Applied Sciences

14 pages, 2128 KiB

Open AccessArticle

Correlation Measures in Metagenomic Data: The Blessing of Dimensionality

by Alessandro Fuschi, Alessandra Merlotti, Thi Dong Binh Tran, Hoan Nguyen, George M. Weinstock and Daniel Remondini

Appl. Sci. 2025, 15(15), 8602; https://doi.org/10.3390/app15158602 (registering DOI) - 2 Aug 2025

Abstract

Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the [...] Read more.

Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathways, and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly in light of the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson’s correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations. Full article

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

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14 pages, 392 KiB

Open AccessArticle

Development of Asymmetrical, Symmetrical Tonic Neck Reflex Test and Tonic Labyrinth Reflex Test (TASHUN) for the Assessment of Neurotypical Children: Validity and Reliability

by Ágnes Virág Nagy, Ferenc Rárosi, Mihály Domokos and Márta Wilhelm

Appl. Sci. 2025, 15(15), 8601; https://doi.org/10.3390/app15158601 (registering DOI) - 2 Aug 2025

Abstract

The ongoing secular changes in human movement development means that an assessment of primitive reflexes is now required not only in disabled but also in neurotypical children. This study had three aims: (1) presenting the TASHUN test battery as suitable for the assessment [...] Read more.

The ongoing secular changes in human movement development means that an assessment of primitive reflexes is now required not only in disabled but also in neurotypical children. This study had three aims: (1) presenting the TASHUN test battery as suitable for the assessment of primitive reflex activity in normal children and child athletes; (2) analyzing reflex characteristics of neurotypical children; (3) verifying validity and reliability of tests. Spearman’s rank correlation and ROC analysis were used for validation. Intraclass Correlation Coefficient and RM ANOVA analyzed reliability. The test on 242 schoolgirls has demonstrated that retained primitive reflexes are present in almost every individual (84.7–95.7%). Correlations showed strong positive association, with all values exceeding 0.8, and ROC analysis demonstrated excellent predictive strength (AUC values over 0.9). Interobserver reliability showed excellent agreement (ICC values above 0.9). No significant offset was present among the scoring by evaluators. Therefore, testing for primitive reflexes is necessary in neurotypical children in order to obtain a realistic image about the physiology of reflexes and their role in motor development. Our screening could be useful for practicing sport professionals, researchers and academics, to identify deficiencies, to further explore reflexes and to train future PE teachers and trainers. Full article

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23 pages, 872 KiB

Open AccessArticle

Performance Optimization of Grounding System for Multi-Voltage Electrical Installation

by Md Tanjil Sarker, Marran Al Qwaid, Md Sabbir Hossen and Gobbi Ramasamy

Appl. Sci. 2025, 15(15), 8600; https://doi.org/10.3390/app15158600 (registering DOI) - 2 Aug 2025

Abstract

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, [...] Read more.

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

23 pages, 1211 KiB

Open AccessReview

Dealuminated Metakaolin in Supplementary Cementitious Material and Alkali-Activated Systems: A Review

by Mostafa Elsebaei, Maria Mavroulidou, Amany Micheal, Maria Astrid Centeno, Rabee Shamass and Ottavia Rispoli

Appl. Sci. 2025, 15(15), 8599; https://doi.org/10.3390/app15158599 (registering DOI) - 2 Aug 2025

Abstract

This paper presents a comprehensive review of dealuminated metakaolin (DK), a hazardous industrial by-product generated by the aluminium sulphate (alum) industry and evaluates its potential as a component in cementitious systems for the partial or full replacement of Portland cement (PC). Positioned within the [...] Read more.

