Applied Sciences

16 pages, 1376 KB

Open AccessArticle

Site-Specific Hydrocarbon-Degrading Bacteria Consortium Developed Using Functional and Genomic Analyses

by Davide Lelli, Cristina Russo, Dario Liberati, Paolo De Angelis, Maurizio Petruccioli and Silvia Crognale

Appl. Sci. 2026, 16(3), 1671; https://doi.org/10.3390/app16031671 - 6 Feb 2026

Viewed by 310

Abstract

Bioaugmentation, defined as the strategic incorporation of specifically selected microbial biomass into contaminated environments, can significantly enhance the biodegradation of pollutants and is extensively employed in soil bioremediation efforts. A multistep screening process was applied to develop an autochthonous microbial consortium, including (i) [...] Read more.

Bioaugmentation, defined as the strategic incorporation of specifically selected microbial biomass into contaminated environments, can significantly enhance the biodegradation of pollutants and is extensively employed in soil bioremediation efforts. A multistep screening process was applied to develop an autochthonous microbial consortium, including (i) hydrocarbonoclastic strain isolation from soil chronically contaminated with petroleum hydrocarbons, (ii) bacterial selection according to genomic and functional traits, and (iii) consortium validation in the native contaminated soil through microcosm experiments. The selection of strains with the ability to degrade alkanes and aromatic hydrocarbons on synthetic media was further supported by genomic analysis, delivering a consortium with complementary degradative properties. The outcomes of the microcosm experiments corroborated the efficacy of the selected indigenous consortium, demonstrating that the combination of Acinetobacter guillouiae, A. radioresistens, and Pseudomonas zarinae as an inoculum in the bioaugmentation strategy was successful in achieving the removal of up to 26% and 76% of linear and polycyclic aromatic hydrocarbons, respectively, thereby effectively addressing areas where natural attenuation was insufficient. Full article

(This article belongs to the Special Issue Human Impacts on Environmental Microbial Communities)

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22 pages, 10092 KB

Open AccessArticle

Numerical Analysis of Fracture Mechanisms in Granite with a Grain Size Gradient Using the GBM–DEM

by Zhijie Zheng and Dan Huang

Appl. Sci. 2026, 16(3), 1669; https://doi.org/10.3390/app16031669 - 6 Feb 2026

Viewed by 234

Abstract

To examine how grain-size distribution affects the mechanical response and fracture behavior of Lac du Bonnet (LdB) granite under uniaxial compression, numerical simulations were conducted using the particle flow code (PFC) with a grain-based model. By displacing grain centroids in different directions along [...] Read more.

To examine how grain-size distribution affects the mechanical response and fracture behavior of Lac du Bonnet (LdB) granite under uniaxial compression, numerical simulations were conducted using the particle flow code (PFC) with a grain-based model. By displacing grain centroids in different directions along the y-axis, four LdB granite models with distinct grain sizes were generated, with grains delineated by Voronoi tessellation. The main findings are as follows: (1) The flat-jointed constitutive model reproduces the experimental response well, and introducing unbonded contacts (micrometer-scale gaps) improves the simulation of crack-closure behavior during loading. (2) Secondary cracks initiate predominantly at grain boundaries, and the yield stress is strongly associated with the evolution of intragranular tensile cracks. (3) Grain size governs the sequence of crack accumulation (tensile vs. shear), the growth rate and spatial correlation of damage, and the distribution and intensity of local failures; smaller grains hinder macroscopic damage, whereas larger grains are more readily penetrated and filled by microcracks. (4) Mechanical cutting tests show that grain-size combinations produce several dominant secondary-failure modes; the failure thickness is controlled by the penetration depth of the subsequent cutting head, and the stress concentration near the cutting head is sensitive to grain size. Full article

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

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28 pages, 642 KB

Open AccessReview

Redefining Cyber Threat Intelligence with Artificial Intelligence: From Data Processing to Predictive Insights and Human–AI Collaboration

by Mateo Barrios-González, Javier Manuel Aguiar-Pérez, María Ángeles Pérez-Juárez and Enrique Castañeda-de-Benito

Appl. Sci. 2026, 16(3), 1668; https://doi.org/10.3390/app16031668 - 6 Feb 2026

Viewed by 614

Abstract

The increasing complexity and scale of cyber threats have pushed Cyber Threat Intelligence (CTI) beyond the capabilities of traditional rule-based systems. This article explores how Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and graph-based analytics, is [...] Read more.

The increasing complexity and scale of cyber threats have pushed Cyber Threat Intelligence (CTI) beyond the capabilities of traditional rule-based systems. This article explores how Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and graph-based analytics, is reshaping the CTI landscape. By automating threat data processing, enhancing attribution, and enabling predictive capabilities, AI is transforming CTI into a proactive and scalable discipline. By analysing CTI architectures, real-world use cases, platform comparisons, and current limitations, this study highlights the emerging opportunities and challenges at the intersection of cybersecurity and AI. This analysis concludes that the future of CTI lies in hybrid systems that seamlessly combine human expertise with intelligent automation. Full article

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

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28 pages, 14955 KB

Open AccessArticle

A Novel Explainable AI–Driven Framework for Parametric Knot Vector Estimation in NURBS Surfaces

by Furkan Bilucan and Bahadir Ergun

Appl. Sci. 2026, 16(3), 1667; https://doi.org/10.3390/app16031667 - 6 Feb 2026

Viewed by 303

Abstract

Non-uniform rational B-spline (NURBS) surfaces are effective for accurately modeling curved geometries, and research in this area has recently increased. In this study, point cloud data obtained from two challenging test environments (a convex wooden object and the widely used Stanford Bunny dataset [...] Read more.

Non-uniform rational B-spline (NURBS) surfaces are effective for accurately modeling curved geometries, and research in this area has recently increased. In this study, point cloud data obtained from two challenging test environments (a convex wooden object and the widely used Stanford Bunny dataset from the literature) were used to predict the u and v parameter values corresponding to positions in the knot vectors, to determine the knot points of NURBS surfaces. The u and v parameters were predicted with accuracies of 92.60% and 93.20% for the wooden object, and 85.50% and 84.40% for the Stanford Bunny. The models’ decision-making processes were analyzed using explainable artificial intelligence (XAI) methods, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Predicted knot points were compared with the calculated knot points, which are considered as actual, yielding root mean square errors (RMSE) of 0.09 mm for the wooden object and 0.02 mm for the Stanford Bunny. This study fills a gap in the literature by predicting knot points and providing XAI-based analyses, demonstrating that the approach effectively preserves the characteristic features of NURBS surfaces across different geometries. Full article

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12 pages, 1476 KB

Open AccessArticle

Thickness Uniformity Improvement in Superplastic Hemispherical Shell Using Truncated Conical Blanks: Numerical and Experimental Analysis

by Gillo Giuliano and Wilma Polini

Appl. Sci. 2026, 16(3), 1666; https://doi.org/10.3390/app16031666 - 6 Feb 2026

Viewed by 172

Abstract

Achieving thickness uniformity is a critical challenge in superplastic forming (SPF) of hemispherical shells, as standard constant-thickness blanks suffer from excessive thinning at the pole. While the literature suggests using variable thickness blanks to mitigate this issue, existing solutions often rely on complex, [...] Read more.

