Applied Sciences

19 pages, 1446 KB

Open AccessArticle

An Observational Study of Age-Related Changes in Bite Force During Stabilization Splint Therapy in Patients with Unilateral Temporomandibular Joint Osteoarthritis

by Kun-Hwa Kang, Jae-Kwang Jung, Jin-Seok Byun and Ji Rak Kim

Appl. Sci. 2026, 16(2), 636; https://doi.org/10.3390/app16020636 (registering DOI) - 7 Jan 2026

Abstract

Age-related differences in temporomandibular joint osteoarthritis (TMJ OA) have been suggested; however, age-specific patterns of functional recovery following occlusal splint therapy remain insufficiently characterized. This retrospective observational study evaluated longitudinal changes in bite force across different age groups in patients with unilateral TMJ [...] Read more.

Age-related differences in temporomandibular joint osteoarthritis (TMJ OA) have been suggested; however, age-specific patterns of functional recovery following occlusal splint therapy remain insufficiently characterized. This retrospective observational study evaluated longitudinal changes in bite force across different age groups in patients with unilateral TMJ OA undergoing stabilization splint therapy. Thirty-two patients diagnosed according to the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) were categorized into three age groups (20–39, 40–59, and ≥60 years). Maximum bite force was measured repeatedly from baseline to 2 weeks and up to 6 months during the observation period following splint application. Patients aged 60 years and older exhibited significantly lower baseline maximum bite force compared with younger groups (p = 0.011), but demonstrated a gradual and statistically significant increase over the observation period (p = 0.011). In contrast, patients aged 20–39 years showed a significant improvement in bite force asymmetry after 2 weeks of treatment (p = 0.047), which was maintained throughout follow-up. These findings suggest that functional recovery patterns in unilateral TMJ OA may vary according to age, with younger patients showing earlier improvement and older patients demonstrating slower but progressive functional gains. Bite force assessment may serve as a complementary functional parameter for characterizing age-related differences in functional change in patients with unilateral TMJ OA. Full article

16 pages, 5655 KB

Open AccessArticle

Wideband Circularly Polarized Slot Antenna Using a Square-Ring Notch and a Nonuniform Metasurface

by Seung-Heon Kim, Yong-Deok Kim, Tu Tuan Le and Tae-Yeoul Yun

Appl. Sci. 2026, 16(2), 634; https://doi.org/10.3390/app16020634 (registering DOI) - 7 Jan 2026

Abstract

Wearable antennas for wireless sensor network (WSN) applications require circularly polarized (CP) radiation to maintain stable communication link under human body movement and complex environments. However, many existing wearable CP antennas rely on either linearly polarized (LP) or CP radiator with a single [...] Read more.

Wearable antennas for wireless sensor network (WSN) applications require circularly polarized (CP) radiation to maintain stable communication link under human body movement and complex environments. However, many existing wearable CP antennas rely on either linearly polarized (LP) or CP radiator with a single axial ratio (AR) mode combined with external polarization conversion structures, which limit the achievable axial ratio bandwidth (ARBW). In this work, an all-textile wideband CP antenna with a square-ring notched slot radiator, a 50 Ω microstrip line, and a 3 × 3 nonuniform metasurface (MTS) is proposed for 5.85 GHz WSN applications. Unlike conventional CP generation approaches, the square-ring notched slot, analyzed using characteristic mode analysis (CMA), directly excites three distinct AR modes, enabling potential wideband CP radiation. The nonuniform MTS further improves IBW performance by exciting additional surface wave resonances. Moreover, the nonuniform MTS further enhances ARBW by redirecting the incident wave into an orthogonal direction with equivalent amplitude and a 90° phase difference at higher frequency region. The proposed antenna is composed of conductive textile and felt substrates, offering flexibility for wearable applications. The proposed antenna is measured in free space, on human bodies, and fresh pork in an anechoic chamber. The measured results show a broad IBW and ARBW of 84.52% and 43.56%, respectively. The measured gain and radiation efficiency are 4.47 dBic and 68%, respectively. The simulated specific absorption rates (SARs) satisfy both US and EU standards. Full article

(This article belongs to the Special Issue Advances in Wireless Sensor Networks and Communication Technology)

23 pages, 7998 KB

Open AccessArticle

Multi-Layer Stiffness Matching of Ballastless Track for Passenger and Freight Railways: An Evaluation Method Based on Multi-Dimensional Parameter Fusion

by Weibin Liu, Jijun Wang, Weitao Cui, Wenda Qin, Ruohan Yin, Chen Hua, Moyan Zhang and Yanglong Zhong

Appl. Sci. 2026, 16(2), 632; https://doi.org/10.3390/app16020632 (registering DOI) - 7 Jan 2026

Abstract

To address the insufficient multi-layer optimization of fastener and cushion stiffness in ballastless tracks for mixed passenger and freight railways, a vehicle–track coupled dynamic model is developed, and the effects of individual and combined stiffness parameters on track and vehicle dynamics are systematically [...] Read more.

To address the insufficient multi-layer optimization of fastener and cushion stiffness in ballastless tracks for mixed passenger and freight railways, a vehicle–track coupled dynamic model is developed, and the effects of individual and combined stiffness parameters on track and vehicle dynamics are systematically analyzed. Based on this model, a multi-dimensional stiffness matching approach is proposed to determine appropriate stiffness ranges for mixed-use railways. Results indicate that fastener stiffness primarily affects the local dynamic response of the rail, whereas cushion stiffness has a stronger influence on overall track performance. When the damping pad stiffness exceeds 600 MPa/m, the fastener force increases sharply, posing a risk of accelerated structural deterioration. Differences in axle load and speed between passenger and freight trains induce distinct excitation patterns, leading to nonlinear variations in interlayer forces. The optimal stiffness combination is 50 kN/mm for fasteners and 600 MPa/m for damping pads under passenger conditions, and 40 kN/mm and 600 MPa/m, respectively, under freight conditions. Considering the operational requirements of mixed lines, a fastener stiffness of 40–50 kN/mm and a damping pad stiffness of 600 MPa/m are recommended. This study provides theoretical support for stiffness design and parameter optimization in ballastless tracks for mixed-use railways. Full article

(This article belongs to the Section Acoustics and Vibrations)

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16 pages, 2974 KB

Open AccessArticle

Video Super-Resolution Combining Dual Motion Compensation and Multi-Scale Structure–Texture Prior

by Xiaolei Liu, Jiawei Shi, Jiayi Xu, Pengfei Song, Hongxia Gao, Fuhai Wang, Meining Ji, Chen Chen and Xianghao Kong

Appl. Sci. 2026, 16(2), 631; https://doi.org/10.3390/app16020631 - 7 Jan 2026

Abstract

Video super-resolution methods based on convolutional kernels or optical flow often face challenges such as limited utilization of multi-frame detail information or strong reliance on accurate optical flow estimation. To address these issues, this paper proposes a novel super-resolution reconstruction network named Dual [...] Read more.

