Applied Sciences

17 pages, 8553 KB

Open AccessArticle

High-Intensity Focused Pressure Wave Generation via Q-Switched Er:YAG Laser with a Water Layer Formed by the Coupled Lens for Optoacoustic Conversion

by Dominik Šavli, Aleš Babnik, Daniele Vella and Matija Jezeršek

Appl. Sci. 2025, 15(19), 10860; https://doi.org/10.3390/app151910860 - 9 Oct 2025

Viewed by 255

Abstract

We demonstrate coating-free optoacoustic generation and focusing of ultrasound using a mechanically Q-switched (MQS) erbium-doped yttrium aluminum garnet (Er:YAG) source (~100 ns, ≤20 mJ) combined with a concave water interface that simultaneously serves as converter and acoustic lens. Axial, lateral, and focal-point measurements [...] Read more.

We demonstrate coating-free optoacoustic generation and focusing of ultrasound using a mechanically Q-switched (MQS) erbium-doped yttrium aluminum garnet (Er:YAG) source (~100 ns, ≤20 mJ) combined with a concave water interface that simultaneously serves as converter and acoustic lens. Axial, lateral, and focal-point measurements mapped the pressure field while varying beam diameter (2w = 5–15 mm) and pulse energy (E = 10–20 mJ). The maximum focal positive pressure (P_max = 7 MPa) occurs at an intermediate diameter (~10 mm), whereas the tightest lateral/axial confinement and strongest spectral enhancement arise at larger diameters (14–15 mm) with f_c = ~5 MHz and −6 dB bandwidth up to 7 MHz. Pressure increases nearly monotonically with energy. For equal fluence, larger diameters yield higher focal pressures due to greater focusing gain. Small beams (2w ≈ 5–7 mm) show shorter apparent time-of-flight (TOF) and waveform broadening, consistent with early shock-like emission from locally vaporizing region. These results provide practical rules for tuning amplitude, spectrum, and confinement, enabling sub-millimeter focusing for contamination-sensitive and therapeutic applications. Full article

(This article belongs to the Section Optics and Lasers)

► Show Figures

Figure 1

12 pages, 2357 KB

Open AccessArticle

D-Band THz A-Scanner for Grout Void Inspection of External Bridge Tendons

by Dae-Su Yee, Ji Sang Yahng and Seung Hyun Cho

Appl. Sci. 2025, 15(19), 10859; https://doi.org/10.3390/app151910859 - 9 Oct 2025

Viewed by 168

Abstract

Grout voids in external tendons of post-tensioned bridges are a critical issue, as they may result in the corrosion of the steel strands and significantly reduce tendon strength. Therefore, preventing tendon failure necessitates thorough inspection for these voids during both construction and operation. [...] Read more.

Grout voids in external tendons of post-tensioned bridges are a critical issue, as they may result in the corrosion of the steel strands and significantly reduce tendon strength. Therefore, preventing tendon failure necessitates thorough inspection for these voids during both construction and operation. Terahertz electromagnetic wave testing is an effective method for detecting voids between the protective duct and the grout in external tendons, as terahertz waves can penetrate through the protective duct. This study introduces a D-band electronic frequency-modulated continuous-wave terahertz A-scanner for enhanced real-time inspection. The proposed method offers key advantages such as miniaturization, cost-effectiveness, and robustness, while providing effective detection of voids beneath the duct in external tendons. It is indicated that voids with a thickness of approximately 2.5 mm or greater can be detected using the D-band THz A-scanner. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

► Show Figures

Figure 1

14 pages, 722 KB

Open AccessArticle

Fermentation of Grapefruit Juice with Lacticaseibacillus rhamnosus and Enzymatic Debittering by Naringinase

by Katarzyna Górska, Joanna Bodakowska-Boczniewicz and Zbigniew Garncarek

Appl. Sci. 2025, 15(19), 10858; https://doi.org/10.3390/app151910858 - 9 Oct 2025

Viewed by 195

Abstract

Growing consumer awareness of the link between diet and health has increased interest in functional foods, including fermented juices. Grapefruit juice has potential health-promoting properties, but its bitter taste limits its acceptance by consumers. This study aimed to develop a fermentation process for [...] Read more.

Growing consumer awareness of the link between diet and health has increased interest in functional foods, including fermented juices. Grapefruit juice has potential health-promoting properties, but its bitter taste limits its acceptance by consumers. This study aimed to develop a fermentation process for debittering grapefruit juice at natural pH using Lacticaseibacillus rhamnosus and naringinase. Grapefruit juice was fermented with Lactic. rhamnosus using free naringinase and naringinase immobilized on carob gum and chitosan supports at 30 ± 0.2 °C for 72 h. Naringin concentration, bacterial cell count, total phenol content, organic acids, carbohydrates, antioxidant activity, and pH were analyzed. Naringinase immobilized on carob gum demonstrated the highest efficiency, hydrolyzing over 42% of naringin after 24 h (from 418.20 to 241.19 μg/mL). The free enzyme reduced the naringin concentration to 155.28 μg/mL after 48 h. The highest Lactic. rhamnosus cell count (2.05 × 10⁹ CFU/mL) was achieved with the free enzyme. Total phenol content decreased from 42.24 to 16.58 mg GAE/100 mL when using naringinase immobilized on chitosan. The combined use of naringinase and Lactic. rhamnosus enables the development of an integrated process that improves consumer acceptance with potential applications in the functional beverage industry. Full article

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

► Show Figures

Figure 1

17 pages, 811 KB

Open AccessArticle

Balancing Privacy and Utility in Artificial Intelligence-Based Clinical Decision Support: Empirical Evaluation Using De-Identified Electronic Health Record Data

by Jungwoo Lee and Kyu Hee Lee

Appl. Sci. 2025, 15(19), 10857; https://doi.org/10.3390/app151910857 - 9 Oct 2025

Viewed by 272

Abstract

The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to re-identification, membership inference attacks, and model extraction attacks. This study examined the [...] Read more.

