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17 pages, 2039 KiB

Open AccessArticle

Protective Effects of Mackerel Protein Hydrolysates Against Oxidative Stress-Induced Atrophy in C2C12 Myotubes

by Gyu-Hyeon Park and Syng-Ook Lee

Foods 2025, 14(14), 2430; https://doi.org/10.3390/foods14142430 (registering DOI) - 10 Jul 2025

Muscle aging and atrophy in the elderly are closely associated with increased oxidative stress in muscle tissue. Bioactive peptides derived from protein hydrolysates have emerged as promising functional ingredients for alleviating sarcopenia due to their antioxidant properties and enrichment in essential amino acids. [...] Read more.

Muscle aging and atrophy in the elderly are closely associated with increased oxidative stress in muscle tissue. Bioactive peptides derived from protein hydrolysates have emerged as promising functional ingredients for alleviating sarcopenia due to their antioxidant properties and enrichment in essential amino acids. In a preliminary screening, mackerel protein hydrolysate (MPH) showed notable protective effects in a myotube atrophy model. This study evaluated the anti-atrophic potential of MPHs produced using different enzymes in H₂O₂-treated C2C12 myotubes. Among five hydrolysates, the alcalase-derived hydrolysate (MHA) demonstrated the most potent effects in maintaining myotube diameter, restoring myosin heavy chain (MYH) expression, and downregulating the atrophy-related genes MAFbx and MuRF1. Mechanistically, MHA activated the Akt/FoxO signaling pathway and inhibited NF-κB activation, thereby reducing muscle protein degradation. Additionally, MHA significantly lowered intracellular ROS levels and showed strong direct antioxidant activity. Amino acid and molecular weight profiling revealed high levels of essential amino acids and low-molecular-weight peptides, suggesting a synergistic contribution to its bioactivity. These findings suggest that MHA is a promising food-derived functional material with anti-atrophic and antioxidant properties and may be useful in preventing or managing age-related muscle loss such as sarcopenia, warranting further preclinical validation. Full article

(This article belongs to the Special Issue Preparation and Functional Activity of Food Bioactive Peptides)

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16 pages, 419 KiB

Open AccessArticle

Energy-Efficient Resource Allocation for Near-Field MIMO Communication Networks

by Tong Lin, Jianyue Zhu, Junfan Zhu, Yaqin Xie, Yao Xu and Xiao Chen

Sensors 2025, 25(14), 4293; https://doi.org/10.3390/s25144293 (registering DOI) - 10 Jul 2025

Abstract

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is [...] Read more.

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article

(This article belongs to the Special Issue Extremely Large-Scale MIMO Technologies for 6G Near-Field Communications)

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20 pages, 2135 KiB

Open AccessArticle

Valorization of Rice-Bran and Corn-Flour Hydrolysates for Optimized Polyhydroxybutyrate Biosynthesis: Statistical Process Design and Structural Verification

by Gaurav Shrimali, Hardik Shah, Kashyap Thummar, Esha Rami, Rajeshkumar Chaudhari, Jens Ejbye Schmidt and Ajit Gangawane

Polymers 2025, 17(14), 1904; https://doi.org/10.3390/polym17141904 (registering DOI) - 10 Jul 2025

Abstract

The extensive environmental pollution caused by petroleum-based plastics highlights the urgent need for sustainable, economically viable alternatives. The practical challenge of enhancing polyhydroxybutyrate (PHB) production with cost-effective agro-industrial residues—rice-bran and corn-flour hydrolysates—has been demonstrated. Bacillus bingmayongensis GS2 was isolated from soil samples collected [...] Read more.

The extensive environmental pollution caused by petroleum-based plastics highlights the urgent need for sustainable, economically viable alternatives. The practical challenge of enhancing polyhydroxybutyrate (PHB) production with cost-effective agro-industrial residues—rice-bran and corn-flour hydrolysates—has been demonstrated. Bacillus bingmayongensis GS2 was isolated from soil samples collected at the Pirana municipal landfill in Ahmedabad, India, and identified through VITEK-2 biochemical profiling and 16S rDNA sequencing (GenBank accession OQ749793). Initial screening for PHB accumulation was performed using Sudan Black B staining. Optimization via a sequential one-variable-at-a-time (OVAT) approach identified optimal cultivation conditions (36 h inoculum age, 37 °C, pH 7.0, 100 rpm agitation), resulting in a PHB yield of 2.77 g L⁻¹ (66% DCW). Further refinement using a central composite response surface methodology (RSM)—varying rice-bran hydrolysate, corn-flour hydrolysate, peptone concentration, and initial pH—significantly improved the PHB yield to 3.18 g L⁻¹(74% DCW), representing more than a threefold enhancement over unoptimized conditions. Structural validation using Fourier Transform Infrared spectroscopy (FTIR) and Proton Nuclear Magnetic Resonance spectroscopy (¹H-NMR) confirmed the molecular integrity of the produced PHB. That Bacillus bingmayongensis GS2 effectively converts low-cost agro-industrial residues into high-value bioplastics has been demonstrated, indicating substantial industrial potential. Future work will focus on bioreactor scale-up, targeted metabolic-engineering strategies, and comprehensive sustainability evaluations, including life-cycle assessment. Full article

(This article belongs to the Section Biobased and Biodegradable Polymers)

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17 pages, 2123 KiB

Open AccessArticle

Challenges and Prospects of Enhanced Oil Recovery Using Acid Gas Injection Technology: Lessons from Case Studies

by Abbas Hashemizadeh, Amirreza Aliasgharzadeh Olyaei, Mehdi Sedighi and Ali Hashemizadeh

Processes 2025, 13(7), 2203; https://doi.org/10.3390/pr13072203 (registering DOI) - 10 Jul 2025

Abstract

Acid gas injection (AGI), which primarily involves injecting hydrogen sulfide (H₂S) and carbon dioxide (CO₂), is recognized as a cost-efficient and environmentally sustainable method for controlling sour gas emissions in oil and gas operations. This review examines case studies [...] Read more.

