Applied Sciences

18 pages, 4244 KB

Open AccessArticle

Semantic-Guided Kernel Low-Rank Sparse Preserving Projections for Hyperspectral Image Dimensionality Reduction and Classification

by Junjun Li, Jinyan Hu, Lin Huang, Chao Hu and Meinan Zheng

Appl. Sci. 2026, 16(1), 561; https://doi.org/10.3390/app16010561 - 5 Jan 2026

Cited by 1 | Viewed by 1299

Abstract

Hyperspectral images present significant challenges for conventional dimensionality reduction methods due to their high dimensionality, spectral redundancy, and complex spatial–spatial dependencies. While kernel-based sparse representation methods have shown promise in handling spectral non-linearities, they often fail to preserve spatial consistency and semantic discriminability [...] Read more.

Hyperspectral images present significant challenges for conventional dimensionality reduction methods due to their high dimensionality, spectral redundancy, and complex spatial–spatial dependencies. While kernel-based sparse representation methods have shown promise in handling spectral non-linearities, they often fail to preserve spatial consistency and semantic discriminability during feature transformation. To address these limitations, we propose a novel semantic-guided kernel low-rank sparse preserving projection (SKLSPP) framework. Unlike previous approaches that primarily focus on spectral information, our method introduces three key innovations: a semantic-aware kernel representation that maintains discriminability through label constraints, a spatially adaptive manifold regularization term that preserves local pixel affinities in the reduced subspace, and an efficient optimization framework that jointly learns sparse codes and projection matrices. Extensive experiments on benchmark datasets demonstrate that SKLSPP achieves superior performance compared to state-of-the-art methods, showing enhanced feature discrimination, reduced redundancy, and improved robustness to noise while maintaining spatial coherence in the dimensionality-reduced features. Full article

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31 pages, 1852 KB

Open AccessArticle

Sentiment Analysis of X Users Regarding Bandung Regency Using Support Vector Machine

by Irlandia Ginanjar, Abdan Mulkan Shabir, Anindya Apriliyanti Pravitasari, Sinta Septi Pangastuti, Gumgum Darmawan and Sukono

Appl. Sci. 2026, 16(1), 560; https://doi.org/10.3390/app16010560 - 5 Jan 2026

Viewed by 1173

Abstract

Social media has the potential to serve beneficial purposes. The abundance of uploaded content and responses from the public generates various opinions, allowing them to be identified as positive or negative regarding the portrayal of Bandung Regency. This research aims to analyse the [...] Read more.

Social media has the potential to serve beneficial purposes. The abundance of uploaded content and responses from the public generates various opinions, allowing them to be identified as positive or negative regarding the portrayal of Bandung Regency. This research aims to analyse the classification and frequency of words for each sentiment expressed by X (Twitter) users regarding Bandung Regency. The research employs the Support Vector Machine (SVM) method. We expect the results to aid in formulating governmental programmes for Bandung Regency. The research revealed that the SVM model, which uses the Sigmoid kernel function with parameters

C

= 10 and gamma (

γ

) = 1, is the most optimal sentiment classification model for handling an imbalanced dataset. This model achieved an 83.01% negative recall value. Furthermore, frequent words appearing in both classes indicate that several positive opinions about Bandung Regency exhibit similar dominance, except for football dominance in negative opinions. This research pertains to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice, and Strong Institutions). The suggested technique facilitates evidence-based policy reviews, transparent governance, and enhanced responsive public services by analysing public sentiment regarding local government performance. The results illustrate how social media analytics can aid local governments in assessing popular sentiment and pinpointing areas for policy response. Full article

(This article belongs to the Special Issue Natural Language Processing and Text Mining)

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22 pages, 2074 KB

Open AccessArticle

Traffic Flow Prediction Model Based on Attention Mechanism Spatio-Temporal Graph Convolutional Network on U.S. Highways

by Ruiying Zhang and Yin Han

Appl. Sci. 2026, 16(1), 559; https://doi.org/10.3390/app16010559 - 5 Jan 2026

Cited by 5 | Viewed by 1710

Abstract

Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these [...] Read more.

Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these challenges, this paper proposes an improved attention-mechanism spatio-temporal graph convolutional network, termed AMSGCN, for highway traffic flow prediction. AMSGCN introduces an adaptive adjacency matrix learning mechanism to overcome the limitations of static graphs and capture time-varying spatial correlations and congestion propagation paths. A hierarchical multi-scale spatial attention mechanism is further designed to jointly model local congestion diffusion and long-range bottleneck effects, enabling an adaptive spatial receptive field under congested conditions. To enhance temporal modeling, a gating-based fusion strategy dynamically balances periodic patterns and recent observations, allowing effective prediction under both regular and abnormal traffic scenarios. In addition, direction-aware encoding is incorporated to suppress interference from opposite-direction lanes, which is essential for directional highway traffic systems. Extensive experiments on multiple benchmark datasets, including PeMS and PEMSF, demonstrate the effectiveness and robustness of AMSGCN. In particular, on the I-24 MOTION dataset, AMSGCN achieves an RMSE reduction of 11.0% compared to ASTGCN and 17.4% relative to the strongest STGCN baseline. Ablation studies further confirm that dynamic and multi-scale spatial attention provides the primary performance gains, while temporal gating and direction-aware modeling offer complementary improvements. These results indicate that AMSGCN is a robust and effective solution for highway traffic flow prediction. Full article

(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)

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18 pages, 3375 KB

Open AccessArticle

Research on the Operating Characteristics of Low-Temperature Return-Water Cogeneration Heating Systems

by Yaohua Zhao, Lei Lei, Shaohua Li and Junfu Lyu

Appl. Sci. 2026, 16(1), 558; https://doi.org/10.3390/app16010558 - 5 Jan 2026

Cited by 1 | Viewed by 691

Abstract

This paper introduces the composition of the combined heat and power (CHP) generation system with low-temperature return water. Focusing on the Gujiao–Taiyuan heating system, it systematically analyzes the effects of key operational parameters, such as supply–return-water temperatures, circulating water flow, maximum back-pressure, and [...] Read more.

