerable road users (VRUs) by promoting walking and cycling. However, despite the documented benefits of shared spaces, concerns were raised about the frequency and severity of road user interactions [...] Read more.

The concept of shared space is proposed to improve the safety and health of vulnerable road users (VRUs) by promoting walking and cycling. However, despite the documented benefits of shared spaces, concerns were raised about the frequency and severity of road user interactions in shared spaces. Thus, the objective of this study is to investigate the microscopic behaviors and psychological characteristics of vulnerable road user interactions (i.e., pedestrian–e-bike interactions and pedestrian–cyclist interactions) in non-motorized shared spaces and their interplay mechanisms. We identify a total of 334 interactions in the same- and opposite-direction using the Dutch Objective Conflict Technique for Operation and Research (DOCTOR) method at four locations in Shenzhen city, China. Trajectories of road users involved in these interactions were extracted to identify key points in trajectories and interaction phases, considering both microscopic behaviors and psychological factors synthetically. The study also compared lateral and longitudinal decision distances, maneuvering distances, maneuvering time, and safety zones across different characteristics, including severity levels, road user types, genders, and whether road users carry large items or not. The results show that the main characteristic of the interaction’s starting and ending points changes in the lateral direction. Road users have a stronger sense of security in swerve-back phases. The average lateral psychological safety distance in shared spaces is about 1.125 m. Moreover, the average safety zone area for road users in opposite and same-direction interactions are 4.83 m² and 9.36 m², respectively. Road users carrying large items perceived a higher risk in shared spaces and required longer lateral psychological safety distances and larger safety zones. The findings of this study can be used to better design shared space facilities, considering the perceived risk of road users and their interactions and psychological behavior. Full article

(This article belongs to the Section Transportation and Future Mobility)

24 pages, 7378 KB

Open AccessArticle

Comparing Multiple Machine Learning Models to Investigate Thermal Drivers in an Arid-Oasis Urban Park and Its Surroundings Using Mobile Monitoring

by Yunyao Feng, Xuegang Chen and Siqi Xie

Appl. Sci. 2025, 15(21), 11417; https://doi.org/10.3390/app152111417 (registering DOI) - 24 Oct 2025

Abstract

At present, the research on the microclimate of urban parks mainly focuses on the univariate or multivariate research contents of park design elements, and there are few analyses that can combine the park with the surrounding regional environment to jointly explore the cooling [...] Read more.

At present, the research on the microclimate of urban parks mainly focuses on the univariate or multivariate research contents of park design elements, and there are few analyses that can combine the park with the surrounding regional environment to jointly explore the cooling mechanism of park design elements. This study takes the People’s Park in Urumqi, a typical oasis city in an arid area, as the research object. Combined with different land use natures (park area/residential area), it analyzes the spatiotemporal variation law of temperature through mobile meteorological monitoring in different periods of summer and autumn and optimizes the buffer zone to further compare the performance of the multiple linear regression model and three machine learning models. The selection of the optimal model for collaborative analysis and comparison revealed the dominant variables and their threshold effects affecting the temperature of the park area and the residential area. The results show that: (1) In multi-scenario comparisons, a larger buffer has a better fitting effect. (2) The random forest model is the best model for temperature prediction in the study area. (3) The dominant factors of temperature in different seasons show significant differences, and only a few periods have cross-seasonal persistence. In the park area, the green coverage rate and road network density play a leading and influential role, while in the residential area, the influence of water cover ratio is more obvious. Furthermore, the influence direction of residential area indicators on temperature shows opposite trends in the morning and afternoon periods. (4) There are obvious limited-threshold effects on the influence of dominant factors on temperature in different regions. It is suggested that in the urban spatial layout, while considering the differences for different utilization Spaces, collaborative planning should be carried out. These findings offer new insights into temperature drivers and provide practical references for urban planners. Full article

(This article belongs to the Section Environmental Sciences)

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32 pages, 5173 KB

Open AccessArticle

Support System Integrating Assistive Technologies for Fire Emergency Evacuation from Workplaces of Visually Impaired People

by Adrian Mocanu, Ioan Valentin Sita, Camelia Avram, Dan Radu and Adina Aștilean

Appl. Sci. 2025, 15(21), 11416; https://doi.org/10.3390/app152111416 (registering DOI) - 24 Oct 2025

Abstract

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with [...] Read more.

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution. Full article

(This article belongs to the Special Issue Assistive Technologies for Rehabilitation: Challenges and Applications)

31 pages, 2021 KB

Open AccessSystematic Review

Cephalometric Assessment and Long-Term Stability of Anterior Open-Bite Correction with Skeletal Anchorage: A Systematic Review and Meta-Analysis

by Alessandro Ugolini, Margherita Donelli, Alessandro Bruni, Nunzio Cirulli, Massimo Berlen, Andrea Abate and Valentina Lanteri

Appl. Sci. 2025, 15(21), 11415; https://doi.org/10.3390/app152111415 (registering DOI) - 24 Oct 2025

Abstract

This systematic review evaluated the dento-skeletal effects and long-term stability of anterior open-bite (AOB) correction with temporary anchorage devices (TADs). A comprehensive search up to May 2025 was conducted in PubMed, Scopus, Web of Science, Embase, Cochrane Library, LILACS, Scielo, Epistemonikos, Google Scholar, [...] Read more.