This paper presents a comprehensive review of dealuminated metakaolin (DK), a hazardous industrial by-product generated by the aluminium sulphate (alum) industry and evaluates its potential as a component in cementitious systems for the partial or full replacement of Portland cement (PC). Positioned within the context of waste valorisation in concrete, the review aims to establish a critical understanding of DK formation, properties, and reactivity, particularly its pozzolanic potential, to assess its suitability for use as a supplementary cementitious material (SCM), or as a precursor in alkali-activated cement (AAC) systems for concrete. A systematic methodology is used to extract and synthesise relevant data from existing literature concerning DK and its potential applications in cement and concrete. The collected information is organised into thematic sections exploring key aspects of DK, beginning with its formation from kaolinite ores, followed by studies on its pozzolanic reactivity. Applications of DK are then reviewed, focusing on its integration into SCMs and alkali-activated cement (AAC) systems. The review consolidates existing knowledge related to DK, identifying scientific gaps and practical challenges that limit its broader adoption for cement and concrete applications, and outlines future research directions to provide a solid foundation for future studies. Overall, this review highlights the potential of DK as a low-carbon, circular-economy material and promotes its integration into efforts to enhance the sustainability of construction practices. The findings aim to support researchers’ and industry stakeholders’ strategies to reduce cement clinker content and mitigate the environmental footprint of concrete in a circular-economy context. Full article

(This article belongs to the Special Issue Applications of Waste Materials and By-Products in Concrete)

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25 pages, 7588 KiB

Open AccessArticle

Electrophoretic Deposition of Green-Synthesized Hydroxyapatite on Thermally Oxidized Titanium: Enhanced Bioactivity and Antibacterial Performance

by Mariana Relva, Daniela Santo, Ricardo Alexandre, Pedro Faia, Sandra Carvalho, Zohra Benzarti and Susana Devesa

Appl. Sci. 2025, 15(15), 8598; https://doi.org/10.3390/app15158598 (registering DOI) - 2 Aug 2025

Abstract

Titanium alloys such as Ti-6Al-4V are widely used in biomedical implants due to their excellent mechanical properties and biocompatibility, but their bioinert nature limits osseointegration and antibacterial performance. This study proposes a multifunctional surface coating system integrating a thermally oxidized TiO₂ interlayer [...] Read more.

Titanium alloys such as Ti-6Al-4V are widely used in biomedical implants due to their excellent mechanical properties and biocompatibility, but their bioinert nature limits osseointegration and antibacterial performance. This study proposes a multifunctional surface coating system integrating a thermally oxidized TiO₂ interlayer with a hydroxyapatite (HAp) top layer synthesized via a green route using Hylocereus undatus extract. The HAp was deposited by electrophoretic deposition (EPD), enabling continuous coverage and strong adhesion to the pre-treated Ti-6Al-4V substrate. Structural, morphological, chemical, and electrical characterizations were performed using XRD, SEM, EDS, Raman spectroscopy, and impedance spectroscopy. Bioactivity was assessed through apatite formation in simulated body fluid (SBF), while antibacterial properties were evaluated against Staphylococcus aureus. The results demonstrated successful formation of crystalline TiO₂ (rutile phase) and calcium-rich HAp with good surface coverage. The HAp-coated surfaces exhibited significantly enhanced bioactivity and strong antibacterial performance, likely due to the combined effects of surface roughness and the bioactive compounds present in the plant extract. This study highlights the potential of eco-friendly, bio-inspired surface engineering to improve the biological performance of titanium-based implants. Full article

(This article belongs to the Special Issue Sustainable Nanomaterials for Biotechnological and Medical Applications: Current Developments)

27 pages, 18859 KiB

Open AccessArticle

Application of a Hierarchical Approach for Architectural Classification and Stratigraphic Evolution in Braided River Systems, Quaternary Strata, Songliao Basin, NE China

by Zhiwen Dong, Zongbao Liu, Yanjia Wu, Yiyao Zhang, Jiacheng Huang and Zekun Li

Appl. Sci. 2025, 15(15), 8597; https://doi.org/10.3390/app15158597 (registering DOI) - 2 Aug 2025

Abstract

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic [...] Read more.

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro–macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits’ preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments. Full article

(This article belongs to the Section Earth Sciences)

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31 pages, 3464 KiB

Open AccessArticle

An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent

by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek and Olena Chernyk

Appl. Sci. 2025, 15(15), 8596; https://doi.org/10.3390/app15158596 (registering DOI) - 2 Aug 2025

Abstract

Ensuring software quality during development requires effective regression testing. However, test suites in open-source libraries often grow large, redundant, and difficult to maintain. Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This [...] Read more.