Achieving thickness uniformity is a critical challenge in superplastic forming (SPF) of hemispherical shells, as standard constant-thickness blanks suffer from excessive thinning at the pole. While the literature suggests using variable thickness blanks to mitigate this issue, existing solutions often rely on complex, non-linear profiles that are expensive and difficult to manufacture. This work proposes a cost-effective, truncated conical blank design (linearly variable thickness) to optimize material distribution. The approach combines Finite Element Method (FEM) analysis and experimental validation on AZ31 magnesium alloy. The study demonstrates that the optimized truncated conical profile (α = 0.2) yields superior structural quality, drastically reducing the thinning factor to 9%. This represents a significant improvement compared to the ~14% thinning observed with conical profile (α = 0) blanks and outperforms constant-thickness blanks (30%). These results demonstrate that a simplified, easily machinable blank geometry can effectively address the thinning problem, providing a practical solution for industrial SPF applications. Full article

(This article belongs to the Section Mechanical Engineering)

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68 pages, 8733 KB

Open AccessArticle

Towards Privacy-Preserving Deep Learning for Intelligent IoT Botnet Detection

by Ariwan M. Rasool, Nader Sohrabi Safa and Consolee Mbarushimana

Appl. Sci. 2026, 16(3), 1665; https://doi.org/10.3390/app16031665 - 6 Feb 2026

Viewed by 218

Abstract

Internet of Things (IoT) botnets are networks of infected smart devices controlled by attackers and posing a serious cybersecurity challenge. Developing detection approaches that maintain high accuracy while protecting privacy presents considerable challenges, particularly in large and heterogeneous IoT networks. This paper empirically [...] Read more.

Internet of Things (IoT) botnets are networks of infected smart devices controlled by attackers and posing a serious cybersecurity challenge. Developing detection approaches that maintain high accuracy while protecting privacy presents considerable challenges, particularly in large and heterogeneous IoT networks. This paper empirically compares three modelling approaches on Bot-IoT and N-BaIoT in binary and multiclass settings: handcrafted machine learning with random forest (RF), centralised deep learning (CDL) with DNN/LSTM/BiLSTM, and federated deep learning (FDL) with the same architectures. Model hyperparameters are selected via randomised search on stratified subsets and then fixed for final training. Results show near-perfect performance for all approaches in binary detection: on Bot-IoT, CDL-DNN attains perfect accuracy, and RF is virtually perfect (only four benign-to-attack false positives), while FDL models are similarly strong with only small false-positive and false-negative counts. On N-BaIoT, RF and CDL (especially LSTM) are near-perfect, and FDL is very close to CDL. For multiclass detection, CDL-DNN leads on Bot-IoT, RF remains near perfect with minimal cross-class confusion, and FDL trails slightly; on N-BaIoT, FDL-BiLSTM and RF are essentially perfect, with CDL-LSTM close behind. Overall, the findings validate RF as a competitive classical approach, show where centralised representation learning adds value, and demonstrate that federated training preserves most of the centralised accuracy while avoiding raw data centralization (data locality) for scalable deployment. Full article

(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing, 2nd Edition)

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25 pages, 3080 KB

Open AccessArticle

Lightweight Vision Transformer for Real-Time Threat Level Assessment in Φ-OTDR-Based Pipeline Monitoring

by Yuhan Zhang, Hao Zeng, Chang Su, Jie Yang, Jianjun Zhu and Jianli Wang

Appl. Sci. 2026, 16(3), 1664; https://doi.org/10.3390/app16031664 - 6 Feb 2026

Viewed by 212

Abstract

Phase-sensitive optical time domain reflectometry (Φ-OTDR) is a highly sensitive distributed vibration sensing technology crucial for pipeline safety monitoring. However, its sensitivity makes it susceptible to environmental interference, leading to frequent false alarms by misclassifying routine activities as threats. To enable accurate threat [...] Read more.

Phase-sensitive optical time domain reflectometry (Φ-OTDR) is a highly sensitive distributed vibration sensing technology crucial for pipeline safety monitoring. However, its sensitivity makes it susceptible to environmental interference, leading to frequent false alarms by misclassifying routine activities as threats. To enable accurate threat identification and rapid response, this study proposes a lightweight LightPatch Vision Transformer (LP-ViT) model suitable for edge deployment. We establish a mapping between excavator-pipeline distance and threat levels: “direct intrusion” (within 5 m), “high-risk operation” (within 10 m), and “background construction” (beyond 15 m). The LP-ViT model is developed through structural optimization and parameter compression of the standard Vision Transformer, achieving a 96.6% reduction in parameter count while maintaining a high classification accuracy of 89.9%. Furthermore, via knowledge distillation, we derive an ultra-lightweight student model with merely 0.37 M parameters, which achieves an inference latency of 5.5 ms per sample, enabling millisecond-level threat detection and response. The proposed solution effectively enhances both the classification accuracy and real-time performance of Φ-OTDR systems in complex environments, providing a practical pathway for implementing edge intelligence in pipeline safety monitoring. Full article

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17 pages, 7105 KB

Open AccessArticle

Evaluation of the Recrystallization Annealing Microstructure of the INCONEL 625 Superalloy Exposed to Cavitation Erosion

by Ion Mitelea, Robert Parmanche, Ion-Dragoș Uțu, Dragoș Buzdugan, Corneliu Marius Crăciunescu and Ilare Bordeașu

Appl. Sci. 2026, 16(3), 1663; https://doi.org/10.3390/app16031663 - 6 Feb 2026

Viewed by 145

Abstract

Cavitation erosion is a critical problem for many engineering components, such as ship propellers, diesel engine exhaust valves, cylinder liners, pump impeller blades, hydraulic turbines, and bearings, which are exposed to high-velocity flowing fluids or to vibratory fluid motion. It represents a mechanical [...] Read more.