The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to re-identification, membership inference attacks, and model extraction attacks. This study examined the balance between privacy protection and model utility by evaluating de-identification strategies and differentially private learning in large-scale electronic health records. De-identified records from a tertiary medical center were analyzed and compared with three strategies—baseline generalization, enhanced generalization, and enhanced generalization with suppression—together with differentially private stochastic gradient descent. Privacy risks were assessed through k-anonymity distributions, membership inference attacks, and model extraction attacks. Model performance was evaluated using standard predictive metrics, and privacy budgets were estimated for differentially private stochastic gradient descent. Enhanced generalization with suppression consistently improved k-anonymity distributions by reducing small, high-risk classes. Membership inference attacks remained at the chance level under all conditions, indicating that patient participation could not be inferred. Model extraction attacks closely replicated victim model outputs under baseline training but were substantially curtailed once differentially private stochastic gradient descent was applied. Notably, privacy-preserving learning maintained clinically relevant performance while mitigating privacy risks. Combining suppression with differentially private stochastic gradient descent reduced re-identification risk and markedly limited model extraction while sustaining predictive accuracy. These findings provide empirical evidence that a privacy–utility balance is achievable in clinical applications. Full article

(This article belongs to the Special Issue Digital Innovations in Healthcare)

► Show Figures

Figure 1

18 pages, 1164 KB

Open AccessArticle

Potential for Improving the Environmental Sustainability of Natural Aggregates Production (Slovenian Case Study)

by Janez Turk, Anja Kodrič, Rok Cajzek and Tjaša Zupančič Hartner

Appl. Sci. 2025, 15(19), 10856; https://doi.org/10.3390/app151910856 - 9 Oct 2025

Viewed by 156

Abstract

The environmental performance of natural aggregates for concrete and road construction, extracted from a dolomite quarry, was investigated. Environmental hotspots were identified, and potential optimization measures to further reduce the environmental footprint were proposed. The natural aggregates extracted from the dolomite quarry have [...] Read more.

The environmental performance of natural aggregates for concrete and road construction, extracted from a dolomite quarry, was investigated. Environmental hotspots were identified, and potential optimization measures to further reduce the environmental footprint were proposed. The natural aggregates extracted from the dolomite quarry have relatively low GWP and a low environmental footprint in general. The GWP of 1 tonne of natural aggregates used in concrete production is 1.13 kg CO₂ equiv., while for 1 tonne of aggregates used in road construction, it is 0.97 kg CO₂ equiv. The dolomite rock in the quarry in question is tectonically fractured, such that very intensive extraction is not required, taking into account the blasting of the rock and further processing. The use of non-road mobile machinery is already optimized. Additional reductions in environmental impact could be achieved by powering the screening process exclusively with electricity from renewable sources, such as a photovoltaic system. In this context, integrating on-site battery storage systems might present a promising solution for addressing the seasonal mismatch between solar energy generation and processing demands. Full article

(This article belongs to the Topic Sustainable Construction Materials, Processes, and Automation Technologies)

► Show Figures

Figure 1

23 pages, 5026 KB

Open AccessArticle

Vibration Control of Passenger Aircraft Active Landing Gear Using Neural Network-Based Fuzzy Inference System

by Aslı Durmuşoğlu and Şahin Yıldırım

Appl. Sci. 2025, 15(19), 10855; https://doi.org/10.3390/app151910855 - 9 Oct 2025

Viewed by 238

Abstract

Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated [...] Read more.

Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems. Full article

(This article belongs to the Special Issue Vibration Analysis of Nonlinear Mechanical Systems)

► Show Figures

Figure 1

22 pages, 3155 KB

Open AccessArticle

Forced Vibration Analysis of a Hydroelastic System with an FGM Plate, Viscous Fluid, and Rigid Wall Using a Discrete Analytical Method

by Mohammed M. Alrubaye and Surkay D. Akbarov

Appl. Sci. 2025, 15(19), 10854; https://doi.org/10.3390/app151910854 - 9 Oct 2025

Viewed by 147

Abstract

This study examines the forced vibration behavior of a hydroelastic system composed of a functionally graded material (FGM) plate, a barotropic compressible Newtonian viscous fluid, and an adjacent rigid wall. The fluid occupies the gap between the plate and the wall. A time-harmonic [...] Read more.

This study examines the forced vibration behavior of a hydroelastic system composed of a functionally graded material (FGM) plate, a barotropic compressible Newtonian viscous fluid, and an adjacent rigid wall. The fluid occupies the gap between the plate and the wall. A time-harmonic force, applied in and along the free surface of the FGM plate, excites vibrations within the system. The plate’s motion is modeled using the exact equations of elastodynamics, while the fluid dynamics are described by the linearized Navier–Stokes equations for compressible viscous flow. The governing equations, which feature variable coefficients, are solved using a discrete analytical approach. Boundary conditions enforce impermeability at the rigid wall and continuity of both forces and velocities at the fluid–plate interface. The investigation focuses on the plane strain state of the plate coupled with the corresponding two-dimensional fluid flow. Numerical analyses are conducted to evaluate normal stresses and velocity distributions along the interface. The primary objective is to assess how the graded material properties of the plate influence the frequency-dependent responses of stresses and velocities at the plate–fluid boundary. Full article

► Show Figures

Figure 1

17 pages, 3346 KB

Open AccessArticle

Enhancing Tree-Based Machine Learning for Personalized Drug Assignment

by Katyna Sada Del Real and Angel Rubio

Appl. Sci. 2025, 15(19), 10853; https://doi.org/10.3390/app151910853 - 9 Oct 2025

Viewed by 203

Abstract

Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug [...] Read more.

Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug from multiple candidates. We present SEATS (Systematic Efficacy Assignment with Treatment Seats), which adapts conventional models like Random Forest and XGBoost for multiclass drug assignment by allocating probabilistic “treatment seats” to drugs based on efficacy. This approach helps models learn clinically relevant distinctions. Additionally, we assess an interpretable Optimal Decision Tree (ODT) model designed specifically for drug assignment. Trained on the BeatAML2 cohort and validated on the GDSC AML cell line dataset, integrating SEATS with Random Forest and XGBoost improved prediction accuracy and consistency. The ODT model offered competitive performance with clear, interpretable decision paths and minimal feature requirements, facilitating clinical use. SEATS reorients standard models towards personalized drug selection. Combined with the ODT framework it provides effective, interpretable strategies for precision oncology, underscoring the potential of tailored machine learning solutions in supporting real-world treatment decisions. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)

► Show Figures

Figure 1

19 pages, 4558 KB

Open AccessArticle

Study on the Effect of Seatback Recline Angle and Connection Stiffness on Occupant Injury in High-Speed Train Collisions

by Fei Yu, Xu Sang, Honglei Tian, Longxi Liu and Wenbin Wang

Appl. Sci. 2025, 15(19), 10852; https://doi.org/10.3390/app151910852 - 9 Oct 2025

Viewed by 217

Abstract

This study investigates occupant–seat interaction dynamics in high-speed train frontal collisions. A finite element model of a second-class double seat was developed and simulated using LS-DYNA R12.1 software with a Hybrid III dummy, applying trapezoidal and triangular acceleration pulses per European and American [...] Read more.

This study investigates occupant–seat interaction dynamics in high-speed train frontal collisions. A finite element model of a second-class double seat was developed and simulated using LS-DYNA R12.1 software with a Hybrid III dummy, applying trapezoidal and triangular acceleration pulses per European and American standards. The research analyzes the impact of front-row seatback recline angles (0°, 10°, 20°) and seatback-to-base connection stiffness (1000 N/mm to 0 N/mm) on head, neck, chest, and leg injury severity. Results show that a 10° recline provides optimal protection under fixed stiffness. When optimizing both parameters, a 0° recline with approximately 300 N/mm stiffness minimizes composite injury metrics (HIC₁₅, N_ij, CTI). However, reducing stiffness at non-zero recline angles increases neck injury risk due to tray table displacement toward the cervical region. These findings emphasize the critical importance of integrated seat design optimization for rail passenger passive safety and highlight the need to mitigate tray table hazards. Full article

► Show Figures

Figure 1

26 pages, 7145 KB

Open AccessArticle

Mechanical Properties of a New Type of Link Slab for Simply Supported Steel–Concrete Composite Bridges

by Liang Xiao, Qingtian Su and Qingquan Wang

Appl. Sci. 2025, 15(19), 10851; https://doi.org/10.3390/app151910851 - 9 Oct 2025

Viewed by 175

Abstract

This study investigates the mechanical behavior of a new type of link slab through experimental testing and numerical simulation. A full-scale segmental specimen of an I-shaped steel–concrete composite beam was designed, and a vertical active plus horizontal follow-up loading system was employed to [...] Read more.

This study investigates the mechanical behavior of a new type of link slab through experimental testing and numerical simulation. A full-scale segmental specimen of an I-shaped steel–concrete composite beam was designed, and a vertical active plus horizontal follow-up loading system was employed to realistically simulate the stress state of the link slab. In parallel, a nonlinear finite element model was established in ABAQUS to validate and extend the experimental findings. Test results indicate that the link slab exhibits favorable static performance with a ductile flexural tensile failure mode. At ultimate load, tensile reinforcement yielded while compressive concrete remained uncrushed, demonstrating high safety reserves. Cracks propagated primarily in the transverse direction, showing a typical flexural tensile cracking pattern. The maximum crack width was limited to 0.4 mm and remained confined within the link slab region, which is beneficial for long-term durability, maintenance, and repair. The FE model successfully reproduced the experimental process, accurately capturing both the crack development and the ultimate bending capacity of the slab. The findings highlight the reliability of the proposed structural system, demonstrate that maximum crack width can be evaluated as an eccentric tension member, and confirm that bending capacity may be assessed using existing design specifications. Full article

(This article belongs to the Special Issue Sustainable Construction Systems in Civil and Environmental Engineering)

► Show Figures

Figure 1

24 pages, 1327 KB

Open AccessArticle

Research on Sem-RAG: A Corn Planting Knowledge Question-Answering Algorithm Based on Fine-Grained Semantic Information Retrieval Enhancement

by Bing Bai, Xiaoyan Meng and Chenzi Zhao

Appl. Sci. 2025, 15(19), 10850; https://doi.org/10.3390/app151910850 - 9 Oct 2025

Viewed by 243

Abstract

Large language models and retrieval-augmented generation (RAG) are widely applied in knowledge question-answering tasks. However, in knowledge-intensive domains such as agriculture, hallucination and insufficient retrieval accuracy remain challenging. To address these issues, we propose Sem-RAG, a corn planting knowledge question-answering algorithm based on [...] Read more.