Ensuring software quality during development requires effective regression testing. However, test suites in open-source libraries often grow large, redundant, and difficult to maintain. Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This paper presents an intelligent method for test case synthesis using a Q-learning agent. The agent learns to construct compact test cases by interacting with an execution environment and receives rewards based on branch coverage improvements and simultaneous reductions in test case length. The training process includes a pretraining phase that transfers knowledge from the original test suite, followed by adaptive learning episodes on individual test cases. As a result, the method requires no formal documentation or API specifications and uses only execution traces of the original test cases. An explicit synthesis algorithm constructs new test cases by selecting API calls from a learned policy encoded in a Q-table. Experiments were conducted on two open-source C++ libraries of differing API complexity and original test suite size. The results show that the proposed method can reach up to 67% test suite reduction while preserving branch coverage, confirming its effectiveness for regression test suite minimization in resource-constrained or specification-limited environments. Full article

(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)

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17 pages, 2828 KiB

Open AccessArticle

Augmented Reality in Cardiovascular Education (HoloHeart): Assessment of Students’ and Lecturers’ Needs and Expectations at Heidelberg University Medical School

by Pascal Philipp Schlegel, Florian Kehrle, Till J. Bugaj, Eberhard Scholz, Alexander Kovacevic, Philippe Grieshaber, Ralph Nawrotzki, Joachim Kirsch, Markus Hecker, Anna L. Meyer, Katharina Seidensaal, Thuy D. Do, Jobst-Hendrik Schultz, Norbert Frey and Ann-Kathrin Rahm

Appl. Sci. 2025, 15(15), 8595; https://doi.org/10.3390/app15158595 (registering DOI) - 2 Aug 2025

Abstract

Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger [...] Read more.

Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger Curriculum Medicinale (HeiCuMed), we conducted a needs assessment among medical students and lecturers at Heidelberg University Medical School. Methods: Our survey aimed to evaluate the perceived benefits of AR-based learning compared to conventional methods and to gather expectations regarding an AR course in cardiovascular medicine. Using LimeSurvey, we developed a questionnaire to assess participants’ prior AR experience, preferred learning methods, and interest in a proposed AR-based, 2 × 90-min in-person course. Results: A total of 101 students and 27 lecturers participated. Support for AR in small-group teaching was strong: 96.3% of students and 90.9% of lecturers saw value in a dedicated AR course. Both groups favored its application in anatomy, cardiac surgery, and internal medicine. Students prioritized congenital heart defects, coronary anomalies, and arrhythmias, while lecturers also emphasized invasive valve interventions. Conclusions: There is significant interest in AR-based teaching in cardiovascular education, suggesting its potential to complement and improve traditional methods in medical curricula. Further studies are needed to assess the potential benefits regarding learning outcomes. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)

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17 pages, 1647 KiB

Open AccessArticle

Application of Iron Oxides in the Photocatalytic Degradation of Real Effluent from Aluminum Anodizing Industries

by Lara K. Ribeiro, Matheus G. Guardiano, Lucia H. Mascaro, Monica Calatayud and Amanda F. Gouveia

Appl. Sci. 2025, 15(15), 8594; https://doi.org/10.3390/app15158594 (registering DOI) - 2 Aug 2025

Abstract

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides [...] Read more.

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

(This article belongs to the Special Issue Application of Nanomaterials in the Field of Photocatalysis)

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15 pages, 2466 KiB

Open AccessArticle

A Capillary-Based Micro Gas Flow Measurement Method Utilizing Laminar Flow Regime

by Yuheng Zheng, Dailiang Xie, Zhengcheng Qin, Zhengwei Huang, Ya Xu, Da Wang and Hong Zheng

Appl. Sci. 2025, 15(15), 8593; https://doi.org/10.3390/app15158593 (registering DOI) - 2 Aug 2025

Abstract

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a [...] Read more.

