Applied Sciences

30 pages, 7627 KB

Open AccessArticle

An Experimental and Numerical Simulation Study on a Three-Hydraulic-Cylinder Synchronous Steering Offset Actuator Driven by a Drilling Fluid Rotary Valve Distributor

by Junfeng Kang, Gonghui Liu, Tian Chen, Chunqing Zha, Wei Wang and Lincong Wang

Appl. Sci. 2026, 16(7), 3612; https://doi.org/10.3390/app16073612 - 7 Apr 2026

Viewed by 792

Abstract

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations [...] Read more.

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator. Full article

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

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24 pages, 2876 KB

Open AccessArticle

High-Performance Computing Optimization of the Maxwell–Stefan Diffusion Model in OpenFOAM

by Zixin Chi, Xin Hui and Bosen Wang

Appl. Sci. 2026, 16(7), 3611; https://doi.org/10.3390/app16073611 - 7 Apr 2026

Viewed by 713

Abstract

Multicomponent diffusion modeling based on the Maxwell–Stefan formulation is widely used in high-fidelity combustion simulations due to its superior physical accuracy compared with simplified diffusion models. However, the computational complexity of the Maxwell–Stefan model, which arises from the solution of coupled multicomponent transport [...] Read more.

Multicomponent diffusion modeling based on the Maxwell–Stefan formulation is widely used in high-fidelity combustion simulations due to its superior physical accuracy compared with simplified diffusion models. However, the computational complexity of the Maxwell–Stefan model, which arises from the solution of coupled multicomponent transport equations, becomes a major performance bottleneck in large-scale CFD simulations. In this work, a high-performance computing optimization strategy for the Maxwell–Stefan diffusion model is developed within the OpenFOAM framework. The proposed method improves computational efficiency through block-based computation and vectorization-oriented data organization to better exploit modern CPU architectures and SIMD instruction capabilities. The optimized implementation enhances memory locality, increases data reuse efficiency, and reduces cache miss penalties. Numerical validation is performed using two-dimensional laminar counterflow flame cases and ammonia–hydrogen turbulent combustion cases, including both premixed and non-premixed jet flames. Results demonstrate that the optimized Maxwell–Stefan implementation preserves numerical accuracy while significantly improving computational performance. Speedups of 2.5×–4.5× are achieved depending on the number of chemical species. The developed approach provides an efficient solution for detailed combustion simulations involving large chemical mechanisms. The test cases and source code are openly shared. Full article

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28 pages, 3267 KB

Open AccessArticle

A Hierarchical Dynamic Path Planning Framework for Autonomous Vehicles Based on Physics-Informed Potential Field and TD3 Reinforcement Learning

by Yan Pan, Yu Wang and Bin Ran

Appl. Sci. 2026, 16(7), 3610; https://doi.org/10.3390/app16073610 - 7 Apr 2026

Cited by 1 | Viewed by 902

Abstract

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This [...] Read more.

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This paper proposes PIPF-TD3, which integrates APF theory with the Twin Delayed Deep Deterministic Policy Gradient (TD3) by embedding composite potential values and Doppler-weighted gradients as physics-informed features into the state vector. A Hybrid A* planner generates a reference path encoded as an attractive field; repulsive fields model nearby obstacles using real-time perception data; and a multi-objective reward function jointly optimizes path tracking, collision avoidance, and ride comfort. Experiments in CARLA 0.9.14 across two scenarios—a highway segment with mixed obstacles and a signalized intersection with conflicting turning movements—show that PIPF-TD3 achieves 100% task completion with zero collisions, whereas TD3 without potential field guidance suffers a 90% collision rate. PIPF-TD3 reduces mean cross-track error to 0.12 m (72.1% reduction over the rule-based FSM baseline), maintains 67.0% larger safety clearance, and yields RMS longitudinal and lateral accelerations of 1.12 and 0.75 m/s², outperforming the FSM by 37.1% and 42.7%. These results confirm that Doppler-weighted physical priors substantially enhance RL-based driving safety and quality in complex traffic conditions. Full article

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

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25 pages, 6398 KB

Open AccessArticle

StageAttn-VTON: Stage-Wise Flow Deformation with Attention for High-Resolution Virtual Try-On

by Li Yao, Wenhui Liang and Yan Wan

Appl. Sci. 2026, 16(7), 3609; https://doi.org/10.3390/app16073609 - 7 Apr 2026

Viewed by 556

Abstract

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often [...] Read more.

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article

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26 pages, 2871 KB

Open AccessArticle

Instability Mechanism of Voussoir Beam and Roof-Cutting Pressure Relief in Parallel Goaf: A Case Study of Shiyangou Coal Mine

by Jie Zhang, Chu Zhang, Tao Yang, Bin Wang, Shoushi Gao, Guang Qin, Jianping Sun, Yiming Zhang, Xiaogang Zhang and Zhengyang Fan

Appl. Sci. 2026, 16(7), 3608; https://doi.org/10.3390/app16073608 - 7 Apr 2026

Viewed by 691

Abstract

During coal mining, parallel voids ahead of an advancing working face often trigger intense dynamic loading and structural instability, posing significant risks to operational safety. Using the 43,201 working face of the Shiyangou Coal Mine as a case study, this research investigates the [...] Read more.

During coal mining, parallel voids ahead of an advancing working face often trigger intense dynamic loading and structural instability, posing significant risks to operational safety. Using the 43,201 working face of the Shiyangou Coal Mine as a case study, this research investigates the mechanisms of surrounding rock instability and proposes an integrated synergistic control strategy. Based on voussoir beam theory, a mechanical model of the roof structure—incorporating the nonlinear coupling between the gangue and immediate roof—was developed to establish the critical thresholds for the rotational instability of key blocks. Analytical results indicate that the limit breaking distance for “Key Block B” in the main roof is 24.49 m, which defines the primary zone for advanced reinforcement and hazard prevention. Furthermore, applying short-arm beam theory, this study clarifies how pre-split roof cutting disrupts the transmission of advance abutment pressure, identifying 8° as the optimal cutting angle. Building on these insights, a multi-faceted control system was implemented, combining hydraulic fracturing for pressure relief, pumpable backfill pillars, and an artificial false roof (utilizing a suspended I-beam structure 1.2 m above the floor). Field monitoring confirms that this collaborative approach effectively stabilizes the surrounding rock, ensuring the safe and continuous passage of the working face through parallel void areas. Full article

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48 pages, 13934 KB

Open AccessArticle

Static and Dynamic Properties of Organic Soils Stabilized with Nano-Silica and Sand

by Gaoliang Tao, Ning Yang, Shaoping Huang, Qingsheng Chen and Eihui Guo

Appl. Sci. 2026, 16(7), 3607; https://doi.org/10.3390/app16073607 - 7 Apr 2026

Viewed by 572

Abstract

The stabilization of soft, organic-rich soils with cement is often hindered by retarded hydration and poor long-term performance under cyclic loads. While nano-silica or sand are known modifiers, their individual efficacy in high-organic environments remains limited, and a systematic comparison of their composite [...] Read more.

