Applied Sciences

24 pages, 10226 KB

Open AccessArticle

Experimental and Numerical Study on Plastic Behavior of Expansion Tubes Subjected to Impact

by Di Jiang, Yiqun Yu, Lihua Wu, Xvdong Zhi, Haiqing Li, Feng Fan and Rong Zhang

Appl. Sci. 2026, 16(11), 5725; https://doi.org/10.3390/app16115725 - 5 Jun 2026

Viewed by 407

Aiming to study the plastic behavior of expansion tubes, this paper presents experimental studies on Q420 and S2205 steel tubes and investigates the influence of key parameters on the responses of the expansion tube. A finite element model is established and validated by [...] Read more.

Aiming to study the plastic behavior of expansion tubes, this paper presents experimental studies on Q420 and S2205 steel tubes and investigates the influence of key parameters on the responses of the expansion tube. A finite element model is established and validated by comparing the numerical results with experimental results. Based on both experimental and numerical approaches, the effects of the coefficient of friction, geometric parameters, tube material and impact velocity are revealed. The results show that the steady-state force increases linearly with increasing friction coefficient and tube thickness. As expansion value increases, the growth rate of steady-state force decreases, and local buckling and splitting become more likely. Numerical simulations examine the response and failure modes under low- to high-speed impacts. The steady-state force is insensitive to impact velocity and expansion angle, but the failure mode under high-speed impact is more severe than that under low-speed impact. Four failure modes and typical deformation stages of the failure process were obtained based on test observations and numerical simulations. The empirical formula for predicting the steady-state force of Q420 steel tubes under quasistatic and low-speed impact expansion is proposed based on similarity criteria and dimensional analysis. Full article

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19 pages, 4114 KB

Open AccessArticle

Design, Implementation and Experimental Evaluation of an Additively Manufactured SiSiC Reactor for Catalytic Steam Reforming

by Alexander Feldner, Jakob Müller, Peter Treiber and Jürgen Karl

Appl. Sci. 2026, 16(11), 5724; https://doi.org/10.3390/app16115724 - 5 Jun 2026

Viewed by 261

Abstract

Hydrogen from biogenic sources is central to the transition to a carbon-neutral energy system, offering flexibility for mobility and industrial applications. Decentralized steam reforming of biogas enables on-site hydrogen production but requires precise heat management due to its strongly endothermic nature. In small-scale [...] Read more.

Hydrogen from biogenic sources is central to the transition to a carbon-neutral energy system, offering flexibility for mobility and industrial applications. Decentralized steam reforming of biogas enables on-site hydrogen production but requires precise heat management due to its strongly endothermic nature. In small-scale systems, conventional manufacturing approaches often limit geometric flexibility and thermal integration, whereas additive manufacturing enables highly integrated reactor structures that overcome these constraints. This study presents the development and experimental evaluation of a compact, monolithic reformer additively manufactured from silicon-infiltrated silicon carbide, combining combustion and reforming zones in a single component to enhance heat transfer and compactness. The reactor features an internal U-shaped reforming channel filled with a nickel-based catalyst and was tested under varying loads. CH₄ conversions of 95–99% close to equilibrium were achieved at gas hourly space velocities up to 75,000 h⁻¹. Stable internal heat supply sustained reforming, although combustion results remain preliminary due to manufacturing-related blockages in the combustion channels, as revealed by computed tomography (CT) analysis. Energy assessments indicate that thermal efficiency is primarily limited by external heat losses of up to 46%, resulting from the high operating temperatures and small reactor dimensions. The results demonstrate the feasibility of the integrated reactor concept while highlighting current limitations related to manufacturability and heat losses, providing a basis for future optimization and scale-up. Full article

(This article belongs to the Section Applied Thermal Engineering)

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26 pages, 38863 KB

Open AccessArticle

U-Net-Based Classification of Patient Sleep Postures Using IMU-Derived RGB Representations

by Rabia Gizemnur Eren, Beyda Tasar, Orhan Yaman, Irfan Kilic, Cetin Gencer and Anuarbek Amanov

Appl. Sci. 2026, 16(11), 5723; https://doi.org/10.3390/app16115723 - 5 Jun 2026

Viewed by 308

Abstract

Patients confined to bed for extended periods must frequently change sleeping posture to prevent pressure ulcers, which are difficult and undesirable to treat. In this study, three IMU sensors were placed on 108 bedridden patients to collect data for five different postures, resulting [...] Read more.

Patients confined to bed for extended periods must frequently change sleeping posture to prevent pressure ulcers, which are difficult and undesirable to treat. In this study, three IMU sensors were placed on 108 bedridden patients to collect data for five different postures, resulting in 1,800,000 data points per sensor. These were converted into Eulerx, Eulery, and Eulerz values. The goal was to detect the sleep posture using a single IMU sensor. Four cases were defined: Case 1 used only IMU1, Case 2 only IMU2, Case 3 only IMU3, and Case 4 combined all three. Euler signals were converted into RGB images, framing the problem as an image classification task. A total of 2000 images were used, with 400 for training and 100 for testing in each case. A U-Net model was applied, achieving high IoU scores: 98.00%, 99.56%, 99.72%, and 98.20% respectively. Accuracy scores were 98.98%, 99.78%, 99.86%, and 99.09%, confirming U-Net’s effectiveness. Full article

(This article belongs to the Special Issue Evolving Wearable and Smart Device Technologies for Healthcare)

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12 pages, 1451 KB

Open AccessArticle

Study on Local Damage Identification of a Masonry Retaining Wall Based on Wavelet Packet Decomposition

by Jin Zhou, Longjian Fang, Jiacheng Li, Ling Mei and Jiapeng Xu

Appl. Sci. 2026, 16(11), 5722; https://doi.org/10.3390/app16115722 - 5 Jun 2026

Viewed by 272

Abstract

Masonry retaining walls are widely used in mountainous regions but are susceptible to progressive internal damage under environmental and operational loads, which is often difficult to detect through conventional visual inspection. To address this problem, this study proposes a baseline-free vibration-based damage identification [...] Read more.

Masonry retaining walls are widely used in mountainous regions but are susceptible to progressive internal damage under environmental and operational loads, which is often difficult to detect through conventional visual inspection. To address this problem, this study proposes a baseline-free vibration-based damage identification method for existing masonry retaining walls. The method combines impulse response function (IRF) estimation with wavelet packet decomposition (WPD) and introduces a scalar damage index, termed the energy ratio standard deviation (ERSD). Unlike conventional WPD energy ratio deviation (ERD) vectors, ERSD condenses multi-band energy redistribution into a single positive scalar for each sensor location, thereby facilitating spatial interpolation and field-level damage localization without modal extraction. The method was validated through four monthly impact hammer tests on a masonry retaining wall in Zhenjiang, China. The results show that non-zero ERD vectors indicate vibration energy redistribution between successive monitoring states, while the spatial peak of ERSD identifies the most likely damage zone. The ERSD maximum occurred at point 5 and was confirmed by post-test visual inspection, which revealed a local crack of approximately 0.8–1.2 mm in the adjacent mortar joint. To avoid overfitting with the limited four-test dataset, the temporal trend of ERSD was evaluated using a linear regression and finite-difference progression rates rather than a high-order polynomial. The proposed method provides a practical preliminary screening tool for field damage localization; however, its quantitative damage severity calibration requires further validation using controlled stiffness-reduction tests and environmental compensation models. Full article

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20 pages, 28708 KB

Open AccessArticle

Material Characterization and Seismic Assessment of the Historic Pamukçular Masonry Bridge

by Fatih Avcil, Ahmet Yılmaz, Ercan Işık and Aydın Büyüksaraç

Appl. Sci. 2026, 16(11), 5721; https://doi.org/10.3390/app16115721 - 5 Jun 2026

Viewed by 223

Abstract

Türkiye has many historically rich cities that host structures of significant cultural value. These structures, especially masonry bridges, reflect the construction techniques and materials of the periods in which they were built. However, studies on the origins of these bridges and the structural [...] Read more.

