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Applied Sciences

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering )
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our authors say about Applied Sciences.
Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal, JETA and AI in Medicine.

Impact Factor: 2.5 (2024); 5-Year Impact Factor: 2.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

22 pages, 2208 KB

Open AccessArticle

Fostering Teachers’ Digital Competence in AI-Supported Learning Environments: Implications for Interactive Teaching and Student Achievement

by Gaukhar Aimicheva, Aigul Shaikhanova, Kainizhamal Iklassova, Parassat Tazabekova, Aizhan Nazyrova and Yenglik Kadyr

Appl. Sci. 2025, 15(23), 12597; https://doi.org/10.3390/app152312597 (registering DOI) - 27 Nov 2025

In this article, the development of in-service teachers’ digital competence is examined within an AI-supported learning environment designed to enhance professional modeling skills and the creation of interactive AR-based instructional content. The study investigates how such an environment supports teachers in developing contextual [...] Read more.

In this article, the development of in-service teachers’ digital competence is examined within an AI-supported learning environment designed to enhance professional modeling skills and the creation of interactive AR-based instructional content. The study investigates how such an environment supports teachers in developing contextual digital skills that enable not only the use of emerging technologies but also their meaningful adaptation to pedagogical goals and instructional needs. A training program involving 916 in-service teachers from Kazakhstani secondary schools was implemented, and survey data were collected to assess changes in digital competence and readiness to integrate AI and AR tools into teaching practices. The findings demonstrate high levels of interest and engagement: 96% of participants expressed readiness for further learning, 86% reported satisfaction with the course content, and 84% showed contextual maturity in applying newly acquired technologies in their instructional processes. These results highlight the potential of AI-supported professional development to strengthen teachers’ capacity to design interactive learning environments, promote equity and quality in digital education, and enhance student engagement. Full article

18 pages, 1962 KB

Open AccessArticle

Stress–Strain–Strength Behavior of Hydraulic Asphalt Concrete at Different Bitumen Grades

by Xing Yang, Zhihao Yang, Congyong Ran and Jianxin He

Appl. Sci. 2025, 15(23), 12596; https://doi.org/10.3390/app152312596 - 27 Nov 2025

The stress–strain–strength behavior of hydraulic asphalt concrete is critical to the safety of the high asphalt concrete core. To study the effect of bitumen grade on the stress–strain–strength behavior of hydraulic asphalt concrete, uniaxial compression tests, direct tension tests, bending tests, and triaxial [...] Read more.

The stress–strain–strength behavior of hydraulic asphalt concrete is critical to the safety of the high asphalt concrete core. To study the effect of bitumen grade on the stress–strain–strength behavior of hydraulic asphalt concrete, uniaxial compression tests, direct tension tests, bending tests, and triaxial compression tests were conducted. The variation patterns of mechanical performance indicators and stress–strain curves of hydraulic asphalt concrete with bitumen grades A70, A90, and A110 were analyzed. The elastic modulus expression of asphalt concrete based on nonlinear failure criteria were proposed. Considering potential issues associated with asphalt concrete core, the selection of bitumen grades was discussed. The results indicate that increasing the bitumen grade enhances the tensile, compressive, bending, and shear deformation properties of hydraulic asphalt concrete, and makes it exhibit more pronounced ductile behavior. However, the strength and modulus decrease. The use of higher-grade bitumen reduces the dilatancy of hydraulic asphalt concrete. As the bitumen grade increases, the nonlinear property of the shear strength of hydraulic asphalt concrete becomes more significant. An elastic modulus expression based on nonlinear failure criterion accurately describes the deviatoric stress–axial strain relationship for hydraulic asphalt concrete of different bitumen grades. When the strength of hydraulic asphalt concrete meets these requirements, it is advisable to select higher-grade bitumen to enhance the safety of the core. Full article

(This article belongs to the Section Civil Engineering)

22 pages, 1615 KB

Open AccessArticle

An Improved Crested Porcupine Optimizer for Path Planning of Mobile Robot

by Chenhui Xing, Bo Tang, Guanhua Xu and Hongyu Wu

Appl. Sci. 2025, 15(23), 12595; https://doi.org/10.3390/app152312595 (registering DOI) - 27 Nov 2025

To address the problem of easily falling into local optimization and low convergence accuracy in the path planning tasks of mobile robots, an Improved Crested Porcupine Optimizer (ICPO) based on chaotic mapping is proposed. The ICPO algorithm employs a three-step optimization process. First, [...] Read more.