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a broad measurement range. Nevertheless, due to the low measurement accuracy under micro gas flow caused by nonlinear errors and a relatively complex structure, traditional laminar flow measurement devices exhibit limitations in micro gas flow measurement scenarios. This study proposes a novel micro gas flow measurement method based on a single capillary laminar flow element, which simplifies the structure and enhances applicability in the field of micro gas flow. Through structural optimization with precise control of the capillary length–diameter ratios and theoretical error correction based on computational analysis, nonlinear errors were effectively reduced while improving the measurement accuracy in the field of micro gas flow. The proposed methodology was systematically validated through computational fluid dynamics simulations (ANSYS Fluent 2021 R1) and experimental investigations using a dedicated test platform. The experimental results show that the relative error of the measurement system within the full measurement range is less than ±0.6% (1–10 cm³/min; cm³/min means cubic centimeter per minute), and its accuracy is superior to 1% of reading (1% Rd) or 1.5% of reading (1.5% Rd) of conventional laminar flowmeters. The fitting curve of the flow rate versus the pressure difference derived from the measurement results maintains an excellent linear correlation (R² > 0.99), thus confirming that this method has practical application value in the field of micro gas flow measurement. Full article

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17 pages, 1635 KiB

Open AccessArticle

Predicting Relative Density of Pure Magnesium Parts Produced by Laser Powder Bed Fusion Using XGBoost

by Kristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko and Igor Drstvenšek

Appl. Sci. 2025, 15(15), 8592; https://doi.org/10.3390/app15158592 (registering DOI) - 2 Aug 2025

Abstract

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve [...] Read more.

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage. Full article

(This article belongs to the Special Issue Additive Manufacturing and Machining Technologies for Engineering Materials)

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27 pages, 4880 KiB

Open AccessArticle

Multi-Objective Optimization of Steel Slag–Ceramsite Foam Concrete via Integrated Orthogonal Experimentation and Multivariate Analytics: A Synergistic Approach Combining Range–Variance Analyses with Partial Least Squares Regression

by Alipujiang Jierula, Haodong Li, Tae-Min Oh, Xiaolong Li, Jin Wu, Shiyi Zhao and Yang Chen

Appl. Sci. 2025, 15(15), 8591; https://doi.org/10.3390/app15158591 (registering DOI) - 2 Aug 2025

Abstract

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal [...] Read more.

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m³, 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramsite replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramsite replacement proportion on the SSCFC’s fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramsite replacement for fluidity. In contrast, W/C ratio > ceramsite replacement > steel slag replacement governed the compressive strength. Verification showed R² values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramsite replacement. Full article

(This article belongs to the Section Civil Engineering)

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14 pages, 2236 KiB

Open AccessArticle

Reducing the Stochastic Coal Output Model Using the Convolution of Probability Density Functions

by Ryszard Snopkowski, Marta Sukiennik and Aneta Napieraj

Appl. Sci. 2025, 15(15), 8590; https://doi.org/10.3390/app15158590 (registering DOI) - 2 Aug 2025

Abstract

The construction of stochastic models and the use of stochastic simulations for their analysis constitute research methods used in the analysis of stochastic processes. These methods can be applied to processes carried out in underground mines. For mining processes, carried out, e.g., in [...] Read more.

The construction of stochastic models and the use of stochastic simulations for their analysis constitute research methods used in the analysis of stochastic processes. These methods can be applied to processes carried out in underground mines. For mining processes, carried out, e.g., in hard coal mines, it is noticed that the influence of factors generally referred to as geological–mining and technical–organizational may cause a given process to be treated as not fully defined (not necessarily in terms of the process technology, but, e.g., taking into account the time taken for its implementation). This article draws attention to the possibilities of reducing the stochastic model based on the use of the properties of the convolution of probability density functions. Functions that were considered appropriate for describing the processes discussed based on time-based studies were presented. The mechanism of operation of the proposed model reduction was discussed. Reduction carried out in this way eliminates the need to analyze these parts of the model, e.g., using the stochastic simulation method. This reduction leads to simplification of the model and the calculations, which translates into the effectiveness of the research. Full article