Cavitation erosion is a critical problem for many engineering components, such as ship propellers, diesel engine exhaust valves, cylinder liners, pump impeller blades, hydraulic turbines, and bearings, which are exposed to high-velocity flowing fluids or to vibratory fluid motion. It represents a mechanical degradation of the surface caused by the continuous collapse of bubbles in the surrounding liquid, which seriously affects flow efficiency and component service life, increasing maintenance frequency and refurbishment costs. The intensity and evolution of the cavitation erosion phenomenon depend on the hydrodynamic conditions to which the component surface is exposed, the properties of the liquid, and the judicious selection of the most suitable material. This paper aims to modify the microstructure of a Ni-based superalloy by applying recrystallization annealing heat treatment in order to obtain surfaces resistant to cavitation erosion for components that handle fluids under local pressure fluctuations. Experimental tests are carried out using a vibratory apparatus with piezoceramic crystals operating at a frequency of 20 kHz and an amplitude of 50 µm. The cavitation erosion performance of the Ni-based superalloy INCONEL 625, heat treated by recrystallization annealing, are compared with that of austenitic stainless steel AISI 316L subjected to solution treatment. For both metallic alloys, based on mass loss measurements, the characteristic time-dependent curves of the mean cumulative erosion penetration depth, MDE(t), and the mean erosion rate, MDER(t), are determined. The comparison of these curves and of the parameters defined and recommended by the ASTM G32 standard demonstrates that, for the Inconel 625 superalloy, resistance to cavitation erosion increases by 77–81% compared to that of AISI 316L austenitic stainless steel. X-ray diffraction analyses (XRD) show that, in the microstructure of the Inconel 625 superalloy, in addition to austenite, MC-type carbides, M₂₃C₆ carbides, and intermetallic phases γ″ = Ni₃(Nb, Al, Ti) and δ = Ni₃(Nb, Mo) are also present. Full article

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

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54 pages, 11159 KB

Open AccessReview

Thermoelectric Transducers: A Promising Method of Energy Generation for Smart Roads

by Tomas Baca, Peter Sarafin, Miroslav Chochul and Michal Kubascik

Appl. Sci. 2026, 16(3), 1662; https://doi.org/10.3390/app16031662 - 6 Feb 2026

Viewed by 246

Abstract

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic [...] Read more.

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research. Full article

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

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19 pages, 2714 KB

Open AccessArticle

Fabrication and Curing Kinetics of Thermal Insulation Material Suitable for Deep-Earth Extreme Environments

by Jianping Yang, Haishu Bai, Zhiqiang He, Bo Yu, Zijie Wei, Ling Chen and Shaoshuai Shi

Appl. Sci. 2026, 16(3), 1661; https://doi.org/10.3390/app16031661 - 6 Feb 2026

Viewed by 141

Abstract

In the extreme high-temperature (up to 150 °C) and high-pressure (up to 140 MPa) conditions of deep in situ condition-preserved coring devices, high-strength epoxy resin was selected as the insulation layer. The non-isothermal DSC method was employed at heating rates of 2.5, 5, [...] Read more.

In the extreme high-temperature (up to 150 °C) and high-pressure (up to 140 MPa) conditions of deep in situ condition-preserved coring devices, high-strength epoxy resin was selected as the insulation layer. The non-isothermal DSC method was employed at heating rates of 2.5, 5, 10, 15, and 20 °C/min, revealing that increasing the heating rate elevates curing temperatures, expands the curing range, and enhances curing rate and heat release. The curing kinetics were modeled using n-order and autocatalytic approaches, with the latter accurately describing the behavior. Optimized integration process conditions (80 °C/4 h + 150 °C/2 h + 180 °C/3 h) yielded epoxy with compressive strength of 204.47 MPa, initial thermal decomposition temperature of 345.9 °C, thermal conductivity of 0.246 W/m·K, and T_g of 193.04 °C (storage modulus 2.41 GPa at 150 °C). As insulation, it reduces rock core heat loss by 32.38% (8.78 × 10⁴ J) and active heating demand by 44 W, enhancing system stability for in situ temperature preservation. Full article

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14 pages, 2997 KB

Open AccessArticle

Impact of Non-Linear CT Resampling on Enhancing Synthetic-CT Generation in Total Marrow and Lymphoid Irradiation

by Monica Bianchi, Nicola Lambri, Daniele Loiacono, Stefano Tomatis, Marta Scorsetti, Cristina Lenardi and Pietro Mancosu

Appl. Sci. 2026, 16(3), 1660; https://doi.org/10.3390/app16031660 - 6 Feb 2026

Viewed by 184

Abstract

Computed tomography (CT) images are stored at a 12-bit depth. However, many deep learning libraries and pre-trained models are designed for 8-bit images, requiring an intermediate compression step before restoring the original 12-bit physical range. This process causes information loss and can compromise [...] Read more.

Computed tomography (CT) images are stored at a 12-bit depth. However, many deep learning libraries and pre-trained models are designed for 8-bit images, requiring an intermediate compression step before restoring the original 12-bit physical range. This process causes information loss and can compromise image reliability. This study investigated the impact of two CT resampling methods (8-bit compression; 12-bit decompression) on dose calculation and image quality. Ten total marrow and lymphoid irradiation patients were selected. CT scans were resampled using linear and non-linear look-up tables (l_LUT/nl_LUT). Original and resampled CTs were evaluated considering: (i) Hounsfield unit (HU) root mean squared error (RMSE); (ii) dose-volume histogram (DVH) statistics for target volume and several organs; (iii) 3D gamma passing rate (GPR) with a 1%/1.25 mm criterion; (iv) lymph nodes contouring and diagnostic quality (scale 1–5). The RMSE for l_LUT vs. nl_LUT was 7 ± 1 vs. 10 ± 1 HU. Maximum differences in DVH statistics were 0.4%, with a 3D-GPR = 100% for all cases. CTs resampled with l_LUT exhibited evident brain pixelation (score = 1), whereas nl_LUT matched the original CT quality (score = 4). Both LUTs were acceptable for lymph nodes delineation. The nl_LUT optimized the CT resampling process, providing a more efficient method for possible deep learning applications in synthetic CT generation. Full article

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

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35 pages, 7010 KB

Open AccessArticle

Seismic Response and Failure Mechanism of Radiator and Conservator Connections in a 154 kV Transformer Based on Shaking Table Tests

by Nakhyun Chun, Sung-Wan Kim, Sung-Jin Chang, U-Jin Kwon, Bub-Gyu Jeon and Su-Won Son

Appl. Sci. 2026, 16(3), 1659; https://doi.org/10.3390/app16031659 - 6 Feb 2026

Viewed by 228

Abstract

Transformers are critical components in power systems, and their functionality must be maintained during seismic events. This study conducted multi-directional shaking table tests on a full-scale 154 kV transformer to investigate the seismic response and failure mechanisms of radiator and conservator connections. Measurements [...] Read more.