To address the problem of easily falling into local optimization and low convergence accuracy in the path planning tasks of mobile robots, an Improved Crested Porcupine Optimizer (ICPO) based on chaotic mapping is proposed. The ICPO algorithm employs a three-step optimization process. First, it utilizes SPM, a piecewise linear chaotic initialization, to optimize the population thereby enhancing its diversity and global coverage. Second, the Cauchy Distribution Inverse Cumulative Operator is incorporated to prevent convergence to local optima and to accelerate the overall convergence rate. Finally, the Gaussian mutation is applied to strengthen ICPO’s local exploitation capabilities. Comparative analysis of five algorithms (PSO, DBO, GOOSE, CPO, and ICPO) is conducted using eight standard benchmark functions. Results demonstrate that ICPO achieves a faster convergence rate and superior convergence accuracy. Furthermore, in path planning experiments within 20 × 20 and 40 × 40 grid maps, ICPO reduced the path length by 4.53% and 8.99%, respectively, compared to the CPO algorithm. Full article

(This article belongs to the Section Robotics and Automation)

16 pages, 656 KB

Open AccessArticle

Integrating Ensemble Learning with Item Response Theory to Improve the Interpretability of Student Learning Outcome Tracing

by Christian Onyeke, Lijun Qian, Pamela Obiomon and Xishuang Dong

Appl. Sci. 2025, 15(23), 12594; https://doi.org/10.3390/app152312594 - 27 Nov 2025

Student learning outcome (SLO) tracing aims to monitor students’ learning progress by predicting their likelihood of passing or failing courses using Deep Knowledge Tracing (DKT). However, conventional DKT models often lack interpretability, limiting their adoption in educational settings that require transparent decision-making. To [...] Read more.

Student learning outcome (SLO) tracing aims to monitor students’ learning progress by predicting their likelihood of passing or failing courses using Deep Knowledge Tracing (DKT). However, conventional DKT models often lack interpretability, limiting their adoption in educational settings that require transparent decision-making. To address this challenge, this quantitative study proposes an interpretable ensemble framework that integrates Item Response Theory (IRT) with DKT. Specifically, multiple IRT-based DKT models are developed to capture student ability and item characteristics, and these models are combined using a bagging strategy to enhance predictive performance and robustness. The framework is evaluated on an SLO tracing dataset from Prairie View A&M University (PVAMU), a historically Black college and university (HBCU). Result analysis includes comparisons of evaluation metrics such as Area Under the Curve (AUC), accuracy (ACC), and precision across individual and ensemble models, as well as visualizations of student ability, item difficulty, and predicted probabilities to assess interpretability. Experimental results demonstrate that the ensemble approach consistently outperforms single models while providing clear, interpretable insights into student learning dynamics. These findings suggest that integrating ensemble methods with IRT can simultaneously improve prediction accuracy and transparency in SLO tracing. Full article

20 pages, 2603 KB

Open AccessArticle

Effect of Different Biostimulant Application Forms on Some Geometrical and Mechanical Properties of Soybean Seeds

by Artur Przywara, Monika Różańska-Boczula, Stanisław Parafiniuk, Sławomir Kocira, Agnieszka Żelazna and Grzegorz Łysiak

Appl. Sci. 2025, 15(23), 12593; https://doi.org/10.3390/app152312593 - 27 Nov 2025

The physical and mechanical properties of soybean seeds are of fundamental importance for their practical use, as they determine the quality of seed material and the efficiency of technological processes in the food, feed, and oil industries, as well as the seeds’ ability [...] Read more.

The physical and mechanical properties of soybean seeds are of fundamental importance for their practical use, as they determine the quality of seed material and the efficiency of technological processes in the food, feed, and oil industries, as well as the seeds’ ability to withstand transport, storage, and processing. Modern agriculture strives to increase productivity sustainably, and plant biostimulants are an innovative solution aiming to support plant development and to improve plant resistance. The purpose of this study was to determine how the form of an application of a biostimulant influences the geometrical and mechanical properties of soybean seeds. Two biostimulants (Asahi SL and Kelpak SL), both applied in three ways (universal spray nozzle, injector spray nozzle, and spray hoses), were tested in conjunction with three levels of moisture (6%, 8%, and 10%) on soybean seeds of the cultivar Abelina. The results demonstrated that the biostimulants did not have a significant effect on the sphericity of seeds, which remained at an average level: 0.74. Lower moisture of seeds resulted in their weaker tolerance to mechanical damage and a higher compression resistance factor. Seeds with a moisture content of 6% treated with Asahi SL using universal nozzle 12004C showed the highest cracking resistance (719.4 N∙mm⁻¹) and ultimate force (245.1 N) compared with untreated seeds (672.4 N∙mm⁻¹ and 216.4 N). The Asahi SL biostimulator increased the compression work up to the maximum force by 12% relative to the control, regardless of the spray application method, while the ceramic universal spray nozzle caused an almost 10% increase in maximum force compared with the control, irrespective of the type of biostimulator used. The findings indicate that biostimulants have a positive effect on the physical quality of seeds, with the choice of spray parameters playing a key role. The results provide practical guidelines for optimising agrotechnical treatments to produce seeds with improved quality parameters. They are also crucial for making an appropriate selection of sowing and seed-processing equipment, minimising seed loss and improving the efficiency of soybean production. Full article

(This article belongs to the Special Issue Crop Yield and Quality Characteristics: Influence of Biostimulants and Fertilizers)

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21 pages, 989 KB

Open AccessArticle

Attention-Based CNN-BiGRU-Transformer Model for Human Activity Recognition

by Mingda Miao, Weijie Yan, Xueshan Gao, Le Yang, Jiaqi Zhou and Wenyi Zhang

Appl. Sci. 2025, 15(23), 12592; https://doi.org/10.3390/app152312592 - 27 Nov 2025

Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced recognition performance and reduced reliance on traditional [...] Read more.

Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced recognition performance and reduced reliance on traditional handcrafted feature engineering. However, current deep learning models still face challenges in effectively capturing complex temporal dependencies in long-term time-series sensor data and addressing information redundancy, which affect model recognition accuracy and generalization ability. To address these issues, this paper proposes an innovative CNN-BiGRU–Transformer hybrid deep learning model aimed at improving the accuracy and robustness of human activity recognition. The proposed model integrates a multi-branch Convolutional Neural Network (CNN) to effectively extract multi-scale local spatial features, and combines a Bidirectional Gated Recurrent Unit (BiGRU) with a Transformer hybrid module for modeling temporal dependencies and extracting temporal features in long-term time-series data. Additionally, an attention mechanism is incorporated to dynamically allocate weights, suppress redundant information, and enhance key features, further improving recognition performance. To demonstrate the capability of the proposed model, evaluations are performed on three public datasets: WISDM, PAMAP2, and UCI-HAR. The model achieved recognition accuracies of 98.41%, 95.62%, and 96.74% on the three datasets, respectively, outperforming several state-of-the-art methods. These results confirm that the proposed approach effectively addresses feature extraction and redundancy challenges in long-term sensor time-series data and provides a robust solution for wearable sensor-based human activity recognition. Full article

20 pages, 634 KB

Open AccessArticle

The Effect of Nut Oil Cakes on Selected Properties of Enriched Wheat Bread and Their Changes During Storage

by Karolina Pycia and Lesław Juszczak

Appl. Sci. 2025, 15(23), 12591; https://doi.org/10.3390/app152312591 - 27 Nov 2025

The aim of this study was to evaluate selected parameters during storage of wheat bread enriched with hazelnut oil cake (HOC) or walnut oil cake (WOC) at levels of 5%, 10%, and 15%. Bread volume and specific volume, chemical composition, crumb moisture, crumb [...] Read more.

The aim of this study was to evaluate selected parameters during storage of wheat bread enriched with hazelnut oil cake (HOC) or walnut oil cake (WOC) at levels of 5%, 10%, and 15%. Bread volume and specific volume, chemical composition, crumb moisture, crumb color in the CIE L*a*b* space, crumb texture, total phenolic content (TPC), and antioxidant activity were determined. Tests were conducted on days 1, 3, and 7 of storage. It was found that the HOC/WOC-enriched breads had lower volume and specific volume compared to the control bread. The addition of HOC or WOC improved the nutritional value of the bread, as they had higher protein, mineral, and dietary fiber contents and lower carbohydrate levels. Crumb moisture decreased during storage. The addition of HOC or WOC to the recipe increased crumb hardness while reducing elasticity and cohesiveness. During storage, an increase in hardness, a decrease in elasticity and cohesiveness, and a darker crumb were observed. The enriched breads were characterized by significantly higher TPC and AA contents, and the values of these parameters increased with the addition of nut oil cake. However, the TPC decreased significantly during storage. Full article

(This article belongs to the Special Issue Advances in Food Processing Technology: Enhancing Quality, Safety, and Sustainability)

45 pages, 57464 KB

Open AccessArticle

Multi-Agent Cooperative Optimisation of Microwave Heating Based on Phase–Power Coordinated Control and Consensus Feedback

by Baowei Song, Biao Yang and Yuling Zhou

Appl. Sci. 2025, 15(23), 12590; https://doi.org/10.3390/app152312590 - 27 Nov 2025

To address the key challenges of non-uniform energy distribution, local overheating, and unstable electromagnetic–thermal coupling in multi-source microwave heating systems, this paper proposes a distributed optimisation cooperative method based on phase–power coordinated control and consensus-feedback constraints. A two-stage multi-agent control mechanism, described as [...] Read more.

To address the key challenges of non-uniform energy distribution, local overheating, and unstable electromagnetic–thermal coupling in multi-source microwave heating systems, this paper proposes a distributed optimisation cooperative method based on phase–power coordinated control and consensus-feedback constraints. A two-stage multi-agent control mechanism, described as “phase leading, power following”, is constructed within a hierarchical architecture to achieve spatiotemporal collaborative optimisation from the perspectives of electromagnetic interference-field shaping and thermal feedback regulation. In the phase-regulation stage (Innovation 1), adaptive reconstruction of the interference field is achieved through relative phase specification and a two-level scanning mechanism, rapidly shaping the spatial energy distribution and enhancing the absorption efficiency of incident electromagnetic energy in the cavity–material system. In the power-regulation stage (Innovation 2), amplitude correction is performed under a stabilised interference-field background, and a consensus-feedback constrained regional energy collaboration network is established to ensure that regional energy states converge within the convex hull of the leader reference set. Power redistribution is driven by the target–region energy deviation and neighbourhood consistency relationships, enabling spatial reverse balancing of energy density, suppressing excessive heating in high-energy regions, and enhancing compensation in low-energy regions. Furthermore, a spatiotemporal dual-scale coupling consensus-optimisation framework (Innovation 3) is developed to form a cooperative loop between fast electromagnetic-field reconstruction and slow thermal-field dynamics, achieving synchronous improvement in energy utilisation efficiency and temperature-field uniformity with stable convergence. Simulation results demonstrate that, compared with conventional constant-power, single-phase, and single-power control strategies, the proposed method improves heating efficiency by 16.62–44.74%, and enhances temperature uniformity in vertical and horizontal sections by 8.84–55.87% and 11.41–40.54%, respectively. Full article