Acid gas injection (AGI), which primarily involves injecting hydrogen sulfide (H₂S) and carbon dioxide (CO₂), is recognized as a cost-efficient and environmentally sustainable method for controlling sour gas emissions in oil and gas operations. This review examines case studies of twelve AGI projects conducted in Canada, Oman, and Kazakhstan, focusing on reservoir selection, leakage potential assessment, and geological suitability evaluation. Globally, several million tonnes of acid gases have already been sequestered, with Canada being a key contributor. The study provides a critical analysis of geochemical modeling data, monitoring activities, and injection performance to assess long-term gas containment potential. It also explores AGI’s role in Enhanced Oil Recovery (EOR), noting that oil production can increase by up to 20% in carbonate rock formations. By integrating technical and regulatory insights, this review offers valuable guidance for implementing AGI in geologically similar regions worldwide. The findings presented here support global efforts to reduce CO₂ emissions, and provide practical direction for scaling-up acid gas storage in deep subsurface environments. Full article

(This article belongs to the Special Issue Recent Developments in Enhanced Oil Recovery (EOR) Processes)

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18 pages, 3556 KiB

Open AccessArticle

Multi-Sensor Fusion for Autonomous Mobile Robot Docking: Integrating LiDAR, YOLO-Based AprilTag Detection, and Depth-Aided Localization

by Yanyan Dai and Kidong Lee

Electronics 2025, 14(14), 2769; https://doi.org/10.3390/electronics14142769 (registering DOI) - 10 Jul 2025

Abstract

Reliable and accurate docking remains a fundamental challenge for autonomous mobile robots (AMRs) operating in complex industrial environments with dynamic lighting, motion blur, and occlusion. This study proposes a novel multi-sensor fusion-based docking framework that significantly enhances robustness and precision by integrating YOLOv8-based [...] Read more.

Reliable and accurate docking remains a fundamental challenge for autonomous mobile robots (AMRs) operating in complex industrial environments with dynamic lighting, motion blur, and occlusion. This study proposes a novel multi-sensor fusion-based docking framework that significantly enhances robustness and precision by integrating YOLOv8-based AprilTag detection, depth-aided 3D localization, and LiDAR-based orientation correction. A key contribution of this work is the construction of a custom AprilTag dataset featuring real-world visual disturbances, enabling the YOLOv8 model to achieve high-accuracy detection and ID classification under challenging conditions. To ensure precise spatial localization, 2D visual tag coordinates are fused with depth data to compute 3D positions in the robot’s frame. A LiDAR group-symmetry mechanism estimates heading deviation, which is combined with visual feedback in a hybrid PID controller to correct angular errors. A finite-state machine governs the docking sequence, including detection, approach, yaw alignment, and final engagement. Simulation and experimental results demonstrate that the proposed system achieves higher docking success rates and improved pose accuracy under various challenging conditions compared to traditional vision- or LiDAR-only approaches. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, 3rd Edition)

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24 pages, 1484 KiB

Open AccessSystematic Review

Advances in Food Quality Management Driven by Industry 4.0: A Systematic Review-Based Framework

by Fernanda Araujo Pimentel Peres, Beniamin Achilles Bondarczuk, Leonardo de Carvalho Gomes, Laurence de Castro Jardim, Ricardo Gonçalves de Faria Corrêa and Ismael Cristofer Baierle

Foods 2025, 14(14), 2429; https://doi.org/10.3390/foods14142429 (registering DOI) - 10 Jul 2025

Abstract

Integrating Industry 4.0 technologies into food manufacturing processes transforms traditional quality management practices. This study aims to understand how these technologies are applied across managerial quality functions in the food industry. A systematic literature review was conducted using the Scopus and Web of [...] Read more.

Integrating Industry 4.0 technologies into food manufacturing processes transforms traditional quality management practices. This study aims to understand how these technologies are applied across managerial quality functions in the food industry. A systematic literature review was conducted using the Scopus and Web of Science databases, selecting 69 peer-reviewed articles. The analysis identified quality control (QC) and quality assurance (QA) as the most frequently addressed functions. Sensor technology was the most cited, followed by blockchain and artificial intelligence, mainly supporting food safety, process monitoring, and traceability. In contrast, quality design (QD), quality improvement (QI), and quality policy and strategy (QPS) were underrepresented, revealing a gap in strategic and innovation-focused applications. Based on these insights, the Food Quality Management 4.0 (FQM 4.0) framework was developed, mapping the relationship between Industry 4.0 technologies and the five managerial quality functions, with food safety positioned as a transversal dimension. The framework contributes to academia and industry by offering a structured view of technological integration in food quality management and identifying future research and implementation directions. This study highlights the need for broader adoption of advanced technologies to improve transparency, responsiveness, and overall quality performance in the food sector. Full article

(This article belongs to the Special Issue Digital Innovation in Food Technology)

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30 pages, 34072 KiB

Open AccessArticle

ARE-PaLED: Augmented Reality-Enhanced Patch-Level Explainable Deep Learning System for Alzheimer’s Disease Diagnosis from 3D Brain sMRI

by Chitrakala S and Bharathi U

Symmetry 2025, 17(7), 1108; https://doi.org/10.3390/sym17071108 (registering DOI) - 10 Jul 2025

Abstract

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human [...] Read more.