This systematic review evaluated the dento-skeletal effects and long-term stability of anterior open-bite (AOB) correction with temporary anchorage devices (TADs). A comprehensive search up to May 2025 was conducted in PubMed, Scopus, Web of Science, Embase, Cochrane Library, LILACS, Scielo, Epistemonikos, Google Scholar, and ScienceDirect. Eligible studies included randomized and non-randomized trials and case series with cephalometric outcomes. Risk of bias was assessed with the MINORS tool. A qualitative synthesis was performed, and studies meeting criteria were included in the meta-analysis. Ot of 1885 records, 22 studies were included qualitatively; 5 entered meta-analysis. Treatment yielded a mean overbite increase of 5.6 mm and reduction in N-Me of 2.8 mm. FMA and SN-GoMe decreased by about 2° and 1.6°, ANB by 1.7°, while SN-Pog increased by 1.4°. Most studies reported stability up to 3 years. Despite heterogeneity and predominance of non-randomized studies, evidence suggests TADs effectively correct AOB through overbite improvement and mandibular counterclockwise rotation. Reported effects appear stable, supporting skeletal anchorage as a reliable, less invasive alternative to surgery in selected patients. Full article

(This article belongs to the Special Issue Innovative Materials and Technologies in Orthodontics)

31 pages, 1556 KB

Open AccessArticle

Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems

by Andrés Fernández-Miguel, Susana Ortíz-Marcos, Mariano Jiménez-Calzado, Alfonso P. Fernández del Hoyo, Fernando E. García-Muiña and Davide Settembre-Blundo

Appl. Sci. 2025, 15(21), 11414; https://doi.org/10.3390/app152111414 (registering DOI) - 24 Oct 2025

Abstract

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, [...] Read more.

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence. Full article

(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)

17 pages, 1425 KB

Open AccessArticle

Dengue Fever Classification Integrating Bird Swarm Algorithm With Gradient Boosting Classifier Along With Feature Selection and SHAP–DiCE Based InterpretabilityBased Interpretability

by Prosenjit Das, Proshenjit Sarker, Jun-Jiat Tiang and Abdullah-Al Nahid

Appl. Sci. 2025, 15(21), 11413; https://doi.org/10.3390/app152111413 (registering DOI) - 24 Oct 2025

Abstract

Dengue is a life-threatening disease that is transmitted by mosquitoes. Dengue fever has no proper treatment. Early, proper diagnosis is essential to minimize complications and enhance outcomes in patients. This research uses a clinical and hematological dataset of dengue to assess the effectiveness [...] Read more.

Dengue is a life-threatening disease that is transmitted by mosquitoes. Dengue fever has no proper treatment. Early, proper diagnosis is essential to minimize complications and enhance outcomes in patients. This research uses a clinical and hematological dataset of dengue to assess the effectiveness of the Gradient Boosting (GB) classification model with and without feature selection. It initially employs a standalone GB model, achieving impeccable results for classification, at 100% accuracy, F1-score, precision, and recall. In addition, the Bird Swarm Algorithm (BSA)-based metaheuristic technique is implemented on the GB classifier to execute wrapper-based feature selection so that features are reduced and achieve better results. The BSA-GB model yielded an accuracy of 99.49%, F1-score of 99.62%, recall of 99.24%, and precision of 100%, but it only selected five features in total. An additional test with a five-fold cross-validation was employed for better performance and model evaluation. Folds 1 and 2 showed especially good results. Although fold 2 selected only four features, it still showed high results, compared to fold 1, which selected five features. In this context, fold 2 achieved an accuracy of 99.49%, F1-score of 99.65%, recall of 99.30%, and precision of 100%. Means of hyperparameters were also calculated across folds to make a generalized GB model, which maintained 99.49% of accuracy with just three features, namely, Hemoglobin, WBC Count, and Platelet Count. To enhance transparency, counterfactual explanations were performed to analyze the misclassified cases, which indicated that minimum changes in input features modify the predictions. Also, an evaluation of the SHAP value result designated WBC Count and Platelet Count as the most important features. Full article

40 pages, 9185 KB

Open AccessArticle

Tongan Speech Recognition Based on Layer-Wise Fine-Tuning Transfer Learning and Lexicon Parameter Enhancement

by Junhao Geng, Dongyao Jia, Ziqi Li, Zihao He, Nengkai Wu, Weijia Zhang and Rongtao Cui

Appl. Sci. 2025, 15(21), 11412; https://doi.org/10.3390/app152111412 (registering DOI) - 24 Oct 2025

Abstract

Speech recognition, as a key driver of artificial intelligence and global communication, has advanced rapidly in major languages, while studies on low-resource languages remain limited. Tongan, a representative Polynesian language, carries significant cultural value. However, Tongan speech recognition faces three main challenges: data [...] Read more.

Speech recognition, as a key driver of artificial intelligence and global communication, has advanced rapidly in major languages, while studies on low-resource languages remain limited. Tongan, a representative Polynesian language, carries significant cultural value. However, Tongan speech recognition faces three main challenges: data scarcity, limited adaptability of transfer learning, and weak dictionary modeling. This study proposes improvements in adaptive transfer learning and NBPE-based dictionary modeling to address these issues. An adaptive transfer learning strategy with layer-wise unfreezing and dynamic learning rate adjustment is introduced, enabling effective adaptation of pretrained models to the target language while improving accuracy and efficiency. In addition, the MEA-AGA is developed by combining the Mind Evolutionary Algorithm (MEA) with the Adaptive Genetic Algorithm (AGA) to optimize the number of byte-pair encoding (NBPE) parameters, thereby enhancing recognition accuracy and speed. The collected Tongan speech data were expanded and preprocessed, after which the experiments were conducted on an NVIDIA RTX 4070 GPU (16 GB) using CUDA 11.8 under the Ubuntu 18.04 operating system. Experimental results show that the proposed method achieved a word error rate (WER) of 26.18% and a word-per-second (WPS) rate of 68, demonstrating clear advantages over baseline methods and confirming its effectiveness for low-resource language applications. Although the proposed approach demonstrates promising performance, this study is still limited by the relatively small corpus size and the early stage of research exploration. Future work will focus on expanding the dataset, refining adaptive transfer strategies, and enhancing cross-lingual generalization to further improve the robustness and scalability of the model. Full article

(This article belongs to the Special Issue Techniques and Applications of Natural Language Processing)

29 pages, 3033 KB

Open AccessArticle

Early Prediction of Student Performance Using an Activation Ensemble Deep Neural Network Model

by Hassan Bin Nuweeji and Ahmad Bassam Alzubi

Appl. Sci. 2025, 15(21), 11411; https://doi.org/10.3390/app152111411 (registering DOI) - 24 Oct 2025

Abstract

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of [...] Read more.

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of the complexity of student profiles as a consequence of single activation functions, which prevent them from effectively learning intricate patterns. In addition, these models could experience obstacles such as the vanishing gradient problem and computational complexity. Therefore, this research study designed an Activation Ensemble Deep Neural Network (AcEnDNN) model to gain control of the previously mentioned challenges. The main contribution is the creation of a credible student performance prediction model that comprises extensive data preprocessing, feature extraction, and an Activation Ensemble DNN. By utilizing various methods of activation functions, such as ReLU, tanh, sigmoid, and swish, the ensembled activation functions are able to learn the complex structure of student data, which leads to more accurate performance prediction. The AcEn-DNN model is trained and evaluated based on the publicly available Student-mat.csv dataset, Student-por.csv dataset, and a real-time dataset. The experimental results revealed that the AcEn-DNN model achieved lower error rates, with an MAE of 1.28, MAPE of 2.36, MSE of 4.55, and RMSE of 2.13 based on a training percentage of 90%, confirming its robustness in modeling nonlinear relationships within student data. The proposed model also gained the minimum error values MAE of 1.28, MAPE of 2.97, MSE of 4.77, and RMSE of 2.18, based on a K-fold value of 10, utilizing the Student-mat.csv dataset. These findings highlight the model’s potential in early identification of at-risk students, enabling educators to develop targeted learning strategies. This research contributes to educational data mining by advancing predictive modeling techniques that evaluate student performance. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

28 pages, 7203 KB

Open AccessArticle

Influence of Fin Spacing and Fin Height in Passive Heat Sinks: Numerical Analysis with Experimental Comparison

by Mateo Kirinčić, Tin Fadiga and Boris Delač

Appl. Sci. 2025, 15(21), 11410; https://doi.org/10.3390/app152111410 (registering DOI) - 24 Oct 2025

Abstract

In this paper, heat dissipation through a passive vertical plate fin heat sink via natural convection was numerically investigated. The influence of two nondimensional geometric parameters, fin spacing-to-thickness ratio and fin height-to-spacing ratio, on the heat sink’s thermal performance was evaluated. A mathematical [...] Read more.

In this paper, heat dissipation through a passive vertical plate fin heat sink via natural convection was numerically investigated. The influence of two nondimensional geometric parameters, fin spacing-to-thickness ratio and fin height-to-spacing ratio, on the heat sink’s thermal performance was evaluated. A mathematical model describing the three-dimensional steady-state problem of buoyancy-driven flow and heat transfer was formulated. The solution was obtained numerically using the finite volume method in Ansys Fluent. The model and numerical procedure were validated by comparing the numerical predictions with measurements acquired on a constructed experimental apparatus. The heat sink thermal performance was assessed based on a series of performance metrics: heat dissipation rate, heat transfer coefficient, overall thermal resistance, and fin efficiency. Fin spacing-to-thickness ratio was varied between 1.86 and 4.8. Heat dissipation rate displayed a clear peak at a value of approximately 2.6, which coincided with a minimum in the overall thermal resistance. The heat transfer coefficient initially grew steadily, but at higher values of fin spacing-to-thickness ratio, it began to stagnate. Fin efficiency consistently decreased across the investigated range. Fin height-to-spacing ratio was varied between 1.11 and 7.78. The heat dissipation rate increased almost linearly across this range, but when the mass-specific heat dissipation rate was considered, it yielded diminishing returns. The heat transfer coefficient likewise exhibited a plateauing trend, while fin efficiency decreased steadily across the investigated range of fin height-to-spacing ratio. The obtained numerical results provide guidelines for geometry selection and can serve as a foundation for further analyses and optimizations of passive heat sinks’ thermal performance. Full article

(This article belongs to the Section Applied Thermal Engineering)

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18 pages, 3024 KB

Open AccessArticle

A Guideline for Selecting Bi-Directional Ground Motions Satisfying KDS 41 Seismic Design Criteria

by Seongjin Ha

Appl. Sci. 2025, 15(21), 11409; https://doi.org/10.3390/app152111409 (registering DOI) - 24 Oct 2025

Abstract

This study proposes an efficient method for selecting bi-directional ground motions in compliance with the KDS 41 criteria. The proposed method sequentially selects the required number of ground motions from available libraries without exhaustively evaluating all possible combinations, thereby improving computational efficiency. To [...] Read more.

This study proposes an efficient method for selecting bi-directional ground motions in compliance with the KDS 41 criteria. The proposed method sequentially selects the required number of ground motions from available libraries without exhaustively evaluating all possible combinations, thereby improving computational efficiency. To validate the method, nonlinear response history analyses were performed on multi-degree-of-freedom (MDF) structures using the selected ground motions. The results demonstrate that the proposed method successfully identifies ground motions that closely match the target response spectrum with minimal variance. When selecting between 3 and 6 ground motions, maximum interstory drift ratio (MIDR) is generally overestimated, with errors increasing as the number of motions increases. However, when 7 or more ground motions are used, the mean MIDR errors remain within 20% for most structural models. This improves the accuracy of seismic demand predictions compared to relying on maximum responses from fewer motions. The results confirm the importance of using a sufficient number of ground motions to ensure reliable and efficient analysis. Full article

(This article belongs to the Special Issue Seismic Response and Safety Assessment of Building Structures)

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17 pages, 1129 KB

Open AccessArticle

Wheat–Oat Bread Enriched with Beetroot-Based Additives: Technological and Quality Aspects

by Zuzanna Posadzka-Siupik, Joanna Kaszuba, Ireneusz Tomasz Kapusta and Grażyna Jaworska

Appl. Sci. 2025, 15(21), 11408; https://doi.org/10.3390/app152111408 (registering DOI) - 24 Oct 2025

Abstract

Beetroot-based additives are interesting for enriching bread in terms of bioactive compounds. The objective of this study was to determine the effect of the following beetroot-based additives: a beetroot lyophilizate powder (wheat–oat baking mix flour was replaced in proportions of 2.5, 5.0, 7.5, [...] Read more.

Beetroot-based additives are interesting for enriching bread in terms of bioactive compounds. The objective of this study was to determine the effect of the following beetroot-based additives: a beetroot lyophilizate powder (wheat–oat baking mix flour was replaced in proportions of 2.5, 5.0, 7.5, 10%), a beetroot juice (water was replaced with juice in proportions of 25, 50, 75, 100%) and a by-product of beetroot juice production, i.e., pomace (wheat–oat baking mix flour was replaced in proportions of 2.5, 5.0, 7.5, 10%) on the quality of wheat–oat bread and the content of bioactive components in this type of bread. The properties of the dough were also assessed. The type and percentage level of partially replacing wheat–oat baking mix flour or water with beetroot-based additives had a significant impact on water absorption, dough development, and stability time of the tested dough. The beetroot juice (BJ) and powder (BLP) had the most significant impact on the rheological properties of the dough, whereas the pomace (BP) had the smallest effect. Beetroot-based additives, especially powder and juice, reduced the volume of bread (from 199 to 148 cm³/100 g of bread) but did not change oven loss [%] and bread crumb porosity index. Breads with these additives showed higher increased values for dough yield [%] and bread yield [%] (for beetroot powder—by 10% compared to the control sample (133.37% and 113.83%)). Tested additives had an impact on the crust and crumb color of the tested wheat–oat breads. The proposed additives significantly increased the antioxidant activity, total phenolic content, and betalain content in the bread samples. The above results showed that, from a technological point of view, replacing water or flour in the wheat–oat bread recipe with beetroot-based additives with a maximum concentration of 5% for BP or BLP and 50% for BJ allows for obtaining a product of good quality. Full article

(This article belongs to the Special Issue Functional Foods: Product Development, Technological Trends and Safety)

27 pages, 6555 KB

Open AccessArticle

Finite Element Model Updating of Axisymmetric Structures

by Pavol Lengvarský, Martin Hagara, Lenka Hagarová and Jaroslav Briančin

Appl. Sci. 2025, 15(21), 11407; https://doi.org/10.3390/app152111407 (registering DOI) - 24 Oct 2025

Abstract

Creating the most accurate numerical models with the same dynamic behavior as real structures plays an important role in the development process of various facilities. This article deals with the use of experimental methods, particularly experimental modal analysis (EMA), scanning, detection, spectral analysis, [...] Read more.

Creating the most accurate numerical models with the same dynamic behavior as real structures plays an important role in the development process of various facilities. This article deals with the use of experimental methods, particularly experimental modal analysis (EMA), scanning, detection, spectral analysis, and mechanical testing in combination with the optimization techniques of the ANSYS 2024 R1 software to calibrate numerical models of axisymmetric structures. The proposed methodology was tested on a steel pipe whose geometric and material properties were both available. Within the updating of finite element models (FEMU) with one or two design variables, the influence of the range of feasible values on the accuracy of the observed parameters was examined. The updating process led to the acquisition of such a pipe model, which natural frequencies differed by less than 1.5% from the results estimated in EMA, and its weight also differed only minimally. The proposed methodology was then used for the FEMU of a pressure vessel whose contour was obtained by a 3D scanning method; material properties were investigated, and all wall thicknesses, i.e., eleven design variables, were unknown and thus determined by an iterative optimization technique. Using the Multi-Objective Genetic Algorithm (MOGA) method, the dimensions of the vessel were first updated for their shell model and subsequently for the 3D model. The resulting natural frequencies of the model with applied internal pressures of 0 bar, 40 bar, and 80 bar differed from those estimated experimentally by less than 1.2%. Full article

(This article belongs to the Section Acoustics and Vibrations)

23 pages, 3747 KB

Open AccessArticle

Target Tracking with Adaptive Morphological Correlation and Neural Predictive Modeling

by Victor H. Diaz-Ramirez and Leopoldo N. Gaxiola-Sanchez

Appl. Sci. 2025, 15(21), 11406; https://doi.org/10.3390/app152111406 (registering DOI) - 24 Oct 2025

Abstract

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering [...] Read more.

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Image Processing)

14 pages, 1287 KB

Open AccessArticle

Comparative Evaluation of Two Dynamic Navigation Systems vs. Freehand Approach and Different Operator Skills in Endodontic Microsurgery: A Cadaver Study

by Umberto Gibello, Elina Mekhdieva, Mario Alovisi, Luca Cortese, Andrea Cemenasco, Anna Cassisa, Caterina Chiara Bianchi, Vittorio Monasterolo, Allegra Comba, Andrea Baldi, Vittorio Fenoglio, Elio Berutti and Damiano Pasqualini

Appl. Sci. 2025, 15(21), 11405; https://doi.org/10.3390/app152111405 (registering DOI) - 24 Oct 2025

Abstract

Background/Objectives: The purpose of the study is to determine and compare the accuracy and efficiency of two dynamic navigation systems (DNS)—Navident (ClaroNav, Canada) and X-Guide (Nobel Biocare, Switzerland)—vs. a free-hand (FH) approach in performing endodontic microsurgery (EMS) on human cadavers. Methods: a total [...] Read more.

Background/Objectives: The purpose of the study is to determine and compare the accuracy and efficiency of two dynamic navigation systems (DNS)—Navident (ClaroNav, Canada) and X-Guide (Nobel Biocare, Switzerland)—vs. a free-hand (FH) approach in performing endodontic microsurgery (EMS) on human cadavers. Methods: a total of 119 roots of six cadavers were randomly divided into three groups (Navident/X-Guide/FH). The cadavers’ jaws were scanned pre-operatively with computed tomography. The DICOM data were uploaded and digitally managed with software interfaces for registration, calibration, and virtual planning of EMS. Osteotomy was performed under DNS control and using a dental operating microscope (FH control group). Post-operative scans were taken with same settings as preoperative. Accuracy was then determined by comparing pre- and post-scans of coronal and apical linear, angular deviation, angle, length, and depth of apical resection. Efficiency was determined by measuring the procedural time of osteotomy, apicectomy, retro-cavity preparation, the volume of substance, and cortical bone loss, as well as iatrogenic complications. Outcomes were also evaluated in relation to different operators’ skill levels. Descriptive statistics and inferential analyses were conducted using R software (4.2.1). Results: DNS demonstrated better efficiency in osteotomy and apicectomy, second only to FH in substance and cortical bone loss. Both DNS approaches had similar accuracy. Experts were faster and more accurate than non-experts in FH, apart from resection angle, length and depth, and retro-cavity preparation time, for which comparison was not statistically significant. The Navident and X-guide groups had similar trends in increasing efficiency and accuracy of EMS. All complications in the FH group were performed by non-experts. The X-guide group demonstrated fewer complications than the Navident group. Conclusions: Both DNS appear beneficial for EMS in terms of accuracy and efficacy in comparison with FH, also demonstrating the decreasing gap of skill expertise between experts and novice operators. Through convenient use X-guide diminishes the level of iatrogenic complications compared to Navident. Full article

(This article belongs to the Special Issue Endodontics and Restorative Dentistry: Advanced Materials, Methods and Technologies)

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23 pages, 4085 KB

Open AccessArticle

Probability Selection-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization

by Siyuan Wang and Jian-Yu Li

Appl. Sci. 2025, 15(21), 11404; https://doi.org/10.3390/app152111404 (registering DOI) - 24 Oct 2025

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a powerful class of optimization methods that utilize surrogate models to address expensive optimization problems (EOPs), where fitness evaluations (FEs) are expensive or limited. By leveraging previously evaluated solutions to learn predictive models, SAEAs enable efficient [...] Read more.

Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a powerful class of optimization methods that utilize surrogate models to address expensive optimization problems (EOPs), where fitness evaluations (FEs) are expensive or limited. By leveraging previously evaluated solutions to learn predictive models, SAEAs enable efficient search under constrained evaluation budgets. However, the performance of SAEAs heavily depends on the quality and utilization of surrogate models, and balancing the accuracy and generalization ability makes effective model construction and management a key challenge. Therefore, this paper introduces a novel probability selection-based surrogate-assisted evolutionary algorithm (PS-SAEA) to enhance optimization performance under FE-constrained conditions. The PS-SAEA has two novel designs. First, a probabilistic model selection (PMS) strategy is proposed to stochastically select surrogate models, striking a balance between prediction accuracy and generalization by avoiding overfitting commonly caused by greedy selection. Second, a weighted model ensemble (WME) mechanism is developed to integrate selected models, assigning weights based on individual prediction errors to improve the accuracy and reliability of fitness estimation. Extensive experiments on benchmark problems with varying dimensionalities demonstrate that PS-SAEA consistently outperforms several state-of-the-art SAEAs, validating its effectiveness and robustness in dealing with various complex EOPs. Full article

(This article belongs to the Special Issue Applications of Genetic and Evolutionary Computation)

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29 pages, 4285 KB

Open AccessReview

Advanced Techniques for Thorium Recovery from Mineral Deposits: A Comprehensive Review

by Tolganay Atamanova, Bakhytzhan Lesbayev, Sandugash Tanirbergenova, Zhanna Alsar, Aisultan Kalybay, Zulkhair Mansurov, Meiram Atamanov and Zinetula Insepov

Appl. Sci. 2025, 15(21), 11403; https://doi.org/10.3390/app152111403 (registering DOI) - 24 Oct 2025

Abstract

Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile ²³³U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium [...] Read more.

Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile ²³³U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium recovery from primary ores and secondary resources. The main mineralogical carriers—including monazite, thorianite, thorite, and cheralite as well as industrial by-products such as rare-earth processing tailings—are critically examined with respect to their occurrence and processing potential. Physical enrichment methods (gravity, magnetic, and electrostatic separation) and hydrometallurgical approaches (acidic and alkaline leaching) are analyzed in detail, highlighting their efficiencies, limitations, and environmental implications. Particular emphasis is placed on modern separation strategies such as solvent extraction with organophosphorus reagents, diglycolamides, and ionic liquids, as well as extraction chromatography, nanocomposite sorbents, ion-imprinted polymers, and electrosorption on carbon-based electrodes. These techniques demonstrate significant progress in enhancing selectivity, reducing reagent consumption, and enabling recovery from low-grade and secondary feedstocks. Environmental and radiological aspects, including waste minimization, immobilization, and regulatory frameworks, are discussed as integral components of sustainable thorium management. Finally, perspectives on hybrid technologies, digital process optimization, and economic feasibility are outlined, underscoring the need for interdisciplinary approaches that combine chemistry, materials science, and environmental engineering. Collectively, the analysis highlights the transition from conventional practices to integrated, scalable, and environmentally responsible technologies for thorium recovery. Full article

(This article belongs to the Special Issue Current Advances in Nuclear Energy and Nuclear Physics)

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32 pages, 2925 KB

Open AccessReview

Site and Formation Selection for CO₂ Geological Sequestration: Research Progress and Case Analyses

by Wei Lian, Hangyu Liu, Jun Li and Yanxian Wu

Appl. Sci. 2025, 15(21), 11402; https://doi.org/10.3390/app152111402 (registering DOI) - 24 Oct 2025

Abstract

Carbon Capture and Storage (CCS) is a key technology for achieving carbon neutrality goals. Relevant foreign research began in the 1970s, but overall it remains in the exploration and demonstration stage. Clarifying the geological parameters and characteristics of reservoir–caprock systems in CCS projects [...] Read more.

Carbon Capture and Storage (CCS) is a key technology for achieving carbon neutrality goals. Relevant foreign research began in the 1970s, but overall it remains in the exploration and demonstration stage. Clarifying the geological parameters and characteristics of reservoir–caprock systems in CCS projects is of great significance to the effectiveness and safety of long-term storage. By reviewing 15 typical global CCS projects, this paper identifies that ideal reservoirs are gently structured sandstones with few faults (characterized by high porosity, high permeability, and large scale, which are conducive to CO₂ diffusion) or basalts (which can react with CO₂ for mineralization, enabling permanent storage). Caprocks are mainly composed of thick mudstone and shale; composite caprocks consisting of multi-layer low-permeability formations and tight interlayers within reservoirs have stronger sealing performance. Additionally, they should be far from faults, and sufficient caprock thickness is required to reduce leakage risks. Meanwhile, this paper points out the challenges faced by CCS technology, such as complex site selection, limitations in long-term monitoring, difficulties in designing injection parameters, and challenges in large-scale deployment. It proposes suggestions including establishing a quantitative site selection system, building a comprehensive monitoring network, and strengthening collaborative optimization of parameters, so as to provide a basis for safe site selection and assessment. Full article

5 pages, 162 KB

Open AccessEditorial

Sustainability in Energy and Buildings: Future Perspectives and Challenges

by Camilla Lops and Sergio Montelpare

Appl. Sci. 2025, 15(21), 11401; https://doi.org/10.3390/app152111401 (registering DOI) - 24 Oct 2025

Abstract

How can we make the built environment more energy-efficient, sustainable, and resilient [...] Full article

(This article belongs to the Special Issue Sustainability in Energy and Buildings: Future Perspectives and Challenges)

15 pages, 11436 KB

Open AccessArticle

Design of a Six-Phase Surface Permanent-Magnet Synchronous Motor with Chamfer-Shaped Magnet to Reduce Cogging Torque and Torque Ripple for Large-Ship Propulsion

by Do-Hyeon Choi, Chaewon Jo, Hyung-Sub Han, Hyo-Gu Kim, Won-Ho Kim and Hyunwoo Kim

Appl. Sci. 2025, 15(21), 11400; https://doi.org/10.3390/app152111400 (registering DOI) - 24 Oct 2025

Abstract

Surface permanent-magnet synchronous motors (SPMSMs) have been widely adopted for ship propulsion due to their high power density and efficiency. However, conventional three-phase open-slot SPMSMs struggle to balance high efficiency with reductions in cogging torque and torque ripple. This paper proposes a design [...] Read more.

Surface permanent-magnet synchronous motors (SPMSMs) have been widely adopted for ship propulsion due to their high power density and efficiency. However, conventional three-phase open-slot SPMSMs struggle to balance high efficiency with reductions in cogging torque and torque ripple. This paper proposes a design of an SPMSM with a six-phase winding configuration and a chamfer-shaped permanent magnet to reduce cogging torque and torque ripple. Electromagnetic performance is evaluated through finite element analysis (FEA). A reference three-phase interior PMSM and three-phase SPMSMs with different magnet shapes are first compared to identify a suitable basic design. Based on the basic machine, three pole–slot combinations for the six-phase winding are analyzed, and the most efficient configuration is selected. A final model is designed to minimize cogging torque and torque ripple for the chamfer-shaped permanent magnet. Finally, the effectiveness of the final model is validated through FEA by comparing its performance with that of the reference model. Full article

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17 pages, 402 KB

Open AccessArticle

Training a Team of Language Models as Options to Build an SQL-Based Memory

by Seokhan Lee and Hanseok Ko

Appl. Sci. 2025, 15(21), 11399; https://doi.org/10.3390/app152111399 (registering DOI) - 24 Oct 2025

Abstract

Despite the rapid progress in the capabilities of large language models, they still lack a reliable and efficient method of storing and retrieving new information conveyed over the course of their interaction with users upon deployment. In this paper, we use reinforcement learning [...] Read more.

Despite the rapid progress in the capabilities of large language models, they still lack a reliable and efficient method of storing and retrieving new information conveyed over the course of their interaction with users upon deployment. In this paper, we use reinforcement learning methods to train a team of smaller language models, which we frame as options, on reward-respecting subtasks, to learn to use SQL commands to store and retrieve relevant information to and from an external SQL database. In particular, we train a storage language model on a subtask for distinguishing between user and assistant in the dialogue history, to learn to store any relevant facts that may be required to answer future user queries. We then train a retrieval language model on a subtask for querying a sufficient number of fields, to learn to retrieve information from the SQL database that could be useful in answering the current user query. We find that training our models on their respective subtasks results in much higher performance than training them to directly optimize the reward signal and that the resulting team of language models is able to achieve performance on memory tasks comparable to existing methods that rely on language models orders of magnitude larger in size. In particular, we were able to able to achieve a 36% gain in accuracy over a prompt engineering baseline and a 13% gain over a strong baseline that uses the much larger GPT-3.5 Turbo on the MSC-Self-Instruct dataset. Full article

(This article belongs to the Topic Challenges and Solutions in Large Language Models)

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31 pages, 5227 KB

Open AccessArticle

Electrodynamics of Carbon Nanotubes with Non-Local Surface Conductivity

by Tomer Berghaus, Touvia Miloh, Oded Gottlieb and Gregory Ya. Slepyan

Appl. Sci. 2025, 15(21), 11398; https://doi.org/10.3390/app152111398 (registering DOI) - 24 Oct 2025

Abstract

A new framework that can be utilized for the electrodynamics of carbon nanotubes (CNTs) with non-local surface conductivity (spatial dispersion) is presented. The model of non-local conductivity is developed on the basis of the Kubo technique applied to the Dirac equation for pseudospins. [...] Read more.