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36 pages, 1010 KiB

Open AccessArticle

SIBERIA: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access

by Daniel Paredes-García, José Álvaro Fernández-Carrasco, Jon Ander Medina López, Juan Camilo Vasquez-Correa, Imanol Jericó Yoldi, Santiago Andrés Moreno-Acevedo, Ander González-Docasal, Haritz Arzelus Irazusta, Aitor Álvarez Muniain and Yeray de Diego Loinaz

Appl. Sci. 2025, 15(15), 8589; https://doi.org/10.3390/app15158589 (registering DOI) - 2 Aug 2025

Abstract

The growing need for secure and privacy-preserving identity management in industrial environments has exposed the limitations of traditional, centralized authentication systems. In this context, SIBERIA was developed as a modular solution that empowers users to control their own digital identities, while ensuring robust [...] Read more.

The growing need for secure and privacy-preserving identity management in industrial environments has exposed the limitations of traditional, centralized authentication systems. In this context, SIBERIA was developed as a modular solution that empowers users to control their own digital identities, while ensuring robust protection of critical services. The system is designed in alignment with European standards and regulations, including EBSI, eIDAS 2.0, and the GDPR. SIBERIA integrates a Self-Sovereign Identity (SSI) framework with a decentralized blockchain-based infrastructure for the issuance and verification of Verifiable Credentials (VCs). It incorporates multi-factor authentication by combining a voice biometric module, enhanced with spoofing-aware techniques to detect synthetic or replayed audio, and a behavioral biometrics module that provides continuous authentication by monitoring user interaction patterns. The system enables secure and user-centric identity management in industrial contexts, ensuring high resistance to impersonation and credential theft while maintaining regulatory compliance. SIBERIA demonstrates that it is possible to achieve both strong security and user autonomy in digital identity systems by leveraging decentralized technologies and advanced biometric verification methods. Full article

(This article belongs to the Special Issue Blockchain and Distributed Systems)

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22 pages, 2498 KiB

Open AccessArticle

SceEmoNet: A Sentiment Analysis Model with Scene Construction Capability

by Yi Liang, Dongfang Han, Zhenzhen He, Bo Kong and Shuanglin Wen

Appl. Sci. 2025, 15(15), 8588; https://doi.org/10.3390/app15158588 (registering DOI) - 2 Aug 2025

Abstract

How do humans analyze the sentiments embedded in text? When attempting to analyze a text, humans construct a “scene” in their minds through imagination based on the text, generating a vague image. They then synthesize the text and the mental image to derive [...] Read more.

How do humans analyze the sentiments embedded in text? When attempting to analyze a text, humans construct a “scene” in their minds through imagination based on the text, generating a vague image. They then synthesize the text and the mental image to derive the final analysis result. However, current sentiment analysis models lack such imagination; they can only analyze based on existing information in the text, which limits their classification accuracy. To address this issue, we propose the SceEmoNet model. This model endows text classification models with imagination through Stable diffusion, enabling the model to generate corresponding visual scenes from input text, thus introducing a new modality of visual information. We then use the Contrastive Language-Image Pre-training (CLIP) model, a multimodal feature extraction model, to extract aligned features from different modalities, preventing significant feature differences caused by data heterogeneity. Finally, we fuse information from different modalities using late fusion to obtain the final classification result. Experiments on six datasets with different classification tasks show improvements of 9.57%, 3.87%, 3.63%, 3.14%, 0.77%, and 0.28%, respectively. Additionally, we set up experiments to deeply analyze the model’s advantages and limitations, providing a new technical path for follow-up research. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications of Emotion Recognition)

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18 pages, 6891 KiB

Open AccessArticle

Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry

by Siqi Liu, Ruina Li, Jiayi Zhou, Chaoyuan Dai, Jingui Yu and Qiaoxin Zhang

Appl. Sci. 2025, 15(15), 8587; https://doi.org/10.3390/app15158587 (registering DOI) - 2 Aug 2025

Abstract

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that [...] Read more.