Türkiye has many historically rich cities that host structures of significant cultural value. These structures, especially masonry bridges, reflect the construction techniques and materials of the periods in which they were built. However, studies on the origins of these bridges and the structural deteriorations that develop over time are limited. This situation may lead to damage and even the risk of collapse if necessary precautions are not taken. In this study, stone and mortar samples were first collected from the historic Pamukçular (Şifalısu) Bridge in Bitlis, and the collected materials were analyzed. The structural behavior of the bridge under seismic effects was then investigated using the Finite Element Method (FEM). A three-dimensional geometric model of the bridge was created, and material parameters were defined based on values from the material analyses. Static analysis under self-weight and modal analysis were performed in the ABAQUS software (Version 6.14) to obtain the natural frequencies. Under the bridge’s self-weight, local stress concentrations were concentrated at the arch crown and pier-arch connections, with maximum tensile and compressive stresses reaching approximately 0.15 MPa and 0.27 MPa, respectively. These low stress levels demonstrate that the structure remains fully stable under static loading conditions. Finally, dynamic analyses in the time domain were carried out. In these analyses, records from the 2011 Van Earthquake and the 2023 Kahramanmaraş Earthquake were used to identify the bridge’s critical regions and evaluate its seismic performance. The results indicate that the overall structural stability is adequate; however, local stress concentrations occur in the arch crown and pier connection regions. The study provides engineering-based recommendations for preserving and strengthening historic masonry bridges. Full article

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20 pages, 12199 KB

Open AccessArticle

Analysis on Time-Dependent Yield Stress Behavior and Influencing Factors in Basalt Fiber-Reinforced Gangue Cemented Slurry

by Bingchao Zhao, Shangyinggang Chen, Di Zhai, Pan Chen and Jie Wen

Appl. Sci. 2026, 16(11), 5720; https://doi.org/10.3390/app16115720 - 5 Jun 2026

Viewed by 189

Abstract

Due to the tendency of backfill slurry to stagnate within pipelines during transportation, a time-dependent rheological model for basalt fiber-reinforced gangue cemented slurry was developed based on the H-B rheological model and flocculation structure theory to ensure unimpeded slurry flow within pipelines over [...] Read more.

Due to the tendency of backfill slurry to stagnate within pipelines during transportation, a time-dependent rheological model for basalt fiber-reinforced gangue cemented slurry was developed based on the H-B rheological model and flocculation structure theory to ensure unimpeded slurry flow within pipelines over specified time periods. Experiments were conducted to investigate the time-dependent yield stress evolution of 9 mm fiber-reinforced gangue cemented slurry over time under varying conditions, specifically examining the effects of adding 9 mm fiber-reinforced (accounting for 0.5% of the total mass of the slurry) gangue cemented slurry under varying conditions. Significant effects of mass concentration, sucrose admixture content, and fly ash concentration on the yield stress of the slurry under different standing times were investigated. Research findings indicate that the yield stress of the paste increases with rising mass concentration and also rises with extended standing time. For slurries with mass concentrations ranging from 76% to 82%, the yield stress after 120 min of standing increased by 81.03%, 80%, 82%, and 97.48%, respectively, compared to freshly mixed slurries. The yield stress decreases with increasing sucrose dosage. Below 0.5% sucrose dosage, the rate of yield stress increase with standing time is relatively slow; above 0.5%, the rate increases more rapidly. After 120 min of standing, the yield stress of slurries with a sucrose dosage ranging from 0.25% to 1.00% increased by 48.66%, 54.42%, 32.90%, and 33.70%, respectively, compared to freshly mixed slurry. Yield stress decreased with increasing fly ash content and exhibited an overall steady upward trend with standing time. Based on the fitting surfaces depicting the variation in yield stress in filling materials over time under different influencing factors, fitting expressions were derived. Analysis of variance revealed that the time-dependent behavior of filling materials is primarily influenced by mass concentration, followed by retarder dosage and fly ash proportion. Full article

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

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19 pages, 1823 KB

Open AccessArticle

VLM-MCPDD: An Interpretable Vision Language Model for Multi-Crop Pests and Disease Diagnosis

by Liang Zhao, Mengwei Li, Xu Ren, Yuting Cheng and Zongxi Hu

Appl. Sci. 2026, 16(11), 5719; https://doi.org/10.3390/app16115719 - 5 Jun 2026

Viewed by 270

Abstract

Deep convolutional neural networks have made substantial progress in automated crop disease diagnosis. However, their practical application remains constrained by limited interpretability and insufficient structured reasoning, as these models largely operate as black boxes. Although they are effective in extracting visual features, they [...] Read more.

Deep convolutional neural networks have made substantial progress in automated crop disease diagnosis. However, their practical application remains constrained by limited interpretability and insufficient structured reasoning, as these models largely operate as black boxes. Although they are effective in extracting visual features, they often fail to provide semantically grounded explanations, which may reduce their reliability in complex and open agricultural environments. To address these issues, this study constructs a Vision Language Model for Multi-Crop Pest and Disease Diagnosis (VLM-MCPDD). Specifically, the LLaVA-1.5 model is fine-tuned using low-rank adaptation (LoRA) to better align visual symptom representations with domain-specific agricultural knowledge. In addition, a Pests and Diseases Semantic Dataset (PDSD) is constructed to support multimodal learning. Based on PDSD, a chain-of-thought (CoT) mechanism is introduced to simulate the diagnostic workflow of agronomists, covering symptom observation, causal analysis, and final decision-making. The experimental results show that compared with comparative models such as Swin Transformer and ConvNeXt, VLM-MCPDD performs better in overall performance and can provide some reference for disease and pest diagnosis in intelligent agriculture. Full article

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42 pages, 12027 KB

Open AccessReview

Resolving the Chemistry and Bioactivity of Juglone: From Natural Extracts to Applications

by Małgorzata Olszowy-Tomczyk and Dorota Wianowska

Appl. Sci. 2026, 16(11), 5718; https://doi.org/10.3390/app16115718 - 5 Jun 2026

Viewed by 250

Abstract

Juglone (5-hydroxynaphthoquinone) is a naturally occurring naphthoquinone broadly present in Juglans species and selected taxa of other plant families. Owing to its redox-active structure, it exhibits a wide range of biological effects, including antimicrobial, anti-inflammatory, and cytotoxic activity. Despite increasing interest in its [...] Read more.