This paper introduces the composition of the combined heat and power (CHP) generation system with low-temperature return water. Focusing on the Gujiao–Taiyuan heating system, it systematically analyzes the effects of key operational parameters, such as supply–return-water temperatures, circulating water flow, maximum back-pressure, and minimum supply-water temperature, on overall system energy consumption. The results indicate that lowering supply and return-water temperatures, increasing circulating water flow, raising the maximum back-pressure, and reducing the minimum supply temperature generally contribute to a decrease in the heating equivalent electricity. Furthermore, the study compares several operation regulation modes of the heating system, including two-stage quality regulation, two-stage quantity regulation, and staged quality–quantity regulation. It is concluded that staged quality–quantity regulation achieves the lowest energy consumption. The impacts of heating steam allocation and variable back-pressure under different heating loads are also examined. Based on these findings, an extraction steam reduction sequence is proposed following the principle of minimizing the system’s heating equivalent electricity, along with a strategy to lower the back-pressure of selected units during the early and late heating periods. Finally, an economic operation strategy for the CHP system with low-temperature return water is developed, integrating recommendations on circulating water flow, temperature settings, steam allocation, and back-pressure regulation across different heating loads, leading to improved energy efficiency and economic performance. Full article

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12 pages, 5349 KB

Open AccessCommunication

Mammary Intraductal Gene Electroporation (MIGE): A Novel Non-Viral Gene Delivery Method Targeting Murine Mammary Epithelial Cells

by Kazunori Morohoshi, Miho Ohba, Masahiro Sato and Shingo Nakamura

Appl. Sci. 2026, 16(1), 557; https://doi.org/10.3390/app16010557 - 5 Jan 2026

Viewed by 861

Abstract

The mammary gland is a valuable model in cancer research and developmental biology. Gene delivery techniques are crucial for mammary tissue research to understand how genes function and study on diseases such as cancer. Viral vector-based approaches provide a high degree of transduction [...] Read more.

The mammary gland is a valuable model in cancer research and developmental biology. Gene delivery techniques are crucial for mammary tissue research to understand how genes function and study on diseases such as cancer. Viral vector-based approaches provide a high degree of transduction efficiency, but they raise safety and immunogenicity concerns, whereas non-viral vector-based approaches are considered safer and have lower immunogenicity than viral methods. Unfortunately, non-viral gene delivery has rarely been applied to the mammary glands because it is technically challenging. Here, we developed a novel method for in vivo transfection of epithelial cells lining murine mammary glands via intraductal injection of plasmid DNA using a breath-controlled glass capillary and subsequent electroporation (EP) of the injected area. Female mice were transfected with plasmids harboring the enhanced green fluorescent protein (EGFP) gene. Widespread EGFP fluorescence was observed in the mammary epithelial cells of the ducts and adipocytes adjacent to the ducts. As this in vivo gene delivery method is simple, safe, and efficient for gene transfer to the mammary glands, we named this technique “Mammary Intraductal Gene Electroporation” (MIGE). The MIGE method is a useful experimental tool for studies on mammary gland development and differentiation as well as breast cancer research. Full article

(This article belongs to the Section Biomedical Engineering)

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25 pages, 7051 KB

Open AccessArticle

Research on Multi-Source Dynamic Stress Data Analysis and Visualization Software for Structural Life Assessment

by Qiming Liu, Yu Chen and Zhiming Liu

Appl. Sci. 2026, 16(1), 556; https://doi.org/10.3390/app16010556 - 5 Jan 2026

Viewed by 1036

Abstract

Dynamic stress data are essential for evaluating structural fatigue life. To address the challenges of complex test data formats, low data reading efficiency, and insufficient visualization, this study systematically analyzes the .raw and .sie file formats from IMC and HBM data acquisition systems [...] Read more.

Dynamic stress data are essential for evaluating structural fatigue life. To address the challenges of complex test data formats, low data reading efficiency, and insufficient visualization, this study systematically analyzes the .raw and .sie file formats from IMC and HBM data acquisition systems and proposes a unified parsing approach. A lightweight .dac format is designed, featuring a “single-channel–single-file” storage strategy that enables rapid, independent retrieval of specific channels and seamless cross-platform sharing, effectively eliminating the inefficiency of the .sie format caused by multi-channel coupling. Based on Python v3.11, an automated format conversion tool and a PyQt5-based visualization platform are developed, integrating graphical plotting, interactive operations, and fatigue strength evaluation functions. The platform supports stress feature extraction, rainflow counting, Goodman correction, and full life-cycle fatigue damage assessment based on the Palmgren–Miner rule. Experimental results demonstrate that the proposed system accurately reproduces both time- and frequency-domain features, with equivalent stress deviations within 2% of nCode results, and achieves a 7–8× improvement in file loading speed compared with the original format. Furthermore, multi-channel scalability tests confirm a linear increase in conversion time (R² > 0.98) and stable throughput across datasets up to 10.20 GB, demonstrating strong performance consistency for large-scale engineering data. The proposed approach establishes a reliable data foundation and efficient analytical tool for fatigue life assessment of structures under complex operating conditions. Full article

(This article belongs to the Special Issue Advances and Applications in Mechanical Fatigue and Life Assessment)

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13 pages, 2483 KB

Open AccessArticle

Automating the Evaluation of Artificial Respiration: A Computer Vision Approach

by Chaofang Wang, Yali Tong, Shuai Ma, Wenlong Dong and Bin Fan

Appl. Sci. 2026, 16(1), 555; https://doi.org/10.3390/app16010555 - 5 Jan 2026

Cited by 1 | Viewed by 1297

Abstract

Traditional cardiopulmonary resuscitation (CPR) training faces limitations such as instructor dependency, low efficiency, and subjective assessment. To address these issues, this study proposes a novel computer vision-based method for the automation and objective evaluation of artificial respiration, shifting focus to the long-overlooked ventilation [...] Read more.

Traditional cardiopulmonary resuscitation (CPR) training faces limitations such as instructor dependency, low efficiency, and subjective assessment. To address these issues, this study proposes a novel computer vision-based method for the automation and objective evaluation of artificial respiration, shifting focus to the long-overlooked ventilation component. We developed an evaluation framework integrating human pose estimation and spatio-temporal graph convolution network (ST-GCN): first, OpenPose is utilized to extract skeletal keypoints of the rescuer, followed by action classification and recognition-including chest compressions, airway opening, and artificial breathing via a ST-GCN. Based on the American Heart Association (AHA) guidelines, this research defines and implements five quantitative metrics for ventilation quality, including CPR operation procedure, chin-frontal angle, interruption time, ventilation time, and ventilation frequency. An automated scoring model was established accordingly. Validated on a self-constructed dataset containing multi-source videos, the model achieved an accuracy of 87.64% in recognizing artificial respiration actions and 84.47% in evaluating action standardization. Experimental results demonstrate that the system can effectively and objectively evaluate the quality of artificial respiration. Compared with traditional instructor-dependent approaches, this study provides a low-cost, scalable technical solution, offering a new pathway for promoting high-quality CPR training. Full article

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20 pages, 1542 KB

Open AccessArticle

Large-Scale Point Cloud Completion Through Registration and Fusion of Object-Level Reconstructions

by Taiming He, Yixuan Fang, Keyuan Li and Lu Yang

Appl. Sci. 2026, 16(1), 554; https://doi.org/10.3390/app16010554 - 5 Jan 2026

Viewed by 1353

Abstract

Existing 3D reconstruction algorithms commonly struggle with modeling specific local objects within large-scale scenes, often resulting in a lack of local detail and incomplete geometric structures. While current mainstream point cloud completion methods can restore these missing structures to some degree, they are [...] Read more.