Transformers are critical components in power systems, and their functionality must be maintained during seismic events. This study conducted multi-directional shaking table tests on a full-scale 154 kV transformer to investigate the seismic response and failure mechanisms of radiator and conservator connections. Measurements of relative displacement, acceleration, and test response spectra (TRS) indicated stable responses of 0.5–1.2 g for the transformer body, whereas the bushings and radiators exhibited amplified accelerations of up to 4 g and 2 g, respectively, along with relative displacements exceeding 20 mm. Under the 500-year return period input motion, leakage was observed at the lower radiator elbow, which is attributed to the combined effects of concentrated relative displacement, acceleration amplification, frequency-dependent energy concentration, and local structural discontinuities. The observed damage patterns were consistent with leakage incidents reported during the 2024 Noto earthquake in Japan. Based on the experimental findings, this study discusses seismic performance enhancement measures for radiator connections and provides experimental evidence to support the seismic safety evaluation of transformers with similar configurations. Full article

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26 pages, 969 KB

Open AccessArticle

Student Learning Outcome Prediction via Sheaflet-Based Graph Learning and LLM

by Dongmei Zhang, Zhanle Zhu, Yukang Cheng and Yongchun Gu

Appl. Sci. 2026, 16(3), 1658; https://doi.org/10.3390/app16031658 - 6 Feb 2026

Viewed by 185

Abstract

Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. [...] Read more.

Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. To bridge this gap, we propose EduSheaf—a unified framework that integrates large language models (LLMs) with a sheaflet-based signed graph neural network. Specifically, LLMs are employed to extract fine-grained semantic embeddings from multiple-choice questions (MCQs), thereby enriching graph representations with contextual knowledge. A signed graph is then constructed to encode student–MCQ interactions, where correct and incorrect responses are represented as positive and negative edges. On top of this, a novel sheaflet-based signed graph neural network performs multi-frequency learning through low-pass and high-pass filters, enabling the joint modeling of global consensus and local variations, while sheaf structures enforce edge-level consistency. Extensive experiments on multiple real-world educational datasets demonstrate that EduSheaf consistently outperforms state-of-the-art baselines, including both semantic-enhanced and signed graph models, in terms of prediction accuracy and robustness. Ablation studies further reveal the complementary roles of semantic embeddings and multi-frequency graph filters. Full article

(This article belongs to the Special Issue Generative AI for Intelligent Knowledge Systems and Adaptive Learning)

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19 pages, 5735 KB

Open AccessArticle

Design of a Broadband Continuous-Mode Doherty Power Amplifier Using a High-Order Filter Integrated Matching Network

by Peng Tao, Hui Lv and Benyuan Chen

Appl. Sci. 2026, 16(3), 1657; https://doi.org/10.3390/app16031657 - 6 Feb 2026

Viewed by 326

Abstract

To meet the demand for high efficiency in modern broadband communication systems, this paper presents a novel continuous-mode Doherty power amplifier design method based on integrated high-order filter prototypes. By deeply merging the filter structure with the output matching network, broadband impedance transformation [...] Read more.

To meet the demand for high efficiency in modern broadband communication systems, this paper presents a novel continuous-mode Doherty power amplifier design method based on integrated high-order filter prototypes. By deeply merging the filter structure with the output matching network, broadband impedance transformation and harmonic suppression are simultaneously achieved within the 1.6–2.2 GHz frequency range. This approach resolves the bandwidth limitations and efficiency degradation caused by the conventional separation of matching and harmonic control stages. Using a CGH40010F GaN transistor, the impedance space was determined through load-pull analysis, and the design flexibility was enhanced by applying continuous Class-F mode theory. The implemented amplifier demonstrates a saturated efficiency of 68–72%, a 6 dB back-off efficiency of 58.9–64.9%, a saturated output power exceeding 45 dBm, an in-band gain greater than 11.2 dB, and a return loss better than −15 dB. The proposed method offers an effective solution for the design of high-performance broadband power amplifiers. Full article

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24 pages, 4667 KB

Open AccessArticle

A Unified Complementary Regularization Framework for Long-Tailed Image Classification

by Xingyu Shen, Lei Zhang, Lituan Wang and Yan Wang

Appl. Sci. 2026, 16(3), 1656; https://doi.org/10.3390/app16031656 - 6 Feb 2026

Viewed by 174

Abstract

Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often [...] Read more.

Class imbalance is a formidable and ongoing challenge in image classification tasks. Existing methods address this issue by emphasizing minority classes through class redistribution in the feature space or adjusting decision boundaries. Although such approaches improve the accuracy of minority classes, they often lead to unstable training and performance degradation on majority classes. To alleviate these challenges, we propose a unified redistribution framework termed as ComReg, which explicitly enforces complementary regularization on feature learning and decision boundary optimization in long-tailed image classification. Specifically, ComReg employs a multi-expert learning framework combined with prior-knowledge-guided online distillation to construct distribution-aware decision boundaries. From the feature space learning perspective, we enhance intra-class compactness and inter-class separability through decoupled-balanced contrastive learning. To further align the distributions in both spaces, we introduce a delay-weighted prototype learning strategy, which incorporates the decision boundary constructed by the head-class expert into the decoupled-balanced contrastive learning process. Extensive experiments on widely used long-tailed benchmarks, including CIFAR10-LT and CIFAR100-LT, as well as the real-world long-tailed datasets such as subsets of MedMNIST v2, demonstrate that our method achieves state-of-the-art performance. Full article

(This article belongs to the Special Issue AI-Driven Image and Signal Processing)

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20 pages, 3617 KB

Open AccessArticle

Wear Analysis of Catenary Dropper Lines Due to Discontinuous Contact

by Cong Chen, Huai Zhao, Duorun Wang, Xingyu Feng, Guilin Liu, Jiliang Mo, Jian Luo and Dabing Luo

Appl. Sci. 2026, 16(3), 1655; https://doi.org/10.3390/app16031655 - 6 Feb 2026

Viewed by 168

Abstract

The service reliability of critical catenary components is strongly influenced by damage evolution at dynamic contact interfaces. In this study, a numerical framework is developed to simulate the dynamic contact behavior and wear progression of catenary droppers by coupling Archard’s wear law with [...] Read more.