The stabilization of soft, organic-rich soils with cement is often hindered by retarded hydration and poor long-term performance under cyclic loads. While nano-silica or sand are known modifiers, their individual efficacy in high-organic environments remains limited, and a systematic comparison of their composite effect across different soil types is lacking. This study investigates the synergistic enhancement of cement-stabilized soils using a combined nano-SiO₂ and sand composite, comparing its effectiveness in high-organic soft soil and low-organic clay. Laboratory tests, including unconfined compressive strength (UCS), cyclic loading, scanning electron microscopy (SEM), and X-ray diffraction (XRD), were conducted. Results showed a stark contrast in 28-day UCS between unmodified soft soil cement (0.13 MPa) and clay cement (1.04 MPa). The optimal composite of 3.5% nano-SiO₂ and 40% sand increased the 28-day UCS to 1.39 MPa for soft soil (a 969% improvement) and 5.51 MPa for clay (a 430% improvement), respectively. Notably, under a cyclic stress ratio (CSR) of 0.7~0.8, unmodified specimens failed after fewer than 120 load cycles, whereas the composite-modified soils withstood 20,000 cycles without failure, demonstrating exceptional fatigue resistance independent of static strength gain. Microstructural analysis revealed that the composite effectively promoted the formation of cementitious hydration products, counteracting the inhibitory effect of organic matter. This research demonstrates that the nano-silica sand composite provides a superior and more broadly applicable improvement for cement-stabilized soils across the tested organic content range (3.3–7.7% LOI) compared to single-additive approaches, significantly enhancing both mechanical strength and long-term durability. Full article

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17 pages, 3101 KB

Open AccessArticle

Study on the Evolution Law of Fracture Seepage Behavior of Granite Under High Temperature and High Pressure

by Zimin Zhang, Zijun Feng, Peihua Jin, Weitao Yin and Guo Xu

Appl. Sci. 2026, 16(7), 3606; https://doi.org/10.3390/app16073606 - 7 Apr 2026

Viewed by 486

Abstract

With the continuous development of drilling and reservoir stimulation technologies, the drilling depth of enhanced geothermal system projects is getting deeper and deeper, and the surrounding rock stress of dry hot rock reservoirs is also increasing. Therefore, it has become an inevitable demand [...] Read more.

With the continuous development of drilling and reservoir stimulation technologies, the drilling depth of enhanced geothermal system projects is getting deeper and deeper, and the surrounding rock stress of dry hot rock reservoirs is also increasing. Therefore, it has become an inevitable demand for geothermal exploitation to study the evolution law of fracture seepage characteristics of granite under high temperature and ultra-high pressure. To reveal the evolutionary patterns of seepage characteristics in deep-seated hot dry rock fractures, an independently developed ultra-high pressure rock triaxial mechanical testing system was employed to investigate the seepage characteristics of fractured granite under varying temperatures (25–150 °C) and triaxial stresses (50–100 MPa). The study explores the influence of temperature on the seepage characteristics of granite fractures under ultra-high triaxial stress conditions. The results indicate that: (1) In the temperature range of 25–125 °C, as the rock temperature increases, the permeability of the Specimens showed a continuously decreasing trend due to the effect of thermal expansion. (2) In the temperature range of 125–150 °C, as the rock temperature increases, the permeability continues to decrease under low triaxial stress (50 MPa). However, under high triaxial stress (75 MPa) and extremely high triaxial stress (100 MPa), the permeability shows a slight increase instead. This phenomenon is attributed to free surface dissolution. (3) Quantitative analysis of the mesoscopic morphological data of the rock fracture surfaces after testing, combined with SEM images from scanning electron microscopy, confirms that within the high-temperature range of 125–150 °C, the differing levels of triaxial stress determine the variation in the dominant mechanism governing the evolution of the Specimen fracture surfaces, which in turn leads to the divergence in the trend of their permeability changes. Full article

(This article belongs to the Section Earth Sciences)

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25 pages, 3942 KB

Open AccessArticle

Deep Reinforcement Learning-Based Scheduling for an Electric–Hydrogen Integrated Station Using a Data-Driven Electrolyzer Model

by Dongdong Li, Liang Liu and Haiyu Liao

Appl. Sci. 2026, 16(7), 3605; https://doi.org/10.3390/app16073605 - 7 Apr 2026

Viewed by 791

Abstract

To address the inaccurate scheduling of electric–hydrogen integrated stations (EHISs) caused by the limited accuracy of conventional mechanistic models for proton exchange membrane (PEM) electrolyzers, this study proposes a deep reinforcement learning (DRL)-based scheduling strategy incorporating a data-driven electrolyzer model. First, a deep [...] Read more.

To address the inaccurate scheduling of electric–hydrogen integrated stations (EHISs) caused by the limited accuracy of conventional mechanistic models for proton exchange membrane (PEM) electrolyzers, this study proposes a deep reinforcement learning (DRL)-based scheduling strategy incorporating a data-driven electrolyzer model. First, a deep XGBoost model is developed to characterize the hydrogen production behavior of the PEM electrolyzer, thereby replacing the traditional mechanistic model and reducing prediction errors. Second, the EHIS scheduling problem is formulated as a constrained Markov decision process (CMDP) that explicitly considers user demand and carbon emission constraints. Third, an improved deep Q-network (DQN) algorithm integrating Lagrangian relaxation and the template policy-based reinforcement learning (TPRL) method is designed to solve the scheduling problem, which enhances convergence speed and generalization performance under similar operating scenarios. The simulation results demonstrate that the proposed method can effectively alleviate the decision-making risks introduced by model inaccuracies and significantly improve the operational profitability of the station while satisfying user demand and carbon emission constraints. Full article

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5 pages, 154 KB

Open AccessEditorial

Editorial for the First Edition of the Special Issue “Risk and Safety of Maritime Transportation”

by Andrea Maternová

Appl. Sci. 2026, 16(7), 3604; https://doi.org/10.3390/app16073604 - 7 Apr 2026

Cited by 1 | Viewed by 439

Abstract

“Shipping is perhaps the most international of all the world’s great industries and one of the most dangerous [...] Full article

(This article belongs to the Special Issue Risk and Safety of Maritime Transportation)

27 pages, 9320 KB

Open AccessArticle

A Study of the Groove Geometry Effects on the Performance of Water-Lubricated Rubber Journal Bearings

by Ahmad Golzar Shahri, Asghar Dashti Rahmatabadi, Mahdi Zare Mehrjardi and Mehrdad Rabani

Appl. Sci. 2026, 16(7), 3603; https://doi.org/10.3390/app16073603 - 7 Apr 2026

Viewed by 634

Abstract

This study aims to investigate the static performance of water-lubricated rubber bearings (WLRBs) with axial grooves. To achieve this objective, an analytical approach is employed that combines a modified Reynolds equation, accounting for surface groove effects and rubber deformation, with a Winkler model [...] Read more.