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human brain exhibits inherent bilateral symmetry, and deviations from this symmetry—such as asymmetric atrophy—are strong indicators of early Alzheimer’s disease (AD). Patch-based methods help capture local brain changes for early AD diagnosis, but they often struggle with fixed-size limitations, potentially missing subtle asymmetries or broader contextual cues. To address these limitations, we propose a novel augmented reality (AR)-enhanced patch-level explainable deep learning (ARE-PaLED) system. It includes an adaptive multi-scale patch extraction network (AMPEN) to adjust patch sizes based on anatomical characteristics and spatial context, as well as an informative patch selection algorithm (IPSA) to identify discriminative patches, including those reflecting asymmetry patterns associated with AD; additionally, an AR module is proposed for future immersive explainability, complementing the patch-level interpretation framework. Evaluated on 1862 subjects from the ADNI and AIBL datasets, the framework achieved an accuracy of 92.5% (AD vs. NC) and 85.9% (AD vs. MCI). The proposed ARE-PaLED demonstrates potential as an interpretable and immersive diagnostic aid for sMRI-based AD diagnosis, supporting the interpretation of model predictions for AD diagnosis. Full article

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18 pages, 681 KiB

Open AccessArticle

Short-Term Effects of Eccentric Strength Training on Hematology and Muscle Ultrasound in University Students

by Juan Carlos Giraldo García, Julián Echeverri Chica, German Campuzano Zuluaga, Gloria María Ruiz Rengifo, Donaldo Cardona Nieto, Juan Cancio Arcila Arango and Oliver Ramos-Álvarez

Youth 2025, 5(3), 72; https://doi.org/10.3390/youth5030072 (registering DOI) - 10 Jul 2025

Abstract

Strength training has established itself as an essential component in physical conditioning programmes, not only to improve sports performance, but also for health purposes. To evaluate the effects of a strength training protocol with a predominance of the eccentric component on blood count, [...] Read more.

Strength training has established itself as an essential component in physical conditioning programmes, not only to improve sports performance, but also for health purposes. To evaluate the effects of a strength training protocol with a predominance of the eccentric component on blood count, blood chemistry, and quadriceps muscle ultrasound in university students. 31 students (22.3 ± 4.14 years) of the professional programme in Sports of the Politécnico Colombiano Jaime Isaza Cadavid participated. A mesocycle was developed with three weekly sessions of eccentric training focused on the lower body and core zone. Pre and post-intervention measurements were taken anthropometry, haemogram, lipid profile, ultrasound of the right quadriceps, Bosco test, and Rockport test. The Wilcoxon signed-rank test was used, and the effect size was calculated using rank correlation. Statistically significant changes were observed in haematocrit, mean corpuscular volume, HDL, muscle thickness and echo-intensity, vertical jump power, and maximal oxygen consumption. A four-week eccentric strength training programme generates improvements in haematology, lipid profile, muscle quality assessed by ultrasound, and functional performance in university students. Full article

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16 pages, 1645 KiB

Open AccessArticle

Carbon Pricing Strategies and Policies for a Unified Global Carbon Market

by Mohammad Imran Azizi, Xize Xu, Xuehui Duan, Haotian Qin and Bin Xu

Atmosphere 2025, 16(7), 836; https://doi.org/10.3390/atmos16070836 (registering DOI) - 10 Jul 2025

Abstract

Driven by the urgent need to mitigate climate change and achieve net-zero emissions, carbon pricing has emerged as a critical policy tool in major economies worldwide. This study compares carbon pricing in the EU, China, Canada, and Singapore, evaluating effectiveness in emission reductions, [...] Read more.

Driven by the urgent need to mitigate climate change and achieve net-zero emissions, carbon pricing has emerged as a critical policy tool in major economies worldwide. This study compares carbon pricing in the EU, China, Canada, and Singapore, evaluating effectiveness in emission reductions, with the EU ranking first with high carbon prices, road market coverage, and strict penalties, based on carbon price per capita. Conversely, Singapore’s position as fourth in carbon price per capita among these four most mature carbon markets, Singapore has a high GDP per capita and lower carbon prices. Canada’s fragmented provincial policies and China’s limited market coverage, despite being the top global emitter. Our analysis reveals three critical success factors: (1) higher carbon prices per capita are essential for carbon reduction, (2) the necessity of penalties on carbon price per capita from EUR 20–EUR 100, and (3) expanded market coverage maximizes impact. To address global disparities, we propose a Uniform Carbon Pricing Mechanism under the Global Carbon Resilience Framework (GCRF), based on carbon price per capita tiered pricing: EUR 100/t (developed), EUR 30–50 (developing), and EUR 5–15 (least-developed countries). This balanced system supports vulnerable regions while cutting emissions, proving that fair carbon pricing is crucial for climate goals and economic stability. Full article

(This article belongs to the Section Air Pollution Control)

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16 pages, 2292 KiB

Open AccessArticle

Passive Synthetic Aperture for Direction-of-Arrival Estimation Using an Underwater Glider with a Single Hydrophone

by Yueming Ma, Jie Sun, Shuo Li, Tianze Hu, Shilong Li and Yuexing Zhang

J. Mar. Sci. Eng. 2025, 13(7), 1322; https://doi.org/10.3390/jmse13071322 (registering DOI) - 10 Jul 2025

Abstract

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the [...] Read more.