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

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18 pages, 7965 KiB

Open AccessArticle

Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram

by Qianlong Ding, Shuangquan Chen, Jinsong Shen and Borui Wang

Appl. Sci. 2025, 15(15), 8586; https://doi.org/10.3390/app15158586 (registering DOI) - 1 Aug 2025

Abstract

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, [...] Read more.

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. Full article

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12 pages, 1862 KiB

Open AccessArticle

Effect of Si Addition on Microstructure and Mechanical Properties of SiC Ceramic Fabricated by Direct LPBF with CVI Technology

by Yipu Wang, Pei Wang, Liqun Li, Jian Zhang, Yulei Zhang, Jin Peng, Xingxing Wang, Nan Kang, Mohamed El Mansori and Konda Gokuldoss Prashanth

Appl. Sci. 2025, 15(15), 8585; https://doi.org/10.3390/app15158585 (registering DOI) - 1 Aug 2025

Abstract

In this paper, SiC and Si/SiC ceramics were fabricated using direct laser powder bed fusion with chemical vapor infiltration. Their microstructure, mechanical properties and the impacts of silicon addition were analyzed. The incorporation of silicon led to an increase in the relative density [...] Read more.

In this paper, SiC and Si/SiC ceramics were fabricated using direct laser powder bed fusion with chemical vapor infiltration. Their microstructure, mechanical properties and the impacts of silicon addition were analyzed. The incorporation of silicon led to an increase in the relative density of the silicon carbide ceramics from 76.4% to 78.3% and the compression strength increased from 39 ± 13 MPa to 90 ± 8 MPa after laser powder bed fusion with chemical vapor infiltration. The melting and re-solidification of silicon allows the silicon to encapsulate the silicon carbide grains, changing the microstructure and the failure mechanism of the silicon carbide ceramics, resulting in a small amount of silicon residue. In the LPBF-CVI SiC ceramic specimen, the LPBF-formed SiC exhibits a microhardness of 24.2 ± 1.0 GPa. In LPBF-CVI Si/SiC, the spherical dual-phase structure displays a moderately increased hardness (25.9 ± 4.4 GPa), and the CVI-formed SiC exhibits a hardness of 55.3 ± 9.3 GPa. Full article

(This article belongs to the Special Issue Advances in High-Strength Materials Processing: Machining, Surface Integrity Management, and Operational Properties)

13 pages, 680 KiB

Open AccessArticle

Anthropometric Characteristics and Somatotype of Young Slovenian Tennis Players

by Ales Germic, Tjasa Filipcic and Ales Filipcic

Appl. Sci. 2025, 15(15), 8584; https://doi.org/10.3390/app15158584 (registering DOI) - 1 Aug 2025

Abstract

Tennis is a demanding sport that requires physical abilities and optimal body composition. The aim of this study was to investigate the anthropometric characteristics, body composition, and somatotype development of young Slovenian tennis players (754 boys and 514 girls aged 12 to 18 [...] Read more.

Tennis is a demanding sport that requires physical abilities and optimal body composition. The aim of this study was to investigate the anthropometric characteristics, body composition, and somatotype development of young Slovenian tennis players (754 boys and 514 girls aged 12 to 18 years) over the last two decades. Using standardised anthropometric measurements and the Heath-Carter method, somatotypes were calculated and analysed by age and gender. The results showed clear age- and gender-specific trends and differences in both somatotype profiles and detailed anthropometric characteristics. Significant differences were found in height, body mass, BMI, skinfolds, girths, and limb lengths, with gender differences becoming more pronounced in the older age groups. In boys, mesomorphy increased with age, reflecting an increase in musculature, while in girls, a shift from ectomorphic to endomorphic profiles was observed during adolescence, probably influenced by pubertal and hormonal changes. Significant sex-specific differences were observed in all three somatotype components in most age groups, especially in fat mass and muscle. The longitudinal design provides valuable data and insights into the evolving physical profiles of adolescent tennis players that support more effective talent identification and training. Despite the changes that have taken place in tennis over time, standardised measurement protocols ensured comparability, making the results relevant for practitioners working with adolescents in tennis development. Full article

(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise—2nd Edition)

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