Large language models and retrieval-augmented generation (RAG) are widely applied in knowledge question-answering tasks. However, in knowledge-intensive domains such as agriculture, hallucination and insufficient retrieval accuracy remain challenging. To address these issues, we propose Sem-RAG, a corn planting knowledge question-answering algorithm based on fine-grained semantic retrieval enhancement. Unlike standard NaiveRAG, which retrieves only fixed-length text chunks, and GraphRAG, which relies solely on graph node connections, Sem-RAG introduces a dual-store retrieval mechanism. It constructs both a surface semantic store (chunk-level embeddings) and a fine-grained semantic store derived from Leiden-based community summaries. These community summaries do not merely shorten contexts; instead, they provide thematic-level semantic aggregation across document chunks, thereby enhancing semantic coverage and reducing noise. During retrieval, user queries are matched against the surface store to locate relevant chunks and simultaneously linked to corresponding thematic summaries in the fine-grained store, ensuring that both local details and higher-level associations are leveraged. We evaluated Sem-RAG on the corn knowledge question-answering dataset CornData. The algorithm achieved Answer-C, Answer-R, and CR scores of 94.6%, 84.6%, and 70.4%, respectively, which were 2.6%, 1.7%, and 1.6% higher than those of traditional NaiveRAG. These results demonstrate that Sem-RAG materially improves the quality and reliability of agricultural knowledge Q&A by combining dual-store retrieval with community-level semantic aggregation. Full article

► Show Figures

Figure 1

13 pages, 729 KB

Open AccessArticle

LLM-Enhanced Semantic Text Segmentation

by Alexander Krassovitskiy, Rustam Mussabayev and Kirill Yakunin

Appl. Sci. 2025, 15(19), 10849; https://doi.org/10.3390/app151910849 - 9 Oct 2025

Viewed by 209

Abstract

This study investigates semantic text segmentation enhanced by large language model (LLM) embeddings. We assess how effectively embeddings capture semantic coherence and topic closure by integrating them into both classical clustering algorithms and a modified graph-based methods. In addition, we propose a simple [...] Read more.

This study investigates semantic text segmentation enhanced by large language model (LLM) embeddings. We assess how effectively embeddings capture semantic coherence and topic closure by integrating them into both classical clustering algorithms and a modified graph-based methods. In addition, we propose a simple magnetic clustering algorithm as a lightweight baseline. Experiments are conducted across multiple datasets and embedding models, with segmentation quality evaluated using the boundary segmentation metric. Results demonstrate that LLM embeddings improve segmentation accuracy, highlight dataset-specific difficulties, and reveal how contextual window size and embedding choice affect performance. These findings clarify the strengths and limitations of embedding-based approaches to segmentation and provide insights relevant to retrieval-augmented generation (RAG). Full article

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

► Show Figures

Figure 1

21 pages, 1084 KB

Open AccessArticle

Adaptive Ensemble Machine Learning Framework for Proactive Blockchain Security

by Babatomiwa Omonayajo, Oluwafemi Ayotunde Oke and Nadire Cavus

Appl. Sci. 2025, 15(19), 10848; https://doi.org/10.3390/app151910848 - 9 Oct 2025

Viewed by 249

Abstract

Blockchain technology has rapidly evolved beyond cryptocurrencies, underpinning diverse applications such as supply chains, healthcare, and finances, yet its security vulnerabilities remain a critical barrier to safe adoption. However, attackers increasingly exploit weaknesses in consensus protocols, smart contracts, and network layers with threats [...] Read more.

Blockchain technology has rapidly evolved beyond cryptocurrencies, underpinning diverse applications such as supply chains, healthcare, and finances, yet its security vulnerabilities remain a critical barrier to safe adoption. However, attackers increasingly exploit weaknesses in consensus protocols, smart contracts, and network layers with threats such as Denial-of-Chain (DoC) and Black Bird attacks, posing serious challenges to blockchain ecosystems. We conducted anomaly detection using two independent datasets (A and B) generated from simulation attack scenarios including hash rate, Sybil, Eclipse, Finney, and Denial-of-Chain (DoC) attacks. Key blockchain metrics such as hash rate, transaction authorization status, and recorded attack consequences were collected for analysis. We compared both class-balanced and imbalanced datasets, applying Synthetic Minority Oversampling Technique (SMOTE) to improve representation of minority-class samples and enhance performance metrics. Supervised models such as Random Forest, Gradient Boosting, and Logistic Regression consistently outperformed unsupervised models, achieving high F1-scores (0.90), while balancing the training data had only a modest effect. The results are based on simulated environment and should be considered as preliminary until the experiment is performed in a real blockchain environment. Based on identified gaps, we recommend the exploration and development of multifaceted defense approaches that combine prevention, detection, and response to strengthen blockchain resilience. Full article

► Show Figures

Figure 1

31 pages, 6918 KB

Open AccessArticle

Three-Dimensional Visualization of Product Manufacturing Information in a Web Browser Based on STEP AP242 and WebGL

by Yazhou Chen, Hongxing Wang, Lin Wang, Songqin Xu, Longxing Liao, Jingyu Mo and Xiaochuan Lin

Appl. Sci. 2025, 15(19), 10847; https://doi.org/10.3390/app151910847 - 9 Oct 2025

Viewed by 182

Abstract

Commercial computer-aided design (CAD) software is often expensive. This paper examines the use of product manufacturing information (PMI) web visualization to address the challenges faced by production site personnel and external partners collaborating on product development. These individuals need to be able to [...] Read more.

Commercial computer-aided design (CAD) software is often expensive. This paper examines the use of product manufacturing information (PMI) web visualization to address the challenges faced by production site personnel and external partners collaborating on product development. These individuals need to be able to view or query PMI in model-based definition models without having to install professional CAD software. A detailed analysis of the relationships between PMI entity attributes in standard for the exchange of product model data (STEP) AP242 files was conducted. An algorithm for the automatic parsing and mapping of PMI semantics to a web browser is presented. Using linear sizes as an example, this paper introduces a prototype system with the following features: PMI web visualization; automatic linkage of PMI to associated geometry; browser-native rendering without the need for dedicated applications; and integration of graphical presentation and semantic representation. The effectiveness and feasibility of the prototype system are validated through case studies. However, the system has limitations when handling large assemblies with compound tolerances, curved dimension placements, and overlapping annotations, which presents areas for future research. Full article

► Show Figures

Figure 1

24 pages, 4289 KB

Open AccessArticle

A Stylus-Based Calibration Method for Robotic Belt Grinding Tools

by Di Chang, Yichao Wang, Yi Chen and Lieshan Zhang

Appl. Sci. 2025, 15(19), 10846; https://doi.org/10.3390/app151910846 - 9 Oct 2025

Viewed by 127

Abstract

To address the tool calibration challenge in robotic systems equipped with grinding tools, this paper proposes a high-precision method utilizing a stylus assembly and the Particle Swarm Optimization (PSO) algorithm. A global optimization strategy is implemented, which simultaneously identifies and compensates for coupled [...] Read more.