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape was adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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15 pages, 914 KiB

Open AccessArticle

Spectral and Photometric Studies of NGC 7469 in the Optical Range

by Saule Shomshekova, Inna Reva, Ludmila Kondratyeva, Nazim Huseynov, Vitaliy Kim and Laura Aktay

Universe 2025, 11(7), 227; https://doi.org/10.3390/universe11070227 (registering DOI) - 10 Jul 2025

Abstract

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50

M_{⊙} {yr}^{- 1}

. [...] Read more.

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50

M_{⊙} {yr}^{- 1}

. This study presents the results of spectral and photometric observations carried out during the period from 2020 to 2024 at the Fesenkov Astrophysical Institute (Almaty, Kazakhstan) and the Nasreddin Tusi Shamakhy Astrophysical Observatory (Shamakhy, Azerbaijan). Photometric data were obtained using B, V, and Rc filters, while spectroscopic observations covered the wavelength range of

λ

4000–7000 Å. Data reduction was performed using the IRAF and MaxIm DL Pro6 software packages. An analysis of the light curves revealed that after the 2019–2020 outburst, the luminosity level of NGC 7469 remained relatively stable until the end of 2024. In November–December 2024, an increase in brightness (∼0.3–0.5 magnitudes) was recorded. Spectral data show variations in the Ha fluxes and an enhancement of them at the end of 2024. On BPT diagrams, the emission line flux ratios [OIII]/H

β

and [NII]/H

α

place NGC 7469 on the boundary between regions dominated by different ionization sources: AGN and star-forming regions. The electron density of the gas, estimated from the intensity ratios of the [SII] 6717, 6731 Ålines, is about

900 - 1000 {cm}^{- 3}

. Continued observations will help to determine whether the trend of increasing brightness and emission line fluxes recorded at the end of 2024 will persist. Full article

(This article belongs to the Special Issue 10th Anniversary of Universe: Galaxies and Their Black Holes)

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14 pages, 6073 KiB

Open AccessArticle

Soil Nitrogen Transformation Pathways Shift Following Deep Tillage in Coastal Wetlands Invaded by Spartina alterniflora

by Jingwen Gao, Pengcheng Jiang, Junzhen Li, Ming Wu, Xuexin Shao and Niu Li

Diversity 2025, 17(7), 473; https://doi.org/10.3390/d17070473 (registering DOI) - 10 Jul 2025

Abstract

Spartina alterniflora invasion has posed severe ecological threats to coastal wetlands. Deep tillage is considered an effective physical method for ecological restoration in such wetlands; however, its effects on sediment nitrogen transformation processes remain unclear. In this study, we investigated the impacts of [...] Read more.

Spartina alterniflora invasion has posed severe ecological threats to coastal wetlands. Deep tillage is considered an effective physical method for ecological restoration in such wetlands; however, its effects on sediment nitrogen transformation processes remain unclear. In this study, we investigated the impacts of deep tillage on soil physicochemical properties and key nitrogen transformation pathways, including nitrification, denitrification, anammox, and DNRA, across different soil depths (0–10, 10–20, 20–30, 30–50, and 50–100 cm) in Spartina alterniflora-invaded coastal wetlands. Deep tillage significantly restructured the distribution of soil moisture (p < 0.05), pH (p > 0.05), electrical conductivity (p < 0.05), and nutrients, promoting NO₃⁻-N accumulation in deeper layers while reducing NH₄⁺-N concentrations in surface soils (p < 0.05). It markedly enhanced denitrification and DNRA rates (p < 0.05), suppressed surface nitrification (p < 0.05), and altered the vertical distribution of anammox activity. Correlation analysis revealed that NH₄⁺-N and NO₃⁻-N concentrations were the primary drivers of nitrogen transformation, with pH and electrical conductivity playing secondary roles. Overall, deep tillage stimulated nitrogen removal processes and affected net ammonium changes. These findings reveal that deep tillage can stimulate nitrogen removal processes by alleviating soil compaction and altering nitrogen transformation pathways, thus supporting biogeochemical recovery mechanisms after deep tillage. These insights provide scientific guidance for the ecological restoration of Spartina alterniflora-invaded coastal wetlands. Full article

(This article belongs to the Section Biodiversity Conservation)

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16 pages, 4138 KiB

Open AccessArticle

Bridging NDT and Laboratory Testing in an Airfield Pavement Structural Evaluation

by Angeliki Armeni

NDT 2025, 3(3), 17; https://doi.org/10.3390/ndt3030017 (registering DOI) - 10 Jul 2025

Abstract

The accurate assessment of the structural condition of airfield pavements is of paramount importance to airport authorities as it determines the planning of maintenance activities. On this basis, Non-Destructive Testing (NDT) techniques provide a powerful tool to assess the mechanical properties of the [...] Read more.

The accurate assessment of the structural condition of airfield pavements is of paramount importance to airport authorities as it determines the planning of maintenance activities. On this basis, Non-Destructive Testing (NDT) techniques provide a powerful tool to assess the mechanical properties of the individual layers of the pavement. However, information from laboratory testing of cores taken from the pavement is expected to provide a more accurate assessment of material properties. Against this background, the present research aims to investigate the accuracy of the mechanical properties of in-situ layers derived from NDT data and the associated back-calculation procedures for airfield pavements, where higher pavement thicknesses are usually required due to the high aircraft loads, while few similar studies have been conducted compared to road pavements. For this reason, the assessment of the structural condition of a flexible runway pavement is presented. The analysis shows that there is a strong correlation between the moduli estimated in the laboratory and the moduli estimated by back-calculation. Furthermore, the back-calculated moduli appear to lead to a conservative approach in assessing the structural condition of the pavement. This conservatism promotes a more proactive pavement management by airport authorities. Full article

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20 pages, 516 KiB

Open AccessArticle

Intelligent System Using Data to Support Decision-Making

by Viera Anderková, František Babič, Zuzana Paraličová and Daniela Javorská

Appl. Sci. 2025, 15(14), 7724; https://doi.org/10.3390/app15147724 (registering DOI) - 10 Jul 2025

Abstract

Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to [...] Read more.

Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamlit interface to generate patient-specific explanations and rate models on accuracy, separability, stability, response time, understandability, and user experience. Random Forest combined with SHAP consistently achieved the highest rankings in Borda count. Clinicians reported reduced evaluation time, enhanced explanation clarity, and increased confidence in model outputs. These results demonstrate that CDSS-EQCM can effectively streamline interpretability assessment and support clinician decision-making in medical diagnostics. Future work will focus on deeper electronic medical record integration and interactive parameter tuning to further enhance real-time diagnostic support. Full article

(This article belongs to the Special Issue Artificial Intelligence in Digital Health)

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15 pages, 1449 KiB

Open AccessArticle

Cochlear Implant in Children with Congenital CMV Infection: Long-Term Results from an Italian Multicentric Study

by Francesca Forli, Silvia Capobianco, Stefano Berrettini, Francesco Lazzerini, Rita Malesci, Anna Rita Fetoni, Serena Salomè, Davide Brotto, Patrizia Trevisi, Leonardo Franz, Elisabetta Genovese, Andrea Ciorba and Silvia Palma

Children 2025, 12(7), 908; https://doi.org/10.3390/children12070908 (registering DOI) - 10 Jul 2025

Abstract

Background/Objectives: Congenital cytomegalovirus (cCMV) infection is the most common non-genetic cause of sensorineural hearing loss (SNHL) in children. In cases of severe-to-profound SNHL, cochlear implantation (CI) is a widely used intervention, but outcomes remain variable due to possible neurodevelopmental comorbidities. This study [...] Read more.

Background/Objectives: Congenital cytomegalovirus (cCMV) infection is the most common non-genetic cause of sensorineural hearing loss (SNHL) in children. In cases of severe-to-profound SNHL, cochlear implantation (CI) is a widely used intervention, but outcomes remain variable due to possible neurodevelopmental comorbidities. This study aimed to evaluate the long-term auditory and language outcomes in children with cCMV after CI and to explore clinical and radiological predictors of post-CI performance. Methods: Fifty-three children with cCMV and bilateral severe-to-profound SNHL who underwent CI at five tertiary referral centers in Italy were included in the study. Auditory and language outcomes were assessed pre- and post-implantation using the Categories of Auditory Performance II (CAP-II) scale, the Nottingham 3-Level Classification, and the Bates Language Development Scale. Brain MRI abnormalities were classified according to the Alarcón classification. Correlations were explored between outcome scores and symptomatic status at birth, MRI findings, and neurodevelopmental comorbidities. Results: At birth, 40 children (75.5%) were symptomatic and 13 (24.5%) asymptomatic. Neurodevelopmental comorbidities were present in 19 children (35.8%). MRI was normal in 15 (28.3%), mildly abnormal in 26 (49%), and moderately to severely abnormal in 12 (22.6%). Auditory and language outcomes improved significantly post-CI (p < 0.001), though the outcomes varied widely. Twenty-five children (47%) reached CAP level ≥ 6, and thirteen (23%) reached Bates Level 6. Symptomatic status at birth correlated weakly with worse CAP (ρ = −0.291, p = 0.038) and Bates (ρ = −0.310, p = 0.028) scores. Higher Alarcón scores were significantly associated with neurodevelopmental comorbidities, though not directly with post-CI auditory and language outcomes. Finally, the presence of neurodevelopmental disabilities was generally associated with lower results, even if without statistical significance. Conclusions: CI provides substantial auditory and language benefit in children with cCMV, even in cases of severe neurodevelopmental comorbidities. MRI and developmental assessments, as well as perinatal history for clinical signs and symptoms, are helpful in guiding expectations and personalizing post-implantation support. Full article

(This article belongs to the Special Issue Treatment Strategies for Hearing Loss in Children)

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17 pages, 4846 KiB

Open AccessArticle

The Air Stability of Sodium Layered Oxide NaTMO₂ (100) Surface Investigated via DFT Calculations

by Hui Li, Qing Xue, Shengyi Li, Xuechun Wang, Yijie Hou, Chang Sun, Cun Wang, Guozheng Sheng, Peng Sheng, Huitao Bai, Li Xu and Yumin Qian

Nanomaterials 2025, 15(14), 1067; https://doi.org/10.3390/nano15141067 (registering DOI) - 10 Jul 2025

Abstract

Air stability caused by the H₂O/CO₂ reaction at the layered oxide NaTMO₂ surface is one of the main obstacles to commercializing sodium-ion batteries (SIBS). The H₂O and CO₂ adsorption properties on the (100) surface of sodium [...] Read more.

Air stability caused by the H₂O/CO₂ reaction at the layered oxide NaTMO₂ surface is one of the main obstacles to commercializing sodium-ion batteries (SIBS). The H₂O and CO₂ adsorption properties on the (100) surface of sodium layered transition metal oxide NaTMO₂ (TM = Co, Ni, Mo, Nd) are calculated using the DFT method to study the surface air stability. This study showed that the material bulk phase (symmetry), surface site, element type, and surface termination are all (though not the only) important factors that affect the adsorption strength. Contrary to previous studies, the P phase is not always more air-stable than the O phase; our calculations showed that the NaNiO₂ O phase is more stable than the P phase. The calculated band center and occupation showed a direct relationship with the adsorption energy. The Na site adsorption for CO₂ and H₂O showed the same V-shape trend. However, the TM adsorption for CO₂ and H₂O showed a different trend. With an increased t₂g band center, CO₂ adsorption strength increases. There is no clear trend for H₂O adsorption. Our calculations showed that the electronic structure of the surface atomic of adsorption site plays a decisive role in CO₂ and H₂O adsorption strength. This study demonstrated an effective method for obtaining a stability parameter regarding the electronic structure, which can be used to screen the air-stable layered oxide sodium cathode in the future. Full article

(This article belongs to the Special Issue Nanostructured Materials for Energy Storage)

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24 pages, 375 KiB

Open AccessReview

Psychological and Physical Health Outcomes Associated with Gender-Affirming Medical Care for Transgender and Gender-Diverse Youth: A Critical Review

by Terri A. Croteau, Jan Gelech, Melanie A. Morrison and Todd G. Morrison

Healthcare 2025, 13(14), 1659; https://doi.org/10.3390/healthcare13141659 (registering DOI) - 10 Jul 2025

Abstract

Introduction: Access of transgender and gender diverse (TGD) youth to gender-affirming medical care (GAMC) has become a contentious topic in the West, with many members of the general population, politicians, and even some experts and academic researchers voicing concerns about possible adverse effects [...] Read more.

Introduction: Access of transgender and gender diverse (TGD) youth to gender-affirming medical care (GAMC) has become a contentious topic in the West, with many members of the general population, politicians, and even some experts and academic researchers voicing concerns about possible adverse effects of GAMC on the mental and physical health of TGD youth. Due to these concerns, recent years have seen a significant rise in legislation restricting TGD youth from accessing GAMC in countries such as the United States, the United Kingdom, and Canada. However, in this critical review of the literature on the psychological (e.g., anxiety, depression, suicide, and body satisfaction) and physical (e.g., bone health, cognitive function, and fertility) health outcomes associated with GAMC among TGD youth, we argue that, given the state of current research, youth should not be restricted from accessing GAMC. Conclusions: Our findings reinforce the importance of close monitoring by doctors, counselling for TGD youth with respect to potential risks, and increased studies on the topic, especially those focusing on reproductive health. Full article

19 pages, 605 KiB

Open AccessArticle

Residents’ Well-Being and Sustainable Governance in Island Tourism: The Evidence from Aceh, Indonesia

by T. Meldi Kesuma, Riha Dedi Priantana, M. Ridha Siregar, Radhia Humaira and Abdul Muzammil

Tour. Hosp. 2025, 6(3), 131; https://doi.org/10.3390/tourhosp6030131 (registering DOI) - 10 Jul 2025

Abstract

This study develops and tests an integrated structural equation model (SEM) linking Butler’s Tourism Area Life Cycle (TALC), residents’ quality of life (QoL), and community participation in sustainable tourism governance (STG) across three emerging island destinations in Aceh, Indonesia. Drawing on survey data [...] Read more.

This study develops and tests an integrated structural equation model (SEM) linking Butler’s Tourism Area Life Cycle (TALC), residents’ quality of life (QoL), and community participation in sustainable tourism governance (STG) across three emerging island destinations in Aceh, Indonesia. Drawing on survey data from 1266 residents, we employ confirmatory factor analysis and covariance-based SEM to (1) assess the direct effects of TALC-derived dimensions on residents’ QoL; (2) examine the influence of residents’ QoL on governance participation; and (3) evaluate both direct and indirect pathways linking TALC to STG. Rather than distinct life cycle stages, we conceptualize and measure residents’ perceptions of destination maturity based on key TALC dimensions, such as infrastructure development, tourism intensity, and institutional coordination. Results indicate that higher perceived destination maturity is positively associated with residents’ QoL (β = 0.21, p < 0.001), and that residents’ QoL strongly predicts governance participation (β = 0.31, p < 0.001). TALC dimensions also directly affect STG (β = 0.23, p < 0.001), with residents’ QoL partially mediating this relationship and accounting for 22.4% of the total effect. Multigroup SEM reveals consistent effect patterns across Weh, Pulo Aceh, and Simeulue. These findings illustrate how TALC-informed perceptions of destination maturity relate to residents’ quality of life and governance participation, suggesting that perceived well-being may play an important role in shaping community engagement in small-island tourism contexts. Full article

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16 pages, 1312 KiB

Open AccessSystematic Review

Measuring Health Inequalities Using the Robin Hood Index: A Systematic Review with Meta-Analysis

by Georgios Farantos, Athanasios Pitis, Maria Diamantopoulou and Fotini Tzavella

Epidemiologia 2025, 6(3), 35; https://doi.org/10.3390/epidemiologia6030035 (registering DOI) - 10 Jul 2025

Abstract

Background/Objectives: Although the Robin Hood Index (RHI) is increasingly used to quantify geographic health inequality and guide resource redistribution, empirical evidence on whether higher physician density reduces RHI-measured inequality remains limited. This study systematically reviews and meta-analyzes RHI-based research to assess the association [...] Read more.

Background/Objectives: Although the Robin Hood Index (RHI) is increasingly used to quantify geographic health inequality and guide resource redistribution, empirical evidence on whether higher physician density reduces RHI-measured inequality remains limited. This study systematically reviews and meta-analyzes RHI-based research to assess the association between physician distribution and health inequalities. Methods: We conducted a systematic review and meta-analysis of studies using the RHI to evaluate health inequalities, without restrictions on country or publication date. Following PRISMA 2020 guidelines and registered in PROSPERO (CRD42024496486), we searched PubMed, Scopus, and OpenGrey literature, extracted data on physician density and RHI outcomes, and conducted a meta-analysis. Odds ratios (ORs), ln(OR), and 95% confidence intervals (CIs) were calculated, and risk of bias was assessed using the Robvis tool. Results: Seventeen studies covering 720 regions and 1.07 billion individuals were included. Three clusters emerged: physician redistribution (10 studies), poverty–mortality associations (six studies), and systematic reviews (one study). Physician redistribution was strongly associated with increased inequality and policy attention (r = 0.73; p = 0.0038). Meta-analysis of eight redistribution studies yielded a pooled OR of 1.24 (95% CI: 0.54–2.86), consistent in sensitivity analysis (OR = 1.26; 95% CI: 0.56–2.89). Poverty–mortality studies also showed a correlation with the number of variables considered (r = 0.59; p = 0.022). Conclusions: A greater physician supply is associated with increased health inequalities, with statistical support but limited certainty. Methodological heterogeneity in RHI-based studies constrains comparability. Standardized methodologies and broader analytic models are needed to inform research and guide health policy. Full article

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24 pages, 539 KiB

Open AccessArticle

Big Data Analytics as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies

by Mohammad Mousa Mousa, Heyam Abdulrahman Al Moosa, Issam Naim Ayyash, Fandi Omeish, Imed Zaiem, Thamer Alzahrani, Samiha Mjahed Hammami and Ahmad M. Zamil

Sustainability 2025, 17(14), 6319; https://doi.org/10.3390/su17146319 (registering DOI) - 10 Jul 2025

Abstract

Facing growing sustainability challenges and the critical priority of digital transformation, this study explores, through the lens of the dynamic capability view, the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in [...] Read more.

Facing growing sustainability challenges and the critical priority of digital transformation, this study explores, through the lens of the dynamic capability view, the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in emerging markets, particularly the pharmaceutical sector. Our study proposes an original conceptual model linking big data analytics to the circular economy, tested with 275 employees from the Saudi pharmaceutical sector. The results, obtained through state-of-the-art PLS-SEM modeling, indicate a significant positive impact of big data analytics on sustainable performance and green supply chain management within the circular economy framework. The study also reveals the crucial mediating role of sustainable performance and green supply chain management in the relationship between big data analytics and the circular economy. Our study proposes an integrated framework for understanding how digital technologies support the circular economy in emerging markets, with practical implications for pharmaceutical sector actors and policymakers, in line with Saudi Arabia’s Vision 2030. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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17 pages, 3082 KiB

Open AccessArticle

Full-Length Transcriptome Sequencing and hsp Gene Family Analysis Provide New Insights into the Stress Response Mechanisms of Mystus guttatus

by Lang Qin, Xueling Zhang, Yusen Li, Jun Shi, Yu Li, Yaoquan Han, Hui Luo, Dapeng Wang, Yong Lin and Hua Ye

Biology 2025, 14(7), 840; https://doi.org/10.3390/biology14070840 (registering DOI) - 10 Jul 2025

Abstract

Mystus guttatus, a second-class protected species in China, has undergone severe population decline due to anthropogenic and environmental pressures, yet conservation efforts are hindered by limited genomic resources and a lack of mechanistic insights into its stress response systems. Here, the first [...] Read more.

Mystus guttatus, a second-class protected species in China, has undergone severe population decline due to anthropogenic and environmental pressures, yet conservation efforts are hindered by limited genomic resources and a lack of mechanistic insights into its stress response systems. Here, the first full-length transcriptome of M. guttatus was generated via SMRT sequencing. A total of 32,647 full-length transcripts were obtained, with an average length of 1783 bp. After structure and function annotation of full-length transcripts, 30,977 genes, 1670 transcription factors (TF), 918 alternative splicing (AS), and 11,830 simple sequence repeats (SSR) were identified. In order to further explore the stress resistance of M. guttatus, 93 genes belonging to the heat shock protein (HSP) family were identified and categorized into HSP70 and HSP90 subgroups. After phylogenetic analysis and selective stress analysis, it was discovered that the hsp family has suffered purifying selection and gene loss, potentially contributing to a decrease in the stress resilience and population of M. guttatus. Using protein interaction network and molecular docking tools, we observed the intricate interplay among HSPs and discovered HSP70-HOP-HSP90 interaction, which is an essential stress response mechanism. Our study sequenced the first full-length transcriptome of M. guttatus to enhance its genomic resources for its conservation and breeding and provide new insights into the future study of stress response mechanisms on M. guttatus. Full article

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12 pages, 486 KiB

Open AccessArticle

Five-Year Retrospective Analysis of Traumatic and Non-Traumatic Pneumothorax in 2797 Patients

by Ayhan Tabur and Alper Tabur

Healthcare 2025, 13(14), 1660; https://doi.org/10.3390/healthcare13141660 (registering DOI) - 10 Jul 2025

Abstract

Objectives: Pneumothorax is a critical condition frequently encountered in emergency departments (EDs), with spontaneous pneumothorax (SP) and traumatic pneumothorax (TP) presenting distinct clinical challenges. This study aimed to evaluate the epidemiological characteristics, clinical outcomes, and treatment strategies for SP and TP across different [...] Read more.

Objectives: Pneumothorax is a critical condition frequently encountered in emergency departments (EDs), with spontaneous pneumothorax (SP) and traumatic pneumothorax (TP) presenting distinct clinical challenges. This study aimed to evaluate the epidemiological characteristics, clinical outcomes, and treatment strategies for SP and TP across different age groups and provide insights for optimizing emergency management protocols. Methods: This retrospective cohort study analyzed 2797 cases of pneumothorax over five years (2018–2023) at a tertiary care center. Patients were stratified by age (18–39, 40–64, and >65 years) and pneumothorax type (SP vs. TP). Data on demographics, clinical presentation, treatment, hospital stay, recurrence, and complications were extracted from medical records. Comparative statistical analyses were also conducted. Results: The mean age of patients with SP was 32.5 ± 14.7 years, whereas patients with TP were older (37.8 ± 16.2 years, p < 0.001). Male predominance was observed in both groups: 2085 (87.0%) in the SP group and 368 (92.0%) in the TP group (p = 0.01). The right lung was more frequently affected in the SP (64.2%) and TP (56.0%) groups (p < 0.001). Age-related differences were evident in both groups of patients. In the SP group, younger patients (18–39 years) represented the majority of cases, whereas older patients (≥65 years) were more likely to present with SSP and required more invasive management (p < 0.01). In the TP group, younger patients often had pneumothorax due to high-energy trauma, whereas older individuals developed pneumothorax due to falls or iatrogenic causes (p < 0.01). SP predominantly affected younger patients, with a history of smoking and male predominance associated with younger age (p < 0.01). TP is more frequent in older patients, often because of falls or iatrogenic injuries. Management strategies varied by age group; younger patients were often managed conservatively, whereas older patients underwent more invasive procedures (p < 0.01). Surgical intervention was more common in younger patients in the TP group, whereas conservative management was more frequent in elderly patients (p < 0.01). The clinical outcomes differed significantly, with older patients having longer hospital stays and higher rates of persistent air leaks (p < 0.01). Recurrence was more common in younger patients with SP, whereas TP recurrence rates were lower across all age groups (p < 0.01). No significant differences were observed in re-expansion pulmonary edema, empyema, or mortality rates between the age groups, suggesting that age alone was not an independent predictor of these complications when adjusted for pneumothorax severity and management strategy (p = 0.22). Conclusions: Age, pneumothorax subtype, and underlying pulmonary comorbidities were identified as key predictors of clinical outcomes. Advanced age, secondary spontaneous pneumothorax, and COPD were independently associated with recurrence, prolonged hospitalization, and in-hospital mortality, respectively. These findings highlight the need for risk-adapted management strategies to improve triaging and treatment decisions for spontaneous and traumatic pneumothorax. Full article

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23 pages, 1590 KiB

Open AccessArticle

A Decision Support System for Classifying Suppliers Based on Machine Learning Techniques: A Case Study in the Aeronautics Industry

by Ana Claudia Andrade Ferreira, Alexandre Ferreira de Pinho, Matheus Brendon Francisco, Laercio Almeida de Siqueira, Jr. and Guilherme Augusto Vilas Boas Vasconcelos

Computers 2025, 14(7), 271; https://doi.org/10.3390/computers14070271 (registering DOI) - 10 Jul 2025

Abstract

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as [...] Read more.

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts. Full article

(This article belongs to the Special Issue Adaptive Decision Making Across Industries with AI and Machine Learning: Frameworks, Challenges, and Innovations)

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17 pages, 1937 KiB

Open AccessArticle

Hybrid Deep Learning Model for Improved Glaucoma Diagnostic Accuracy

by Nahum Flores, José La Rosa, Sebastian Tuesta, Luis Izquierdo, María Henriquez and David Mauricio

Information 2025, 16(7), 593; https://doi.org/10.3390/info16070593 (registering DOI) - 10 Jul 2025

Abstract

Glaucoma is an irreversible neurodegenerative disease that affects the optic nerve, leading to partial or complete vision loss. Early and accurate detection is crucial to prevent vision impairment, which necessitates the development of highly precise diagnostic tools. Deep learning (DL) has emerged as [...] Read more.

Glaucoma is an irreversible neurodegenerative disease that affects the optic nerve, leading to partial or complete vision loss. Early and accurate detection is crucial to prevent vision impairment, which necessitates the development of highly precise diagnostic tools. Deep learning (DL) has emerged as a promising approach for glaucoma diagnosis, where the model is trained on datasets of fundus images. To improve the detection accuracy, we propose a hybrid model for glaucoma detection that combines multiple DL models with two fine-tuning strategies and uses a majority voting scheme to determine the final prediction. In experiments, the hybrid model achieved a detection accuracy of 96.55%, a sensitivity of 98.84%, and a specificity of 94.32%. Integrating datasets was found to improve the performance compared to using them separately even with transfer learning. When compared to individual DL models, the hybrid model achieved a 20.69% improvement in accuracy compared to the best model when applied to a single dataset, a 13.22% improvement when applied with transfer learning across all datasets, and a 1.72% improvement when applied to all datasets. These results demonstrate the potential of hybrid DL models to detect glaucoma more accurately than individual models. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 6890 KiB

Open AccessTechnical Note

Research on Task Interleaving Scheduling Method for Space Station Protection Radar with Shifting Constraints

by Guiqiang Zhang, Haocheng Zhou, Hong Yang, Jiacheng Hou, Guangyuan Xu and Dawei Wang

Telecom 2025, 6(3), 49; https://doi.org/10.3390/telecom6030049 (registering DOI) - 10 Jul 2025

Abstract

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation [...] Read more.

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station’s protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance. Full article

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20 pages, 11158 KiB

Open AccessArticle

Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction

by Bo Li, Zhongfa Zhou, Tianjun Wu and Jiancheng Luo

Remote Sens. 2025, 17(14), 2368; https://doi.org/10.3390/rs17142368 (registering DOI) - 10 Jul 2025

Abstract

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological [...] Read more.

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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