Juglone (5-hydroxynaphthoquinone) is a naturally occurring naphthoquinone broadly present in Juglans species and selected taxa of other plant families. Owing to its redox-active structure, it exhibits a wide range of biological effects, including antimicrobial, anti-inflammatory, and cytotoxic activity. Despite increasing interest in its potential applications, research on juglone remains dispersed, particularly with regard to extraction strategies, analytical approaches, and the relationship between composition and biological activity. This review provides an integrated overview of juglone, focusing on its natural occurrence, modern extraction techniques, analytical determination, and reported biological properties. Special attention is given to emerging green extraction approaches and challenges related to process standardization and reproducibility. The paper also highlights current limitations in linking phytochemical composition with biological effects and outlines future directions for improving the reliability and applicability of juglone-based systems. By combining chemical, analytical, and application-oriented perspectives, this paper offers a concise framework for further research and supports the development of safe, efficient, and sustainable strategies for the utilization of juglone in various industrial sectors. Full article

(This article belongs to the Special Issue Bioactive Natural Compounds: From Discovery to Applications)

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14 pages, 2094 KB

Open AccessArticle

Fused Filament Fabrication of COC/Aluminum Composites for Structured Reactor Components

by Elizabeta Forjan, Marijan-Pere Marković, Klara Cvitkušić and Domagoj Vrsaljko

Appl. Sci. 2026, 16(11), 5717; https://doi.org/10.3390/app16115717 - 5 Jun 2026

Viewed by 261

Abstract

The development of 3D-printable polymer–metal composites offers new opportunities for structured catalytic reactor design and process intensification. Here, cyclic olefin copolymer (COC) composites filled with micron-scale aluminum particles (1–15 wt%, 160 µm) were prepared via a two-step compounding and extrusion process to produce [...] Read more.

The development of 3D-printable polymer–metal composites offers new opportunities for structured catalytic reactor design and process intensification. Here, cyclic olefin copolymer (COC) composites filled with micron-scale aluminum particles (1–15 wt%, 160 µm) were prepared via a two-step compounding and extrusion process to produce filaments suitable for fused filament fabrication (FFF). Thermal analysis confirmed that aluminum incorporation does not significantly alter the glass transition (T_g = 76–77 °C) or thermal stability of the polymer. Melt flow rate measurements indicated processable viscosity (MFR 3.82–4.57 g/10 min), while tensile testing revealed Young’s modulus of 1277 MPa–1783 MPa, maximum stress of 27 MPa–39 MPa, and enhanced strain at break for the 1 wt% Al composite (ε_B = 5.33%). Composites containing up to 15 wt% Al were successfully printed into mechanically robust static mixers, demonstrating complex geometries without particle sedimentation issues. The incorporation of aluminum particles introduces potential functionalities related to thermal management, surface modification, and future catalytic or photocatalytic applications. This work establishes a scalable polymer–metal platform integrating structural stability, geometric complexity, and prospective multifunctional behavior for advanced flow-reactor applications. Full article

(This article belongs to the Special Issue 15th Anniversary of Applied Sciences—Emerging Applications in the Recent 2 Years)

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19 pages, 774 KB

Open AccessArticle

Chemical Elements—Identifiers for Honey Quality

by Elisaveta Mladenova, Konstantina Priboyska, Ina Yotkovska and Irina Karadjova

Appl. Sci. 2026, 16(11), 5716; https://doi.org/10.3390/app16115716 - 5 Jun 2026

Viewed by 317

Abstract

Honey is a natural food product which in traditional production represents a clear example of the “farm-to-table” principle, as it excludes any processing of the original product. This study proposes an analytical approach for determining 30 most frequently determined chemical elements (Ag, Al, [...] Read more.

Honey is a natural food product which in traditional production represents a clear example of the “farm-to-table” principle, as it excludes any processing of the original product. This study proposes an analytical approach for determining 30 most frequently determined chemical elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Co, Cr, Cs, Cu, Ga, In, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Rb, S, Se, Sr, Te, V, and Zn) in honey, emphasizing the use of a relatively large sample mass to overcome sample heterogeneity and ensure accurate and reliable results. About 31 linden and 16 rapeseed honey samples from different Bulgarian regions were analyzed. Pollen analysis data showed that pollen content ranged from 30 to 78% for linden and 30 to 93% for rapeseed honey. The results identify a group of elements—K, Ca, Mg, Sr, and Rb—whose concentrations show statistically significant dependence on the floral origin and purity of the honey. Based on these findings, these elements are proposed as potential markers for identifying the botanical origin of honey. Furthermore, macronutrients and micronutrients (P, S, B, Cu, Fe, Mn, and Zn), which are generally subject to homeostatic regulation, as well as micro-elements (Al, As, Cd, Co, Cr, and Pb), which are more strongly influenced by environmental factors, showed limited discriminatory potential and no clear correlation with floral purity and botanical origin. Therefore, they should not be used as criteria when assessing the botanical origin of honey, but rather as indicators of environmental pollution and potential quality or safety concerns. Overall, the research contributes to improving the reliability of botanical classification of honey by combining robust analytical methodology with statistically validated elemental markers, while also distinguishing between natural compositional features and contamination-related signals. Full article

(This article belongs to the Special Issue Advanced Food Detection Technology)

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16 pages, 9191 KB

Open AccessArticle

A Physically Guided Porosity-Compensated Model for Shear-Wave Velocity Prediction in Sandstone Reservoirs

by Mohamed Almabrouk Alhashi and Cavit Atalar

Appl. Sci. 2026, 16(11), 5715; https://doi.org/10.3390/app16115715 - 5 Jun 2026

Viewed by 207

Abstract

Accurate estimation of shear-wave velocity (Vs) is fundamental for reservoir geomechanics, as it directly influences the calculation of elastic properties used in Mechanical Earth Models (MEMs). However, shear-wave sonic logs are frequently unavailable in legacy or data-limited wells due to high [...] Read more.

Accurate estimation of shear-wave velocity (Vs) is fundamental for reservoir geomechanics, as it directly influences the calculation of elastic properties used in Mechanical Earth Models (MEMs). However, shear-wave sonic logs are frequently unavailable in legacy or data-limited wells due to high operational costs and technical constraints. Therefore, reliable prediction of Vs has become essential. This study proposes a physically guided porosity-compensated compressional-wave predictor, Vp (1 − PHIT), derived from the Wyllie time-average equation, to mitigate porosity-induced variability and enhance sensitivity to rock-frame stiffness. The proposed model was evaluated using a multi-well sandstone and shaly sand dataset comprising 29,426 data points from 19 wells in the Sirte Basin, Libya. Its performance was benchmarked against five widely used global correlations using statistical metrics including R², RMSE, MAE, and MAPE. The results demonstrate that the proposed model achieves superior predictive performance: R² = 0.908, root-mean-square error (RMSE) = 0.00047 ft/µs, mean absolute error (MAE) = 0.00037 ft/µs, and mean absolute percentage error (MAPE) = 4.05%, outperforming conventional empirical correlations. The developed correlation provides a simple, physically interpretable, and field-applicable solution for predicting Vs in sandstone reservoirs and similar formations where shear-wave measurements are unavailable. Full article

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24 pages, 3604 KB

Open AccessArticle

Design and Safety Simulation of the Integrated Ventilation System for “Excavation–Backfilling–Retention” of Inter-Section Coal Pillar and Gate Roads

by Bingchao Zhao, Jin Ren, Shenglin He, Yufeng Guo, Wenshuo Yuan, Liang Ren and Zhen Zhang

Appl. Sci. 2026, 16(11), 5714; https://doi.org/10.3390/app16115714 - 5 Jun 2026

Viewed by 195

Abstract

Traditional coal mining methods have led to prominent issues of coal resource waste and large-scale solid waste emissions. The integrated “excavation–backfilling–retention” mining technology for inter-section coal pillars and gate roads is one of the key technologies to solve these problems. However, the excavation [...] Read more.