The service reliability of critical catenary components is strongly influenced by damage evolution at dynamic contact interfaces. In this study, a numerical framework is developed to simulate the dynamic contact behavior and wear progression of catenary droppers by coupling Archard’s wear law with an adaptive remeshing strategy. Surface degradation is explicitly incorporated into the contact formulation through an improved boundary representation, enabling a quantitative linkage between interface damage and the corresponding mechanical responses. The simulations indicate that, after geometric reconstruction of the worn surface, the contact interface exhibits a pronounced stress-gradient evolution. The most severe damage is predicted at the contact region between the central strand and one outer strand, and the spatial damage pattern is primarily governed by discontinuous contact. Moreover, thermally induced material softening has a limited effect on the peak contact stress, which is dominated instead by the applied load and local contact geometry. The proposed framework provides a computational basis for assessing dropper wear and estimating catenary lifetime, thereby supporting reliability-oriented maintenance and safer rail operations. Full article

(This article belongs to the Special Issue Advanced Finite Element Method and Its Applications, Second Edition)

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19 pages, 2037 KB

Open AccessSystematic Review

Aerogels and Oleogels as Functional Fat Replacers in Spreads—A Systematic Review

by Andrea Karlović, Marija Banožić, Đurđica Ačkar, Sanda Hasenay and Drago Šubarić

Appl. Sci. 2026, 16(3), 1654; https://doi.org/10.3390/app16031654 - 6 Feb 2026

Viewed by 273

Abstract

The growing demand for healthier food options has accelerated the development of innovative fat-replacement strategies in spreadable products. Oleogels are semi-solid systems formed by structuring edible oils. Recently, these systems have emerged as a promising solution for reducing saturated fat content without compromising [...] Read more.

The growing demand for healthier food options has accelerated the development of innovative fat-replacement strategies in spreadable products. Oleogels are semi-solid systems formed by structuring edible oils. Recently, these systems have emerged as a promising solution for reducing saturated fat content without compromising product quality, texture, or sensory attributes. A systematic review was conducted following the PRISMA 2020 protocol, supplemented by a bibliometric analysis. Research was identified through searches in Web of Science, Scopus, Wiley, Springer, MDPI, and Google Scholar for studies published between 2020 and 2024. Inclusion criteria focused on original research articles in English involving food-sector applications of oleogels and aerogels in sweet spreads. Study quality and risk of bias were assessed by two independent reviewers based on methodological relevance and data integrity. Results were synthesized through a narrative approach and bibliometric mapping. After screening 490 records, 34 original research articles were included. Bibliometric data highlighted a clear trend shifting from foundational lipid structuring research in 2020 toward complex, product-specific functional applications by 2024. Overall, the results suggest that these structured systems are viable replacements for traditional saturated fats, providing comparable spreadability and stability. Funding: This work was supported by the Croatian Science Foundation under the project IP-2022-10-1960. This systematic review was not registered in a public database. Full article

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13 pages, 2100 KB

Open AccessArticle

The Effect of Rice Husk-Derived Graphene-like Materials on the Mechanical Properties of Oil Well Cement-Based Composite Materials

by Weifeng Yan, Xijie Wang, XiuJin Yuan, Wei Liu and Jianjian Song

Appl. Sci. 2026, 16(3), 1653; https://doi.org/10.3390/app16031653 - 6 Feb 2026

Viewed by 166

Abstract

The early mechanical properties of a cement sheath are not good when the temperature of the oil and gas wells is low, and it is easily damaged. The rice husk-derived graphene-like (RCG) materials were used to improve the mechanical properties of oil well [...] Read more.

The early mechanical properties of a cement sheath are not good when the temperature of the oil and gas wells is low, and it is easily damaged. The rice husk-derived graphene-like (RCG) materials were used to improve the mechanical properties of oil well cement-based composite materials. The rice husk-derived graphene-like materials were prepared using agricultural waste rice husks with a lower cost. The rice husk-derived graphene-like materials were analyzed using X-ray diffraction and Raman spectroscopy. The effect of the rice husk-derived graphene-like materials on the mechanical properties and microstructure of oil well cement was studied. The results show that the prepared graphene-like materials are a type of multi-layer graphene with certain defects. The compressive strength of the cement sample after curing for 28 days increases by 35.58%; its flexural strength increases by 25.33%, and its impact strength increases by 40.94% with 0.06 wt% of the graphene-like materials. The graphene-like materials derived from rice husks do not lead to the generation of a new hydration product in oil well cement. It mainly enhances the mechanical properties of cement paste by affecting hydration crystallization. This article provides a reference for studying the improvement of mechanical properties of oil well cement-based composites using eco-friendly materials. Full article

(This article belongs to the Special Issue New Technologies and Theories Applied in Oil and Gas Development Under Complex Conditions)

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13 pages, 876 KB

Open AccessArticle

Evaluation of the Precision and Accuracy of Computer-Guided Implant Surgery: A Prospective Clinical Study Comparing .STL Files from the Intraoral Rehabilitation Scanning with the Digital Project

by Francesca Argenta, Antonino Palazzolo, Eugenio Romeo, Saturnino Marco Lupi, Tommaso Risciotti, Massimo Scanferla and Stefano Storelli

Appl. Sci. 2026, 16(3), 1652; https://doi.org/10.3390/app16031652 - 6 Feb 2026

Viewed by 190

Abstract

Objectives: This prospective cohort study aimed to evaluate the accuracy and precision of static computer-guided, flapless implant surgery in partially edentulous patients, comparing the virtually planned and clinically achieved implant positions. Materials and Methods: From 2017 to 2022, 40 patients (20 males and [...] Read more.

Objectives: This prospective cohort study aimed to evaluate the accuracy and precision of static computer-guided, flapless implant surgery in partially edentulous patients, comparing the virtually planned and clinically achieved implant positions. Materials and Methods: From 2017 to 2022, 40 patients (20 males and 20 females) received a total of 129 implants across 59 partial rehabilitations, with 62 implants placed in the maxilla and 67 in the mandible. All interventions were performed by a single experienced operator using dental-supported stereolithographic guides and a flapless protocol. The discrepancy between planned and actual implant positions was measured using reverse engineering software, assessing linear deviations at the implant Platform (coronal) and apex, as well as angular deviations. Subgroup analyses were conducted based on the jaw (maxilla vs. mandible) and the type of surgical guide support (Kennedy classes I–IV). Results: The mean linear deviation was 1.16 ± 0.58 mm at the apex and 0.80 ± 0.41 mm at the implant Platform (coronal). The mean angular deviation was 3.23° ± 1.86°. Slightly higher deviations were observed in the mandible than in the maxilla. Group-wise analysis showed minor variations depending on the type of guide support. Conclusions: Static computer-guided surgery demonstrated measurable linear and angular deviations between planned and achieved implant positions. These discrepancies should be considered during treatment planning, especially in narrow ridges or Class I configurations. Full article

(This article belongs to the Special Issue Recent Development and Emerging Trends in Dental Implants)

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19 pages, 6327 KB

Open AccessArticle

Finite Element Analysis of the Connection Between Prefabricated Large-Diameter Steel-Reinforced Concrete Hollow Tubular Columns and Foundations

by Bailing Chen, Zifan Bai, Yu He, Lianguang Wang and Chuang Shao

Appl. Sci. 2026, 16(3), 1651; https://doi.org/10.3390/app16031651 - 6 Feb 2026

Viewed by 192

Abstract

The extensive use of prefabricated large-diameter steel-reinforced concrete (SRC) hollow tubular columns in major infrastructure projects creates a critical demand for efficient and reliable column-to-foundation connections with satisfactory seismic performance. To address this, three novel prefabricated connection details are proposed herein. A refined [...] Read more.

The extensive use of prefabricated large-diameter steel-reinforced concrete (SRC) hollow tubular columns in major infrastructure projects creates a critical demand for efficient and reliable column-to-foundation connections with satisfactory seismic performance. To address this, three novel prefabricated connection details are proposed herein. A refined three-dimensional nonlinear finite element model was developed using ABAQUS to assess their mechanical behavior under quasi-static cyclic loading. The model was established based on widely accepted constitutive models, contact algorithms, and loading protocols consistent with relevant codes and international research. The results demonstrate that the proposed prefabricated connections significantly outperform conventional cast-in-place connections in terms of ultimate bearing capacity, with an increase of approximately 79%. A comprehensive parametric analysis was conducted, identifying an optimal design configuration comprising a socket depth of 600 mm, six embedded steel sections, an axial compression ratio of 0.1, and a hollow core radius of 600 mm, which achieves an optimal balance between mechanical performance and cost-effectiveness. These findings provide a reliable theoretical basis and practical guidance for designing and implementing high-performance prefabricated connections in engineering practice. Full article

(This article belongs to the Topic Advances on Structural Engineering, 3rd Edition)

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30 pages, 3213 KB

Open AccessArticle

Contextual Reuse of Big Data Systems: A Case Study Assessing Groundwater Recharge Influences

by Agustina Buccella, Alejandra Cechich, Walter Garrido and Ayelén Montenegro

Appl. Sci. 2026, 16(3), 1650; https://doi.org/10.3390/app16031650 - 6 Feb 2026

Viewed by 205

Abstract

The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this [...] Read more.

The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition. Full article

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

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21 pages, 4440 KB

Open AccessArticle

A Fitting Study on the Growth Boundary of an Underground Coal Gasification Cavity Based on Numerical Simulation

by Xiao Ma, Zhiyi Zhang, Xin Li, Shuo Feng and Baiye Li

Appl. Sci. 2026, 16(3), 1649; https://doi.org/10.3390/app16031649 - 6 Feb 2026

Viewed by 158

Abstract

Underground coal gasification (UCG) is a coal utilization technology that has attracted extensive attention over the years. In order to study the distribution and evolution law of the growth boundary of a coal gasification cavity under UCG, COMSOL numerical simulation software was used [...] Read more.

Underground coal gasification (UCG) is a coal utilization technology that has attracted extensive attention over the years. In order to study the distribution and evolution law of the growth boundary of a coal gasification cavity under UCG, COMSOL numerical simulation software was used to conduct a multi-physical field-coupling numerical simulation of its growth process. In this study, we established a gasification reaction model of the cavity, and after simulation calculation, the growth boundary of the gasification cavity was obtained. Multiple data points were taken from the growth boundary of the gasification cavity for the fitting calculation, and the fitting function

y = F (x)

of the gasification boundary growth was obtained. The core insight from this study is that a gasification boundary growth fitting function

y = F (x)

was cross-fitted based on seven different gasification times

t

(5 d, 20 d, 40 d, 60 d, 80 d, 110 d, 150 d) and 10 different gasification agent inflow velocities

v

(0.1 m/s, 0.3 m/s, 0.5 m/s, 0.7 m/s, 1 m/s, 2 m/s, 4 m/s, 6 m/s, 8 m/s, 10 m/s) as orthogonal independent variables. An innovative multi-parameter fitting equation was constructed,

y = F (x, t, v)

, with the gasification time

t

and the gasification agent inflow velocity

v

as independent variables. This fitting equation,

y = F (x, t, v)

, can dynamically depict the gasification cavity boundary during the UCG process when different gasification times

t

and gasification agent inflow velocities

v

are inputted. The novelty of this study lies in the fact that it breaks through the limitations of traditional numerical simulation models that rely on a single variable, have limited adaptability, and focus on gasification cavities that lie mostly in the side-view direction. Moreover, through a multi-physics field-coupling numerical simulation in the top-view direction of the gasification cavity, we have improved the construction of the UCG numerical simulation model and cross-fitted the gasification boundary with respect to the gasification time

t

and gasification agent inflow velocity

v

to construct a fitting equation, achieving the quantitative representation of the nonlinear relationship between variables. Full article

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

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25 pages, 1409 KB

Open AccessArticle

MDMF: A Market-Mainline-Driven Multi-Feature Fusion Model for Stock Trend Forecasting

by Zichen Shi, Yuli Zhao, Yin Zhang and Hongfei Wu

Appl. Sci. 2026, 16(3), 1648; https://doi.org/10.3390/app16031648 - 6 Feb 2026

Viewed by 258

Abstract

Accurate stock prediction is crucial for investment decisions and risk management, yet remains challenging due to the non-stationary, nonlinear, and noisy nature of financial markets. Although deep learning has advanced forecasting by modeling temporal patterns and stock relationships, most methods fail to capture [...] Read more.

Accurate stock prediction is crucial for investment decisions and risk management, yet remains challenging due to the non-stationary, nonlinear, and noisy nature of financial markets. Although deep learning has advanced forecasting by modeling temporal patterns and stock relationships, most methods fail to capture structured, market-wide forces. Specifically, they miss the emergence and influence of “market mainlines”—persistent directional trends in groups of stocks with shared attributes that collectively drive market movement. To address this, we propose the Market-mainline-Driven Multi-feature Fusion Model (MDMF), which dynamically identifies multidimensional market mainline characteristics and captures their differential impact on individual stocks. The model incorporates a dual-channel encoding mechanism, a dynamic stock aggregation algorithm, and a differential influence module to integrate temporal, fundamental, and stock-specific features. Extensive experiments on real-world stock datasets show that MDMF outperforms state-of-the-art baselines in predictive accuracy and profitability, demonstrating its robustness and practical utility. Our study highlights the value of explicitly modeling market mainlines for enhancing stock prediction and offers insights into systematic market behavior. Full article

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

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15 pages, 4686 KB

Open AccessArticle

Petrogenesis of Quartz Diorite in the Datian Complex, Western Yangtze Block: Evidence from U-Pb Geochronology, Geochemistry, and Sr-Nd-Hf Isotopes

by Jian Yao, Youliang Chen, Yu Wu, Jing Zhao, Luyu Huang and Minghui Yin

Appl. Sci. 2026, 16(3), 1647; https://doi.org/10.3390/app16031647 - 6 Feb 2026

Viewed by 172

Abstract

This study presents integrated zircon U-Pb geochronology, whole-rock geochemistry, and Sr-Nd-Hf isotopic investigations of quartz diorite and gneissic quartz diorite from the Datian Complex along the western Yangtze Block, elucidating their petrogenesis and tectonic implications. Key findings reveal: (1) The crystallization ages of [...] Read more.

This study presents integrated zircon U-Pb geochronology, whole-rock geochemistry, and Sr-Nd-Hf isotopic investigations of quartz diorite and gneissic quartz diorite from the Datian Complex along the western Yangtze Block, elucidating their petrogenesis and tectonic implications. Key findings reveal: (1) The crystallization ages of the Datian Complex (~770–755 Ma) record episodic magmatic activity over a ~16 Ma period, indicating a multi-stage tectonic evolution; (2) Both rock types exhibit intermediate SiO₂ (57–64.58 wt.%), high Al₂O₃ (15.44–17.80 wt.%), and MgO (2.18–3.67 wt.%; Mg# = 47.41–52.65) with calc-alkaline signatures (Na₂O/K₂O = 1.14–2.65), coupled with adakitic traits including pronounced LREE/HREE fractionation (La_N/Yb_N = 3.83–26.4), negative Eu anomalies (δEu = 0.61–1.05), elevated Sr (372–701 ppm), and Sr/Y ratios (24.6–56.2), collectively classifying the complex as high-Si adakite; (3) The isotopic homogeneity (whole-rock Sr-Nd: ⁸⁷Sr/⁸⁶Sr(i) = 0.7038–0.7048, εNd(t) = −1.5 to–3.8; zircon Hf: εHf(t) = 1.24–6.88) supports a two-stage petrogenetic model involving partial melting of subducted oceanic slab, followed by mantle wedge metasomatism during magma ascent. These results position the Datian Complex as a Neoproterozoic arc-related adakitic magmatic system within the active continental margin of the Yangtze Block. Full article

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16 pages, 2129 KB

Open AccessArticle

Design and Optimization of Highly Efficient RF Power Amplifiers for Acousto-Optic Tunable Filters in Spaceborne Applications

by Pallab Kr Gogoi, Jurgen Vanhamel and Jérôme Loicq

Appl. Sci. 2026, 16(3), 1646; https://doi.org/10.3390/app16031646 - 6 Feb 2026

Viewed by 159

Abstract

This paper presents the design and optimization of highly efficient radio frequency power amplifiers (RFPAs) for driving acousto-optic tunable filters (AOTFs) in spaceborne applications. High efficiency is critical in such applications to minimize power consumption, heat dissipation, and enhance system reliability. However, RFPAs [...] Read more.

This paper presents the design and optimization of highly efficient radio frequency power amplifiers (RFPAs) for driving acousto-optic tunable filters (AOTFs) in spaceborne applications. High efficiency is critical in such applications to minimize power consumption, heat dissipation, and enhance system reliability. However, RFPAs typically generate significant harmonic content and heat, which can induce thermal effects and compromise the optical measurement accuracy of AOTFs. This work investigates the trade-offs among efficiency, bandwidth, harmonic suppression, and tunable output power. Analytical modeling and parametric optimization are employed to derive practical design strategies. The results offer valuable insights for the development of efficient RF driving systems for AOTFs. Full article

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

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26 pages, 2807 KB

Open AccessArticle

An Engineering Framework for Adaptive Winglet Design: Identification of the Optimal Morphing Mode and Envelope

by Wei Li, Benjamin King Sutton Woods and Dazhong Wang

Appl. Sci. 2026, 16(3), 1645; https://doi.org/10.3390/app16031645 - 6 Feb 2026

Viewed by 240

Abstract

Adaptive winglets improve aerodynamic efficiency by enabling geometry adjustments tailored to flight conditions. In this study, an engineering-oriented optimization framework is developed and applied to numerical aerodynamic evaluations based on the wing–winglet configuration of a KC-135 aircraft, under representative takeoff, climb, and cruise [...] Read more.

Adaptive winglets improve aerodynamic efficiency by enabling geometry adjustments tailored to flight conditions. In this study, an engineering-oriented optimization framework is developed and applied to numerical aerodynamic evaluations based on the wing–winglet configuration of a KC-135 aircraft, under representative takeoff, climb, and cruise conditions. A Plackett–Burman design is employed to screen the 10 kinds of winglet geometric parameters, from which the dominant variables affecting drag are identified. Subsequently, response surface methodology is used to construct surrogate models and determine optimal parameter combinations for each flight phase, thereby defining a feasible morphing envelope for adaptive winglet operation. The results indicate that a coupled morphing of winglet height and cant angle constitutes the most effective morphing mode. Across the takeoff, climb, and cruise phases, the optimal morphing envelope involves a continuous transition from Height = 0.20b/2 and Cant angle = 86.3° at takeoff, to Height = 0.192b/2 and Cant angle = 8.2° during climb, and finally approaching the baseline configuration (Height = 0.135b/2, Cant angle = 20°) at cruise, while achieving a maximum drag reduction efficiency improvement of up to 8.8% at the climb phase. Full article

(This article belongs to the Special Issue Morphing-Enabling Technologies for Aerospace Systems: 2nd Edition)

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27 pages, 9814 KB

Open AccessArticle

ffstruc2vec: Flat, Flexible, and Scalable Learning of Node Representations from Structural Identities

by Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer and Gonzalo Wandosell Fernández de Bobadilla

Appl. Sci. 2026, 16(3), 1644; https://doi.org/10.3390/app16031644 - 6 Feb 2026

Viewed by 244

Abstract

Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific node properties and is widely used in applied domains such as fraud detection and social network analysis. A key challenge is the development of scalable [...] Read more.

Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific node properties and is widely used in applied domains such as fraud detection and social network analysis. A key challenge is the development of scalable methods that can capture structural characteristics relevant to diverse downstream application tasks. While most existing approaches focus on preserving node proximity, methods that aim to preserve structural properties often lack the flexibility required to represent different types of structural patterns. In this work, we introduce ffstruc2vec, a scalable deep learning framework for learning node embeddings that preserve structural identities, designed for real-world network analysis tasks. The proposed approach employs a flat and efficient architecture that enables flexible modeling of a wide range of structural patterns and facilitates adaptation to different downstream tasks. The experimental results across unsupervised and supervised settings demonstrate that ffstruc2vec achieves competitive and often superior performance compared to existing structure-preserving node embedding methods. In addition, the framework enhances interpretability by quantifying the influence of individual structural patterns on task outcomes, supporting the interpretation of the learned representations. These results indicate that ffstruc2vec offers a flexible and scalable solution for structure-preserving node embedding with practical applicability. Full article

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1 pages, 116 KB

Open AccessExpression of Concern

Expression of Concern: Svynos et al. Effects of Vertical and Horizontal Jumping Asymmetries on Linear and Change-of-Direction Speed Performance of Female Soccer Players. Appl. Sci. 2024, 14, 3901

by Applied Sciences Editorial Office

Appl. Sci. 2026, 16(3), 1643; https://doi.org/10.3390/app16031643 - 6 Feb 2026

Viewed by 126

Abstract

With this notice, the Applied Sciences Editorial Office alerts the readers to concerns related to this article [...] Full article

17 pages, 2936 KB

Open AccessArticle

Bioactive Glasses Based on SiO₂-CaO-Na₂O-P₂O₅-ZrO₂ System: Effects of ZrO₂ on the Glass Structure, Solubility and Mineral Precipitation in Simulated Body Fluid

by Sahar Mokhtari, Cieran A. Rody and Anthony W. Wren

Appl. Sci. 2026, 16(3), 1642; https://doi.org/10.3390/app16031642 - 6 Feb 2026

Viewed by 161

Abstract

Zirconia (ZrO₂) containing bioactive glasses (BG’s) have been synthesized to determine their influence on the structure of a 0.56SiO_2–0.15Na₂O-0.25CaO-0.04P₂O₅ glass and the resulting solubility within a hydrated environment. In this study, the SiO₂ [...] Read more.

Zirconia (ZrO₂) containing bioactive glasses (BG’s) have been synthesized to determine their influence on the structure of a 0.56SiO_2–0.15Na₂O-0.25CaO-0.04P₂O₅ glass and the resulting solubility within a hydrated environment. In this study, the SiO₂ content was directly substituted with 0.04 ZrO₄ (Mol. Fr.) and structural analysis of the Control and Zr-Glasses was conducted using X-ray Photoelectron Spectroscopy (XPS) and Magic Angle Spinning-Nuclear Magnetic Resonance (MAS-NMR). These techniques indicate that the overall network connectivity (NC) of the glass increases with ZrO₂/SiO₂ substitution, suggesting that ZrO₂ acts predominantly as a network former in the glass structure. The ion release profiles of the glasses incubated in de-ionized water from 1 to 1000 h showed decreased dissolution rates for the Zr-containing glasses. The in vitro bioactivity of glasses tested in Simulated Body Fluid (SBF) showed calcium phosphate (CaP) formation on the surface of all glasses after 100–1000 h incubation; however, the Zr-glass experienced delayed CaP precipitation compared to the Zr-free Control. Full article

(This article belongs to the Special Issue Advancements in Sustainable Silicate Materials and Their Applications)

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18 pages, 4253 KB

Open AccessArticle

Optimization of Transmission Parameters for a Tractor Equipped with HMCVT Based on Power Flow Analysis to Improve Efficiency

by Huadong Zhou, Zhixiong Lu and Zhun Cheng

Appl. Sci. 2026, 16(3), 1641; https://doi.org/10.3390/app16031641 - 6 Feb 2026

Viewed by 157

Abstract

In order to match the transmission parameters of HMCVT more reasonably, this paper takes a three-planetary-row parallel scheme as the research object, and proposes the concept of ‘hydraulic circuit power coefficient’, that is, the ratio of the power transmitted from the hydraulic circuit [...] Read more.

In order to match the transmission parameters of HMCVT more reasonably, this paper takes a three-planetary-row parallel scheme as the research object, and proposes the concept of ‘hydraulic circuit power coefficient’, that is, the ratio of the power transmitted from the hydraulic circuit to the confluence mechanism to the output power of the HMCVT system. Under the condition of satisfying the variable speed range, the proportion of the power transmitted by the hydraulic circuit with lower efficiency is reduced as much as possible. The optimization function with the minimum sum of the hydraulic circuit power coefficients as the objective is established, and the PSO algorithm with compression factor is used for iterative optimization. The objective function is optimal when iterating 85 times, after parameter optimization, the hydraulic power coefficients of the three sections are reduced to varying degrees. When the displacement ratio

e

= −0.7, the HM1 section is reduced by 1.07, a decrease of 42%, and the sum of the hydraulic power coefficients at the endpoints of the three working sections is reduced from 4.24 to 2.91, a decrease of 32%. The physical simulation model of a three-planetary parallel HMCVT is established by AMESim software, and the speed ratio characteristics obtained by simulation are consistent with the theoretical analysis results, which verifies the correctness of the model. The parameters before and after optimization are substituted into the model for a simulation test, and the transmission efficiency of three working sections is obtained. The simulation results show that the efficiency of the three working sections after parameter optimization is higher than that before optimization. When the displacement ratio

e

= −0.8, the transmission efficiency of the HM1 section increases from 0.3 to 0.45, which is increased by 50%. When the displacement ratio

e

= 0.98, the transmission efficiency of the HM2 section increases from 0.78 to 0.83, which is increased by 6.4%, and the optimization effect is obvious. Full article

(This article belongs to the Special Issue The Advances and Applications of Computational Fluid Dynamics and Heat Transfer)

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