To address the tool calibration challenge in robotic systems equipped with grinding tools, this paper proposes a high-precision method utilizing a stylus assembly and the Particle Swarm Optimization (PSO) algorithm. A global optimization strategy is implemented, which simultaneously identifies and compensates for coupled error sources, including the robot’s kinematic (DH) parameters, the tool coordinate frame (TCF), and the stylus tip’s spatial position. This approach transforms the complex calibration task into a constrained, high-dimensional optimization problem. The experimental results demonstrate the method’s effectiveness, reducing the final calibration Root Mean Square Error (RMSE) to below 0.1 mm. Validation through a practical grinding experiment confirmed a significant improvement in machining accuracy, with the workpiece’s axis deviation from the ideal model decreasing from 1.477° to 0.326°, and the maximum contour error being reduced from 1.4 mm to under 0.3 mm. This study provides a robust, low-cost technical solution for tool calibration in complex industrial applications. Full article

► Show Figures

Figure 1

17 pages, 3028 KB

Open AccessArticle

YOLO-Based Object and Keypoint Detection for Autonomous Traffic Cone Placement and Retrieval for Industrial Robots

by János Hollósi

Appl. Sci. 2025, 15(19), 10845; https://doi.org/10.3390/app151910845 - 9 Oct 2025

Viewed by 293

Abstract

The accurate and efficient placement of traffic cones is a critical safety and logistical requirement in diverse industrial environments. This study introduces a novel dataset specifically designed for the near-overhead detection of traffic cones, containing both bounding box annotations and apex keypoints. Leveraging [...] Read more.

The accurate and efficient placement of traffic cones is a critical safety and logistical requirement in diverse industrial environments. This study introduces a novel dataset specifically designed for the near-overhead detection of traffic cones, containing both bounding box annotations and apex keypoints. Leveraging this dataset, we systematically evaluated whether classical object detection methods or keypoint-based detection methods are more effective for the task of cone apex localization. Several state-of-the-art YOLO-based architectures (YOLOv8, YOLOv11, YOLOv12) were trained and tested under identical conditions. The comparative experiments showed that both approaches can achieve high accuracy, but they differ in their trade-offs between robustness, computational cost, and suitability for real-time embedded deployment. These findings highlight the importance of dataset design for specialized viewpoints and confirm that lightweight YOLO models are particularly well-suited for resource-constrained robotic platforms. The key contributions of this work are the introduction of a new annotated dataset for overhead cone detection and a systematic comparison of object detection and keypoint detection paradigms for apex localization in real-world robotic applications. Full article

(This article belongs to the Special Issue Sustainable Mobility and Transportation (SMTS 2025))

► Show Figures

Figure 1

15 pages, 617 KB

Open AccessArticle

Contract-Graph Fusion and Cross-Graph Matching for Smart-Contract Vulnerability Detection

by Xue Liang, Yao Tan, Jun Song and Fan Yang

Appl. Sci. 2025, 15(19), 10844; https://doi.org/10.3390/app151910844 - 9 Oct 2025

Viewed by 167

Abstract

Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an [...] Read more.

Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an across-graph corresponding contract-graph method for vulnerability detection: abstract syntax, control flow, and data flow are fused into a typed, directed contract-graph whose nodes are enriched with pre-code embeddings (GraphCodeBERT or CodeT5+). A Graph Matching Network (GMN) with cross-graph attention compares contract-graphs, aligns homologous sub-graphs associated with vulnerabilities, and supports the interpretation of statements at the level of balance between a broad structural coverage and a discriminative pairwise alignment. The evaluation follows a deployment-oriented protocol with thresholds fixed for validation, multi-seed averaging, and a conservative estimate of sensitivity under low-false-positive budgets. On SmartBugs Wild, the method consistently and markedly exceeds strong rule-based and learning baselines and maintains a higher sensitivity to matching false-positive rates; ablations track the gains to multi-graph fusion, pre-trained encoders, and cross-graph matching, stable through seeds. Full article

► Show Figures

Figure 1

24 pages, 889 KB

Open AccessSystematic Review

From BIM to UAVs: A Systematic Review of Digital Solutions for Productivity Challenges in Construction

by Victor Francisco Saraiva Landim, João Poças Martins and Diego Calvetti

Appl. Sci. 2025, 15(19), 10843; https://doi.org/10.3390/app151910843 - 9 Oct 2025

Viewed by 291

Abstract

The construction industry faces persistent productivity challenges despite the widespread adoption of advanced digital technologies. This systematic review examines how digital technologies contribute to improving on-site labor productivity within the Architecture, Engineering, Construction, and Operations (AECOs) sector. Following the PRISMA methodology, 431 records [...] Read more.