(This article belongs to the Section Applied Thermal Engineering)

35 pages, 614 KB

Open AccessReview

Paradigm Shift in Bioenergy: Addressing the System of Biomass Wastage and Environmental Pollution with Biomaterial Valorisation into Biochar

by Chiugo Claret Aduba, Johnson Kalu Ndukwe, Kenechi Onyejiaka Chukwu, Evelyn Chizoba Sam, Adline Eberechukwu Ani, Helen Onyeaka and Ogueri Nwaiwu

Appl. Sci. 2025, 15(23), 12589; https://doi.org/10.3390/app152312589 - 27 Nov 2025

The universal need for sustainable and renewable energy sources has accelerated the shift towards bioenergy as a valuable option to fossil fuels. However, a significant challenge remains in the underutilisation of biomass resources and the environmental pollution caused by improper biomass disposal methods. [...] Read more.

The universal need for sustainable and renewable energy sources has accelerated the shift towards bioenergy as a valuable option to fossil fuels. However, a significant challenge remains in the underutilisation of biomass resources and the environmental pollution caused by improper biomass disposal methods. Biochar, a by-product of biomass pyrolysis rich with carbon, serves as a means to convert underused biomass into valuable energy and a tool for environmental remediation. Biochar can be integrated into a biorefinery for improved bioelectricity and biogas production, but there are challenges with regard to its production scalability, quality control, and standardisation. This article provides a comprehensive review of the prospective processes useful in the valorisation of biomass into biochar for bioenergy, co-firing potential with fossil fuels, and in waste biomass transformation. This article also provides insight into business development and policy-making by bioentrepreneurs, bioengineers, and the government, as it identifies grey opportunities for bioenergy production and improvement. The prospect of AI technology in improving the production, quality, and yield of biochar, by identifying the most efficient parameters and conditions, as well as optimising the application of biochar in various industries, is also highlighted. The transition to biofuels in aviation, a step towards a future in the industry that is more sustainable, is also suggested in this review. Full article

(This article belongs to the Special Issue Unlocking the Potential of Agri-Food Waste for Innovative Applications and Bio-Based Materials, 2nd Edition)

14 pages, 23346 KB

Open AccessArticle

Augmented Reality-Assisted Micro-Invasive Apicectomy with Markerless Visual–Inertial Odometry: An In Vivo Pilot Study

by Marco Farronato, Davide Farronato, Federico Michelini and Giulio Rasperini

Appl. Sci. 2025, 15(23), 12588; https://doi.org/10.3390/app152312588 - 27 Nov 2025

Introduction: Apicectomy is an endodontic surgical procedure prescribed for persistent periapical pathologies when conventional root canal therapy or retreatment have failed. Accurate intraoperative visualization of the root apex and surrounding structures remains challenging and subject to possible errors. Augmented reality (AR) allows for [...] Read more.

Introduction: Apicectomy is an endodontic surgical procedure prescribed for persistent periapical pathologies when conventional root canal therapy or retreatment have failed. Accurate intraoperative visualization of the root apex and surrounding structures remains challenging and subject to possible errors. Augmented reality (AR) allows for the addition of real-time digital overlays of the anatomical region, thus potentially improving surgical precision and reducing invasiveness. The purpose of this pilot study is to describe the application of an AR method in cases requiring apicectomy. Materials and Methods: Patients presenting with chronic persistent apical radio-translucency associated with pain underwent AR-assisted apicectomy. Cone-beam computed tomography (CBCT) scans were obtained preoperatively for segmentation of the target root apex and adjacent anatomical structures. A custom visual–inertial odometry (VIO) algorithm was used to map and stabilize the segmented digital 3D models on a portable device in real time, enabling an overlay of digital guides onto the operative field. The duration of preoperative procedures, was recorded. Postoperative pain measured by a Visual Analogue Scale (VAS), and periapical healing assessed with radiographic evaluations, were recorded at baseline (T0) and at 6 weeks and 6 months (T1–T2) after surgery. Results: AR-assisted apicectomies were successfully performed in all three patients without intraoperative complications. The digital overlap procedure required an average of [1.49 ± 0.34] minutes. VAS scores decreased significantly from T0 to T2, and patients showed radiographic evidence of progressive periapical healing. No patient reported persistent discomfort at follow-up. Conclusion: This preliminary pilot study indicates that AR-assisted apicectomy is feasible and may improve intraoperative visualization with low additional surgical time. Future larger-scale studies with control groups are needed to validate the method proposed and to quantify the outcomes. Clinical Significance: By integrating real-time digital images of bony structures and root morphology, AR guidance during apicectomy may offer enhanced precision for apical resection and may decrease the risk of iatrogenic damage. The use of a visual–inertial odometry-based AR method is a novel technique that demonstrated promising results in terms of VAS and final outcomes, especially in anatomically challenging cases in this preliminary pilot study. Full article