Traditional coal mining methods have led to prominent issues of coal resource waste and large-scale solid waste emissions. The integrated “excavation–backfilling–retention” mining technology for inter-section coal pillars and gate roads is one of the key technologies to solve these problems. However, the excavation and mining process associated with this technology imposes higher requirements on the ventilation system. Aiming at addressing the ventilation challenges existing during the implementation of the “excavation–backfilling–retention” method, research on ventilation safety assurance technology for inter-section coal pillars was carried out. Using COMSOL5.5 software, a full-stage ventilation system design model was constructed, adopting a ventilation mode that combines full-air-pressure ventilation with auxiliary local ventilation. The dynamic variation characteristics of the ventilation system under the “excavation–backfilling–retention” method and its capability to prevent and control the risks of O₂ and CO gas accumulation and coal spontaneous combustion were studied. The results show that during the bypass excavation period, the air supply from the auxiliary fan is sufficient, and during the excavation period for the two gate roads, due to the increased ventilation distance, insufficient airflow occurs near the heading face, accompanied by temperature rise, O₂ concentration decrease, and local CO accumulation, posing risks of coal spontaneous combustion and toxic gas accumulation. During the inter-section coal pillar excavation period and the cyclic operation period, after the full-air-pressure ventilation system is established, the airflow becomes stable, ventilation resistance decreases, and both temperature and gas concentrations are controlled within safe limits. However, in the corner areas, auxiliary local ventilation measures are still required due to insufficient O₂ and CO accumulation. The study verifies the feasibility and safety of the integrated “excavation–backfilling–retention” ventilation system, providing a safe ventilation approach for the integrated mining method and supporting the green mining of coal mines and the synergistic development of coal-based solid waste resource utilization. Full article

(This article belongs to the Topic Advances in Mining and Geotechnical Engineering)

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16 pages, 7860 KB

Open AccessArticle

Stability Maintenance of Gravity Comparison Sites (2017–2024): Environmental Factors and Data Processing Strategies

by Lishuang Mou, Dong Wang, Jinyang Feng, Qiyu Wang, Jiamin Yao, Huijuan Ma, Xiaodong Chen, Chunjian Li and Miaomiao Zhang

Appl. Sci. 2026, 16(11), 5713; https://doi.org/10.3390/app16115713 - 5 Jun 2026

Viewed by 276

Abstract

To ensure the sustained stability of absolute gravity benchmark points from 2017 to 2024, observational records from superconducting gravimeters (SGs) and absolute gravimeters were comprehensively examined in this work, and the environmental effects on gravitational acceleration were quantitatively assessed. The annual fluctuation of [...] Read more.

To ensure the sustained stability of absolute gravity benchmark points from 2017 to 2024, observational records from superconducting gravimeters (SGs) and absolute gravimeters were comprehensively examined in this work, and the environmental effects on gravitational acceleration were quantitatively assessed. The annual fluctuation of the SG (iGrav-012k) scale factor reached 0.268 μGal/V, with a weighted average of (–92.8702 ± 0.0265) μGal/V (relative precision of 0.3‰), providing a precise scale factor for long-term SG monitoring. By removing step discontinuities in the SG data using FG5-X249 absolute gravimeter measurements, the residual fitting error decreased to 6.3 μGal. In addition, the SG drift was estimated as 1.0 μGal/year through international comparison datasets and FG5 measurements, substantially improving the consistency of the time series. Further investigation showed that the SG residuals exhibited clear seasonal oscillations, which were mainly attributed to local hydrological processes and ground deformation near the benchmark sites. By integrating groundwater level and deformation monitoring data and applying a neural network model to separate hydrological load components, the peak-to-peak residual amplitude was reduced from 13 μGal to 3.5 μGal. Quantitative analysis indicated that hydrological effects contributed about 9.5 μGal to the seasonal variation, whereas surface deformation had only a minor impact (<2 μGal). The findings confirm that careful data correction and isolation of environmental effects are effective for maintaining the long-term stability of gravity benchmarks. The developed workflow provides a reproducible framework for high-precision gravity site maintenance and supports future dynamic monitoring of regional environmental load responses. Full article

(This article belongs to the Section Earth Sciences)

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25 pages, 1473 KB

Open AccessArticle

From Heuristics to Reinforcement Learning: Integrated Operational–Financial Control of Supply Chains Under Demand Disruption

by Ali Badakhshan, Ehsan Badakhshan, Sameh Saad and Ramin Bahadori

Appl. Sci. 2026, 16(11), 5712; https://doi.org/10.3390/app16115712 - 5 Jun 2026

Viewed by 246

Abstract

Supply chain control requires balancing operational performance and financial efficiency when decisions are made using delayed and imperfect demand information. Although fixed heuristics, adaptive policies, and reinforcement learning approaches have been proposed, their relative effectiveness and robustness under temporary informational mismatch remain unclear. [...] Read more.

Supply chain control requires balancing operational performance and financial efficiency when decisions are made using delayed and imperfect demand information. Although fixed heuristics, adaptive policies, and reinforcement learning approaches have been proposed, their relative effectiveness and robustness under temporary informational mismatch remain unclear. This study addresses this gap by developing an integrated simulation–reinforcement learning framework that jointly captures operational and financial dynamics in supply chains, which enables adaptive optimisation of working capital policies under uncertainty. A unified simulation framework is developed for a multi-echelon supply chain that jointly models service levels, backlog, customer retention, and working capital exposure through the cash conversion cycle. Five classes of controllers are evaluated: fixed-threshold heuristics, adaptive threshold policies optimised using stochastic and evolutionary search, and a reinforcement learning controller based on proximal policy optimisation. Performance is assessed under stationary demand and under demand disruptions. The results reveal a clear hierarchy of performance. Fixed heuristics provide transparent and stable baselines but suffer from structural rigidity. Adaptive threshold policies substantially improve coordination, with evolutionary search yielding the strongest performance among structured approaches. The reinforcement learning controller achieves the best overall outcomes by learning a nonlinear state–action mapping that sharply reduces backlog and service shortfalls while maintaining comparable working capital exposure. These gains arise from improved coordination across operational and financial decisions rather than single-metric optimisation. Practically, adaptive heuristics offer robust baselines, while learning-based controllers are most valuable in more volatile environments. Full article

(This article belongs to the Special Issue Novel Approaches for Future Supply Chains and Smart Logistics)

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23 pages, 1100 KB

Open AccessArticle

Pob-CFR: A Population-Based Counterfactual Regret Minimization Approach for Strategy Optimization in Two-Player Zero-Sum Imperfect-Information Games

by Lei Zhang, Dingzhong Cai and Xuan Wang

Appl. Sci. 2026, 16(11), 5711; https://doi.org/10.3390/app16115711 - 5 Jun 2026

Viewed by 254

Abstract

Sequentialdecision-making under imperfect information is naturally modeled as an extensive-form game, where the Nash equilibrium serves as the predominant solution concept for two-player zero-sum settings. Counterfactual regret minimization (CFR) is a widely used framework for this purpose, iteratively reducing regret through regret matching [...] Read more.

Sequentialdecision-making under imperfect information is naturally modeled as an extensive-form game, where the Nash equilibrium serves as the predominant solution concept for two-player zero-sum settings. Counterfactual regret minimization (CFR) is a widely used framework for this purpose, iteratively reducing regret through regret matching so that the average strategy approaches a Nash equilibrium. However, the convergence efficiency of CFR remains a practical challenge. In this work, we refine and reformulate an advantage-based exponential weighting scheme, Exponential CFR (ExpCFR), which accelerates convergence by allocating greater attention to highly profitable actions during the regret-accumulation process. Building on this heuristic, we further introduce Pob-CFR, a framework that integrates population-based evolutionary training with CFR. Pob-CFR maintains a diverse population of heterogeneous CFR variants, periodically evaluating them by exploitability and replacing underperforming individuals with the elite to synchronize strategy exploration. Systematic evaluations across five benchmark games demonstrate that these methods accelerate early-to-mid convergence compared to standard CFR baselines. Furthermore, within the evaluated benchmarks, the relative advantage of the population-based architecture appears more evident in the games with larger strategic complexity. Full article

(This article belongs to the Special Issue Advances in Intelligent Decision-Making Systems)

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31 pages, 6830 KB

Open AccessArticle

ACTA-AOD: Asymmetric Convolution–Triple Attention Network for Non-Uniform Single-Image Dehazing via Windowed Efficient Multi-Scale Attention

by Yuanying Zhang, Fuxing Yu and Yina Suo

Appl. Sci. 2026, 16(11), 5710; https://doi.org/10.3390/app16115710 - 5 Jun 2026

Viewed by 193

Abstract

Single image dehazing remains a fundamental challenge in computer vision due to the ill-posed nature of the inverse problem and the spatial heterogeneity of real atmospheric haze. Existing convolutional approaches suffer from two structural deficiencies: bounded receptive fields that fail to model large-scale [...] Read more.

Single image dehazing remains a fundamental challenge in computer vision due to the ill-posed nature of the inverse problem and the spatial heterogeneity of real atmospheric haze. Existing convolutional approaches suffer from two structural deficiencies: bounded receptive fields that fail to model large-scale haze gradients, and isotropic kernels insensitive to the directional patterns of atmospheric scattering. This paper proposes ACTA-AOD, a lightweight end-to-end dehazing network that addresses both limitations within a unified framework built upon the AOD-Net K-parameterization. The network integrates two complementary modules: (1) W-EMSAv2, a windowed efficient multi-scale attention module that reduces attention complexity from

O (N^{2} C)

to

O (N M^{2} C / 4)

while preserving full-spectrum spatial information through pixel-shuffle reconstruction; and (2) the ACTA Fusion module, which combines structural-reparameterization-based asymmetric convolution with cross-dimensional Triple Attention for direction-sensitive local detail recovery at zero inference-time overhead. On the RESIDE benchmark, ACTA-AOD achieves peak signal-to-noise ratio (PSNR) of 26.02 dB and structural similarity index measure (SSIM) of 0.910 on indoor synthetic data, and 26.13 dB/0.910 on outdoor synthetic data, surpassing the AOD-Net baseline by

+ 3.41

dB (indoor) and

+ 3.58

dB (outdoor) in PSNR, and exceeding the strongest learning-based baseline (AECRNet, CVPR 2021) by

+ 1.17

dB (indoor) and

+ 1.75

dB (outdoor). The model processes images at 81 frames per second on a single GPU. Ablation studies and stratified robustness evaluation across five haze density levels confirm the complementary, synergistic contribution of each module. Full article

(This article belongs to the Special Issue Intelligence Image Processing and Patterns Recognition)

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40 pages, 2155 KB

Open AccessReview

Cutaneous Thermography in the Diagnosis and Management of Arthropathies: Pathophysiology, Diagnostic Pathways, and Multimodal Imaging Correlations

by Constantin-Adrian Andrei, Serban Dragosloveanu, Alex-Gabriel Grigore, Iosif-Aliodor Timofticiuc, Rares-Mircea Birlutiu, Catalin Anghel, Adelina-Elena Moise, Mihai Emanuel Gherghe, Łukasz Pulik, Adrian Iftime, Romica Cergan, Constantin Caruntu and Cristian Scheau

Appl. Sci. 2026, 16(11), 5709; https://doi.org/10.3390/app16115709 - 5 Jun 2026

Cited by 1 | Viewed by 326

Abstract

Background: Arthropathies are a substantial source of global morbidity and healthcare costs, and there is a clinical need for accessible tools capable of detecting inflammatory and metabolic changes beyond conventional structural imaging. This review consolidates the recent evidence on infrared thermography (IRT) [...] Read more.

Background: Arthropathies are a substantial source of global morbidity and healthcare costs, and there is a clinical need for accessible tools capable of detecting inflammatory and metabolic changes beyond conventional structural imaging. This review consolidates the recent evidence on infrared thermography (IRT) as a diagnostic and monitoring adjunct in the major arthropathies. Methods: A structured narrative review was conducted. A literature search of PubMed, Web of Science Core Collection, and Scopus was performed to identify relevant studies published between January 2016 and December 2025 using thermography- and arthropathy-related keywords and controlled-vocabulary terms combined with Boolean operators; only original full-text studies in English published within the previous decade were eligible. The structured search yielded 53 primary studies. Additional sources, including narrative and systematic reviews, methodological references, and book chapters, were drawn upon to inform the Introduction, Discussion, and interpretation but were not included in the primary evidence synthesis. Results: Across the included studies, IRT detected clinically meaningful thermal changes in most cases of osteoarthritis, rheumatoid arthritis, juvenile idiopathic arthritis, Charcot neuroarthropathy, and post-arthroplasty states, with thermal signals correlating moderately with ultrasound-detected synovitis, inflammatory biomarkers, and symptom distribution. Discussion: The evidence base is heterogeneous, however: temperature distributions overlap substantially between patients and controls, well-conducted negative results exist for hand thermography in low-activity rheumatoid arthritis, and reported effect sizes vary widely across devices and protocols. Quantitative thermographic metrics and machine-learning approaches may further refine diagnostic performance and enable remote monitoring. Conclusions: IRT is a promising rapid, non-invasive, radiation-free adjunctive imaging modality, but its clinical adoption is constrained by methodological variability, environmental and vascular confounders, and the absence of prospective validation. Standardised acquisition protocols and prospective multi-site validation are required before routine clinical use. Full article

(This article belongs to the Special Issue Telerehabilitation and Its Therapeutic Applications)

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24 pages, 2598 KB

Open AccessArticle

SAM 2-Assisted Vision Transformer and Morphometric Feature Engineering for Pig Weight Estimation from RGB Images

by Yurui Li, Longhu Ma, Tingting Li, Shengyuan Zhi, Ran Peng, Yan Sun, Mengxin Chen and Jiong Mu

Appl. Sci. 2026, 16(11), 5708; https://doi.org/10.3390/app16115708 - 5 Jun 2026

Viewed by 357

Abstract

Accurate body-weight measurement is important for precision pig farming, but conventional weighing methods are labor-intensive and may disturb normal animal activity. Although three-dimensional sensing systems can provide reliable geometric information, their deployment cost limits large-scale application in commercial farms. This study proposes a [...] Read more.

Accurate body-weight measurement is important for precision pig farming, but conventional weighing methods are labor-intensive and may disturb normal animal activity. Although three-dimensional sensing systems can provide reliable geometric information, their deployment cost limits large-scale application in commercial farms. This study proposes a non-contact pig weight estimation framework based on standard RGB images. The framework combines SAM 2 foreground extraction with a transformer-based dorsal segmentation network to obtain stable body contours under complex farm conditions. Cross-covariance attention and local patch interaction modules are introduced to preserve both global body structure and local boundary details during segmentation. A hybrid loss function combining focal loss and label-distribution-aware margin loss is further adopted to address foreground-background imbalance. After segmentation, 17 morphometric features are extracted from the dorsal region and used for weight prediction with XGBoost regression. Experiments were conducted on the public PIGRGB-Weight dataset containing 12,476 RGB images from 124 pigs. The proposed method achieved a mean absolute error of 2.983 kg and an

R^{2}

value of 0.9891. Compared with a DeepLabV3+-based baseline under the same regression protocol, the proposed framework reduced the prediction error by 24.1%. The results indicate that improving dorsal segmentation quality can substantially enhance the stability of morphometric feature extraction from low-cost RGB images. Full article

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

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33 pages, 3813 KB

Open AccessArticle

Ultra-Short-Term Power Load Forecasting Based on Multi-Scale Decomposition Clustering and Heterogeneous Gated Fusion

by Ganglong Duan, Yongcheng Shao, Xinjie Gao, Yujian Mi and Zhenhao Wang

Appl. Sci. 2026, 16(11), 5707; https://doi.org/10.3390/app16115707 - 5 Jun 2026

Viewed by 214

Abstract

Ultra-short-term electricity load forecasting is crucial for real-time power system operation, but its accuracy is limited by the nonstationary and multiscale characteristics of load data. To address this issue, this study proposes a multi-scale decomposition–clustering and heterogeneous gated fusion framework. The original load [...] Read more.

Ultra-short-term electricity load forecasting is crucial for real-time power system operation, but its accuracy is limited by the nonstationary and multiscale characteristics of load data. To address this issue, this study proposes a multi-scale decomposition–clustering and heterogeneous gated fusion framework. The original load sequence is decomposed by ICEEMDAN and then grouped into high-, mid-, and low-frequency components using K-means clustering. MS-gTCN is used to capture high-frequency fluctuations, adaptive DLinear is used to model low-frequency trends, and a gated fusion mechanism is designed for mid-frequency components. A lightweight error correction network is further introduced to reduce residual prediction errors. Experiments on two real-world datasets show that the proposed method achieves the best performance across 1-, 4-, 8-, and 12-step horizons. For the 12-step task, it reduces MAE by 29.3% on Dataset A and 26.2% on Dataset B compared with the second-best baselines. Compared with ICEEMDAN-LSTM on Dataset A, it reduces MAE by 17.7% and improves

R^{2}

from 0.9127 to 0.9418. Ablation, sensitivity, significance, and complexity analyses further verify the effectiveness, robustness, and real-time feasibility of the proposed framework. Full article

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29 pages, 2508 KB

Open AccessArticle

Effects of Target Material Properties on Acceleration Characteristics During Sequential Multiple-Target Impacts Based on Quantitative Prediction Models

by Huifa Shi, Feiyin Li, Kunming Jia, Shaojie Ma and Xinping Zhang

Appl. Sci. 2026, 16(11), 5706; https://doi.org/10.3390/app16115706 - 5 Jun 2026

Cited by 1 | Viewed by 186

Abstract

To address the damage and failure of electromechanical structures such as Printed Circuit Board (PCB) modules and battery assemblies under multiple impacts, this study combined experimental and modeling approaches to quantitatively investigate the influence of target material mechanical properties on impact acceleration characteristics. [...] Read more.

To address the damage and failure of electromechanical structures such as Printed Circuit Board (PCB) modules and battery assemblies under multiple impacts, this study combined experimental and modeling approaches to quantitatively investigate the influence of target material mechanical properties on impact acceleration characteristics. Quasi-static tensile/compression tests, split-Hopkinson pressure bar dynamic compression tests, and sequential multiple-target impact experiments were conducted on nine metallic materials, providing constitutive parameters and impact response data. Variance analysis revealed that material type significantly affected acceleration characteristics (p ≤ 1.62 × 10⁻⁵), whereas the target position in the impact sequence was statistically insignificant (p ≥ 0.89). Quantitative prediction models were established for different acceleration characteristics: Ridge regression (α = 0.1) was employed for Peak 1–Peak 3, Duration 1, and Duration 3, while linear regression was used for Duration 2. The results quantitatively demonstrated that the elastic modulus was positively associated with both peak acceleration and duration, while dynamic compressive yield strength exhibited a significant negative influence. This work establishes a preliminary quantitative predictive framework that provides guidance for target material selection in sequential multiple-target impact experiments and offers an experimental approach for generating tunable overload responses in high-intensity impact testing of electromechanical components. Full article

(This article belongs to the Section Mechanical Engineering)

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36 pages, 1305 KB

Open AccessArticle

Multi-ROI Multimodal 3D Vision Transformer for Alzheimer’s Disease Classification with Attention-Based Interpretability

by Juan A. Castro-Silva, María N. Moreno-García and Diego H. Peluffo-Ordóñez

Appl. Sci. 2026, 16(11), 5705; https://doi.org/10.3390/app16115705 - 5 Jun 2026

Viewed by 246

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within [...] Read more.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within a unified attention-based framework. The proposed approach leverages anatomically guided multi-region-of-interest (ROI) decomposition to focus on disease-relevant brain structures, including the hippocampus, entorhinal cortex, fornix, and major cortical lobes. Each ROI is encoded using 3D tubelet embeddings, while clinical and volumetric features are transformed into feature-wise tokens, enabling seamless multimodal fusion through self-attention mechanisms. A hemisphere-aware selection strategy is introduced to identify the most discriminative ROI representations, enhancing both performance and interpretability. The model is evaluated on a merged multi-cohort dataset combining ADNI, AIBL, and OASIS using a 7-fold cross-validation protocol. Experimental results demonstrate that the proposed method achieves high classification performance, reaching an accuracy of 97.62% and an AUC of 0.9940, outperforming single-modality and whole-brain baselines. Furthermore, attention-based analysis provides interpretable insights into the relative importance of clinical and neuroanatomical features, revealing consistency with established AD biomarkers. These findings highlight the effectiveness of multimodal integration and ROI-based representation for robust and explainable AD classification. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)

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46 pages, 958 KB

Open AccessReview

Reimagining Transportation Infrastructure for the Autonomous Era: A Comprehensive Review

by Saki Rezwana, Rawja Alam, Devin Rhoads and Nicholas Lownes

Appl. Sci. 2026, 16(11), 5704; https://doi.org/10.3390/app16115704 - 5 Jun 2026

Viewed by 243

Abstract

Autonomous vehicles (AVs) are expected to reshape transportation systems by influencing roadway design, traffic operations, digital infrastructure, public transport integration, and regulatory frameworks. The implications of AV deployment extend well beyond vehicle technology itself and raise broader questions for infrastructure planning, safety governance, [...] Read more.