The construction industry faces persistent productivity challenges despite the widespread adoption of advanced digital technologies. This systematic review examines how digital technologies contribute to improving on-site labor productivity within the Architecture, Engineering, Construction, and Operations (AECOs) sector. Following the PRISMA methodology, 431 records were initially identified, with 28 high-quality articles ultimately selected for analysis through rigorous screening and snowballing techniques. The reviewed technologies include Building Information Modeling (BIM), photogrammetry, LiDAR, augmented reality (AR), global navigation satellite systems (GNSSs), radio frequency identification (RFID), and unmanned aerial vehicles (UAVs), which were categorized into three key areas: factors affecting productivity, modeling and evaluation, and productivity improvement methods. Findings highlight that these technologies collectively enhance resource allocation, reduce labor costs, and improve project scheduling through better coordination. Whilst digital technologies demonstrate substantial impact on construction productivity, further research is needed to quantify long-term benefits and address scalability challenges across different project contexts and organizational structures. Ultimately, the review concludes that digital technologies play a crucial role in enhancing construction productivity, highlighting the need for further research to assess long-term advantages and scalability across diverse construction environments. These technological advancements are essential for modernizing the industry and supporting sustainable growth in the digital transition era. Full article

(This article belongs to the Special Issue Technology and Management Applied in Construction Engineering Projects II)

► Show Figures

Figure 1

18 pages, 2227 KB

Open AccessArticle

Assessment of Heavy Metal Concentrations in Urban Soil of Novi Sad: Correlation Analysis and Leaching Potential

by Ivana Jelić, Dušan Topalović, Maja Rajković, Danica Jovašević, Kristina Pavićević, Marija Janković and Marija Šljivić-Ivanović

Appl. Sci. 2025, 15(19), 10842; https://doi.org/10.3390/app151910842 - 9 Oct 2025

Viewed by 214

Abstract

Soil samples from the urban area of Novi Sad were analyzed to determine the total concentrations of heavy metals including Cr, Pb, Cu, Zn, As, Mn, Ni, Co, Cd and Fe. In addition, leaching tests according to CEN 12457-2—Milli-Q deionized leaching procedure and [...] Read more.

Soil samples from the urban area of Novi Sad were analyzed to determine the total concentrations of heavy metals including Cr, Pb, Cu, Zn, As, Mn, Ni, Co, Cd and Fe. In addition, leaching tests according to CEN 12457-2—Milli-Q deionized leaching procedure and ISO/TS 21268-2—CaCl₂ solution leaching procedure were conducted to assess the mobility of these metals. Multivariate statistical methods, including Pearson’s correlation, Principal Component Analysis (PCA) and Cluster Analysis, were applied to identify pollution sources and grouping patterns among elements. The results revealed a distinct clustering of Pb and Zn, separate from other metals, indicating their predominant origin from anthropogenic activities. Contamination Factor (CF), Pollution Load Index (PLI), and Geoaccumulation Index (Igeo) were calculated to evaluate the degree of pollution. Combining total concentration, mobility, and multivariate analyses offers a more comprehensive insight into the extent and origin of pollution in the urban area of Novi Sad. The results obtained are valuable for evaluating the soil conditions in the Western Balkans, which have been recognized as a necessity by the EU. Full article

► Show Figures

Figure 1

21 pages, 1160 KB

Open AccessArticle

Near Real-Time Ethereum Fraud Detection Using Explainable AI in Blockchain Networks

by Fatih Ertam

Appl. Sci. 2025, 15(19), 10841; https://doi.org/10.3390/app151910841 - 9 Oct 2025

Viewed by 334

Abstract

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit [...] Read more.

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities. Full article

(This article belongs to the Special Issue Artificial Intelligence on the Edge for Industry 4.0)

► Show Figures

Figure 1

40 pages, 1668 KB

Open AccessReview

A Comprehensive Review of Biological Properties of Flavonoids and Their Role in the Prevention of Metabolic, Cancer and Neurodegenerative Diseases

by Milena Alicja Stachelska, Piotr Karpiński and Bartosz Kruszewski

Appl. Sci. 2025, 15(19), 10840; https://doi.org/10.3390/app151910840 - 9 Oct 2025

Viewed by 178

Abstract

Dietary flavonoids are emerging as multifunctional bioactive compounds with significant implications for the prevention and management of chronic diseases. Integrating the latest experimental, clinical, and epidemiological evidence, this review provides a comprehensive synthesis of flavonoid classification, chemistry, dietary sources, and bioavailability, with special [...] Read more.

Dietary flavonoids are emerging as multifunctional bioactive compounds with significant implications for the prevention and management of chronic diseases. Integrating the latest experimental, clinical, and epidemiological evidence, this review provides a comprehensive synthesis of flavonoid classification, chemistry, dietary sources, and bioavailability, with special attention to their structural diversity and core mechanisms. Mechanistic advances related to antioxidant, anti-inflammatory, antimicrobial, anti-obesity, neuroprotective, cardioprotective, and anticancer activities are highlighted, focusing on the modulation of critical cellular pathways such as PI3K/Akt/mTOR, NF-κB, and AMPK. Evidence from in vitro and in vivo models, supported by clinical data, demonstrates flavonoids’ capacity to regulate oxidative stress, inflammation, metabolic syndrome, adipogenesis, cell proliferation, apoptosis, autophagy, and angiogenesis. An inverse correlation between flavonoid-rich dietary patterns and the risk of obesity, cancer, cardiovascular, and neurodegenerative diseases is substantiated. However, translational challenges persist, including bioavailability and the optimization of delivery strategies. In conclusion, a varied dietary intake of flavonoids constitutes a scientifically grounded approach to non-communicable disease prevention, though further research is warranted to refine clinical applications and elucidate molecular mechanisms. Full article

(This article belongs to the Special Issue Innovations in Natural Products and Functional Foods)

► Show Figures

Figure 1

12 pages, 3484 KB

Open AccessArticle

Realization and Validation of Wide-Band Two-Type Unit Cell Reconfigurable Metasurface Reflect Array Antenna at E-Band Frequency

by Oleg Torgovitsky, Daniel Rozban, Gil Kedar, Ariel Etinger, Tamir Rabinovitz and Amir Abramovich

Appl. Sci. 2025, 15(19), 10839; https://doi.org/10.3390/app151910839 - 9 Oct 2025

Viewed by 177

Abstract

A novel tunable E-band (78–82 GHz) Reconfigurable Electro-Mechanical Reflectarray (REMR) is demonstrated for beam focusing and steering. This design is based on our previous study, which achieved a 350° phase dynamic range in simulations, and is experimentally validated here. The results confirm precise [...] Read more.