Video super-resolution methods based on convolutional kernels or optical flow often face challenges such as limited utilization of multi-frame detail information or strong reliance on accurate optical flow estimation. To address these issues, this paper proposes a novel super-resolution reconstruction network named Dual Motion Compensation and Multi-scale Structure–Texture Prior (DCST-Net). Dual motion compensation performs direct and progressive motion mapping in parallel, effectively mitigating estimation bias in motion modeling. A multi-scale structure–texture prior is introduced to enhance high-frequency details through feature fusion, alleviating over-smoothing caused by warping and fusion processes. The proposed DCST-Net method is validated on datasets containing both large and small targets, demonstrating its effectiveness and robustness. Full article

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

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16 pages, 5335 KB

Open AccessArticle

Vibrational Transport of Granular Materials Achieved by Dynamic Dry Friction Manipulations

by Ribal El Banna, Kristina Liutkauskienė, Ramūnas Česnavičius, Martynas Lendraitis, Mindaugas Dagilis and Sigitas Kilikevičius

Appl. Sci. 2026, 16(2), 630; https://doi.org/10.3390/app16020630 - 7 Jan 2026

Abstract

The use of vibrational transport for granular materials has significantly increased in the technological industry due to its reliability, operational efficiency, cost-effectiveness, and relatively uncomplicated technological setup. These transportation methods typically utilize various forms of asymmetry, such as kinematic, temporal (time), wave, and [...] Read more.

The use of vibrational transport for granular materials has significantly increased in the technological industry due to its reliability, operational efficiency, cost-effectiveness, and relatively uncomplicated technological setup. These transportation methods typically utilize various forms of asymmetry, such as kinematic, temporal (time), wave, and power asymmetry, to induce controlled motion on oscillating surfaces. This study analyses the motion of the granular materials on an inclined plane, where the central innovation lies in the creation of an additional system asymmetry of frictional conditions that enables the granular materials to move upward. This asymmetry is created by introducing dry friction dynamic manipulations. A mathematical model has been developed to describe the motion of particles under these conditions. The modelling results proved that in an inclined transportation system, the asymmetry of frictional conditions during the oscillation cycle—created through dynamic dry friction manipulations—generates a net frictional force exceeding the gravitational force, thereby enabling the upward movement of granular particles. Additionally, the findings highlighted the key control parameters governing the motion of granular particles. λ, which represents the segment of the sinusoidal period over which the friction is dynamically louvered, serves as a parameter that controls the velocity of a moving particle on an inclined surface. The phase shift ϕ serves as a parameter that controls the direction of the particle’s motion at various inclination angles. Experimental investigations were conducted to assess the practicality of this method. The experimental results confirmed that the granular particles can be transported upward along the inclined surface with an inclination angle of up to 6 degrees, as well as provided both qualitative and quantitative validation of the model by illustrating that motion parameters exhibit comparable responses to the control parameters, and strongly agree with the theoretical findings. The primary advantage of the proposed vibrational transport method is the capacity for precise control of both the direction and velocity of granular particle transport using relatively simple mechanical setups. This method offers mechanical simplicity, low cost, and high reliability. It is well-suited to assembly line and manufacturing environments, as well as to industries involved in the processing and handling of granular materials, where controlled transport, repositioning, or recirculation of granular materials or small discrete components is required. Full article

(This article belongs to the Section Mechanical Engineering)

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21 pages, 1437 KB

Open AccessArticle

Finite Element Modelling of Variable Bitumen Content in Asphalt Mixtures

by Mohammad Fahad

Appl. Sci. 2026, 16(2), 629; https://doi.org/10.3390/app16020629 - 7 Jan 2026

Abstract

Bitumen content is a critical factor influencing the long-term performance and durability of asphalt pavements. This study evaluates how different binder percentages affect the mechanical behaviour of asphalt mixtures. Mixtures containing 4.7%, 5.1% and 5.5% binder were tested through an extensive experimental program [...] Read more.

Bitumen content is a critical factor influencing the long-term performance and durability of asphalt pavements. This study evaluates how different binder percentages affect the mechanical behaviour of asphalt mixtures. Mixtures containing 4.7%, 5.1% and 5.5% binder were tested through an extensive experimental program that included Marshall stability and flow, semi-circular bending, PAV aging, wheel rutting, dynamic modulus, creep compliance and fatigue resistance, supported by finite element simulations. To model the nonlinear viscoplastic and damage behaviour, a Perzyna-type viscoplastic formulation and Lemaitre’s isotropic damage model were applied. Model parameters were further refined using Bayesian estimation, based on 10,000 samples generated with a Markov Chain Monte Carlo procedure employing the Metropolis–Hastings algorithm. The findings indicate that mixtures with 4.7% binder content develop fatigue damage earlier, while increasing the binder above 5.1% leads to greater rutting susceptibility and higher creep compliance, as seen in the 5.5% mixture. Among the three, the 5.1% binder content delivered the best overall performance, reducing plastic strain-related damage by 40% compared with the 4.7% mixture and by 27% compared with the 5.5% mixture. Full article

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27 pages, 712 KB

Open AccessReview

Segmentation and Classification of Lung Cancer Images Using Deep Learning

by Xiaoli Yang, Angchao Duan, Ziyan Jiang, Xiao Li, Chenchen Wang, Jiawen Wang and Jiayi Zhou

Appl. Sci. 2026, 16(2), 628; https://doi.org/10.3390/app16020628 (registering DOI) - 7 Jan 2026

Abstract

Lung cancer ranks among the world’s most prevalent and deadly diseases. Early detection is crucial for improving patient survival rates. Computed tomography (CT) is a common method for lung cancer screening and diagnosis. With the advancement of computer-aided diagnosis (CAD) systems, deep learning [...] Read more.

Lung cancer ranks among the world’s most prevalent and deadly diseases. Early detection is crucial for improving patient survival rates. Computed tomography (CT) is a common method for lung cancer screening and diagnosis. With the advancement of computer-aided diagnosis (CAD) systems, deep learning (DL) technologies have been extensively explored to aid in interpreting CT images for lung cancer identification. Therefore, this review aims to comprehensively examine DL techniques developed for lung cancer screening and diagnosis. It explores various datasets that play a crucial role in lung cancer CT image segmentation and classification tasks, analyzing their differences in aspects such as scale. Next, various evaluation metrics for measuring model performance are discussed. The segmentation section details convolutional neural network-based (CNN-based) segmentation methods, segmentation approaches using U-shaped network (U-Net) architectures, and the application and improvements of Transformer models in this domain. The classification section covers CNN-based classification methods, classification methods incorporating attention mechanisms, Transformer-based classification methods, and ensemble learning approaches. Finally, the paper summarizes the development of segmentation and classification techniques for lung cancer CT images, identifies current challenges, and outlines future research directions in areas such as dataset annotation, multimodal dataset construction, multi-model fusion, and model interpretability. Full article

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19 pages, 2628 KB

Open AccessArticle

DOA Estimation Based on Circular-Attention Residual Network

by Min Zhang, Hong Jiang, Jia Li and Jianglong Qu

Appl. Sci. 2026, 16(2), 627; https://doi.org/10.3390/app16020627 (registering DOI) - 7 Jan 2026

Abstract

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from [...] Read more.

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

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18 pages, 6272 KB

Open AccessArticle

Using Virtual Reality to Promote Cognitive Engagement in Rett Syndrome: Eye-Tracking Evidence from Immersive Forest Tasks

by Rosa Angela Fabio, Michela Perina, Andrea Nucita, Giancarlo Iannizzotto and Martina Semino

Appl. Sci. 2026, 16(2), 626; https://doi.org/10.3390/app16020626 - 7 Jan 2026

Abstract

Rett syndrome (RTT) is a rare neurodevelopmental disorder that causes severe motor and cognitive impairments, limiting voluntary communication. Gaze-based technologies and virtual reality (VR) offer innovative ways to assess and enhance attention, happiness, and learning in individuals with minimal motor control. This study [...] Read more.

Rett syndrome (RTT) is a rare neurodevelopmental disorder that causes severe motor and cognitive impairments, limiting voluntary communication. Gaze-based technologies and virtual reality (VR) offer innovative ways to assess and enhance attention, happiness, and learning in individuals with minimal motor control. This study investigated and compared visual-attentional and emotional engagement in girls with RTT and typically developing (TD) peers during exploration of a virtual forest presented in 2D and immersive 3D (VR) formats across four progressively complex tasks. Twelve girls with RTT and 12 TD peers completed eye-tracking tasks measuring reaction time, fixation duration, disengagement events, and observed happiness. Girls with RTT showed slower responses and more disengagements overall, but VR significantly improved attentional efficiency in both groups, resulting in faster reaction times (η²_p = 0.36), longer fixations (η²_p = 0.31), and fewer disengagements (η²_p = 0.27). These effects were stronger in the RTT group. Both groups also showed greater happiness in VR settings (RTT: p = 0.011; TD: p = 0.015), and in participants with RTT, peaks in attention coincided with peak happiness, indicating a link between happiness and cognitive engagement. Immersive VR thus appears to enhance attention and affect in RTT, supporting its integration into personalized neurorehabilitation. Full article

(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)

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20 pages, 3182 KB

Open AccessArticle

Testing a Farm Animal Model for Experimental Kidney Graft Transplantation: Gut Microbiota, Mycobiome and Metabolic Profiles as Indicators of Model Stability and Suitability

by Sona Gancarcikova, Vlasta Demeckova, Stanislav Lauko, Maria Rynikova, Vanda Hajduckova, Pavel Gomulec, David Adandedjan, Eva Petrovova, Rastislav Kalanin, Stefan Hulik, Igor Gala, Jozef Brezina, Jaroslav Novotny, Gabriela Conkova Skybova and Jana Katuchova

Appl. Sci. 2026, 16(2), 625; https://doi.org/10.3390/app16020625 - 7 Jan 2026

Abstract

The aim of this pilot study was to comprehensively evaluate the gut microbiota, mycobiome, and metabolomic profile of six 4-month-old crossbred pigs (A–F) originating from the same litter and from a specific breeding facility intended for preclinical transplantation experiments, in order to assess [...] Read more.

The aim of this pilot study was to comprehensively evaluate the gut microbiota, mycobiome, and metabolomic profile of six 4-month-old crossbred pigs (A–F) originating from the same litter and from a specific breeding facility intended for preclinical transplantation experiments, in order to assess their physiological uniformity and identify potential health-related risks prior to inclusion in a kidney transplantation study. The results demonstrated an overall high degree of microbial and metabolic uniformity among the animals, confirming the stability and suitability of the selected breeding source for experimental purposes. At the same time, several individual differences of potential clinical relevance were observed. Animals A, E, and F exhibited signs of microbial and metabolic imbalance, including reduced diversity, increased oxidative activity, and the presence of potentially pathogenic taxa (Porphyromonadaceae bacterium DJF B175, Aspergillus). In contrast, animals B, C, and D showed a balanced metabolic and microbial profile without pathological deviations. The obtained results highlight the importance of preoperative assessment of the gut bacteriome, mycobiome, and metabolome when selecting animals for transplantation experiments. Such a selective screening approach may contribute to the early identification of physiological deviations, reduction of interindividual variability, and increased reliability and translational potential of preclinical studies. Full article

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26 pages, 4361 KB

Open AccessArticle

Multifaceted Characterization of Olive-Associated Endophytic Fungi with Potential Applications in Growth Promotion and Disease Management

by Tasos-Nektarios Spantidos, Dimitra Douka, Panagiotis Katinakis and Anastasia Venieraki

Appl. Sci. 2026, 16(2), 624; https://doi.org/10.3390/app16020624 - 7 Jan 2026

Abstract

The olive tree hosts diverse endophytic fungi that may contribute to plant protection and growth. In this study, a preliminary screening of olive-associated fungal endophytes was conducted. A total of 67 fungal endophytes were isolated from the leaves and roots of the Greek [...] Read more.

The olive tree hosts diverse endophytic fungi that may contribute to plant protection and growth. In this study, a preliminary screening of olive-associated fungal endophytes was conducted. A total of 67 fungal endophytes were isolated from the leaves and roots of the Greek cultivars Amfissa and Kalamon and identified using morphological and molecular approaches; 28 representative strains were selected for functional evaluation. Dual culture assays revealed substantial antagonistic activity against major phytopathogens, with growth inhibition ranging from 19.05% to 100%. Notably, strains F.KALl.8 and F.AMFr.15 showed the strongest suppression across pathogens. Interaction phenotyping revealed all major interaction types (A, B, C) and subtype C1/C2, with several strains producing pigmentation zone lines or hyphal ridges at contact sites. The assessment of plant growth-related effects using Arabidopsis thaliana as a model system showed that three strains (F.AMFr.15, F.KALr.4, F.KALr.38A) significantly increased seedling biomass (up to ~16% above the control), whereas nine strains caused severe growth reduction and disease symptoms. Beneficial strains also altered root architecture, inhibiting primary root elongation while inducing extensive lateral root formation. Collectively, these findings highlight the functional diversity of olive-associated fungal endophytes and identify promising candidate strains, particularly F.AMFr.15 (identified as Clonostachys sp.), for further host-specific validation as potential biological control and plant growth-promoting agents. Full article

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

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18 pages, 9640 KB

Open AccessArticle

KT-NAS: Knowledge Transfer for Efficient Neural Architecture Search

by Linh-Tam Tran, A. F. M. Shahab Uddin, Younho Jang and Sung-Ho Bae

Appl. Sci. 2026, 16(2), 623; https://doi.org/10.3390/app16020623 - 7 Jan 2026

Abstract

Pre-trained models have played important roles in many tasks, such as domain adaptation and out-of-distribution generalization, by transferring matured knowledge. In this paper, we study Neural Architecture Search (NAS) in the feature space level and observe that low-level features of NAS-based networks (generated [...] Read more.

Pre-trained models have played important roles in many tasks, such as domain adaptation and out-of-distribution generalization, by transferring matured knowledge. In this paper, we study Neural Architecture Search (NAS) in the feature space level and observe that low-level features of NAS-based networks (generated networks from a NAS space) become stable in the earlier stage of training. In addition, these low-level features are similar to those from hand-crafted networks such as VGG, ResNet, and DenseNet. This phenomenon is consistent over different search spaces and datasets. Motivated by these observations, we propose a new architectural method for NAS, called Knowledge-Transfer NAS, which utilizes the features from a pre-trained hand-crafted network. Specifically, we replace the first few cells of NAS-based networks with pre-trained manually designed blocks and freeze them, and then only train the remaining cells. We perform extensive experiments using various NAS algorithms and search spaces, and show that Knowledge-Transfer NAS achieves higher/comparable performance while requiring less memory footprint and search time, offering a new perspective on the applicability of pre-trained models for improved NAS algorithms. Full article

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

13 pages, 1139 KB

Open AccessArticle

Temporary Hearing Threshold Shifts and Cognitive Effects Induced by Ultrasonic Noise Exposure

by Jan Radosz

Appl. Sci. 2026, 16(2), 622; https://doi.org/10.3390/app16020622 - 7 Jan 2026

Abstract

This study examined the auditory and cognitive effects of occupational ultrasonic noise exposure through controlled laboratory experiments simulating workplace conditions. A group of 20 participants aged 18–35 underwent pure-tone audiometry (PTA) in both standard (1–8 kHz) and extended high-frequency (9–16 kHz) ranges before [...] Read more.

This study examined the auditory and cognitive effects of occupational ultrasonic noise exposure through controlled laboratory experiments simulating workplace conditions. A group of 20 participants aged 18–35 underwent pure-tone audiometry (PTA) in both standard (1–8 kHz) and extended high-frequency (9–16 kHz) ranges before and after exposure to airborne ultrasound emitted by an ultrasonic cleaner. The exposure was conducted at two sound pressure levels: at the current permissible occupational limit and at a level 5 dB below it. The results demonstrated statistically significant temporary threshold shifts (TTS) in hearing sensitivity (bilaterally) at 8 kHz and 16 kHz only at the higher exposure level, with mean shifts reaching 3.8 dB and 5.8 dB, respectively. No significant hearing threshold changes were observed at the reduced exposure level. Additionally, participants completed a battery of Abilitest cognitive tests during exposure. Comparisons with standardized normative data showed that reaction times were approximately 20% longer in simple response tasks and 13% longer in selective attention tasks, suggesting a potential deviation in cognitive performance associated with ultrasonic noise. These findings support the need to reevaluate current occupational exposure limits and highlight the potential health and performance risks associated with airborne ultrasound. Full article

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29 pages, 4280 KB

Open AccessArticle

Benefits and Concerns Related to the Implementation of Artificial Intelligence Technology in Enterprises Located in the West Pomeranian Voivodeship of Poland

by Ludmiła Filina-Dawidowicz, Agnieszka Barczak, Joanna Sęk, Piotr Trojanowski, Anna Wiktorowska-Jasik and Dorota Ciesielczyk

Appl. Sci. 2026, 16(2), 621; https://doi.org/10.3390/app16020621 - 7 Jan 2026

Abstract

Artificial intelligence (AI) technologies are actively implemented in companies to support their operation. However, the adoption of AI technologies brings both benefits and concerns, which influence the decisions made by business managers. The aim of this article is to examine the benefits and [...] Read more.

Artificial intelligence (AI) technologies are actively implemented in companies to support their operation. However, the adoption of AI technologies brings both benefits and concerns, which influence the decisions made by business managers. The aim of this article is to examine the benefits and concerns associated with the use of AI technologies based on the opinions of representatives of Polish enterprises located in the West Pomeranian Voivodeship of Poland. In order to conduct the study, a questionnaire was developed and a survey was carried out among representatives of companies operating in the West Pomeranian Voivodeship of Poland. Multivariate correspondence analysis was used as a research tool to analyze the collected data. It was found that a customized approach to the implementation of AI technology is needed, depending on the organizational context, market type, and company’s size. Furthermore, it was stated that respondents’ perception of benefits and concerns varies depending on the number of employees and sector in which the company operates. The results of the study may be of interest to companies’ leaders interested in implementing artificial intelligence technologies. Full article

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17 pages, 4696 KB

Open AccessArticle

Analysis of Adaptive Fractional-Order Sliding-Mode Control Method Based on Smith Predictor for Voice Coil Motor

by Ziyi Shi and Xiaobao Yang

Appl. Sci. 2026, 16(2), 620; https://doi.org/10.3390/app16020620 - 7 Jan 2026

Abstract

To satisfy the stringent requirements of ultra-precision systems for high accuracy and rapid dynamic response, the chatter and system delay of voice coil motor (VCM) under operating conditions have become key bottlenecks restricting overall performance enhancement. To address these challenges, this paper proposes [...] Read more.

To satisfy the stringent requirements of ultra-precision systems for high accuracy and rapid dynamic response, the chatter and system delay of voice coil motor (VCM) under operating conditions have become key bottlenecks restricting overall performance enhancement. To address these challenges, this paper proposes an adaptive convergence rate fractional-order sliding mode control strategy based on a Smith predictor (AFOSMC-SP). The strategy constructs a fractional-order sliding mode controller using an improved Oustaloup method, introduces a novel adaptive reaching law based on a saturation function with dynamic gain, and integrates a Smith predictor to compensate for the current-loop delay in the VCM system. This approach reduces algorithmic complexity and position-tracking error while enabling the adaptive adjustment of the system convergence rate to enhance robustness. In addition, the parameter boundary conditions and the global asymptotic stability of the closed-loop system are analyzed using Lyapunov stability theory. The simulation results show that, compared to conventional sliding mode control methods, the proposed AFOSMC-SP strategy provides superior parameter adaptability, higher tracking accuracy, and stronger suppression of external disturbances. Full article

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24 pages, 2088 KB

Open AccessSystematic Review

Natural Language Processing (NLP)-Based Frameworks for Cyber Threat Intelligence and Early Prediction of Cyberattacks in Industry 4.0: A Systematic Literature Review

by Majed Albarrak, Konstantinos Salonitis and Sandeep Jagtap

Appl. Sci. 2026, 16(2), 619; https://doi.org/10.3390/app16020619 - 7 Jan 2026

Abstract

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an [...] Read more.

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

(This article belongs to the Special Issue Advances in Cyber Security)

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70 pages, 1271 KB

Open AccessSystematic Review

A Systematic Review of Computational and Data-Driven Approaches for Energy-Efficient Battery Management in Electrified Vehicles

by Milos Poliak, Damian Frej, Piotr Łagowski and Justyna Jaśkiewicz

Appl. Sci. 2026, 16(2), 618; https://doi.org/10.3390/app16020618 - 7 Jan 2026

Abstract

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, [...] Read more.

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

23 pages, 1035 KB

Open AccessReview

Mushroom-Derived Compounds as Inhibitors of Advanced Glycation End-Products

by Filip Šupljika, Monika Kovačević and Mojca Čakić Semenčić

Appl. Sci. 2026, 16(2), 617; https://doi.org/10.3390/app16020617 - 7 Jan 2026

Abstract

Mushrooms like Inonotus obliquus and Ganoderma lucidum show significant pharmacological promise. This review analyzes fungi as sources of natural inhibitors against Advanced Glycation End-products (AGEs)—key drivers of diabetes and neurodegeneration. We highlight that extracts from Lignosus rhinocerus and Auricularia auricula exhibit antiglycation potency [...] Read more.

Mushrooms like Inonotus obliquus and Ganoderma lucidum show significant pharmacological promise. This review analyzes fungi as sources of natural inhibitors against Advanced Glycation End-products (AGEs)—key drivers of diabetes and neurodegeneration. We highlight that extracts from Lignosus rhinocerus and Auricularia auricula exhibit antiglycation potency (IC₅₀ as low as 0.001 mg/mL) superior to aminoguanidine. Inhibitory effects are attributed to bioactive fractions including FYGL proteoglycans, uronic acid-rich polysaccharides, and fungal-specific metabolites like ergothioneine. These compounds act through multi-target mechanisms across the glycation cascade: competitive inhibition of Schiff base formation, trapping reactive dicarbonyls (e.g., methylglyoxal), transition metal chelation, and direct scavenging of reactive oxygen species (ROS). Furthermore, the review addresses the transition from in vitro potency to in vivo efficacy (RAGE pathway modulation), stability during food processing (UV-B irradiation), and critical safety issues regarding heavy metal bioaccumulation. Mushroom-derived inhibitors represent a sustainable therapeutic alternative to synthetic agents, offering broader protection against glycative stress. This synthesis provides a foundation for developing standardized mushroom-based nutraceuticals for managing AGE-related chronic disorders. Full article

(This article belongs to the Special Issue The Recent Development of Functional Foods and Natural Products—Impacts on Human Health)

34 pages, 30496 KB

Open AccessArticle

Convolutional Neural Network-Based Detection of Booming Noise in Internal Combustion Engine Vehicles Using Simulated Acoustic Spectrograms

by Pedro Leite, Joaquim Mendes, Filipe Pereira, António Mendes Lopes and António Ramos Silva

Appl. Sci. 2026, 16(2), 616; https://doi.org/10.3390/app16020616 - 7 Jan 2026

Abstract

In this work, we tested the use of Convolutional Neural Networks (CNNs) to classify booming noise inside vehicles. Instead of relying only on long experimental campaigns, we generated a synthetic dataset from Sound Quality Equivalent (SQE) models that were originally built from real [...] Read more.

In this work, we tested the use of Convolutional Neural Networks (CNNs) to classify booming noise inside vehicles. Instead of relying only on long experimental campaigns, we generated a synthetic dataset from Sound Quality Equivalent (SQE) models that were originally built from real acoustic measurements collected with sensors. By applying smoothing functions and Hann windows, we were able to vary the intensity of the booming effect across different mission profiles. The CNNs were trained on spectrograms derived from these signals, with labels informed by psychoacoustic evaluations. The best model reached about 95.5% accuracy in the binary task (booming vs. no booming) and around 93.3% when using three classes (severe, mild, none). Tests with data from three different car models showed that the method can generalize across platforms. These results suggest that CNNs may become a practical tool for NVH analysis, offering a simpler and cheaper complement to traditional end-of-line testing, and one that could be adapted for real-time embedded systems. Full article

21 pages, 10154 KB

Open AccessArticle

CRS-Y: A Study and Application of a Target Detection Method for Underwater Blasting Construction Sites

by Xiaowu Huang, Han Gao, Linna Li, Yucheng Zhao and Chen Men

Appl. Sci. 2026, 16(2), 615; https://doi.org/10.3390/app16020615 - 7 Jan 2026

Abstract

To strengthen the safety management and control of explosives in underwater blasting construction sites, this study proposes an improved YOLOv11-based network named CRS-Y, designed to enhance the detection accuracy of explosives in complex underwater environments and improve the recognition capability of multi-scale targets. [...] Read more.

To strengthen the safety management and control of explosives in underwater blasting construction sites, this study proposes an improved YOLOv11-based network named CRS-Y, designed to enhance the detection accuracy of explosives in complex underwater environments and improve the recognition capability of multi-scale targets. To address the limitations of traditional object detection methods in handling complex backgrounds and low-resolution targets, a lightweight re-parameterized vision transformer was integrated into the C3K module, forming a novel CSP structure (C3K-RepViT) that enhances feature extraction under small receptive fields. In combination with the Efficient Multi-Scale Attention (EMSA) mechanism, the model’s spatial feature representation is further strengthened, enabling a more effective understanding of objects in complex scenes. Furthermore, to reduce the computational cost of the P2 feature layer, a new convolutional structure named SPD-DSConv (Space-to-Depth Depthwise Separable Convolution) is proposed, which integrates downsampling and channel expansion within depthwise separable convolution. This design achieves a balance between parameter reduction and multidimensional feature learning. Finally, the Inner-IoU loss function is introduced to dynamically adjust auxiliary bounding box scales, accelerating regression convergence for both high-IoU and low-IoU samples, thereby optimizing bounding box shapes and localization accuracy while improving overall detection performance and robustness. Experimental results demonstrate that the proposed CRS-Y model achieved superior performance on the VOC2012, URPC2020, and self-constructed underwater blasting datasets, effectively meeting the real-time detection requirements of underwater blasting construction scenarios while exhibiting strong generalization ability and practical value. Full article

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23 pages, 1941 KB

Open AccessArticle

Quantitative Analysis of Spatiotemporal Variations in Ecological Water-Supplementation Benefits of Rivers Based on Remote Sensing: A Case Study of the Yongding River (Beijing Section)

by Lisheng Li, Qinghua Qiao and Hongping Zhang

Appl. Sci. 2026, 16(2), 614; https://doi.org/10.3390/app16020614 - 7 Jan 2026

Abstract

River ecosystems play a crucial role in the global water cycle and regional ecological security, yet they face severe challenges under the dual pressures of human activities and climate change. To systematically assess the spatiotemporal characteristics and driving mechanisms of river ecological impacts, [...] Read more.