(This article belongs to the Special Issue Advanced Dental Imaging Technology)

24 pages, 4382 KB

Open AccessArticle

Explainable Machine Learning for Bubble Leakage Detection at Tube Array Surfaces in Pool

by Yosei Ota, Shun Nukaga, Yuna Kanda and Masahiro Furuya

Appl. Sci. 2025, 15(23), 12587; https://doi.org/10.3390/app152312587 - 27 Nov 2025

Early detection of bubble generation from tube arrays in systems such as fast reactor steam generators, Pressurized Water Reactor (PWR) cores, and Liquefied Natural Gas (LNG) regasification units is critical for safety. While various methods have been proposed, they face challenges such as [...] Read more.

Early detection of bubble generation from tube arrays in systems such as fast reactor steam generators, Pressurized Water Reactor (PWR) cores, and Liquefied Natural Gas (LNG) regasification units is critical for safety. While various methods have been proposed, they face challenges such as high spatial resolution requirements, rapid response times, and varying strengths and weaknesses, suggesting the need for a combined approach. This study integrates ultrasonic testing (UT) with Machine Learning (ML) to identify the presence, location, and direction of bubbles within a complex tube array that cause signal attenuation. A Convolutional Neural Network (CNN) successfully achieved 100% identification accuracy. Furthermore, a method was developed that uses an autoencoder as a feature extractor, combined with a One-Class Support Vector Machine (SVM) and k-means. This approach achieved high accuracy and a correct decision basis. It also demonstrated strong generalization, successfully detecting anomalies without requiring labels for anomalous data, enabling robust bubble identification. Full article

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

19 pages, 4613 KB

Open AccessArticle

Numerical Investigation of the Phase Change Behavior of Liquefied CO₂ in a Type-C Cryogenic Tank

by Seoyeon Ahn, Geunchul Choi and Sunho Park

Appl. Sci. 2025, 15(23), 12586; https://doi.org/10.3390/app152312586 - 27 Nov 2025

As global warming accelerates, the Paris Agreement has emphasized the urgent need for technologies that reduce and manage carbon dioxide emissions. Consequently, carbon capture and storage (CCS) has emerged as a critical area of research. For the safe and efficient transportation of captured [...] Read more.

As global warming accelerates, the Paris Agreement has emphasized the urgent need for technologies that reduce and manage carbon dioxide emissions. Consequently, carbon capture and storage (CCS) has emerged as a critical area of research. For the safe and efficient transportation of captured carbon dioxide in cryogenic tanks, the design must accurately account for the phase change behavior of liquefied carbon dioxide (LCO₂). This study proposes a numerical approach to evaluate the thermal insulation performance of cryogenic tanks by simulating the phase change process of LCO₂. The phase transition of LCO₂ was simulated in a horizontally oriented Type-C cryogenic tank using the open-source computational fluid dynamics (CFD) framework OpenFOAM (v2312). To validate the numerical methodology, the phase change in liquefied nitrogen (LN₂) inside a tank was first simulated and compared with available experimental data. A mesh-independence study was then conducted to determine the optimal grid resolution, and the effects of different equations of state (EOS) for both liquid and gaseous phases, as well as various turbulence models, were examined. The boil-off rate (BOR) and boil-off gas (BOG) generation within the tank were predicted, and variations in internal pressure and flow fields were analyzed. The simulation results over 5000 s showed that the internal tank pressure increased from 7.8 bar to 8.1 bar, and the average temperature rose by approximately 1.3 K. The total mass of LCO₂ decreased from 1439.3 kg to 1431.0 kg. Full article

22 pages, 858 KB

Open AccessArticle

Antidiabetic and Anti-Inflammatory Potential of Sorbus Aucuparia Fruits (Rowanberries) from Romania

by Elena Neagu, Gabriela Paun, Camelia Albu, Georgiana Badea, Ana Maria Seciu-Grama and Gabriel Lucian Radu

Appl. Sci. 2025, 15(23), 12585; https://doi.org/10.3390/app152312585 - 27 Nov 2025

This study aimed to obtain extracts concentrated in polyphenolic compounds from Sorbus aucuparia fruits and evaluate their antioxidant, antidiabetic, anti-inflammatory, and cytotoxic potential. Two modern extraction methods were used, ultrasound-assisted extraction (UAE) and accelerated solvent extraction (ASE), to obtain hydroalcoholic extracts (50% EtOH [...] Read more.