Autonomous vehicles (AVs) are expected to reshape transportation systems by influencing roadway design, traffic operations, digital infrastructure, public transport integration, and regulatory frameworks. The implications of AV deployment extend well beyond vehicle technology itself and raise broader questions for infrastructure planning, safety governance, and urban policy. This study presents a comprehensive literature review of AV impacts on transportation infrastructure and planning, with the aim of synthesizing current knowledge across physical, operational, digital, and policy dimensions. A structured concept-centric review approach, informed by PRISMA-based screening procedures, was used to identify and analyze relevant studies published between 2010 and 2025. The review covers major themes including intersections, geometric design, pavement and bridge performance, parking, signage and lane markings, communication networks, traffic efficiency, lane-changing behavior, safety, public acceptance, public transport, mobility patterns, maintenance, testing, deployment, liability, and insurance. The synthesis indicates that AVs may improve traffic flow, reduce some forms of human-error-related risk, and alter long-standing assumptions in infrastructure design through enhanced sensing, coordination, and connectivity. Simultaneously, these benefits remain highly conditional on penetration rates, mixed-traffic interactions, infrastructure quality, cybersecurity, regulatory readiness, and public trust. The review further shows how AV adoption is likely to shift transportation planning away from purely human-centered geometric and operational design toward more integrated, digitally supported, and system-level approaches. This synthesis highlights the need for adaptive planning frameworks that can respond to transitional traffic conditions while addressing equity, safety, governance, and infrastructure resilience in the autonomous era. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Mobility)

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28 pages, 10714 KB

Open AccessArticle

An Adaptive Rotation Operation Strategy for Photovoltaic Hydrogen Production Systems Based on a Composite Degradation Model of Electrolyzer Clusters

by Jiasheng Wang, Pengcheng Zhao, Jun Yang, Haiting Xia and Jingang Wang

Appl. Sci. 2026, 16(11), 5703; https://doi.org/10.3390/app16115703 - 5 Jun 2026

Viewed by 178

Abstract

Alkaline water electrolysis has become a major technology for large-scale photovoltaic (PV) hydrogen production due to its maturity and low cost. However, PV power fluctuations can cause short-term load imbalances and long-term degradation imbalances in alkaline water electrolyzer (AWE) clusters. To address this [...] Read more.

Alkaline water electrolysis has become a major technology for large-scale photovoltaic (PV) hydrogen production due to its maturity and low cost. However, PV power fluctuations can cause short-term load imbalances and long-term degradation imbalances in alkaline water electrolyzer (AWE) clusters. To address this problem, this paper proposes an adaptive rotation operation strategy based on a composite degradation model. The model considers energy throughput, hot starts, cold starts, and low-load operation to characterize the relative degradation stress of AWEs under fluctuating PV input. Based on this model, virtual rotation is first used to redistribute power among online AWEs, while physical rotation is performed when necessary according to optimal start–stop decisions. A PV hydrogen production experimental platform is built to verify the feasibility of power redistribution, physical rotation, and load balancing. The measured PV power curve is further used for simulation–experiment comparison, and the results show that the model can capture the main operating process of the PV hydrogen production system. Large-scale simulation results show that, compared with S1, S2, and S4, the proposed strategy increases PV utilization by 8.13%, 4.91%, and 2.85%, improves system efficiency by 5.42%, 3.10%, and 1.56%, reduces start–stop cycles by 12.16%, 8.49%, and 3.39%, reduces the average composite degradation index by 29.4%, 21.4%, and 10.1%, and reduces the composite degradation imbalance index by 56.9%, 40.4%, and 22.2%, respectively. The proposed strategy can improve PV utilization and system efficiency while reducing start–stop frequency and degradation imbalances among AWEs. Full article

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

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17 pages, 642 KB

Open AccessReview

Purification and Detection of Bacterial Endospores: Current Methods and Challenges

by Souichirou Kawai

Appl. Sci. 2026, 16(11), 5702; https://doi.org/10.3390/app16115702 - 5 Jun 2026

Viewed by 310

Abstract

Bacterial endospores are highly resistant, dormant forms that pose persistent challenges in food safety, environmental microbiology, and industrial hygiene. Accurate evaluation of endospore resistance, physiology, and inactivation depends on both purification and detection methods; however, these processes are typically examined independently, limiting methodological [...] Read more.

Bacterial endospores are highly resistant, dormant forms that pose persistent challenges in food safety, environmental microbiology, and industrial hygiene. Accurate evaluation of endospore resistance, physiology, and inactivation depends on both purification and detection methods; however, these processes are typically examined independently, limiting methodological consistency and contributing to variability across studies. In this review, current approaches for endospore purification and detection are critically examined, including washing-based methods, density gradient centrifugation, enzymatic treatments, culture-based enumeration, molecular assays, flow cytometry, and emerging biosensor technologies. In addition, these methods are compared using metrics such as purity, recovery yield, sensitivity, and specificity, and their advantages and limitations are summarized to clarify performance. It is further proposed that endospore purification and detection should be considered as a single, end-to-end analytical workflow and optimized accordingly. Purification strategies influence sample cleanliness and aspects of endospore quality, including viability, structural integrity, and physiological state, which affect detection performance and quantitative accuracy. Based on this integrated perspective, a conceptual framework linking purification efficiency to detection outcomes is presented, along with practical considerations for method selection across relevant application contexts. Finally, gaps in standardization are identified, and future research directions are outlined to improve reproducibility and cross-study comparability in endospore-related studies. Full article

(This article belongs to the Special Issue Innovative Perspectives on Food Microbiology and Biotechnology)

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33 pages, 406233 KB

Open AccessArticle

Early Identification of Geological Hazards for Oil and Gas Pipelines Based on SBAS-InSAR and GIS

by Minghao Gao, Jian Liang, Jian Ai, Zhongdi Liu and Xingwei Ren

Appl. Sci. 2026, 16(11), 5701; https://doi.org/10.3390/app16115701 - 5 Jun 2026

Viewed by 252

Abstract

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional [...] Read more.

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

(This article belongs to the Special Issue Geological Disasters: Mechanisms, Detection, and Prevention)

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22 pages, 4500 KB

Open AccessArticle

Research on Cooling and Hazardous Gas Dilution Performance of Underground Mining Culvert Ventilation System

by Yexian Liu, Zhenlei Zhu, Hongtao Wang, Zhaobiao Luan, Delong Meng, Qiang Li, Zhenneng Lu and Cantao Ye

Appl. Sci. 2026, 16(11), 5700; https://doi.org/10.3390/app16115700 - 5 Jun 2026

Viewed by 175

Abstract

The ventilation system of a mine determines the comfort and safety of the underground working environment. Although many studies have been devoted to reducing the impact of underground heat damage, there are still few comprehensive studies or optimizations aimed at simultaneously considering heat [...] Read more.

The ventilation system of a mine determines the comfort and safety of the underground working environment. Although many studies have been devoted to reducing the impact of underground heat damage, there are still few comprehensive studies or optimizations aimed at simultaneously considering heat damage prevention and control, exhaust of mechanical equipment, and methane leakage. To address this knowledge gap, a mine ventilation model was built and validated to analyze the impact of different numbers of top fans on the distribution characteristics of temperature and gas mass fraction. Subsequently, the impact of different blowing duct inlet temperatures and velocities on the capacity to cool and dilute hazardous gases was investigated. Finally, a comprehensive coefficient that removes the effect of dimension was proposed for evaluating the cooling and dilution performance of different top fan cases. The results show that a top fan is the most advantageous for cooling the mine, but has a poor ability to dilute hazardous gases. Three top fans have the best performance for diluting hazardous gases, which leads to some degree of heat diffusion, but obtains the maximum total comprehensive coefficient of 0.71246. Full article

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16 pages, 4016 KB

Open AccessArticle

Form-Stable Phase Change Material Integrated with PVA/CMC-Na Hydrogel for 5 °C Cold Chain Logistics

by Jin-Feng Wang, Xin-Guo Zhang, Xiao-Lin Sun, Da-Zhang Yang and Yuan-Yuan Pan

Appl. Sci. 2026, 16(11), 5699; https://doi.org/10.3390/app16115699 - 5 Jun 2026

Viewed by 210

Abstract

The rapid development of cold chain logistics has generated a strong demand for high-performance phase change materials (PCMs). In this study, a composite PCM (CPCM) applicable to 5 °C cold chain logistics, integrated with PVA/CMC-Na hydrogel to maintain form stability, is developed. N-Tetradecane [...] Read more.