A novel tunable E-band (78–82 GHz) Reconfigurable Electro-Mechanical Reflectarray (REMR) is demonstrated for beam focusing and steering. This design is based on our previous study, which achieved a 350° phase dynamic range in simulations, and is experimentally validated here. The results confirm precise beam focusing and ±3° beam steering, showing excellent agreement with simulations. To further enhance performance, an innovative dual-patch unit cell with a flat ground plane is introduced, enabling nearly 360° phase coverage and extended beam steering up to ±6°. The simplified architecture reduces fabrication complexity and cost while providing a compact, robust, and power-efficient solution for E-band and millimeter-wave communication systems. Full article

► Show Figures

Figure 1

17 pages, 4555 KB

Open AccessArticle

Optimization Study of Gas Supply Pipeline Systems Based on Swarm Intelligence Optimization Algorithms

by Li Dai, Chao Xu, Yiqun Liu and Liang Zeng

Appl. Sci. 2025, 15(19), 10838; https://doi.org/10.3390/app151910838 - 9 Oct 2025

Viewed by 154

Abstract

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error [...] Read more.

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error correction, the pipe friction coefficient is adjusted to minimize the deviation between calculated and actual flows. The GWO reduces average relative error to 0.01% with satisfactory iteration speed and efficiency. For pressure distribution, supply-end pressures are optimized to reduce energy consumption and enhance system performance. The ZOA shows strong convergence and global search capabilities. These methods provide valuable theoretical and practical insights for optimizing gas supply networks, supporting green transformation and sustainable development. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems—3rd Edition)

► Show Figures

Figure 1

30 pages, 6170 KB

Open AccessArticle

Resource Scheduling Algorithm for Edge Computing Networks Based on Multi-Objective Optimization

by Wenrui Liu, Jiale Zhu, Xiangming Li, Yichao Fei, Hai Wang, Shangdong Liu, Xiaoyao Zheng and Yimu Ji

Appl. Sci. 2025, 15(19), 10837; https://doi.org/10.3390/app151910837 - 9 Oct 2025

Viewed by 131

Abstract

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge [...] Read more.

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

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

► Show Figures

Figure 1

24 pages, 2538 KB

Open AccessArticle

Exploring Patterns in Quality Alerts via Random Forest and Multiple Correspondence Analysis

by Iliana Ramírez-Velásquez, Carlos Mario Restrepo, Héctor Herrera and Paola Silva-Cadavid

Appl. Sci. 2025, 15(19), 10836; https://doi.org/10.3390/app151910836 - 9 Oct 2025

Viewed by 146

Abstract

This study presents a multivariate and machine learning-based approach to analyze quality alerts in an industrial manufacturing context. Based on data from recorded quality alerts, this research integrates exploratory data analysis, Multiple Correspondence Analysis (MCA), and Random Forest modeling to uncover hidden patterns [...] Read more.

This study presents a multivariate and machine learning-based approach to analyze quality alerts in an industrial manufacturing context. Based on data from recorded quality alerts, this research integrates exploratory data analysis, Multiple Correspondence Analysis (MCA), and Random Forest modeling to uncover hidden patterns among key categorical variables, including process, section, and priority. The analysis highlights structural associations and frequency distributions that differentiate alert behavior across various production units. Visualization tools such as heatmaps and bar charts are employed to provide actionable insights into the operational environment. The study has practical applications in the monitoring and continuous improvement of quality management systems in manufacturing environments. Identifying patterns in quality alerts through multivariate and machine learning techniques leads to a deeper understanding of the origin and frequency of quality issues across machines, processes, and plant sections. The findings can support preventive actions, efficient resource allocation, and targeted maintenance strategies, ultimately enhancing product consistency. Full article

► Show Figures

Figure 1

27 pages, 32087 KB

Open AccessArticle

A Label-Free Panel Recognition Method Based on Close-Range Photogrammetry and Feature Fusion

by Enshun Lu, Zhe Guo, Xiaofeng Li, Daode Zhang and Rui Lu

Appl. Sci. 2025, 15(19), 10835; https://doi.org/10.3390/app151910835 - 9 Oct 2025

Viewed by 109

Abstract

In the interior decoration panel industry, automated production lines have become the standard configuration for large-scale enterprises. However, during the panel processing procedures such as sanding and painting, the loss of traditional identification markers like QR codes or barcodes is inevitable. This creates [...] Read more.

In the interior decoration panel industry, automated production lines have become the standard configuration for large-scale enterprises. However, during the panel processing procedures such as sanding and painting, the loss of traditional identification markers like QR codes or barcodes is inevitable. This creates a critical technical bottleneck in the assembly stage of customized or multi-model parallel production lines, where identifying individual panels significantly limits production efficiency. To address this issue, this paper proposes a high-precision measurement method based on close-range photogrammetry for capturing panel dimensions and hole position features, enabling accurate extraction of identification markers. Building on this foundation, an identity discrimination method that integrates weighted dimension and hole position IDs has been developed, making it feasible to efficiently and automatically identify panels without physical identification markers. Experimental results demonstrate that the proposed method exhibits significant advantages in both recognition accuracy and production adaptability, providing an effective solution for intelligent manufacturing in the home decoration panel industry. Full article

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

► Show Figures

Figure 1

19 pages, 4133 KB

Open AccessArticle

FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction

by Jinghan Su, Li Xiao and Jingyu Wang

Appl. Sci. 2025, 15(19), 10834; https://doi.org/10.3390/app151910834 - 9 Oct 2025

Viewed by 101

Abstract

Accurate prediction of the flow field is crucial to evaluating the aerodynamic performance of an aircraft. While traditional computational fluid dynamics (CFD) methods solve the governing equations to capture both global flow structures and localized gradients, they are computationally intensive. Deep learning-based surrogate [...] Read more.