A new framework that can be utilized for the electrodynamics of carbon nanotubes (CNTs) with non-local surface conductivity (spatial dispersion) is presented. The model of non-local conductivity is developed on the basis of the Kubo technique applied to the Dirac equation for pseudospins. As a result, the effective boundary conditions for the electromagnetic (EM) field on a CNT surface are formulated. The dispersion relation for the eigenmodes of an infinitely long CNT is obtained and analyzed. It is shown that due to nonlocality, a new type of eigenmode is created that disappears in the local conductivity limit. These eigenmodes should be properly accounted for in the correct formulation of the CNT end conditions for the surface current, which are manifested in the EM-field scattering problem. Additional boundary conditions that consider nonlocality effects are also formulated based on the exact solution obtained for the surface current by means of using the Wiener–Hopf (WH) technique for a semi-infinite CNT. The scattering pattern of the EM-field is simulated by a finite-length model of a CNT, using a numerically solved integral equation for the surface current density and its approximate analytical solution. Thus, the scattering field of a CNT, prevailing in a wide frequency range from THz to infrared light, is analytically solved and analyzed. Potential applications for the design of nanoantennas and other electronic devices, including pointing out some future directions, are also discussed. Full article

(This article belongs to the Special Issue Advances in Nanomaterials and Nanostructures: Synthesis, Characterization, and Applications)

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27 pages, 1008 KB

Open AccessArticle

Efficient Reliability Block Diagram Evaluation Through Improved Algorithms and Parallel Computing

by Gloria Gori, Marco Papini and Alessandro Fantechi

Appl. Sci. 2025, 15(21), 11397; https://doi.org/10.3390/app152111397 (registering DOI) - 24 Oct 2025

Abstract

Quantitative reliability evaluation is essential for optimizing control policies and maintenance strategies in complex industrial systems. While Reliability Block Diagrams (RBDs) are a natural formalism for modeling these hierarchical systems, modern applications require highly efficient, online reliability assessment on resource-constrained embedded hardware. This [...] Read more.

Quantitative reliability evaluation is essential for optimizing control policies and maintenance strategies in complex industrial systems. While Reliability Block Diagrams (RBDs) are a natural formalism for modeling these hierarchical systems, modern applications require highly efficient, online reliability assessment on resource-constrained embedded hardware. This demand presents two fundamental challenges: developing algorithmically efficient RBD evaluation methods that can handle diverse custom distributions while preserving numerical accuracy, and ensuring platform-agnostic performance across diverse multicore architectures. This paper investigates these issues by developing a new version of the librbd open-source RBD library. This version includes advances in efficiency of evaluation algorithms, as well as restructured computation sequences, cache-aware data structures to minimize memory overhead, and an adaptive parallelization framework that scales automatically from embedded processors to high-performance systems. Comprehensive validation demonstrates that these advances significantly reduce computational complexity and improve performance over the original implementation, enabling real-time analysis of substantially larger systems. Full article

(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)

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12 pages, 771 KB

Open AccessReview

Role of Aberrant GLI as a Biomarker and Signaling Pathway in Cancers

by Diti Patel, Olivia Lewis, Bidyut K. Mohanty, David Eagerton, Jaime A. Foushee and Kaushlendra Tripathi

Appl. Sci. 2025, 15(21), 11396; https://doi.org/10.3390/app152111396 (registering DOI) - 24 Oct 2025

Abstract

The Hedgehog (HH) signaling pathway is an evolutionarily conserved, multi-component signaling pathway. Its activation is initiated by the Hh protein, which signals upstream regulators PATCH and SMO to activate the transcription factor GLI. Upon activation, GLI translocates to the nucleus to induce the [...] Read more.

The Hedgehog (HH) signaling pathway is an evolutionarily conserved, multi-component signaling pathway. Its activation is initiated by the Hh protein, which signals upstream regulators PATCH and SMO to activate the transcription factor GLI. Upon activation, GLI translocates to the nucleus to induce the transcription of Hh/GLI target genes. Under normal conditions, the HH pathway plays a crucial role in embryogenesis, development, tissue patterning, and stem cell maintenance. Deregulation of the HH signaling pathway leads to various diseases, including cancer. However, in many human cancers, GLI1 is upregulated through a non-canonical pathway (independent of the HH pathway). This aberrant regulation of GLI1 via a non-canonical pathway is linked to the increased expression of various oncogenes. Aberrant expression of GLI not only affects the genes of several DNA repair pathways but also cancer stem cell pathways, which can contribute to genome instability and ultimately lead to cancer. The ineffectiveness of current HH pathway inhibitors in clinical trials necessitates the discovery of new HH pathway inhibitors. In this review, we will discuss our current understanding of the aberrant signaling of the HH-GLI pathway and focus on GLI1-mediated HH signaling in cancers, cancer stem cells, and carcinogenesis. We will also discuss the effectiveness of current HH inhibitors/drugs and combination therapies based on recent advances in this field. Furthermore, we will also review the role of HH-GLI in cancer stem cell markers, DNA damage response, gene regulation, tumor initiation, metastasis, cancer pathogenesis, and the role of drugs/inhibitors on this pathway. Full article

(This article belongs to the Special Issue Emerging Topics in Precision Medicine: Non-invasive Innovations Shaping Cancer and Immunotherapy Progress)

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Appl. Sci., Volume 15, Issue 21 (November-1 2025) – 126 articles

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