This study aims to investigate the static performance of water-lubricated rubber bearings (WLRBs) with axial grooves. To achieve this objective, an analytical approach is employed that combines a modified Reynolds equation, accounting for surface groove effects and rubber deformation, with a Winkler model and finite element analysis of pressure distribution. By developing a fluid–structure interaction model that incorporates rubber liner deformation, this research reveals the interaction between WLRB geometry and steady-state performance parameters. The investigation evaluates the influence of geometric characteristics, including groove shape, number, and size, on the performance of elastomeric liner WLRBs, while assessing optimal groove depths under various conditions. The study analyzes five distinct groove geometries, including semi-cylindrical, rectangular prism, and three pyramidal types with different apex positions, in a six-groove bearing configuration, presenting their qualitative effects on the behavior of the examined bearings. The key findings indicate that increasing groove size or quantity reduces maximum pressure and load-carrying capacity while elevating friction coefficients. As groove count rises, supporting surfaces diminish, causing pressure distribution to intensify and minimum film thickness to decrease under a specified external load. A notable result reveals that when groove depth exceeds film thickness, performance becomes geometry-independent; however, shallower grooves exhibit significant geometric effects. Additionally, the study identifies groove ends as critical functional zones where film thickness reduction substantially enhances pressure distribution and static performance. Comparative analysis shows that longitudinal grooves with triangular cross sections outperform semi-circular and rectangular variants, with the backward triangular configuration demonstrating superior characteristics due to optimal end-film properties. In conclusion, this research provides a detailed understanding of how groove geometry influences the static performance of WLRBs, highlighting the importance of groove design, particularly at the groove ends, in optimizing bearing functionality. The findings offer valuable insights for the design and selection of groove configurations in water-lubricated rubber bearing applications. Full article

(This article belongs to the Special Issue Advanced Surface Engineering for Tribological Applications)

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14 pages, 1461 KB

Open AccessArticle

Wild and Cultivated Fabaceae Plants in Livestock Feeding: Chemical Contents and Antiparasitic Activity

by Eleni D. Myrtsi, Epameinondas Evergetis and Serkos A. Haroutounian

Appl. Sci. 2026, 16(7), 3602; https://doi.org/10.3390/app16073602 - 7 Apr 2026

Viewed by 403

Abstract

Parasitic infections constitute a common concern for livestock breeders, since they induce animals with various physiological, behavioral, and neurological alterations. Consequently, they negatively affect their health, productivity, and welfare, leading to reduced productivity and increased mortality, and causing severe economic losses to livestock [...] Read more.

Parasitic infections constitute a common concern for livestock breeders, since they induce animals with various physiological, behavioral, and neurological alterations. Consequently, they negatively affect their health, productivity, and welfare, leading to reduced productivity and increased mortality, and causing severe economic losses to livestock breeders. In the context of recent interest in the development of functional animal feeds and/or feed supplements with potent antiparasitic activity, we exploited the chemical contents and bioactivities of 21 wild and cultivated Fabaceae plant species against Haemonchus contortus and Trichostrongylus colubriformis, two widely prevalent gastrointestinal nematode parasites of small ruminants. The respective results revealed that four wildly grown plants exhibit potent antiparasitic activity, with Lathyrus laxiflorus exerting the most significant protective effect against both parasites tested. This plant was also found to display the highest antioxidant potency and the richest phenolic and tannin contents, with rutin molecules being the most abundant. Moreover, the extracts of 11 cultivated species were found to display potent antiparasitic activity, while Trifolium repens, Medicago sativa, and Lathyrus sativus species were determined to provide higher extraction yields and display the most potent antiparasitic activities. Results herein are indicative of Fabaceae plants’ potential to act against parasitic infections, either as grazing plants in pasture-based systems, or as bioactive dietary supplements in intensive farming systems, eventually contributing to reductions in antiparasitic drug utilization. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

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24 pages, 3925 KB

Open AccessArticle

Personal Identification Using Eye Movements During Manga Reading: Effects of Stimulus Variation and Template Aging

by Yuichi Wada

Appl. Sci. 2026, 16(7), 3601; https://doi.org/10.3390/app16073601 - 7 Apr 2026

Viewed by 624

Abstract

Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich [...] Read more.

Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich behavioral data from diverse reading behaviors. Eye movement data from 59 participants were recorded while they read two manga works on a screen. A comprehensive set of gaze features was extracted and evaluated using five machine learning classifiers, among which Random Forest (RF) consistently achieved the best performance. Under constrained experimental conditions, the RF classifier achieved a Rank-1 identification rate of 95.0% and an equal error rate (EER) of 1.9%. Furthermore, this study systematically investigated two critical challenges for practical deployment: stimulus dependency and template aging. Cross-stimulus evaluation revealed substantial performance degradation when training and testing used different manga works, and template aging analysis over an approximately 90-day interval demonstrated notable declines in identification accuracy. These results provide preliminary evidence supporting the potential of natural reading behaviors for biometric continuous authentication systems while highlighting the need for further research into cross-stimulus generalization and temporal stability. Full article

(This article belongs to the Special Issue Eye Tracking Technology and Its Applications)

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18 pages, 3085 KB

Open AccessArticle

Decentralized Multi-Robot Cooperative Exploration with Convex Polygon Expansion and Hierarchical Frontier Selection

by Dicheng Shen, Jun Hu, Shaohua Chen, Chengwei Zheng, Shunyu Tian and Changyun Wei

Appl. Sci. 2026, 16(7), 3600; https://doi.org/10.3390/app16073600 - 7 Apr 2026

Viewed by 717

Abstract

Cooperative exploration of unknown environments in multi-robot systems poses significant challenges, particularly in terms of efficiency and redundancy. Current approaches primarily rely on centralized systems for target point allocation and the construction of 2D grid maps, which often result in overlapping exploration efforts [...] Read more.

Cooperative exploration of unknown environments in multi-robot systems poses significant challenges, particularly in terms of efficiency and redundancy. Current approaches primarily rely on centralized systems for target point allocation and the construction of 2D grid maps, which often result in overlapping exploration efforts and reduced efficiency. This paper aims to enhance the cooperative behaviors of decentralized multi-robot systems, enabling effective exploration in large-scale and complex scenarios. We propose a decentralized multi-robot cooperative exploration framework that includes: (1) a trajectory-point extraction strategy for sequentially identifying key navigation points, (2) a dynamic convex polygon expansion method for delineating explored regions among robots, and (3) a novel hierarchical frontier selection mechanism to guide robots toward unexplored areas. By integrating these components, our framework enables coordinated exploration through the sharing of information about explored regions. Experimental results demonstrate that our approach reduces exploration time by 61.43% and overall travel distance by 56.14% compared to recent advancements in multi-robot exploration tasks. Full article

(This article belongs to the Section Robotics and Automation)

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28 pages, 346 KB

Open AccessArticle

Drivers’ Safety Perception in Autonomous Vehicle Road Sharing: A Knowledge-Segmented TPB and Ordered Logit Analysis

by Boxin Tang, Qiming Yu and Zhiwei Liu

Appl. Sci. 2026, 16(7), 3599; https://doi.org/10.3390/app16073599 - 7 Apr 2026

Viewed by 515

Abstract

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when [...] Read more.

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

22 pages, 22745 KB

Open AccessArticle

Spectral Phenological Typologies for Improving Cross-Dataset in Mediterranean Winter Cereals

by Patricia Arizo-García, Sergio Castiñeira-Ibáñez, Beatriz Ricarte, Alberto San Bautista and Constanza Rubio

Appl. Sci. 2026, 16(7), 3598; https://doi.org/10.3390/app16073598 - 7 Apr 2026

Viewed by 507

Abstract

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, [...] Read more.

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

(This article belongs to the Special Issue Digital Technologies in Smart Agriculture)

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25 pages, 4812 KB

Open AccessArticle

Catalytic Upgrading of Vacuum Residue over Metal-Loaded Iraqi Kaolin Using a Fixed-Bed Reactor

by Osamah Basil Al-Ameri, Abdelhakim Elmouwahidi, Mohammed Alzuhairi, Esther Bailón-García, Juan Amaro-Gahete and Francisco Carrasco-Marín

Appl. Sci. 2026, 16(7), 3597; https://doi.org/10.3390/app16073597 - 7 Apr 2026

Viewed by 725

Abstract

The catalytic upgrading of vacuum residue (VR) is constrained by the high cost, diffusional limitations, and rapid deactivation of conventional zeolite-based catalysts due to severe coking. Addressing this, we developed novel, low-cost, and coke-resistant catalysts utilizing naturally abundant Iraqi kaolin. A composite support [...] Read more.

The catalytic upgrading of vacuum residue (VR) is constrained by the high cost, diffusional limitations, and rapid deactivation of conventional zeolite-based catalysts due to severe coking. Addressing this, we developed novel, low-cost, and coke-resistant catalysts utilizing naturally abundant Iraqi kaolin. A composite support comprising 80 wt.% Iraqi red kaolin and 20 wt.% white kaolin was synthesized via thermal activation at 800 °C and acid leaching. This support was subsequently impregnated with transition and rare-earth metals (Ni, Co, Ce) at 3–40 wt.% loadings, and comprehensively characterized using XRD, BET, SEM-EDX, and XPS. Catalytic performance was evaluated during VR upgrading in a fixed-bed batch reactor at 450 °C. Among the formulations, the 20 wt.% Ce-loaded catalyst (MKRW-800A@Ce20%) exhibited superior efficiency, achieving 80.15% VR conversion, 61.04% liquid yield, and minimal coke formation (3.81 g) compared to Ni and Co counterparts. This enhanced activity is attributed to synergistic effects of improved surface acidity, textural accessibility, and the Ce³⁺/Ce⁴⁺ redox couple, which promotes selective cracking while suppressing coke precursors. These findings provide new insights into the rational design of natural clay-based catalysts, establishing Ce-modified metakaolin as a viable, sustainable alternative to zeolites for industrial heavy-oil processing. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

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20 pages, 2173 KB

Open AccessArticle

Effects of AI-Assisted Physical Exercise on the Health of Elderly Women: A Randomized Controlled Trial Based on Smart Devices and Personalized Exercise Guidance

by Wen Qi, Hongli Yu and Dominika Wilczyńska

Appl. Sci. 2026, 16(7), 3596; https://doi.org/10.3390/app16073596 - 7 Apr 2026

Viewed by 909

Abstract

Background: Elderly women face significant health challenges, including knee osteoarthritis (KOA) and balance disorders. Artificial intelligence (AI)-assisted exercise intervention can address limitations of traditional intervention methods, such as low compliance and high economic costs. Objective: This randomized controlled trial (RCT) evaluated the effects [...] Read more.

Background: Elderly women face significant health challenges, including knee osteoarthritis (KOA) and balance disorders. Artificial intelligence (AI)-assisted exercise intervention can address limitations of traditional intervention methods, such as low compliance and high economic costs. Objective: This randomized controlled trial (RCT) evaluated the effects of AI-assisted Baduanjin exercise on physical health (balance and knee function) in elderly women, comparing it with offline manual guidance and health education. The group of 79 elderly women (60–74 years) were randomly assigned into three groups: AI-assisted Baduanjin (AI group, n = 25), offline instructor-led Baduanjin (Offline group, n = 27), and health education (Education group, n = 27). Methods: Interventions lasted 12 weeks, with three 45-min sessions per week. Two outcome measures were evaluated pre- and post-interventions: postural stability assessed by the unipedal stance test and knee function measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). This study considers two measurement methods. One is a two-way repeated-measures analysis of variance used to evaluate the effects on the three intervention groups. The other is an independent-samples t-test, with post hoc testing (Bonferroni), used to assess differences among the three groups. Results: Both the AI and Offline groups showed significant improvements in WOMAC pain and function scores at 12 weeks (p < 0.05), with the Offline group demonstrating greater functional improvement (decrease in WOMAC function score: 6.7 points, Cohen’s d = 1.23, 95% CI 0.81–1.65). No serious adverse events (e.g., falls, exacerbation of joint pain) were reported in any group. The Offline group also showed immediate balance enhancement (closed-eye stance improvement, effect size d ≈1.57), while the AI group exhibited progressive pain relief. The Education group showed minimal improvements. Inter-group comparisons showed the AI and Offline groups outperformed the Education group in balance and knee function (p < 0.05). Conclusions: AI-assisted and offline Baduanjin interventions effectively improve balance and knee function in elderly women, with offline guidance offering improvement of balance ability. AI intervention is suitable for rural elderly women with low digital literacy, as it provides simplified operation and voice prompts to ensure adherence. Full article

(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)

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14 pages, 1329 KB

Open AccessArticle

Differential Effects of Desktop and Immersive Virtual Reality on Learning, Cognitive Load and Attitudes of University Students

by Julio Cabero-Almenara, Mª Victoria Fernández-Scagliusi, Antonio Palacios-Rodríguez and Rocío Piñero-Virué

Appl. Sci. 2026, 16(7), 3595; https://doi.org/10.3390/app16073595 - 7 Apr 2026

Viewed by 1808

Abstract

Virtual reality (VR) has emerged as a technology with growing presence in education, driven by its potential to increase motivation, promote learning, and offer immersive experiences that are challenging to replicate in traditional settings. However, the literature shows contradictory results regarding its impact [...] Read more.

Virtual reality (VR) has emerged as a technology with growing presence in education, driven by its potential to increase motivation, promote learning, and offer immersive experiences that are challenging to replicate in traditional settings. However, the literature shows contradictory results regarding its impact on academic performance, cognitive load, and student attitudes, particularly when comparing immersive and non-immersive (desktop) modalities. Against this backdrop, this study aimed to examine whether interaction with VR-based learning objects improves knowledge acquisition, whether differences exist between immersive and desktop versions, what cognitive load is associated with each modality, and what attitudes students develop toward VR. A total of 136 Education students participated, randomly assigned to either the immersive (n = 70) or non-immersive (n = 66) condition, following a pretest–posttest experimental design. Data were collected using a performance test, the NASA-TLX questionnaire, and a semantic differential scale. Results indicated significant improvements in learning across both modalities with no statistically significant differences between them, a slightly higher—yet low-to-moderate—cognitive load in the immersive condition, and highly positive attitudes in both groups. These findings suggest that both modalities are effective and well accepted, although immersive VR requires somewhat greater cognitive effort. The discussion highlights the need to clarify the factors that moderate these effects and to advance theoretical frameworks for instructional design in VR environments. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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23 pages, 1296 KB

Open AccessReview

Clinical Readiness of Additively Manufactured Dental Ceramics for Crowns, Veneers, and Partial-Coverage Restorations: A Scoping Review and Evidence Map

by Andrei Vorovenci, Oana Eftene, Mihai Burlibașa, Andi Ciprian Drăguș, Mădălina Adriana Malița, Mihaela Romanița Gligor, Viorel Ștefan Perieanu, Camelia Ionescu, Ruxandra Stănescu, Elena-Cristina Marcov, Cristina Maria Șerbănescu, Mircea Popescu, Andrei Burlibașa and Iuliana Babiuc

Appl. Sci. 2026, 16(7), 3594; https://doi.org/10.3390/app16073594 - 7 Apr 2026

Cited by 1 | Viewed by 853

Abstract

This scoping review mapped the clinical readiness of directly additively manufactured (AM) dental ceramics for single-unit definitive restorations (crowns, veneers, and partial-coverage restorations) using a predefined review-specific five-tier readiness framework (R1–R5) designed to organize evidence maturity from restoration-relevant foundational studies to comparative clinical [...] Read more.

This scoping review mapped the clinical readiness of directly additively manufactured (AM) dental ceramics for single-unit definitive restorations (crowns, veneers, and partial-coverage restorations) using a predefined review-specific five-tier readiness framework (R1–R5) designed to organize evidence maturity from restoration-relevant foundational studies to comparative clinical evidence. MEDLINE (PubMed), Scopus, Web of Science Core Collection, EBSCO (Dentistry and Oral Sciences Sources), and ClinicalTrials.gov were searched from inception to February 2026, with citation tracking. Thirty-five sources were included: 31 in vitro studies and 4 clinical studies. Evidence clustered in preclinical tiers, with most studies classified as restoration-level in vitro investigations (R2, 22/35) or foundational specimen-level studies explicitly linked to restorative performance (R1, 9/35); only one feasibility study reached R3 (1/35), three studies provided comparative clinical evidence (R4, 3/35), and no R5-level evidence was identified. The additively manufactured definitive restorations evaluated were zirconia-based. Most restoration-level studies addressed zirconia crowns (18/35), with fewer studies focusing on veneers/laminates (5/35) and occlusal veneers/tabletops (2/35). Across AM routes (most commonly vat photopolymerization ceramic workflows and nanoparticle jetting) outcomes focused on fit/adaptation, manufacturing accuracy, mechanical performance, and aging simulations; clinical studies reported short- to mid-term performance using standardized evaluation criteria. Overall, the evidence suggests technical feasibility and increasing restoration-level evaluation under controlled conditions, but clinical applicability remains preliminary because higher-readiness clinical evidence is still limited. Future work should prioritize standardized reporting, clinically relevant aging/fatigue paradigms, and longer-term comparative clinical studies. Full article

(This article belongs to the Special Issue Recent Advancements in Novel Dental Materials)

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11 pages, 3742 KB

Open AccessArticle

Numerical Simulation Analysis of Elbow Erosion in Underground Gas Storage Process System

by Chengli Song, Wei Li, Jin Wang, Lifeng Li and Xinbao Liu

Appl. Sci. 2026, 16(7), 3593; https://doi.org/10.3390/app16073593 - 7 Apr 2026

Viewed by 646

Abstract

Aiming at the erosion failure risk of key elbow components in the process system of underground gas storage (UGS), numerical simulation was adopted to investigate the erosion behavior and mechanism of elbows under the following three typical working conditions: gas injection, gas production, [...] Read more.