The rapid development of cold chain logistics has generated a strong demand for high-performance phase change materials (PCMs). In this study, a composite PCM (CPCM) applicable to 5 °C cold chain logistics, integrated with PVA/CMC-Na hydrogel to maintain form stability, is developed. N-Tetradecane and water are employed as the primary cold storage media in the composite. Span 80, Tween 80 and borax are introduced into the composite as homogenizing agents and supercooling depressant, respectively. The main preparation steps of the CPCM include aqueous phase preparation, emulsifier compounding, oil-phase preparation, blending, homogenization, and molding, in sequence. Experimental results demonstrate that the CPCM exhibits a phase transition temperature of 0–5 °C, a latent heat of 236.2 J/g, a supercooling degree of no more than 0.5 °C, and a volume expansion ratio of 3%. Therefore, the CPCM is able to satisfy the cold storage demand for cold chain transportation with a target temperature of approximately 5 °C, and can serve as a superior-performance alternative to the PCMs currently used for similar applications in the market. Full article

(This article belongs to the Special Issue Modern Trends and Applications in Thermal Energy Storage)

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17 pages, 11824 KB

Open AccessArticle

Structural Optimization of Expandable Screen Pipe Based on an NSGA-II Algorithm

by Xiaofeng Wang, Dajun Zhao, Weifeng Wan and Shulei Zhang

Appl. Sci. 2026, 16(11), 5698; https://doi.org/10.3390/app16115698 - 5 Jun 2026

Viewed by 128

Abstract

Expansion screen pipe is a new sand control completion technology. In this paper, the optimization design of the slotted structure of the screen pipe is studied. Mechanical models for both seamless base pipes and slotted base pipes have been established based on elastic-plastic [...] Read more.

Expansion screen pipe is a new sand control completion technology. In this paper, the optimization design of the slotted structure of the screen pipe is studied. Mechanical models for both seamless base pipes and slotted base pipes have been established based on elastic-plastic mechanics. The finite element simulation analysis of the expansion process of screen pipes with different slotted structures is carried out, and the relationship among the length of the slot L, the number of slots N, the internal pressure P and the maximum stress at the slot end σ is obtained. The principle and optimization process of the NSGA-II algorithm are introduced. Furthermore, a multi-objective optimization model is formulated, taking P and σ as the objective functions, and the NSGA-II algorithm is applied to optimize this model. By analyzing the obtained optimization results for the screen pipe with an outer diameter of 68 mm, the optimal slotting parameters are L = 137 mm and n = 12. For the optimal slotted structure obtained, P is greater than the minimum expansion initiation pressure, and σ is less than the ultimate tensile strength, which indicates that the optimal solution meets the requirements of both theoretical analysis and engineering practice. In this paper, the NSGA-II multi-objective optimization algorithm is introduced into screen pipe structure optimization for the first time. The structural optimization method of expandable screen pipe proposed in this paper improves the design level and efficiency of the screen pipe. At the same time, it provides a new means for research on screen pipe structures in the fields of petroleum and exploration. Full article

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29 pages, 4728 KB

Open AccessArticle

Probabilistic Assessment of Downtime-Related Energy-Service Unavailability, Production Loss and Economic Impact in Continuous Material-Handling Systems

by Maksym Mykhei, Daniela Marasová, Jr., Bohdana Bobinics, Daniela Marasová, Marcela Taušová and Dušan Kudelas

Appl. Sci. 2026, 16(11), 5697; https://doi.org/10.3390/app16115697 - 5 Jun 2026

Viewed by 215

Abstract

Continuous industrial material-handling systems are operationally and energy-intensive technological structures in which downtime affecting one equipment group can reduce the availability of the entire production chain. This study develops a probabilistic framework for assessing downtime impacts when detailed historical event-level downtime records are [...] Read more.

Continuous industrial material-handling systems are operationally and energy-intensive technological structures in which downtime affecting one equipment group can reduce the availability of the entire production chain. This study develops a probabilistic framework for assessing downtime impacts when detailed historical event-level downtime records are available, but complete technical and economic equipment parameters are missing. The analysis is based on 6605 downtime records for conveyors, excavators and stackers observed between 2017 and 2025. Historical downtime records were combined with interval-based assumptions for power demand, load factor, handling capacity, electricity price and commodity value, and were propagated through a Monte Carlo simulation with 10,000 iterations. The results revealed a strong concentration of downtime burden. The combination of P–Conveyor–Material Collapse accounted for 32.58% of total downtime, while the top five equipment–fault combinations explained 67.86% of cumulative downtime. At the system level, the median modelled energy-service unavailability reached approximately 4339 MWh, the median production-loss equivalent reached approximately 9279 kt, and the median total economic loss was approximately EUR 209.5 million. The proposed Energy–Economic Impact Index integrated event frequency, downtime severity, energy-service unavailability and economic loss into a single maintenance-prioritisation indicator. The highest-ranked maintenance target was P–Conveyor–Material Collapse, confirming that maintenance priorities should be determined by combined operational, energy-related and economic consequences rather than by event frequency alone. The study demonstrates that historical downtime records can be transformed into a probabilistic decision-support tool for risk-based maintenance planning in industrial systems with incomplete technical and economic data. Full article

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

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20 pages, 10509 KB

Open AccessArticle

A Geometry-Aware Deep Learning Framework for Atmospheric Phase Screen Denoising in SAR Interferograms

by Panpan Tang, Bo Zhao, Xiaogang Song and Yanyan Luo

Appl. Sci. 2026, 16(11), 5696; https://doi.org/10.3390/app16115696 - 5 Jun 2026

Viewed by 183

Abstract

A geometry-aware deep learning framework for the reduction of atmospheric noise in SAR (Synthetic Aperture Radar) interferograms has been proposed and validated in this study. Our model has obvious advantages over existing ones in the following three aspects: (1) our objective is to [...] Read more.

A geometry-aware deep learning framework for the reduction of atmospheric noise in SAR (Synthetic Aperture Radar) interferograms has been proposed and validated in this study. Our model has obvious advantages over existing ones in the following three aspects: (1) our objective is to reconstruct the original SAR imagery using an autoencoder and then eliminate noise by subtracting the reconstructed data from the raw data. However, our network architecture is not symmetric, and we choose to employ HRNet-w32 to preserve the details of the input dataset. (2) A deep supervision module equipped with diverse feature-unleashing mechanisms (including geometric, multispectral, and sematic features) is also developed to enhance the model’s predictive capability and interpretability. (3) We emphasize the significance of fractal geometry and variogram inference in the loss function, given that atmospheric disturbances, specifically humidity, clouds, and fogs, often exhibit statistically fractal characteristics. Compared with existing methods and ablation studies, our framework achieves relatively robust APS suppression performance across multiple quantitative metrics, including the Mean Squared Error (MSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Coefficient of Correlation (CoC), with improvements of at least 5.0% over the baselines. Full article

(This article belongs to the Topic Big Data and AI for Geoscience)

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