This study aimed to obtain extracts concentrated in polyphenolic compounds from Sorbus aucuparia fruits and evaluate their antioxidant, antidiabetic, anti-inflammatory, and cytotoxic potential. Two modern extraction methods were used, ultrasound-assisted extraction (UAE) and accelerated solvent extraction (ASE), to obtain hydroalcoholic extracts (50% EtOH v/v, 15% mass), then the extracts were purified and concentrated by membrane technologies and analyzed spectrophotometrically and chromatographically. HPLC analysis revealed the predominant polyphenolic compounds as chlorogenic acid (526.08 ± 23.35 µg/mL), rutin (36.07 ± 1.23 µg/mL), and caffeic acid (34.41 ± 1.21 µg/mL). The antidiabetic and anti-inflammatory potential of the extracts was analyzed spectrophotometrically by testing their capacity to inhibit α-amylase and α-glucosidase, and, respectively, hyaluronidase (HYA) and lipoxygenase (LOX). The cytotoxic potential of the extracts was tested on the mouse fibroblast NCTC clone L929 cell line. The concentrated ASE extracts showed a pronounced inhibitory activity on the tested enzymes: IC₅₀a-glucosidase was 13.50 ± 0.96 µg/mL, (IC₅₀acarbose was 20.19 ± 1.67 µg/mL), IC₅₀a-amylase was 23.74 ± 1.32 µg/mL (IC ₅₀acarbose was 22.65 ± 1.27 µg/mL), and IC_50LOX was 24.30 ± 1.54 µg/mL (IC₅₀ibuprofen was 26.91 ± 1.27 µg/mL), IC₅₀HYA was 43.04 ± 2.19 µg/mL (IC₅₀ibuprofen was 51.54 ± 3.67 µg/mL). Also, the concentrated UAE extracts presented inhibitory activity superior to or close to that of the standard used, as follows: IC₅₀HYA was 48.49 ± 3.15 µg/mL (IC₅₀ibuprofen was 51.54 ± 3.67 µg/mL) and IC₅₀a glucosidase was 21.53 ± 1.25 µg/mL (IC₅₀acarbose was 20.19 ± 1.67 µg/mL). The results obtained showed that Sorbus aucuparia fruits could be used in products for diabetes and inflammatory diseases. Full article

(This article belongs to the Special Issue Advances in Natural Products: Extraction, Bioactivity, Biotransformation, and Applications)

23 pages, 23534 KB

Open AccessArticle

Unraveling the Patterns and Drivers of Multi-Geohazards in Tangshan, China, by Integrating InSAR and ICA

by Bingtai Ma, Yang Wang, Jianqing Zhao, Qiang Shan, Degang Zhao, Yiwen Zhou and Fuwei Jiang

Appl. Sci. 2025, 15(23), 12584; https://doi.org/10.3390/app152312584 - 27 Nov 2025

This study establishes an integrated “Detection–Decomposition–Interpretation” framework for geohazard assessment, with Tangshan City serving as a representative case. Using Sentinel-1 SAR images from 2020 to 2024, regional surface deformation was derived via the Small Baseline Subset InSAR (SBAS-InSAR) technique. Six categories of geohazards [...] Read more.

This study establishes an integrated “Detection–Decomposition–Interpretation” framework for geohazard assessment, with Tangshan City serving as a representative case. Using Sentinel-1 SAR images from 2020 to 2024, regional surface deformation was derived via the Small Baseline Subset InSAR (SBAS-InSAR) technique. Six categories of geohazards were systematically identified and classified: landslides, open-pit slope deformation, mining-induced subsidence, spoil heap deformation, tailings pond deformation, and reclamation settlement. A total of 115 potential hazards were spatially cataloged, revealing distinct zonation characteristics: the northern mountainous area is predominantly affected by landslides and open-pit mining hazards; the central plain exhibits concentrated mining subsidence; and the southern coastal zone is marked by large-scale reclamation settlement. For the southern reclamation area, where settlement mechanisms are complex, the Independent Component Analysis (ICA) method was applied to successfully decompose the deformation signals into three independent components: IC1, representing the dominant long-term irreversible settlement driven by fill consolidation, building loads, and groundwater extraction; IC2, reflecting seasonal deformation coupled with groundwater level fluctuations; and IC3, comprising residual noise. Time series analysis further reveals the coexistence of “decelerating” and “accelerating” settlement trends across different zones, indicative of their respective evolutionary stages—from decaying to actively progressing settlement. This study not only offers a scientific basis for geohazard prevention and control in Tangshan, but also provides a transferable framework for analyzing hazard mechanisms in other complex geographic settings. Full article

(This article belongs to the Topic Earth Observation Systems in Geology Mass Identification, Investigation and Inventory Mapping)

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13 pages, 2796 KB

Open AccessArticle

Complex Network Responses to Regulation of a Brain-Computer Interface During Semi-Naturalistic Behavior

by Tengfei Feng, Halim Ibrahim Baqapuri, Jana Zweerings and Klaus Mathiak

Appl. Sci. 2025, 15(23), 12583; https://doi.org/10.3390/app152312583 - 27 Nov 2025

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly [...] Read more.

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

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

22 pages, 3906 KB

Open AccessArticle

Control System for an Open-Winding Permanent Magnet Synchronous Motor Fed by a Four-Leg Inverter

by Hai Lin, Siyi Cheng, Zhixin Jing and Weiyu Liu

Appl. Sci. 2025, 15(23), 12582; https://doi.org/10.3390/app152312582 - 27 Nov 2025

This paper employs a four-leg inverter topology to mitigate the high cost and zero-sequence current suppression challenges associated with dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) systems. Building on this topology, an improved current hysteresis control strategy incorporating a switching-state lookup table is [...] Read more.

This paper employs a four-leg inverter topology to mitigate the high cost and zero-sequence current suppression challenges associated with dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) systems. Building on this topology, an improved current hysteresis control strategy incorporating a switching-state lookup table is proposed to suppress switching frequency fluctuations and current ripple. The developed system maintains high DC-link utilization and low cost while addressing the modulation complexity of conventional vector control and the switching frequency instability inherent in traditional hysteresis control. The study establishes a mathematical model of the OW-PMSM, analyzes the voltage vector distribution of the four-leg inverter, and designs an enhanced hysteresis control algorithm. By utilizing a predefined switching table to regulate switching logic in real time, the strategy achieves fixed switching frequency and effective harmonic suppression while preserving the fast-response characteristics of conventional hysteresis control. The experimental results demonstrate that the proposed control strategy achieves superior performance, effectively suppressing current ripple and providing ample stability margin, thereby validating its feasibility and effectiveness for practical engineering applications. Full article

22 pages, 6895 KB

Open AccessArticle

A Study on Fractional-Order Adaptive Super-Twisting Sliding Mode Control for an Excavator Working Device

by Shunjie Zhou, Zhong Liu, Mengyi Li, Deqing Liu, Chongyu Wang and Hao Li

Appl. Sci. 2025, 15(23), 12581; https://doi.org/10.3390/app152312581 - 27 Nov 2025

This study proposes a fractional-order adaptive super-twisting sliding mode control (FO-ASTSMC) strategy to mitigate the difficulties arising from nonlinearity, uncertain parameters, and substantial external interferences during path-following operations of a hydraulic excavator working device. The developed approach merges a high-order sliding mode differentiator [...] Read more.

This study proposes a fractional-order adaptive super-twisting sliding mode control (FO-ASTSMC) strategy to mitigate the difficulties arising from nonlinearity, uncertain parameters, and substantial external interferences during path-following operations of a hydraulic excavator working device. The developed approach merges a high-order sliding mode differentiator aimed at state observation, a fresh fractional-order sliding manifold that embeds a memory component for bolstering transient performance and equilibrium accuracy, together with an adaptable super-twisting coefficient. This adaptive gain eliminates the requirement for prior awareness of disturbance limits, all the while mitigating chattering effects and bolstering system robustness. Utilizing Lyapunov theory, the finite-time stability of the overall closed-loop framework has been thoroughly demonstrated. For controller verification, joint simulations employing AMESim and Simulink platforms were performed, pitting its efficacy against both terminal sliding mode control (TSMC) and adaptive fuzzy sliding mode control (AFSMC). In nominal scenarios, the FO-ASTSMC method yielded the lowest root mean square error (RMSE) along with maximum error (MAXE) across boom, arm, and bucket articulations, registering mean decreases of

60 %

in RMSE and

58.2 %

in MAXE when benchmarked against AFSMC, alongside

41.8 %

in RMSE and

43.6 %

in MAXE versus TSMC. Facing sudden variations in loading, it exhibited enhanced robustness, achieving reductions of

64.2 %

in RMSE and

54.5 %

in MAXE beyond AFSMC, as well as

39 %

in RMSE and

36.5 %

in MAXE in comparison to TSMC. Outcomes from the simulations affirm that the suggested controller exhibits elevated precision, formidable robustness, and good applicability to actuators, thereby highlighting its considerable promise for implementation in actual engineering scenarios. Full article

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61 pages, 3338 KB

Open AccessArticle

A Novel Hybrid Metaheuristic Algorithm for Real-World Mechanical Engineering Optimization Problems

by Chiara Furio, Luciano Lamberti and Catalin I. Pruncu

Appl. Sci. 2025, 15(23), 12580; https://doi.org/10.3390/app152312580 - 27 Nov 2025

Real-world constrained optimization problems often are highly nonlinear and present non-convex design spaces. Metaheuristic algorithms (MHOAs) are naturally suited to solving real-world optimization problems in view of their global optimization capability, but may require too many analyses to complete the optimization process. Hybrid [...] Read more.

Real-world constrained optimization problems often are highly nonlinear and present non-convex design spaces. Metaheuristic algorithms (MHOAs) are naturally suited to solving real-world optimization problems in view of their global optimization capability, but may require too many analyses to complete the optimization process. Hybrid methods enhance searching by combining two or more algorithms to better balance exploration and exploitation. Elitist strategies may be utilized to generate high-quality trial designs, yet with no guarantee that each new design always improves the current best record. In order to solve these issues and minimize the number of analyses, this study presents the novel HALSGWJA (Hybrid Approximate Line Search Grey Wolf JAYA) algorithm. HALSGWJA combined grey wolf optimizer (GWO) and JAYA (two powerful MHOAs still attracting optimization experts), enhanced by approximate line search. HALSGWJA utilized approximate gradient information to perform line searches, providing descent directions with respect to the current best record. This results in a complete renewal of the current population and a much higher probability of improving all individuals with respect to the previous iteration. The proposed HALSGWJA algorithm was successfully tested on 20 real-world mechanical engineering problems: (i) the CEC2020 test suite of 19 real-world mechanical engineering examples with up to 30 optimization variables and 86 nonlinear constraints and (ii) the optimal crashworthiness design of a vehicle subject to side impact with 11 optimization variables and 10 highly nonlinear constraints. Sizing and topology optimization problems, as well as problems with discrete variables, were considered. Remarkably, HALSGWJA outperformed 18 other state-of-the-art metaheuristic algorithms in the CEC2020 problems and 25 other algorithms in the crashworthiness design problem. HALSGWJA practically converged to target optima in all test cases (the largest penalty on target optimized cost was only 0.0263% in problem 13 of the CEC2020 library). Furthermore, it obtained in many cases 0 or nearly 0 standard deviation on optimized cost. Lastly, HALSGWJA always ranked first in terms of computational speed, requiring fewer analyses than its competitors and exhibiting, in most cases, a moderate dispersion on the number of analyses entailed by the optimization process. Full article