Existing 3D reconstruction algorithms commonly struggle with modeling specific local objects within large-scale scenes, often resulting in a lack of local detail and incomplete geometric structures. While current mainstream point cloud completion methods can restore these missing structures to some degree, they are fundamentally based on generative in-filling, a process that relies on geometric priors learned from large-scale datasets. Consequently, the physical realism and geometric accuracy of the results cannot be guaranteed. To address these limitations, this paper proposes a novel, data-driven framework for point cloud completion. Our core method involves the high-precision, heterogeneous data registration and seamless fusion of an object-level point cloud—reconstructed with high-fidelity appearance and geometry by our optimized Neural Radiance Fields (NeRF) framework—with our target large-scale scene point cloud. By using high-precision, physically based data as a strong prior for geometric completion, we offer an alternative route to conventional generative completion methods. Concurrently, we employ unsupervised evaluation metrics to assess the intrinsic quality of the final results. This work provides a robust and high-fidelity solution to the problem of completing local objects within large-scale scenes. Evaluated on our self-constructed UAV-Recon dataset, the proposed method achieved a Structural Plausibility ≥ 0.995, Geometric Smoothness ≤ 0.19, and Distribution Uniformity ≈ 1.2, offering a robust solution for the high-fidelity completion of local objects within large-scale scenes. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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16 pages, 8426 KB

Open AccessArticle

Design Optimization of a Small-Scaled Vortex-Induced Vibration Bladeless Wind Turbine with Binary Resonance Controller

by Heeyun Kang, Susung Han and Young-Keun Kim

Appl. Sci. 2026, 16(1), 553; https://doi.org/10.3390/app16010553 - 5 Jan 2026

Viewed by 1839

Abstract

This study presents the design optimization and semi-active resonance control of a small-scale vortex-induced vibration (VIV) bladeless wind turbine (BWT) equipped with a power efficient binary resonance controller. The proposed system integrates a smart-material-based stiffness-tuning module that adaptively adjusts the structure frequency of [...] Read more.

This study presents the design optimization and semi-active resonance control of a small-scale vortex-induced vibration (VIV) bladeless wind turbine (BWT) equipped with a power efficient binary resonance controller. The proposed system integrates a smart-material-based stiffness-tuning module that adaptively adjusts the structure frequency of the BWT to match varying wind speeds. A coupled mechanical–electromagnetic model for BWT was formulated to quantify the relationships among key design parameters, including mast geometry, pivot length, and rod dimensions, and the resulting induced voltage. Multi-parameter optimization was performed to maximize energy-harvesting efficiency under mass and geometric constraints. Experimental evaluation verified an 88.9 % resonance shift capability, broadening the operational lock-in wind speed range from 1.7 to 3.2 m/s. The results confirm the potential of the semi-active BWT control concept for compact, low-noise, and adaptive wind-energy harvesters. Full article

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18 pages, 2593 KB

Open AccessArticle

Data-Driven Estimation of Cerchar Abrasivity Index Using Rock Geomechanical and Mineralogical Characteristics

by Soon-Wook Choi and Tae Young Ko

Appl. Sci. 2026, 16(1), 552; https://doi.org/10.3390/app16010552 - 5 Jan 2026

Viewed by 897

Abstract

The Cerchar Abrasivity Index (CAI) is essential for predicting tool wear in mechanized tunneling and mining, but direct measurement requires time-consuming laboratory procedures. We developed a data-driven framework to estimate CAI from standard geomechanical and mineralogical properties using 193 rock samples covering igneous, [...] Read more.

The Cerchar Abrasivity Index (CAI) is essential for predicting tool wear in mechanized tunneling and mining, but direct measurement requires time-consuming laboratory procedures. We developed a data-driven framework to estimate CAI from standard geomechanical and mineralogical properties using 193 rock samples covering igneous, metamorphic, and sedimentary lithologies. After evaluating 278 feature combinations with multicollinearity constraints (VIF < 10.0), we identified an optimal four-variable subset: brittleness index B₁, density, Equivalent Quartz Content (EQC), and Uniaxial Compressive Strength (UCS), with rock type indicators. CatBoost achieved the best performance (Test R² = 0.907, RMSE = 0.420), and SHAP analysis confirmed that density and EQC are primary drivers of abrasivity. Additionally, symbolic regression derived an explicit formula using only three variables (density, EQC, B₁) without rock type classification (Test R² = 0.720). The proposed framework offers a practical approach for assessing rock abrasivity at early project stages. Full article

(This article belongs to the Section Civil Engineering)

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14 pages, 2838 KB

Open AccessArticle

Comparative Biofilm Profiling of Healthy and Cavitated Root Surfaces Across Age Groups Using 16S rRNA Sequencing

by Darianna Mashi, Lamia Sami Mokeem, Qoot Alkhubaizi, Guilherme Arossi and Mary Anne S. Melo

Appl. Sci. 2026, 16(1), 551; https://doi.org/10.3390/app16010551 - 5 Jan 2026

Viewed by 778

Abstract

This preliminary clinical study investigated the microbial composition of dental biofilms on healthy root surfaces and cavitated root caries lesions in two age cohorts: adults under 65 years and those aged 65 and older. The goal was to assess how aging and caries [...] Read more.

This preliminary clinical study investigated the microbial composition of dental biofilms on healthy root surfaces and cavitated root caries lesions in two age cohorts: adults under 65 years and those aged 65 and older. The goal was to assess how aging and caries status influence root surface biofilm diversity and structure. For that, forty adults (23 women, 17 men) were enrolled. Biofilm samples were collected directly from clinically healthy and cavitated root surfaces. Microbial profiling was performed using 16S rRNA gene sequencing to evaluate diversity metrics and community composition. The results show that cavitated root surfaces harbored significantly higher microbial diversity compared to healthy root surfaces, as indicated by the Shannon diversity index. In contrast, healthy surfaces exhibited lower diversity and greater species dominance, confirmed by Simpson’s index. Age-related differences in biofilm composition were also evident, with older adults showing distinct microbial communities compared to younger participants. In conclusion, both age and cavitation presence significantly influence biofilm composition on root surfaces. These differences in microbial diversity and dominance may inform future clinical strategies for managing root caries, particularly in older adults. Further research is needed to assess the implications of these microbial patterns on treatment outcomes. Full article

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

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16 pages, 63609 KB

Open AccessArticle

An Automated Framework for Estimating Building Height Changes Using Multi-Temporal Street View Imagery

by Jiqiu Deng, Qiqi Gu and Xiaoyan Chen

Appl. Sci. 2026, 16(1), 550; https://doi.org/10.3390/app16010550 - 5 Jan 2026

Cited by 1 | Viewed by 1090

Abstract

Building height is an important indicator for describing the three-dimensional structure of cities. However, monitoring its changes is still difficult due to high labor costs, low efficiency, and the limited resolution and viewing angles of remote sensing images. This study proposes an automatic [...] Read more.

Building height is an important indicator for describing the three-dimensional structure of cities. However, monitoring its changes is still difficult due to high labor costs, low efficiency, and the limited resolution and viewing angles of remote sensing images. This study proposes an automatic framework for estimating building height changes using multi-temporal street view images. First, buildings are detected by the YOLO-v5 model, and their contours are extracted through edge detection and hole filling. To reduce false detections, greenness and depth information are combined to filter out pseudo changes. Then, a neighboring region resampling strategy is used to select visually similar images for better alignment, which helps to reduce the influence of sampling errors. In addition, the framework applies cylindrical projection correction and introduces a triangulation-based method (HCAOT) for building height estimation. Experimental results show that the proposed framework achieves an accuracy of 85.11% in detecting real changes and 91.23% in identifying unchanged areas. For height estimation, the HCAOT method reaches an RMSE of 0.65 m and an NRMSE of 0.04, which performs better than several comparison methods. Overall, the proposed framework provides an efficient and reliable approach for dynamically updating 3D urban information and supporting spatial monitoring in smart cities. Full article

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16 pages, 2324 KB

Open AccessArticle

High-Fidelity Finite Element Modelling (FEM) and Dynamic Analysis of a Hybrid Aluminium–Honeycomb Railway Vehicle Carbody

by Alessio Cascino, Enrico Meli and Andrea Rindi

Appl. Sci. 2026, 16(1), 549; https://doi.org/10.3390/app16010549 - 5 Jan 2026

Cited by 13 | Viewed by 1303

Abstract

This study presents the development and high-fidelity finite element modelling of an innovative hybrid railway carbody structure, designed to achieve a substantial reduction in mass while maintaining the required mechanical performance under service conditions. The proposed concept integrates a traditional aluminium frame with [...] Read more.

This study presents the development and high-fidelity finite element modelling of an innovative hybrid railway carbody structure, designed to achieve a substantial reduction in mass while maintaining the required mechanical performance under service conditions. The proposed concept integrates a traditional aluminium frame with an advanced honeycomb sandwich panel, joined through adhesive bonding to ensure structural continuity, compensate for thermal effects, and minimize over constraining stresses. Detailed numerical simulations were conducted to evaluate both the static and dynamic behaviour of the structure under the most demanding load cases prescribed by standards. Modal analysis showed excellent agreement with the original carbody, with variations in the first natural frequency about 3%, while a change in the nature of the corresponding eigenvector was observed. Static simulations under maximum vertical loading confirmed comparable stiffness and stress distributions. Localised stress peaks increased by approximately 19%; the corresponding material utilization factor remained below unity, demonstrating that the structure operates safely within its allowable limits. The introduction of the sandwich panel enabled a mass saving of approximately 60% in the replaced components, corresponding to 3.9% if referred to the whole structure. The results validate the structural feasibility and mechanical reliability of the proposed hybrid concept, laying the foundations for the subsequent experimental phase and for refining its predictive accuracy and industrial applicability. Full article

(This article belongs to the Special Issue Simulations and Experiments in the Design of Transport Vehicles: 2nd Edition)

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28 pages, 833 KB

Open AccessReview

Mechanisms and Integrated Pathways for Tropical Low-Carbon Healthy Building Envelopes: From Multi-Scale Coupling to Intelligent Optimization

by Qiankun Wang, Chao Tang and Ke Zhu

Appl. Sci. 2026, 16(1), 548; https://doi.org/10.3390/app16010548 - 5 Jan 2026

Viewed by 984

Abstract

Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by [...] Read more.

Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by linking materials, structures, and buildings across scales. It identifies three key scientific questions: (1) Establishing a multi-scale parametric design model that couples materials, structures, and architecture. (2) Elucidating experimental and simulated multi-scale equivalent relationships under the coupled effects of temperature, humidity, radiation, and salinity. (3) Design multi-objective optimization strategies balancing energy efficiency, comfort, indoor air quality, and carbon emissions. Based on this, a technical implementation pathway is proposed, integrating multi-scale unified parametric design, multi-physics testing and simulation, machine learning, and intelligent optimization technologies. This aims to achieve multi-scale parametric design, data–model fusion, interpretable decision-making, and robust performance prediction under tropical climatic conditions, providing a systematic technical solution to address the key scientific questions. This framework not only provides scientific guidance and engineering references for designing, retrofitting, and evaluating low-carbon healthy buildings in tropical regions but also aligns with China’s dual carbon goals and healthy building development strategies. Full article

(This article belongs to the Special Issue AI-Assisted Building Design and Environment Control)

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23 pages, 3056 KB

Open AccessArticle

A Fatigue-Crack Growth Prediction Model Considering Stress Ratio Effects Based on Material Properties

by Panpan Wu, Chunguo Zhang, Xing Yang and Zhonghong Dong

Appl. Sci. 2026, 16(1), 547; https://doi.org/10.3390/app16010547 - 5 Jan 2026

Cited by 1 | Viewed by 1550

Abstract

To overcome the limitation of the Paris law in capturing stress-ratio (R) effects, a modification of the Goodman model is introduced to account for the nonlinear variation of the fatigue limit with mean stress in this study. Based on the modified [...] Read more.

To overcome the limitation of the Paris law in capturing stress-ratio (R) effects, a modification of the Goodman model is introduced to account for the nonlinear variation of the fatigue limit with mean stress in this study. Based on the modified formulation, an equivalent crack driving force model incorporating R-effects is subsequently derived for fatigue-crack growth (FCG). The model unifies the stress-intensity factor ranges at different values of R into an equivalent value at R = 0 without introducing fitting parameters other than the Paris constants, relying solely on basic material properties (fatigue limit and tensile strength). This feature facilitates practical application and avoids extensive experimental calibration. Validation using FCG test results of Q345qD steel and 23 datasets show that the model outperforms classical models, achieving a goodness of fit up to 0.98 and demonstrating strong robustness and practical value for FCG prediction and residual-life assessment in engineering structures. Full article

(This article belongs to the Section Mechanical Engineering)

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23 pages, 5131 KB

Open AccessArticle

Shape-Constrained ResU-Net for Old Landslides Detection in the Loess Plateau

by Lulu Peng, Mingtao Ding, Qiang Xue, Ying Dong, Yunlong Li, Pengxiang Zhou and Zhenhong Li

Appl. Sci. 2026, 16(1), 546; https://doi.org/10.3390/app16010546 - 5 Jan 2026

Cited by 2 | Viewed by 656

Abstract

The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images become blurred over time, leading to huge challenges in [...] Read more.

The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images become blurred over time, leading to huge challenges in detection. Considering that old landslides exhibit obvious shape characteristics, we propose ResU-SPMNet, a deep learning model that integrates shape characteristics into the baseline ResU-Net. The proposed model consists of three components: ResU-Net, shape prior module (SPM), and the atrous spatial pyramid pooling (ASPP) module, which jointly enhance segmentation performance from the perspectives of shape constraints and multi-scale feature representation. To validate the effectiveness of the proposed approach, old landslides in representative regions of the Loess Plateau were selected as the study targets. Results show that the proposed model outperforms ResU-Net, SegNet, MultiResUnet, and DeepLabv3+ in old landslide segmentation, achieving an F1-score of 0.6669 and an MCC of 0.6167. Moreover, generalization tests conducted in independent regions indicate that the model exhibits strong robustness across different seasons. The best performance is achieved in summer, whereas performance declines in winter due to adverse factors such as reduced illumination and snow or ice cover. Full article

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21 pages, 4727 KB

Open AccessArticle

Influence of Structural Parameters on Thermal Stress and Performance of High-Temperature SiGe Thermoelectric Modules

by Zhenghao Liu, Heng Yan, Jing Li, Xiaoxi Chen, Hang Jing, Yingzeng Zhang, Qingpei Xiang and Mingyang Ma

Appl. Sci. 2026, 16(1), 545; https://doi.org/10.3390/app16010545 - 5 Jan 2026

Cited by 1 | Viewed by 792

Abstract

Mechanical and thermoelectric performance of a SiGe thermoelectric module were investigated through finite element analysis. N-type and P-type SiGe thermoelectric materials were synthesized, and their mechanical and thermoelectric properties were experimentally measured. Thermal stress distributions within the SiGe module and the integrated “heat [...] Read more.

Mechanical and thermoelectric performance of a SiGe thermoelectric module were investigated through finite element analysis. N-type and P-type SiGe thermoelectric materials were synthesized, and their mechanical and thermoelectric properties were experimentally measured. Thermal stress distributions within the SiGe module and the integrated “heat collector–module–heat sink” assembly are simulated, and the results were compared with the measured mechanical strength of the SiGe materials. The simulations show that among the three electrode structures evaluated—C/W/C sandwich, 0.5 mm W/C, and 0.1 mm W/C—the C/W/C sandwich configuration yields the lowest thermal stress. An inter-leg spacing of 0.5 mm also leads to reduced stress compared to a 0.1 mm gap. However, fully constraining the cold end or directly integrating the module with heat collection and dissipation components significantly increases thermal stress. The use of copper cooling plates induces higher stress than C-C plates, exceeding the tolerable strength of the materials. Simulation of a module with 28 SiGe legs (each 10 mm × 10 mm × 1.5 mm) predicts an output power of 7.42 W and a conversion efficiency of 7.11% at a hot-side temperature of 967 °C and a cold-side temperature of 412 °C. Full article

(This article belongs to the Section Energy Science and Technology)

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16 pages, 602 KB

Open AccessArticle

Telecom Fraud Detection Based on Large Language Models: A Multi-Role, Multi-Layer Prompting Strategy

by Jianpeng Ding and Houpan Zhou

Appl. Sci. 2026, 16(1), 544; https://doi.org/10.3390/app16010544 - 5 Jan 2026

Viewed by 2508

Abstract

Telecom network fraud continues to evolve, and its textual expressions have become increasingly concealed, making automated detection more challenging. When combined with mainstream prompting strategies, large language models (LLMs) often exhibit unstable performance when handling diverse fraud texts, particularly for long-tail categories and [...] Read more.

Telecom network fraud continues to evolve, and its textual expressions have become increasingly concealed, making automated detection more challenging. When combined with mainstream prompting strategies, large language models (LLMs) often exhibit unstable performance when handling diverse fraud texts, particularly for long-tail categories and confusing cases where consistent detection is difficult to maintain. To address this limitation, this study proposes a Multi-Role, Multi-Layer (MRML) prompting strategy. The strategy constructs three expert roles—text analysis, business process analysis, and security analysis—and adopts a conditional hierarchical reasoning mechanism to achieve a structured detection process that transitions from rapid binary screening to deep multi-class classification. This design systematically organizes the LLM’s inference steps and enhances its ability to distinguish different types of telecom fraud. Experiments conducted on two public datasets show that the proposed framework significantly outperforms mainstream prompting strategies and surpasses deep learning baselines such as BERT, TextCNN, and Transformer in terms of precision, recall, and F1-score, demonstrating superior performance and robustness. Overall, the results indicate that the proposed prompting strategy provides an effective and practically applicable solution for telecom fraud text detection in real-world scenarios. Full article

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35 pages, 4409 KB

Open AccessArticle

Hybrid Object-Based Augmentation and Histogram Matching for Cross-Domain Building Segmentation in Remote Sensing

by Chulsoo Ye and Youngman Ahn

Appl. Sci. 2026, 16(1), 543; https://doi.org/10.3390/app16010543 - 5 Jan 2026

Cited by 1 | Viewed by 1334

Abstract

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with [...] Read more.

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural, Earth and Environmental Science, 2nd Edition)

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15 pages, 4598 KB

Open AccessArticle

Improved PPIM—A Method to Further Improve the Measurement Accuracy of the Cross-Sectional Area of the Conveying Material Load

by Ning Jiang, Boxuan Shang, Qinghe Ji, Mengchao Zhang and Yuan Zhang

Appl. Sci. 2026, 16(1), 542; https://doi.org/10.3390/app16010542 - 5 Jan 2026

Viewed by 777

Abstract

Timely adjustment of belt conveyor speed according to the conveyed load is a key approach to achieving energy-efficient operation. Line laser-assisted vision has been widely adopted for load measurement, in which image processing techniques are employed to extract and analyze the outer contour [...] Read more.

Timely adjustment of belt conveyor speed according to the conveyed load is a key approach to achieving energy-efficient operation. Line laser-assisted vision has been widely adopted for load measurement, in which image processing techniques are employed to extract and analyze the outer contour of material piles highlighted by laser stripes. To address issues such as laser stripe thinning and breakpoint handling, the point-by-point interpolation method (PPIM) was previously proposed, enabling column-wise extraction of laser stripe pixels by incorporating the geometric characteristics of material accumulation, thereby improving real-time performance. However, its adaptability remains limited under complex pile geometries and strong reflective interference. In this paper, the pixel traversal strategy is further optimized to achieve efficient and robust extraction of the laser stripe centerline. By performing a single, non-global image traversal, laser stripe thinning, breakpoint identification, interpolation, continuity reconstruction, and cross-sectional area calculation are integrated into a unified processing framework. Experimental results demonstrate that the improved method achieves a 0.3% increase in measurement accuracy compared with the original PPIM, while maintaining excellent real-time performance with a processing speed of up to 94 frames per second (FPS). The proposed approach provides a more reliable load perception basis for intelligent speed regulation of belt conveyors, contributing to energy-efficient and stable operation. Full article

(This article belongs to the Special Issue Precision Measurement Technology)

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20 pages, 5915 KB

Open AccessArticle

An Improved Damage–Plasticity Constitutive Model Capturing Rock Softening Behavior Under Various Confining Pressures

by Wei Huang, Juntao Ren, Peiyong Wang, Bingxiang Yuan, Junxuan Long, Haiqing Zhou, Rui Chen and Hao Li

Appl. Sci. 2026, 16(1), 541; https://doi.org/10.3390/app16010541 - 5 Jan 2026

Viewed by 1025

Abstract

With the gradual depletion of shallow high-grade mineral resources, global mining activities are shifting toward deeper regions with more complex geological conditions. Rocks exhibit significantly different mechanical responses under high confining pressure environments, posing new challenges to deep mining safety. To address this, [...] Read more.

With the gradual depletion of shallow high-grade mineral resources, global mining activities are shifting toward deeper regions with more complex geological conditions. Rocks exhibit significantly different mechanical responses under high confining pressure environments, posing new challenges to deep mining safety. To address this, this study proposes an optimized rock damage constitutive model that characterizes the influence of confining pressure on rock mechanical behavior, incorporating its peak strength enhancement effect and regulatory mechanism on post-peak softening behavior. The core innovation lies in establishing an inverse relationship between softening parameters and confining pressure, as confining pressure increases, softening parameters decrease. This inverse relationship enables the model to reasonably reflect the inhibitory effect of confining pressure on the rock softening process, meaning that under higher confining pressure, the material exhibits slower stress decay and more pronounced ductile characteristics. This model can consistently describe brittle responses under low confining pressure and ductile responses under high confining pressure. The findings provide reliable theoretical support for predicting rock mass failure and conducting stability analysis under deep mining conditions. Full article

(This article belongs to the Special Issue Novel Insights into Rock Mechanics and Geotechnical Engineering)

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25 pages, 1673 KB

Open AccessArticle

Comparative Analysis of Clustering Algorithms for Unsupervised Segmentation of Dental Radiographs

by Priscilla T. Awosina, Peter O. Olukanmi and Pitshou N. Bokoro

Appl. Sci. 2026, 16(1), 540; https://doi.org/10.3390/app16010540 - 5 Jan 2026

Cited by 2 | Viewed by 1334

Abstract

In medical diagnostics and decision-making, particularly in dentistry where structural interpretation of radiographs plays a crucial role, accurate image segmentation is a fundamental step. One established approach to segmentation is the use of clustering techniques. This study evaluates the performance of five clustering [...] Read more.

In medical diagnostics and decision-making, particularly in dentistry where structural interpretation of radiographs plays a crucial role, accurate image segmentation is a fundamental step. One established approach to segmentation is the use of clustering techniques. This study evaluates the performance of five clustering algorithms, namely, K-Means, Fuzzy C-Means, DBSCAN, Gaussian Mixture Models (GMM), and Agglomerative Hierarchical Clustering for image segmentation. Our study uses two sets of real-world dental data comprising 140 adult tooth images and 70 children’s tooth images, including professionally annotated ground truth masks. Preprocessing involved grayscale conversion, normalization, and image downscaling to accommodate computational constraints for complex algorithms. The algorithms were accessed using a variety of metrics including Rand Index, Fowlkes-Mallows Index, Recall, Precision, F1-Score, and Jaccard Index. DBSCAN achieved the highest performance on adult data in terms of structural fidelity and cluster compactness, while Fuzzy C-Means excelled on the children dataset, capturing soft tissue boundaries more effectively. The results highlight distinct performance behaviours tied to morphological differences between adult and pediatric dental anatomy. This study offers practical insights for selecting clustering algorithms tailored to dental imaging challenges, advancing efforts in automated, label-free medical image analysis. Full article

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26 pages, 7417 KB

Open AccessArticle

Beam Damage Detection and Characterization Using Rotation Response from a Moving Load and Damage Candidate Grid Search (DCGS)

by Muath Y. Alhumaidi and Brett A. Story

Appl. Sci. 2026, 16(1), 539; https://doi.org/10.3390/app16010539 - 5 Jan 2026

Viewed by 750

Abstract

Structural health monitoring (SHM) increasingly contributes to the safety and durability of key infrastructure, especially bridges. This research introduces a rotation-based approach for damage detection and quantification using a damage candidate grid search technique (DCGS) on simply supported girder bridges under quasi-static or [...] Read more.

Structural health monitoring (SHM) increasingly contributes to the safety and durability of key infrastructure, especially bridges. This research introduces a rotation-based approach for damage detection and quantification using a damage candidate grid search technique (DCGS) on simply supported girder bridges under quasi-static or slowly moving loading conditions. Applying the principle of virtual work, the healthy and candidate-damaged rotation responses are analytically obtained and compared with the rotation observed directly at the moving load location. Damage is defined in terms of three key parameters: the start and the end of the damage,

L_{1}

and

L_{2}

, respectively, and the damage severity

β

. The DCGS method is validated using finite element model simulations of 12 damage scenarios subjected to different noise levels. A statistical analysis and confidence interval characterize the accuracy and consistency of the top ten estimations produced by the DCGS method. A damage length ratio (DLR), defined from the span of the beam,

L

, and the damage location,

L_{1}

and

L_{2}

, improves the robustness of the methodology against measurement noise by reducing possible false positive estimations. Additionally, the experimental results on two beam structures further validate the method. Absolute relative errors (AREs) of about 6% and absolute errors (AEs) of around 0.16 between the estimated and real damage parameters characterize the performance of the technique, considering damage location and damage severity, respectively. The results show that the DCGS methodology can effectively locate damage and estimate its severity in the presence of noise. The developed framework provides a sensitive and practical SHM tool that is suitable for early damage detection in railway and road bridges. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring in Civil Engineering)

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20 pages, 2603 KB

Open AccessArticle

A Comparative Evaluation of Greek Fig Cultivars Utilizing Instrumental Analytical Methodologies, In Silico Studies and Machine Learning Prediction

by Paris Christodoulou, Ioanna Stefanaki, Konstantinos Aouant, Georgia Ladika, Marina Tsiokou, Dionisis Cavouras, Eftichia Kritsi and Vassilia J. Sinanoglou

Appl. Sci. 2026, 16(1), 538; https://doi.org/10.3390/app16010538 - 5 Jan 2026

Viewed by 1491

Abstract

The present study provides a comprehensive comparative evaluation of three Greek fig cultivars through integrated instrumental, computational, and chemometric approaches. Fresh fig peel and flesh samples were analyzed to determine total soluble solids, total phenolic and flavonoid content, as well as antioxidant and [...] Read more.

The present study provides a comprehensive comparative evaluation of three Greek fig cultivars through integrated instrumental, computational, and chemometric approaches. Fresh fig peel and flesh samples were analyzed to determine total soluble solids, total phenolic and flavonoid content, as well as antioxidant and antiradical activities, complemented by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for structural profiling. Significant varietal and tissue-dependent differences were observed, with fig peel exhibiting higher levels of phenolic compounds and antioxidant activity compared to flesh. ATR-FTIR spectral patterns revealed the presence of characteristic functional groups associated with carbohydrates, phenolic compounds, carboxylic acids, and volatile compounds, reflecting the influence of variety, pollination requirements, and geographical origin. In parallel, to explore potential neuroprotective relevance, 30 phytochemicals reported in figs were subjected to molecular docking against human β-secretase 1 (hBACE1), a key enzyme in Alzheimer’s disease (AD) pathogenesis. Phenolic acids and flavonoids displayed favorable binding affinities and interaction profiles with the catalytic Asp32–Asp228 dyad and with the flap domain. A machine learning model (XGBoost) trained on known BACE1 inhibitors further classified all examined fig metabolites as active candidates. Collectively, these findings highlight Greek figs as chemically rich fruits with potential biological properties, supporting future targeted studies on their bioactive potential. Full article

(This article belongs to the Special Issue Recent Advances in Natural Phenolic Compounds Derived from Plant Extracts)

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11 pages, 245 KB

Open AccessReview

Digital Surgical Guides in Bone Regeneration: Literature Review and Clinical Case Report

by Óscar Iglesias-Velázquez, Baoluo Xing Gao, Francisco G. F. Tresguerres, Luis Miguel Sáez Alcaide, Isabel Leco Berrocal and Jesús Torres García-Denche

Appl. Sci. 2026, 16(1), 537; https://doi.org/10.3390/app16010537 - 5 Jan 2026

Viewed by 1483

Abstract

The present study describes a digitally guided workflow for the Split Bone Block Technique (SBBT) using standardized cortical and particulate allogeneic grafts in combination with custom-designed, 3D-printed surgical guides. The aim was to illustrate the feasibility of a donor-site-free alternative to the conventional [...] Read more.

The present study describes a digitally guided workflow for the Split Bone Block Technique (SBBT) using standardized cortical and particulate allogeneic grafts in combination with custom-designed, 3D-printed surgical guides. The aim was to illustrate the feasibility of a donor-site-free alternative to the conventional autologous approach, which remains technically demanding and associated with increased morbidity. A narrative literature review and a single clinical case report were conducted to contextualize the proposed workflow. Digital planning was performed by merging DICOM and STL datasets to design cutting boxes for standardized allogeneic laminae and a transporter guide for intraoperative positioning. The technique was applied in a patient with severe horizontal ridge atrophy. Primary wound closure and uneventful healing were achieved. Six-month CBCT evaluation demonstrated an increase in horizontal ridge width from 2 mm to 8 mm. Within the limitations of a single illustrative case, this report suggests that a fully guided allogeneic SBBT workflow is feasible and may facilitate controlled graft adaptation while avoiding autologous bone harvesting. Further controlled clinical studies are required to evaluate accuracy, reproducibility, and long-term outcomes. Full article

(This article belongs to the Special Issue Advancements and Updates in Digital Dentistry)

20 pages, 4656 KB

Open AccessArticle

Investigation of Axial–Torsional Vibration Characteristics and Vibration Mitigation Mechanism in Compound Percussive Drilling

by Wei Wang, Boyu Guo, Gonghui Liu, Chunqing Zha, Tian Chen and Jun Li

Appl. Sci. 2026, 16(1), 536; https://doi.org/10.3390/app16010536 - 5 Jan 2026

Cited by 3 | Viewed by 1184

Abstract

Severe drill string vibrations, particularly stick–slip, significantly compromise drilling efficiency and tool longevity in deep hard formations. Compound percussive drilling (CPD) has emerged as a promising technique to mitigate these vibrations and enhance the rate of penetration (ROP). However, the complex coupling mechanisms [...] Read more.

Severe drill string vibrations, particularly stick–slip, significantly compromise drilling efficiency and tool longevity in deep hard formations. Compound percussive drilling (CPD) has emerged as a promising technique to mitigate these vibrations and enhance the rate of penetration (ROP). However, the complex coupling mechanisms between impact loads and bit dynamics remain insufficiently understood. This study aims to elucidate the axial–torsional vibration characteristics of the drill bit and the underlying vibration reduction mechanisms under CPD conditions. A multi-degree-of-freedom (MDOF) dynamic model was first established, integrating both the dynamics of the CPD tool and the regenerative cutting effects inherent in bit–rock interactions. The governing equations were then solved numerically using the fourth-order Runge–Kutta method, followed by a systematic parametric sensitivity analysis to quantify the influence of impact parameters on vibration mitigation. The results show that while CPD induces detrimental axial–torsional vibrations in soft rock formations, it effectively suppresses stick–slip and enhances ROP in hard rock formations. Notably, coupled axial–torsional impact loading exhibits superior vibration suppression capabilities compared to singular axial or torsional impacts. A critical proportional relationship for parameter optimization was identified; specifically, maximizing vibration mitigation requires scaling the axial impact load proportionally with the torsional impact load. For example, when the axial impact load amplitudes are 5 kN and 10 kN, the corresponding optimal torsional impact load amplitudes are approximately 500 N·m and 1000 N·m, respectively. Furthermore, maintaining the impact frequency within the range of 10–30 Hz yields optimal vibration reduction effects. The benefits of CPD become increasingly pronounced with higher rock strength and longer drill strings. These findings confirm the suitability of CPD technology for deep hard rock environments and provide theoretical guidelines for the optimal selection of impact parameters in engineering applications. Full article

(This article belongs to the Special Issue Development of Intelligent Software in Geotechnical Engineering)

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20 pages, 1047 KB

Open AccessArticle

The Influence of One-Time Physical Activity at a Temperature of −10 °C on Erythrocyte Deformability in Young Men

by Aneta Teległów, Konrad Rembiasz, Janusz Pobędza, Iga Wilczyńska, Zygmunt Dziechciowski, Andrzej Czerwiński, Jakub Leśniowski, Jakub Marchewka and Piotr Mika

Appl. Sci. 2026, 16(1), 535; https://doi.org/10.3390/app16010535 - 5 Jan 2026

Viewed by 1084

Abstract

The study aimed to determine the effect of acute, one-time physical effort performed under different environmental temperature conditions on erythrocyte deformability in healthy young men. This exploratory randomized parallel-group study involved 30 men randomly assigned to an experimental group exercising at −10 °C [...] Read more.

The study aimed to determine the effect of acute, one-time physical effort performed under different environmental temperature conditions on erythrocyte deformability in healthy young men. This exploratory randomized parallel-group study involved 30 men randomly assigned to an experimental group exercising at −10 °C in a climatic chamber and a control group exercising under thermoneutral outdoor conditions. Erythrocyte deformability was assessed using the elongation index (EI), reflecting erythrocyte elasticity and the ability to pass through microcirculation vessels. Participants performed an incremental 20 m shuttle run test. Venous blood samples were collected before and immediately after exercise, and erythrocyte deformability was analyzed using a Lorrca analyzer across a shear stress range of 0.30–60.00 Pa. A two-factor repeated-measures analysis of variance was applied. An increase in EI after exercise was observed in both groups, predominantly at higher shear stress values, indicating enhanced erythrocyte deformability under conditions of increased shear forces. However, the magnitude of post-exertion changes differed between groups. At lower shear stress levels (0.30 Pa and 0.58 Pa), EI tended to decrease after exercise. These findings indicate that a single bout of physical effort influences erythrocyte deformability, while the potential effects of cold exposure on this response remain uncertain and warrant further investigation. Full article

(This article belongs to the Special Issue Exercise Physiology and Rheology—New Experience)

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18 pages, 2918 KB

Open AccessArticle

Study on Tire–Road Wear Interface Behavior of Porous Elastic Road Surface Based on Image Processing

by Hongjin Liu, Ke Zhong, Jing Gu, Ting Gan and Yuchun Li

Appl. Sci. 2026, 16(1), 534; https://doi.org/10.3390/app16010534 - 5 Jan 2026

Viewed by 716

Abstract

The use of waste rubbers and polyurethane has a significant impact on the abrasion resistance of the porous elastic road surface (PERS) mixture. The purpose of this work is to study the anti-abrasion performance of the PERS mixture under different contents of waste [...] Read more.

The use of waste rubbers and polyurethane has a significant impact on the abrasion resistance of the porous elastic road surface (PERS) mixture. The purpose of this work is to study the anti-abrasion performance of the PERS mixture under different contents of waste rubbers. First, features of the surface of the PERS mixture were collected by image processing technology. Then, the abrasion performance of the mixture was studied by image processing and wear tests. The correlation between the surface texture parameters and the anti-abrasion performance of the mixture was analyzed by the gray entropy correlation method. It is found that the change of convex particle area in the equivalent diameter range of 2–5 mm had the greatest correlation with the abrasion resistance of the PERS mixture. The effect of the waste rubber content of the mixture on the anti-abrasion performance was investigated, and a waste rubber content of 10% showed the best anti-abrasion performance. It is expected that this work can provide a new method for analyzing the anti-abrasion performance of functional pavement. Full article

(This article belongs to the Special Issue Advanced Materials and Technologies in Pavement Engineering)

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13 pages, 2618 KB

Open AccessArticle

Multi-Domain Perception Transformer for Generalized Forgery Image Detection

by Qiaoyue Man, Seok-Jeong Gee and Young-Im Cho

Appl. Sci. 2026, 16(1), 533; https://doi.org/10.3390/app16010533 - 5 Jan 2026

Cited by 4 | Viewed by 1627

Abstract

With the rapid advancement of generative AI (AIGC) technology, synthetic images are increasingly approaching real pictures in terms of resolution and semantic consistency. Traditional detection methods face numerous challenges, such as insufficient cross-modal generalization capabilities and difficulty in identifying hidden generative traces. Existing [...] Read more.

With the rapid advancement of generative AI (AIGC) technology, synthetic images are increasingly approaching real pictures in terms of resolution and semantic consistency. Traditional detection methods face numerous challenges, such as insufficient cross-modal generalization capabilities and difficulty in identifying hidden generative traces. Existing solutions primarily design feature extractors for single generative models, struggling to address the complexity of multimodal forgeries. Therefore, we propose a multi-domain feature fusion Transformer network that integrates spatial, frequency, and wavelet transform features and introduce a cross-domain feature fusion module (CDAF) to detect subtle forgery traces in deepfake images. This model demonstrates superior detection performance on current forged images generated by generative adversarial networks (GANs) and diffusion models while exhibiting enhanced robustness. Full article

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17 pages, 2315 KB

Open AccessArticle

Studies of Akt/mTOR–Autophagy–Apoptosis Crosstalk in Human Gingival Fibroblasts Around New Digital Processed Dental Composites

by Florentina Rus, Radu Radulescu, Alexandra Popa, Monica Musteanu, Melis Izet, Corina Muscurel, Lucian Toma Ciocan, Sebastian-Andrei Bancu, Marina Imre and Alexandra Ripszky

Appl. Sci. 2026, 16(1), 532; https://doi.org/10.3390/app16010532 - 5 Jan 2026

Cited by 1 | Viewed by 1216

Abstract

Introduction: Incomplete polymerization of in vivo composite resins (CR) poses a significant problem, with monomer-to-polymer conversion rates ranging from around 60 to 75%. Furthermore, oxygen exposure hampers polymerization in the surface layers. This research aims to evaluate the autophagy-inducing potential of three types [...] Read more.

Introduction: Incomplete polymerization of in vivo composite resins (CR) poses a significant problem, with monomer-to-polymer conversion rates ranging from around 60 to 75%. Furthermore, oxygen exposure hampers polymerization in the surface layers. This research aims to evaluate the autophagy-inducing potential of three types of CRS and to explore the role of the Akt/mTOR–autophagy–apoptosis crosstalk in composite resin-induced autophagy. The study uses human gingival fibroblasts and three composite materials (M1 and M2, which are 3D printed, and M3, which is milled). Materials and Methods: SEM analysis was performed on the dental materials, and cells kept in contact for 24 h were subjected to tests including the following: MTT, LDH, NO, immunological detection of proteins involved in autophagy and apoptosis, as well as immunofluorescence tests (Annexin V and nucleus; mitochondria and caspase 3/7; detection of autophagosomes). Results: The results showed statistically significant decreases in cell viability with M1 and M2, linked to increases in cytotoxicity and oxidative stress (LDH and NO). Using multiplex techniques, significant increases in glycogen synthase kinase 3 beta (GSK3b) protein were observed in both M1 and M2; a decrease in mTOR (mechanistic target of rapamycin) expression was noted in M1 and M3. Immunofluorescence tests revealed an increase in Annexin V across all materials studied, and an increase in autophagosomes in M1 and M2, whereas a decrease was observed in M3. Conclusions: The relationship between apoptosis and autophagy is highly complex, indicating they may occur sequentially, coexist, or be mutually exclusive. Understanding this complex interplay can help in designing new 3D-printing protocols and monomer compositions to prevent autophagy imbalance. Full article

(This article belongs to the Special Issue Advanced Biomaterials: Characterization and Applications)

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