River ecosystems play a crucial role in the global water cycle and regional ecological security, yet they face severe challenges under the dual pressures of human activities and climate change. To systematically assess the spatiotemporal characteristics and driving mechanisms of river ecological impacts, this study proposes a modular and transferable method, which is Quantitative Analysis of Spatiotemporal Variations in Ecological Water-Supplementation Benefits of Rivers Based on Remote Sensing (QASViewSBR). Taking the Yongding River (Beijing section) from 2016 to 2023 as a case study, this research integrates multi-source remote sensing and ground monitoring data to extract river water bodies using an improved Normalized Difference Water Index and Vertical–Horizontal polarization characteristics. The Seasonal and Trend decomposition using Loess (STL) method was employed for time-series trend decomposition, Pearson correlation analysis was applied to identify driving factors of area changes, and the Pelt algorithm was used to quantify the response range of riparian vegetation to changes of river water levels. An integrated analytical framework of “dynamic monitoring—time series analysis—driving factor identification—spatial heterogeneity assessment” was established, enabling standardized end-to-end analysis from data acquisition to evaluation. The results indicate that the river water area in the basin increased significantly after 2019, with enhanced seasonal fluctuations. Under the ecological water supplementation policy, the “human-initiated, natural-response” mechanism was clearly observed, and the ecological responses along both riverbanks exhibited significant spatial heterogeneity due to variations in surface features and topography. QASViewSBR exhibits good universality and transferability, providing methodological support for ecological restoration and management in different river basins. Full article

(This article belongs to the Section Ecology Science and Engineering)

13 pages, 1912 KB

Open AccessArticle

Research on the Backscattering Prediction Mechanism for Underwater Turbulent Channels

by Yongjie Li, Jingjing Luo, Siguang Zong, Mengxue Lin and Shaopeng Yang

Appl. Sci. 2026, 16(2), 613; https://doi.org/10.3390/app16020613 - 7 Jan 2026

Abstract

In the field of underwater laser detection, turbulence causes beam wandering and intensity scintillation, which subsequently alter the angle of incidence and ultimately degrade the quality of the target echo signal. By establishing an experimental platform that simulates oceanic turbulent channels, this study [...] Read more.

In the field of underwater laser detection, turbulence causes beam wandering and intensity scintillation, which subsequently alter the angle of incidence and ultimately degrade the quality of the target echo signal. By establishing an experimental platform that simulates oceanic turbulent channels, this study investigates the correlation between turbulence location and the backscattered optical scintillation index. This work lays the foundation for developing reliable assessment techniques for laser backscattering detection channels. Using a thermally driven turbulence simulator and an off-axis blue-green laser, a backscattering model was developed via echo signal analysis. This model captures the relationship between turbulence spatial distribution and the optical scintillation coefficient, revealing distinct nonlinear behavior in this relationship. Experimental results revealed a non-monotonic trend in the optical scintillation coefficient, characterized by an initial decrease followed by an increase, with the distance from the turbulence region. While increased water turbidity preserved this overall trend, it resulted in a dampened response. The proposed model demonstrated high reliability, with R² values of 0.8579 and 0.8844 for the open-sea and coastal environments, respectively. The turbulent laser detection backscattering channel prediction model supports the evaluation of oceanic blue-green laser detection channels. Full article

(This article belongs to the Section Optics and Lasers)

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18 pages, 16226 KB

Open AccessArticle

Liquefaction Hazard Assessment and Mapping Across the Korean Peninsula Using Amplified Liquefaction Potential Index

by Woo-Hyun Baek and Jae-Soon Choi

Appl. Sci. 2026, 16(2), 612; https://doi.org/10.3390/app16020612 - 7 Jan 2026

Abstract

Liquefaction is a critical mechanism amplifying earthquake-induced damage, necessitating systematic hazard assessment through spatially distributed mapping. This study presents a nationwide liquefaction hazard assessment framework for South Korea, integrating site classification, liquefaction potential index (LPI) computation, and probabilistic damage evaluation. Sites across the [...] Read more.

Liquefaction is a critical mechanism amplifying earthquake-induced damage, necessitating systematic hazard assessment through spatially distributed mapping. This study presents a nationwide liquefaction hazard assessment framework for South Korea, integrating site classification, liquefaction potential index (LPI) computation, and probabilistic damage evaluation. Sites across the Korean Peninsula were stratified into five geotechnical categories (S1–S5) based on soil characteristics. LPI values were computed incorporating site-specific amplification coefficients for nine bedrock acceleration levels corresponding to seismic recurrence intervals of 500, 1000, 2400, and 4800 years per Korean seismic design specifications. Subsurface characterization utilized standard penetration test (SPT) data from 121,821 boreholes, with an R-based analytical program enabling statistical processing and spatial visualization. Damage probability assessment employed Iwasaki’s LPI severity classification across site categories. Results indicate that at 0.10 g peak ground acceleration (500-year event), four regions exhibit severe liquefaction susceptibility. This geographic footprint expands to seven regions at 0.14 g (1000-year event) and eight regions at 0.18 g. For the 2400-year design basis earthquake (0.22 g), all eight identified high-risk zones reach critical thresholds simultaneously. Site-specific analysis reveals stark contrasts in vulnerability: S2 sites demonstrate 99% very low to low damage probability, whereas S3, S4, and S5 sites face 33%, 51%, and 99% severe damage risk, respectively. This study establishes a scalable, evidence-based framework enabling efficient large-scale liquefaction hazard assessment for governmental risk management applications. Full article

(This article belongs to the Special Issue Soil Dynamics and Earthquake Engineering)

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21 pages, 1772 KB

Open AccessArticle

Towards Patient Anatomy-Based Simulation of Net Cerebrospinal Fluid Flow in the Intracranial Compartment

by Edgaras Misiulis, Algis Džiugys, Alina Barkauskienė, Aidanas Preikšaitis, Vytenis Ratkūnas, Gediminas Skarbalius, Robertas Navakas, Tomas Iešmantas, Robertas Alzbutas, Saulius Lukoševičius, Mindaugas Šerpytis, Indrė Lapinskienė, Jewel Sengupta and Vytautas Petkus

Appl. Sci. 2026, 16(2), 611; https://doi.org/10.3390/app16020611 - 7 Jan 2026

Abstract

Biophysics-based, patient-specific modeling remains challenging for clinical translation, particularly for cerebrospinal fluid (CSF) flow where anatomical detail and computational cost are tightly coupled. We present a computational framework for steady net CSF redistribution in an MRI-derived cranial CSF domain reconstructed from T₂ [...] Read more.

Biophysics-based, patient-specific modeling remains challenging for clinical translation, particularly for cerebrospinal fluid (CSF) flow where anatomical detail and computational cost are tightly coupled. We present a computational framework for steady net CSF redistribution in an MRI-derived cranial CSF domain reconstructed from T₂-weighted imaging, including the ventricular system, cranial subarachnoid space, and periarterial pathways, to the extent resolvable by clinical MRI. Cranial CSF spaces were segmented in 3D Slicer and a steady Darcy formulation with prescribed CSF production/absorption was solved in COMSOL Multiphysics^®. Geometrical and flow descriptors were quantified using region-based projection operations. We assessed discretization cost–accuracy trade-offs by comparing first- and second-order finite elements. First-order elements produced a 1.4% difference in transmantle pressure and a <10% difference in element-wise mass-weighted velocity metric for 90% of elements, while reducing computation time by 75% (20 to 5 min) and peak memory usage five-fold (150 to 30 GB). This proof-of-concept framework provides a computationally tractable baseline for studying steady net CSF pathway redistribution and sensitivity to boundary assumptions, and may support future patient-specific investigations in pathological conditions such as subarachnoid hemorrhage, hydrocephalus and brain tumors. Full article

(This article belongs to the Special Issue Numerical Simulation in Biomechanics and Biomedical Engineering-3rd Edition)

20 pages, 2092 KB

Open AccessArticle

Calibration of Snow Particle Contact Parameters for Simulation Analysis of Membrane Structure Snow Removal Robot

by Jiangtao Dong, Fuxiang Zhang, Fengshan Huang and Xiaofei Man

Appl. Sci. 2026, 16(2), 610; https://doi.org/10.3390/app16020610 - 7 Jan 2026

Abstract

To enhance the accuracy of discrete element method (DEM) simulation for the snow removal process performed by autonomous robots on membrane structures, this study calibrated the key contact parameters of snow particles used in the simulation. Through literature research, the intrinsic parameters and [...] Read more.

To enhance the accuracy of discrete element method (DEM) simulation for the snow removal process performed by autonomous robots on membrane structures, this study calibrated the key contact parameters of snow particles used in the simulation. Through literature research, the intrinsic parameters and contact parameter ranges for snow particles and membrane structures were determined. A discrete element model of snow particles was established, and the Hertz–Mindlin with Johnson–Kendall–Robert contact model was selected to simulate the formation process of the repose angle. Using the actual repose angle of snow particles as the target, four significant factors were identified through the P-B experiment, and other factors were set at the intermediate level. Through the steepest slope climbing experiment and response surface design, second-order response equations of the four significant factors were obtained. The optimal parameter combination was calculated as follows: the surface energy of snow particles was 0.23 J/m²; the restitution coefficient, static friction coefficient, and rolling friction coefficient of snow–snow were 0.141, 0.05, and 0.03; and the restitution coefficient, static friction coefficient, and rolling friction coefficient of snow–membrane were 0.2, 0.18, and 0.03. The simulated repose angle was 40.62°, and the relative error with the actual repose angle was 0.32%. These calibration results are reliable and can provide a reliable simulation basis and essential data support for the optimal design of a snow removal robot and the dynamic simulation of the operation process. Full article

(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)

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24 pages, 30915 KB

Open AccessArticle

A Surface Defect Detection System for Industrial Conveyor Belt Inspection Using Apple’s TrueDepth Camera Technology

by Mohammad Siami, Przemysław Dąbek, Hamid Shiri, Tomasz Barszcz and Radosław Zimroz

Appl. Sci. 2026, 16(2), 609; https://doi.org/10.3390/app16020609 - 7 Jan 2026

Abstract

Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh [...] Read more.

Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh mining environments characterized by dust and variable lighting. This study introduces a smartphone-driven defect detection system for the cost-effective, geometric inspection of conveyor belt surfaces. Using Apple’s iPhone 12 Pro Max (Apple Inc., Cupertino, CA, USA), the system captures 3D point cloud data from a moving belt with induced damage via the integrated TrueDepth camera. A key innovation is a 3D-to-2D projection pipeline that converts point cloud data into structured representations compatible with standard 2D Convolutional Neural Networks (CNNs). We then propose a hybrid deep learning and machine learning model, where features extracted by pre-trained CNNs (VGG16, ResNet50, InceptionV3, Xception) are classified by ensemble methods (Random Forest, XGBoost, LightGBM). The proposed system achieves high detection accuracy exceeding 0.97 F1 score in the case of all proposed model implementations with TrueDepth F1 score over 0.05 higher than RGB approach. Applied cost-effective smartphone-based sensing platform proved to support near-real-time maintenance decisions. Laboratory results demonstrate the method’s reliability, with measurement errors for defect dimensions within 3 mm. This approach shows significant potential to improve conveyor belt management, reduce maintenance costs, and enhance operational safety. Full article

(This article belongs to the Special Issue Mining Engineering: Present and Future Prospectives)

26 pages, 9984 KB

Open AccessArticle

Multi-Fidelity Data and Prior-Enhanced Physics-Informed Neural Networks for Multi-Parameter Identification of Prestressed Concrete Beams with Unquantifiable Noise

by Yuping Zhang, Yifan Yang, Yubo Hu and Zengwei Guo

Appl. Sci. 2026, 16(2), 608; https://doi.org/10.3390/app16020608 - 7 Jan 2026

Abstract

Although PINNs have demonstrated strong predictive capabilities in forward problems, their performance in inverse problems remains inadequate, largely due to unquantifiable noise encountered during the multi-parameter identification of prestressed concrete beams. Experimental measurements are often noisy, sparse, or asymmetric, while numerical or analytical [...] Read more.

Although PINNs have demonstrated strong predictive capabilities in forward problems, their performance in inverse problems remains inadequate, largely due to unquantifiable noise encountered during the multi-parameter identification of prestressed concrete beams. Experimental measurements are often noisy, sparse, or asymmetric, while numerical or analytical models, although physically consistent, are typically approximate and lack regularization from well-defined multi-fidelity data. To address this limitation, this paper proposed a multi-fidelity data and prior-enhanced physics-informed neural network (MF-rPINN), which integrates multi-fidelity data with physics prior relational constraints to guide parameter identification using only sparse experimental observations. The MF-rPINN architecture is designed to enforce consistency between each training iteration and a prescribed set of experimental measurements, while embedding the second-order displacement function into the loss function. Experimental results demonstrate that the proposed MF-rPINN achieves accurate parameter identification even under noisy and incomplete observations, owing to the combined regularization effects of governing physical laws and the second-order displacement prior. The minimum relative errors of the elastic modulus are −6.49% and −9.32% under different and identical loading conditions, respectively, while the minimum relative errors of the prestress force are 0.65% and 4.51%. Compared with classical analytical approaches, MF-rPINN exhibits superior robustness and is capable of predicting continuous displacement fields of prestressed concrete beams while simultaneously identifying prestress force and elastic modulus. These advantages highlight the potential of MF-rPINN as a reliable surrogate modeling tool for practical engineering applications. Full article

(This article belongs to the Section Civil Engineering)

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37 pages, 1750 KB

Open AccessArticle

An Ontology-Driven Framework for Road Technical Condition Assessment and Maintenance Decision-Making

by Rujie Zhang, Jianwei Wang and Haijiang Li

Appl. Sci. 2026, 16(2), 607; https://doi.org/10.3390/app16020607 - 7 Jan 2026

Abstract

Road technical condition assessment and maintenance decision-making rely heavily on technical standards whose clauses, computational formulas, and decision logic are often expressed in unstructured formats, leading to fragmented knowledge representation, isolated indicator calculation procedures, and limited interpretability of decision outcomes. To address these [...] Read more.

Road technical condition assessment and maintenance decision-making rely heavily on technical standards whose clauses, computational formulas, and decision logic are often expressed in unstructured formats, leading to fragmented knowledge representation, isolated indicator calculation procedures, and limited interpretability of decision outcomes. To address these challenges, a semantic framework with executable reasoning and computation components, Road Performance and Maintenance Ontology (RPMO), was developed, composed of a core ontology, an assessment ontology, and a maintenance ontology. The framework formalized clauses, computational formulas, and decision rules from standards and integrated semantic web rule language (SWRL) rules with external computational programs to automate distress identification and the computation and write-back of performance indicators. Validation through three use case scenarios conducted on eleven expressway asphalt pavement segments demonstrated that the framework produced distress severity inference, indicator computation, performance rating, and maintenance recommendations that were highly consistent with technical standards and expert judgment, with all reasoning results traceable to specific clauses and rule instances. This research established a methodological foundation for semantic transformation of road technical standards and automated execution of assessment and decision logic, enhancing the efficiency, transparency, and consistency of maintenance decision-making to support explicit, reliable, and knowledge-driven intelligent systems. Full article

(This article belongs to the Section Civil Engineering)

22 pages, 4532 KB

Open AccessArticle

Ray Tracing Calibration Based on Local Phase Error Estimates for Rail Transit Wireless Channel Modeling

by Meng Lan, Jianfeng Liu, Meng Mei and Zhongwei Xu

Appl. Sci. 2026, 16(2), 606; https://doi.org/10.3390/app16020606 - 7 Jan 2026

Abstract

Ray tracing (RT) has become an important method for train-to-ground (T2G) wireless channel modeling due to its physical interpretability. In rail transit scenarios, RT suffers from modeling errors that arise due to environmental reconstruction and uncertainties in electromagnetic parameters, as well as dynamic [...] Read more.

Ray tracing (RT) has become an important method for train-to-ground (T2G) wireless channel modeling due to its physical interpretability. In rail transit scenarios, RT suffers from modeling errors that arise due to environmental reconstruction and uncertainties in electromagnetic parameters, as well as dynamic phase errors caused by coherent multi-path superposition that is further triggered by such modeling errors. Phase errors significantly affect both the calibration accuracy and prediction precision of RT. Therefore, this paper proposes an intelligent RT calibration method based on local phase errors. The method builds a phase error distribution model and uses constraints from limited measurements to explicitly estimate and correct phase errors in RT-generated channel responses. Firstly, the method applies the Variational Expectation–Maximization (VEM) algorithm to optimize the phase error model, where the expectation step derives an approximate posterior distribution and the maximization step updates parameters conditioned on this posterior. Secondly, experiments are conducted using differentiable RT implemented in the Sionna library, which explicitly provides gradients of environmental and link parameters with respect to channel frequency responses, enabling end-to-end calibration. Finally, experimental results show that in railway scenarios, compared with calibration methods based on phase error-oblivious and uniform phase error, the proposed approach achieves average gains of about 10 dB at SNR = 0 dB and 20 dB at SNR = 30 dB. Full article

(This article belongs to the Special Issue Wireless Communication: Applications, Security and Reliability—2nd Edition)

22 pages, 4042 KB

Open AccessArticle

The Concept of a Hierarchical Digital Twin

by Magdalena Jarzyńska, Andrzej Nierychlok and Małgorzata Olender-Skóra

Appl. Sci. 2026, 16(2), 605; https://doi.org/10.3390/app16020605 - 7 Jan 2026

Abstract

The concept of a digital twin has become a key driver of industrial transformation, enabling a seamless connection between physical systems and their virtual counterparts. The growing need for adaptability has accelerated the use of advanced technologies and tools to maintain competitiveness. In [...] Read more.

The concept of a digital twin has become a key driver of industrial transformation, enabling a seamless connection between physical systems and their virtual counterparts. The growing need for adaptability has accelerated the use of advanced technologies and tools to maintain competitiveness. In this context, the article introduces the concept of a hierarchical digital twin and illustrates its operation through a practical example. Production resource structures and timing data were generated in the KbRS (Knowledge-based Rescheduling System), which will serve as the Level II digital twin in this article. The acquired data is transferred via Excel to the FlexSim simulation environment, which represents the Level I digital twin responsible for modeling the flow of production processes. Because a digital twin must accurately reflect a specific production system, the study begins by formulating a general mathematical model. Algorithms for product ordering and for constructing the digital twin of the production processes were developed. Furthermore, three implementation scenarios for the hierarchical digital twin were proposed using the KbRS and FlexSim tools. The implementation of the hierarchical digital twin concept facilitated the development of the more comprehensive virtual model. At the same time, the integration of data between the two software environments enabled the generation of more detailed and precise results. Traditionally, a digital twin created solely within a single simulation platform is unable to represent all the structural components of a production system—an issue addressed by the hierarchical approach presented in this study. Full article

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Appl. Sci., Volume 16, Issue 2 (January-2 2026) – 73 articles

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