(This article belongs to the Section Mechanical Engineering)

21 pages, 3214 KB

Open AccessReview

Superconductivity and Cryogenics in Medical Diagnostics and Treatment: An Overview of Selected Applications

by Oleksandr Boiko and Henryka Danuta Stryczewska

Appl. Sci. 2025, 15(23), 12579; https://doi.org/10.3390/app152312579 - 27 Nov 2025

This article presents a comprehensive overview of the current and emerging roles of cryogenics and superconductivity in medical diagnostics, imaging, and therapy. Beginning with the historical foundations of both fields and their technological maturation, this review emphasizes how cryogenic engineering and superconducting materials [...] Read more.

This article presents a comprehensive overview of the current and emerging roles of cryogenics and superconductivity in medical diagnostics, imaging, and therapy. Beginning with the historical foundations of both fields and their technological maturation, this review emphasizes how cryogenic engineering and superconducting materials have become indispensable to modern medical systems. Cryogenic technologies are highlighted in applications such as cryosurgery, cryotherapy, cryostimulation, and cryopreservation, all of which rely on controlled exposure to extremely low temperatures for therapeutic or biological preservation purposes. This article outlines the operating principles of cryomedical devices, the refrigerants and cooling methods used, and the technological barriers. This paper reviews the latest applications of superconductivity phenomena in medicine and identifies those that could be used in the future. These include cryogenic therapy, radiotherapy (cyclotrons, particle accelerators, synchrotron radiation generation, isotope production, and proton and ion beam delivery), magnetic resonance imaging (MRI), nuclear magnetic resonance spectroscopy (NMR), positron emission tomography (PET), and ultra-sensitive magnetic signal transducers based on SQUIDs for detecting ultra-low bio-signals emitted by human body organs. CT, MRI/NMR, and PET features are compared using the operation principle, specific applications, safety, contraindications for patients, examination time, and additional valued peculiarities. This article outlines the prospects for the development of superconducting and cryogenic materials and technologies in medical applications. Advances in diagnostic imaging are reviewed, with particular attention on the progression from conventional MRI scanners to ultra-high-field (UHF) systems exceeding 7–10.5 T, culminating in the 11.7 T Iseult whole-body MRI magnet. Another important application area described in this article includes biofunctionalized magnetic nanoparticles and superconducting quantum interference devices (SQUIDs), which enable the ultrasensitive detection of biomagnetic fields and targeted cancer diagnostics. Finally, this article identifies future directions of development in superconducting and cryogenic technologies for medicine. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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15 pages, 1263 KB

Open AccessArticle

Liver-VLM: Enhancing Focal Liver Lesion Classification with Self-Supervised Vision-Language Pretraining

by Jian Song, Yuchang Hu, Hui Wang and Yen-Wei Chen

Appl. Sci. 2025, 15(23), 12578; https://doi.org/10.3390/app152312578 - 27 Nov 2025

Accurate classification of focal liver lesions (FLLs) is crucial for reliable clinical decision-making. Inspired by contrastive vision-language models such as CLIP and MedCLIP, we propose Liver-VLM for FLLs classification, trained on a dedicated multi-phase 2D CT dataset. Liver-VLM aligns multi-phase CT image representations [...] Read more.

Accurate classification of focal liver lesions (FLLs) is crucial for reliable clinical decision-making. Inspired by contrastive vision-language models such as CLIP and MedCLIP, we propose Liver-VLM for FLLs classification, trained on a dedicated multi-phase 2D CT dataset. Liver-VLM aligns multi-phase CT image representations with class-specific textual descriptions by calculating their similarity under a cross-entropy loss. Furthermore, we design tailored, enriched textual prompts to stabilize optimization and enable robust classification even with limited labeled data. Additionally, self-supervised pretraining and data augmentation strategies are incorporated to further improve classification performance. Experimental results on an in-house MPCT-FLLs dataset demonstrate that Liver-VLM consistently outperforms existing VLMs, achieving an accuracy of 85.63 ± 3.18% and an AUC of 0.94 ± 0.01. Our findings highlight the efficacy of self-supervised learning and task-specific augmentation in overcoming data scarcity and distributional biases in medical image analysis. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions)

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