Aiming at the erosion failure risk of key elbow components in the process system of underground gas storage (UGS), numerical simulation was adopted to investigate the erosion behavior and mechanism of elbows under the following three typical working conditions: gas injection, gas production, and wastewater treatment. The results show that the elbow in the gas injection system is under gas–solid two-phase flow, and the most severely eroded area is located at 45–50° on the outer arc side of the elbow. Small particles have stronger flow-following ability than large particles and collide with the wall more sufficiently, resulting in a higher erosion rate. For the tandem elbows in the gas production system, affected by centrifugal force and secondary flow, the outer arc side shows high pressure while the inner arc side shows low pressure. As the particle size increases, the erosion rates of both elbows decrease, with a larger reduction for the second elbow. The most severely eroded positions of the first and second elbows are at 50–55° and 40–45° on the outer arc side, respectively. The elbow in the wastewater treatment system has relatively slight erosion with a symmetrical distribution, but a small amount of natural gas accumulated on the inner side easily induces cavitation corrosion. Full article

(This article belongs to the Special Issue Sustainability and Challenges of Underground Gas Storage Engineering)

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25 pages, 4504 KB

Open AccessArticle

Discrete Element Modelling of Thermal Evolution of Forsmark Repository for Spent Nuclear Fuel Disposal and Long-Term Response of Discrete Fracture Network

by Jeoung Seok Yoon, Haimeng Shen, Arno Zang and Flavio Lanaro

Appl. Sci. 2026, 16(7), 3592; https://doi.org/10.3390/app16073592 - 7 Apr 2026

Viewed by 729

Abstract

Long-term safety assessment of deep geological repositories for spent nuclear fuel requires explicit evaluation of thermo-mechanical (TM) processes induced by decay heat and their influence on fractured host rock. A safety-relevant, though low-probability, scenario concerns shear reactivation of fractures intersecting deposition holes, which [...] Read more.

Long-term safety assessment of deep geological repositories for spent nuclear fuel requires explicit evaluation of thermo-mechanical (TM) processes induced by decay heat and their influence on fractured host rock. A safety-relevant, though low-probability, scenario concerns shear reactivation of fractures intersecting deposition holes, which could compromise canister integrity if displacement exceeds design limits. This study presents a three-dimensional discrete element modelling approach to analyze the thermal evolution of the Forsmark repository (Sweden) and the associated long-term response of a discrete fracture network (DFN) during the post-closure phase. The model explicitly represents repository panel, deterministic deformation zones, and a stochastically generated fracture network embedded in a bonded particle assembly representing the rock for Particle Flow Code (PFC) numerical simulations. Time-dependent heat release from spent nuclear fuel canisters is implemented using a physically based decay power function. A deposition panel-scale heat-loading formulation accounts for deposition-hole and tunnel spacing. Two emplacement scenarios are analyzed: (a) a simultaneous all-panel heating scenario, used as a conservative bounding case, and (b) a sequential panel heating scenario representing staged emplacement and closure. The simulations show that temperature and thermally induced stress evolution are sensitive to the emplacement and closure sequence. Sequential heating produces a more gradual thermal build-up and lower peak temperatures than simultaneous heating, indicating that thermal and stress perturbations in the host rock can be influenced not only through repository design, but also by operational strategy. Thermally induced fracture shear displacement displays a systematic temporal response. Fractures located within the deposition panel footprint develop shear displacement rapidly during the early post-closure period, reaching peak values at approximately 200 years, followed by gradual relaxation as temperatures decline. The average peak shear displacement on fractures is on the order of 2–3 mm, while fractures outside the panel footprint show smaller early-time displacements and a more prolonged long-term response. All simulated shear displacements remain more than one order of magnitude below the commonly cited canister damage threshold for Forsmark of approximately 50 mm, even for the conservative simultaneous heating case. These results indicate that thermally induced fracture shear is unlikely to cause direct mechanical damage to canisters. At the same time, the persistence of residual shear displacement after heating implies permanent fracture dilation, which may influence long-term hydraulic properties and indirectly affect processes such as groundwater flow and canister corrosion. The modelling framework and results presented here were conducted for review purposes independently from the Swedish safety case, and provide a mechanistic basis for evaluating thermally induced fracture deformation in crystalline rock repositories and contribute to bounding the role of thermo-mechanical processes in the safety assessment of spent nuclear fuel disposal at Forsmark. Full article

(This article belongs to the Special Issue Progress and Challenges of Rock Engineering)

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13 pages, 2383 KB

Open AccessArticle

Novel Quantitative Approach for Age Estimation Using Facial Suture Closure and Modified Scoring Systems

by Siriwat Thunyacharoen, Chirapat Inchai and Pasuk Mahakkanukrauh

Appl. Sci. 2026, 16(7), 3591; https://doi.org/10.3390/app16073591 - 7 Apr 2026

Viewed by 1082

Abstract

Background: While human cranial sutures are well-established indicators for age-at-death estimation in forensic anthropology, facial sutures remain an underutilized resource despite their critical role in facial growth and development. Macroscopic examination of craniofacial suture closure patterns reflects physiological aging processes and can [...] Read more.

Background: While human cranial sutures are well-established indicators for age-at-death estimation in forensic anthropology, facial sutures remain an underutilized resource despite their critical role in facial growth and development. Macroscopic examination of craniofacial suture closure patterns reflects physiological aging processes and can provide valuable information at crime scenes. This study aimed to address the gap of knowledge by quantitatively evaluating the efficacy of facial suture closure patterns for age estimation. Methods: A sample consisting of 296 Thai skulls was analyzed to assess facial suture closure based on anatomical morphology. The sutures were evaluated using various established classification systems to determine the most effective method for predicting age ranges. To ensure consistency and reliability, the evaluations were conducted by three independent raters. Results: The assessment demonstrated good Intraclass Correlation (ICC = 0.755, df = 14, p < 0.05). Among the classification methods tested, the Modified Meindl and Lovejoy Scoring System yielded the highest sensitivity, ranging from 90.9% to 100% in males and 75.4% to 96.1% in females. Specifically, the zygomaticomaxillary suture showed the highest sensitivity in males, whereas the frontonasal and sphenozygomatic sutures were the most sensitive indicators in females. Utilizing the total sum score (TSS), the following sex-specific linear regression formulas for age-at-death were generated: (Males: Age-at-death = 1.7625(TSS) − 17.094. Females: Age-at-death = 1.7325(TSS) − 12.865). Conclusions: Facial sutures exhibit distinct, sex-specific closure patterns that serve as robust and reliable indicators for estimating age, with higher sensitivity generally observed in males. The utility of this novel method is heavily dependent on the scoring system employed, highlighting the critical importance of utilizing modified, sex-specific analyses. While these population-specific models tailored to the Thai demographic effectively refine age estimation outcomes, integrating this methodology with broader biological profiling remains essential for high-confidence forensic identification. Full article

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19 pages, 4785 KB

Open AccessArticle

Circadian Biomarkers for Epilepsy Subtyping: Multi-Band EEG Rhythm Disruptions as Novel Diagnostic Signatures

by Lejun Li and Changgui Gu

Appl. Sci. 2026, 16(7), 3590; https://doi.org/10.3390/app16073590 - 7 Apr 2026

Viewed by 675

Abstract

Circadian rhythms maintain healthy neural function, and their disruption links to pathological brain states including epilepsy. Current diagnostic approaches for epilepsy, which predominantly focus on transient ictal events or static spectral features in intracranial EEG, suffer from a temporal myopia that neglects the [...] Read more.

Circadian rhythms maintain healthy neural function, and their disruption links to pathological brain states including epilepsy. Current diagnostic approaches for epilepsy, which predominantly focus on transient ictal events or static spectral features in intracranial EEG, suffer from a temporal myopia that neglects the rich spatiotemporal dynamics of long-term neural activity. To address this limitation, this study aims to establish multi-band circadian biomarkers as diagnostic signatures for epileptogenic tissue identification and patient subtyping. In this article, we developed a comprehensive biomarker extraction pipeline that analyzes long-term intracranial EEG recordings (72+ h) from 38 drug-resistant epilepsy patients, quantifying multi-band rhythm features from delta to gamma frequencies (1–100 Hz). The pipeline captures three circadian signatures: rhythm amplitude, temporal stability, and cross-frequency coupling. Epileptogenic tissue showed systematic circadian dysregulation: 43.2% reduction in delta band circadian amplitude (p < 0.001), 31.5% impairment in delta–gamma coupling (quantified as a power–envelope correlation proxy for phase–amplitude coupling), and progressive temporal instability across sleep–wake transitions. Using unsupervised clustering, we identified three chronobiological subtypes—Circadian-Preserved (36.8%), Coupling-Deficient (39.5%), and Pan-Dysrhythmic (23.7%)—each with distinct pathophysiological mechanisms and surgical outcomes. Our machine learning classification achieved clinically significant discrimination (AUC = 0.865), with circadian amplitude and coupling strength as the most informative features. These multi-band circadian biomarkers provide interpretable, physiologically grounded signatures for epilepsy diagnosis and subtype stratification, offering a temporal framework for personalized surgical planning and chronotherapy interventions. Full article

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21 pages, 31287 KB

Open AccessArticle

A Cross-Scale Study of Data-Driven Micro-to-Macro Mechanical Heterogeneity in Sandstone

by Binwei Xia, Yulin Zhang, Xinqin Xu, Lei Wang, Rui Li and Xiong Zheng

Appl. Sci. 2026, 16(7), 3589; https://doi.org/10.3390/app16073589 - 7 Apr 2026

Viewed by 634

Abstract

Tight sandstone gas development is largely governed by mineral composition and micromechanical heterogeneity. This study proposes a cross-scale method integrating these two factors to characterize macroscopic sandstone heterogeneity. First, a CNN–Transformer model was trained on thin-section images to identify mineral types and contents. [...] Read more.

Tight sandstone gas development is largely governed by mineral composition and micromechanical heterogeneity. This study proposes a cross-scale method integrating these two factors to characterize macroscopic sandstone heterogeneity. First, a CNN–Transformer model was trained on thin-section images to identify mineral types and contents. Second, probability density functions of Young’s modulus for each mineral were derived from nanoindentation data, and stochastic sampling was used to assign mechanical properties to mineral grains in an FDEM-GBM uniaxial compression model. Finally, numerical results validated against experiments show that the random spatial distribution of micromechanical parameters leads to a normal distribution of the macroscopic Young’s modulus. Decreasing high-strength mineral content reduces the mean Young’s modulus while increasing its standard deviation, indicating greater mechanical heterogeneity, with cracks preferentially propagating in low-strength minerals. Mineral composition and content are the primary controls on macroscopic behavior, while micromechanical heterogeneity plays a secondary role. A brittleness index integrating mineral composition and multi-scale Young’s modulus distribution is proposed, providing a theoretical basis for evaluating heterogeneity and fracability in tight sandstone reservoirs. Full article

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27 pages, 26065 KB

Open AccessArticle

AEFOP: Adversarial Energy Field Optimization for Adversarial Example Purification

by Heqi Peng, Shengpeng Xiao and Yuanfang Guo

Appl. Sci. 2026, 16(7), 3588; https://doi.org/10.3390/app16073588 - 7 Apr 2026

Viewed by 525

Abstract

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, [...] Read more.

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd Edition)

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23 pages, 14612 KB

Open AccessArticle

Hydrochemical Evolution of Qilian Mountain Snowmelt Interacting with Beishan Granite: Implications for Deep Groundwater Recharge in the Beishan Geological Repository for High-Level Radioactive Waste

by Qi Wang, Zhongkui Zhou, Jiale Li, Yan Xin, Zhanxue Sun, Yubo Ge and Jinhui Liu

Appl. Sci. 2026, 16(7), 3587; https://doi.org/10.3390/app16073587 - 7 Apr 2026

Viewed by 708

Abstract

The Beishan area of Gansu, China, is the primary candidate site for the geological disposal of China’s high-level radioactive waste (HLW). To assess the long-term safety of this repository, the evolutionary patterns of groundwater and the primary migration vector of radionuclides must be [...] Read more.

The Beishan area of Gansu, China, is the primary candidate site for the geological disposal of China’s high-level radioactive waste (HLW). To assess the long-term safety of this repository, the evolutionary patterns of groundwater and the primary migration vector of radionuclides must be understood. Through experiments and hydrogeochemical simulations of snowmelt samples from the Qilian Mountains and deep rock samples from Beishan, we reveal different hydrochemical compositions and types of the snowmelt and deep groundwater. The results show that the hydrochemical type of Qilian Mountain snowmelt is SO₄–Na·Ca, whereas that of the deep groundwater in the Beishan is Cl·SO₄–Na, indicating substantial differences in the hydrochemical characteristics of the two samples. The water–rock interactions between snowmelt and granite are dominated by the dissolution of silicate minerals and the precipitation of carbonate minerals, accompanied by cation exchange and adsorption. After the interaction, the hydrochemical type of the snowmelt becomes SO₄–Na, with total dissolved solids (TDS) consistently maintained at ~500 mg/L, which is distinct from the TDS range of 1540–2045 mg/L observed for the deep groundwater in the Beishan. Under the experimental and simulation conditions set in this study, the water–rock interactions between Qilian Mountain snowmelt and Beishan granite cannot reproduce the hydrochemical characteristics of the deep groundwater in the Beishan. This study provides theoretical support for the hydrogeological safety assessment of HLW geological repositories. Full article

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15 pages, 1148 KB

Open AccessArticle

Early Prediction of Well-Being Outcomes in Older Adults Using Explainable AI and Emotional Intelligence Measures

by Evgenia Kouli, Evangelos Bebetsos, Maria Michalopoulou and Filippos Filippou

Appl. Sci. 2026, 16(7), 3586; https://doi.org/10.3390/app16073586 - 7 Apr 2026

Viewed by 843

Abstract

Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status [...] Read more.

Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status 5 months later using explainable machine learning models. Methods: A cohort of elderly participants aged 60 to 89 years completed emotional intelligence measures at baseline, and well-being was assessed 5 months later using the POMS questionnaire. Four machine learning algorithms, Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were developed using 5-fold stratified cross-validation. Model performance was evaluated through accuracy, precision, recall, F1-score, ROC AUC, and normalized confusion matrices. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution and directionality of each predictor. Results: XGBoost achieved the highest predictive performance (accuracy = 0.789; F1 = 0.778) and demonstrated balanced classification across well-being categories. SVM also performed robustly (accuracy = 0.760), while LR showed reduced sensitivity for detecting those with poorer well-being. SHAP analysis identified self-control, emotionality, sociability, self-motivation, and well-being components as the most influential predictors. Lower emotionality, higher sociability, and higher self-control scores were linked to a greater probability of favorable well-being outcomes. Conclusions: The findings demonstrate the feasibility of using explainable machine learning models to predict 5-month well-being status within this sample of older adults using emotional intelligence indicators. XGBoost provided the strongest and most balanced performance, while SHAP analysis clarified how specific emotional intelligence dimensions influenced predictions. These findings suggest that interpretable machine learning approaches may support future efforts toward early recognition of older adults who may be at risk for reduced well-being and guide personalized intervention strategies. Full article

(This article belongs to the Special Issue AI-Driven Innovations in Rehabilitation: Integrating Neurological, Musculoskeletal, Sports Medicine and Occupational Therapy Interventions)

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17 pages, 1841 KB

Open AccessArticle

Dynamic Event-Triggered Output Feedback Control for Switched Systems via Switched Lyapunov Functions

by Xinyue Wang, Yanhui Tong and Yuyuan Li

Appl. Sci. 2026, 16(7), 3585; https://doi.org/10.3390/app16073585 - 7 Apr 2026

Viewed by 761

Abstract

This study carries out the research on event-triggered output feedback control tailored for discrete-time switched linear systems. A dynamic event-triggered mechanism (DETM) is utilized to mitigate the triggering frequency. To ensure stability and control performance, it is assumed that an event is triggered [...] Read more.

This study carries out the research on event-triggered output feedback control tailored for discrete-time switched linear systems. A dynamic event-triggered mechanism (DETM) is utilized to mitigate the triggering frequency. To ensure stability and control performance, it is assumed that an event is triggered whenever the system undergoes a switch. First, the closed-loop stability of the underlying switched system with DETM is analyzed via the switched Lyapunov function method, followed by the establishment of a stability criterion for the system under arbitrary switching. Based on this criterion, a dynamic event-triggered output feedback control strategy is devised. The viability and application potential of our proposed control strategy is validated through simulation trials using a morphing aircraft model. Furthermore, when we pit dynamic event-triggered control (DETC) against its static (SETC) version, the proposed DETM reduces the trigger events and prolongs the inter-event intervals versus the SETM, while retaining nearly identical control accuracy and energy consumption, thus providing an efficient solution for resource-constrained networked control systems. Full article

(This article belongs to the Collection Advances in Automation and Robotics)

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24 pages, 1790 KB

Open AccessArticle

Effect of the Recycled HIPS Surface Yellowing Phenomenon on Its Properties

by Benita Malinowska, Michał Chodkowski and Konrad Terpiłowski

Appl. Sci. 2026, 16(7), 3584; https://doi.org/10.3390/app16073584 - 7 Apr 2026

Viewed by 606

Abstract

The paper investigates the effect of the degree of HIPS surface yellowness on its properties: colorimetric, surface, rheological, and mechanical. In order to prepare three naturally degraded samples, about 1 kg of white HIPS flakes, semi-yellow HIPS flakes, and yellow HIPS flakes, segregation [...] Read more.

The paper investigates the effect of the degree of HIPS surface yellowness on its properties: colorimetric, surface, rheological, and mechanical. In order to prepare three naturally degraded samples, about 1 kg of white HIPS flakes, semi-yellow HIPS flakes, and yellow HIPS flakes, segregation based on colorimetric analysis was applied. Then, these samples were subjected to ATR-FTIR analysis, sessile drop contact angle measurements, and MFI analysis. These analyses were repeated for standardized specimens made of the segregated HIPS flakes. The average absorbances were determined for 50 HIPS samples of each type in the form flakes. Finally, mechanical tests were carried out on the standardized specimens. As follows from the research, yellowing of the HIPS surface affects the final color of the standardized specimens, which is confirmed by optical colorimetry. Moreover, material degradation demonstrated by yellowing of its surface and confirmed by a decrease in ATR-FTIR spectra absorbance, is associated with changes in mechanical and rheological properties, as well as in surface characteristics. The novelty of this study lies in the investigation of naturally degraded HIPS samples under laboratory conditions (the HIPS materials were not subjected to artificial aging using laboratory equipment), obtained from waste post-consumer cooling devices used in consumers’ homes, representing natural wear and tear of the material. The tests provide insight into both the visual and mechanical properties of components manufactured from recycled HIPS originating from degraded refrigeration equipment. They also constitute a valuable source of information for processors and manufacturers. Full article

(This article belongs to the Section Surface Sciences and Technology)

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26 pages, 991 KB

Open AccessArticle

Experimental Quantification of Authentication Enforcement Correctness and ACL Misconfiguration Impact in Standards-Compliant MQTT Deployments

by Nael M. Radwan and Frederick T. Sheldon

Appl. Sci. 2026, 16(7), 3583; https://doi.org/10.3390/app16073583 - 7 Apr 2026

Cited by 1 | Viewed by 1158

Abstract

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally [...] Read more.

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions. Full article

(This article belongs to the Special Issue Security, Privacy and Application in New Intelligence Techniques: 2nd Edition)

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