Accurate prediction of the flow field is crucial to evaluating the aerodynamic performance of an aircraft. While traditional computational fluid dynamics (CFD) methods solve the governing equations to capture both global flow structures and localized gradients, they are computationally intensive. Deep learning-based surrogate models offer a promising alternative, yet often struggle to simultaneously model long-range dependencies and near-wall flow gradients with sufficient fidelity. To address this challenge, this paper introduces the Message-passing And Global-attention block (MAG-BLOCK), a graph neural network module that combines local message passing with global self-attention mechanisms to jointly learn fine-scale features and large-scale flow patterns. Building on MAG-BLOCK, we propose FLOW-GLIDE, a cross-architecture deep learning framework that learns a mapping from initial conditions to steady-state flow fields in a latent space. Evaluated on the AirfRANS dataset, FLOW-GLIDE outperforms existing models on key performance metrics. Specifically, it reduces the error in the volumetric flow field by 62% and surface pressure prediction by 82% compared to the state-of-the-art. Full article

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

► Show Figures

Figure 1

17 pages, 5472 KB

Open AccessArticle

An Automated Approach for Calibrating Gafchromic EBT3 Films and Mapping 3D Doses in HDR Brachytherapy

by Labinot Kastrati, Burim Uka, Polikron Dhoqina, Gezim Hodolli, Sehad Kadiri, Behar Raci, Faton Sermaxhaj, Kjani Guri and Hekuran Sejdiu

Appl. Sci. 2025, 15(19), 10833; https://doi.org/10.3390/app151910833 - 9 Oct 2025

Viewed by 169

Abstract

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at [...] Read more.

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at nominal doses of 0–10 Gy and evaluated using a MATLAB (R2024b)-based tool that allows for both manual and automated ROI selection. The calibration curves were modeled with a second-order polynomial and rational model, and performance was assessed using statistical measures. The study found that the rational model fits better than the polynomial model. Additionally, the automatic ROI approach outperformed the manual method in both models, resulting in higher calibration accuracy and reproducibility (R² = 0.999, RMSE = 0.118 Gy, MAE = 0.103 Gy vs. R² = 0.986, RMSE = 0.448 Gy, MAE = 0.388 Gy). Although manual ROI occasionally produced greater dose–response slopes at higher doses, it was more susceptible to operator bias and film non-uniformity. In contrast, automatic ROI reduced variability by consistently picking homogeneous sections, resulting in steady curve fitting across the entire dose range. Furthermore, a companion module transformed calibrated films into 2D false-color maps and 3D dosage surfaces, allowing for visual assessment of dose uniformity, detection of scanner-related aberrations, and quantitative verification for quality assurance. These findings demonstrate that automated ROI selection provides a more stable and reproducible foundation for film calibration in HDR brachytherapy, minimizing operator dependency while facilitating routine clinical quality assurance. Full article

(This article belongs to the Section Applied Physics General)

► Show Figures

Figure 1

25 pages, 5773 KB

Open AccessArticle

Mobile Data Visualisation Interface Design for Industrial Automation and Control: A User-Centred Usability Study

by Chih-Feng Cheng, Chiuhsiang Joe Lin and I-Chin Liu

Appl. Sci. 2025, 15(19), 10832; https://doi.org/10.3390/app151910832 - 9 Oct 2025

Viewed by 192

Abstract

With the increasing integration of mobile technologies into manufacturing automation environments, the effective visualisation of data on small-screen devices has emerged as an important consideration. This study investigates the usability and readability of common visualisation types (bar charts, line charts, and tables) on [...] Read more.

With the increasing integration of mobile technologies into manufacturing automation environments, the effective visualisation of data on small-screen devices has emerged as an important consideration. This study investigates the usability and readability of common visualisation types (bar charts, line charts, and tables) on mobile devices, comparing different interface designs and interaction methods. Using a within-subject experimental design with 11 participants, we evaluated two primary approaches for handling large visualisations on mobile screens: segmented (cutting) displays versus continuous (dragging) displays. Results indicate that segmented displays generally improve task completion time and reduce mental workload for bar charts and tables. In contrast, line charts exhibit more complex patterns that depend on the size of the data. These findings provide practical guidelines for designing responsive data visualisations optimised for mobile interfaces. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

► Show Figures

Figure 1

21 pages, 722 KB

Open AccessArticle

Detecting the File Encryption Algorithms Using Artificial Intelligence

by Jakub Kowalewski and Tomasz Grześ

Appl. Sci. 2025, 15(19), 10831; https://doi.org/10.3390/app151910831 - 9 Oct 2025

Viewed by 242

Abstract

In this paper, the authors analyze the applicability of artificial intelligence algorithms for classifying file encryption methods based on statistical features extracted from the binary content of files. The prepared datasets included both unencrypted files and files encrypted using selected cryptographic algorithms in [...] Read more.

In this paper, the authors analyze the applicability of artificial intelligence algorithms for classifying file encryption methods based on statistical features extracted from the binary content of files. The prepared datasets included both unencrypted files and files encrypted using selected cryptographic algorithms in Electronic Codebook (ECB) and Cipher Block Chaining (CBC) modes. These datasets were further diversified by varying the number of encryption keys and the sample sizes. Feature extraction focused solely on basic statistical parameters, excluding an analysis of file headers, keys, or internal structures. The study evaluated the performance of several models, including Random Forest, Bagging, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and AdaBoost. Among these, Random Forest and Bagging achieved the highest accuracy and demonstrated the most stable results. The classification performance was notably better in ECB mode, where no random initialization vector was used. In contrast, the increased randomness of data in CBC mode resulted in lower classification effectiveness, particularly as the number of encryption keys increased. This paper provides a comprehensive analysis of the classifiers’ performance across various encryption configurations and suggests potential directions for further experiments. Full article

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

► Show Figures

Figure 1

Journal Menu

Journal Browser

Appl. Sci., Volume 15, Issue 19 (October-1 2025) – 559 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI