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Gliders are engineless aircraft capable of maintaining altitude for extended periods and achieving long ranges. This paper presents an optimal aerodynamic design method for flying wing gliders, leveraging a combination of artificial neural networks (ANNs) as a surrogate model and genetic algorithms (GAs) for optimization. Data for training the ANN is generated using the vortex-lattice method (VLM). The study identifies optimal aerodynamic shapes for two objectives: maximum flight endurance and maximum range. A key finding is the inherent conflict between aerodynamic performance and static stability in tailless designs. By introducing a stability constraint via a penalty function, we successfully generate stable and high-performance configurations. For instance, the stabilized RG15 airfoil design achieves a maximum glide ratio of 24.1 with a robust 5.1% static margin. This represents a calculated 11.5% performance reduction compared to its unstable theoretical optimum, quantitatively demonstrating the crucial trade-off between stability and performance. The methodology provides a computationally efficient path to designing practical, high-performance, and inherently stable flying wing gliders. Full article

(This article belongs to the Section Aeronautics)

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20 pages, 1596 KB

Open AccessArticle

D3S3real: Enhancing Student Success and Security Through Real-Time Data-Driven Decision Systems for Educational Intelligence

by Aimina Ali Eli, Abdur Rahman and Naresh Kshetri

Digital 2025, 5(3), 42; https://doi.org/10.3390/digital5030042 (registering DOI) - 10 Sep 2025

Abstract

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

17 pages, 3115 KB

Open AccessArticle

High-Strength, Stable, and Energy-Efficient Bacterial Nanocellulose Composite Films for Building-Integrated Photovoltaics Facade System

by Chenguang Wang, Libin Deng and Yanjie Zhou

Coatings 2025, 15(9), 1063; https://doi.org/10.3390/coatings15091063 - 10 Sep 2025

Abstract

Bacterial nanocellulose (BNC) composite films have emerged as promising candidates for sustainable building materials, yet their practical application in building-integrated photovoltaics (BIPV) facade systems is hindered by insufficient mechanical strength, poor environmental stability, and limited energy efficiency. Here, we developed bacterial nanocellulose/zinc oxide–phenolic resin (BNC/ZnO–PF) composite films with high-strength, stability, and energy efficiency for BIPV facade system through a simple strategy. Specifically, we first prepared BNC films, then in-situ grew ZnO nanoparticles on BNC films via ultrasound assistance, and finally hot-pressed the BNC/ZnO films with PF resin. The BNC/–PF composite films exhibit high mechanical strength (tensile strength of 93.8 MPa), exceptional sturdiness (wet strength of 92.3 MPa), and thermal properties, demonstrating their durability for long-term outdoor applications. Furthermore, the BNC/ZnO–PF composite films show high transparency (86.47%) and haze (82.02%) in the visible light range, enabling effective light propagation and scattering, as well as soft, uniform, and large-area light distribution. Meanwhile, a low thermal conductivity of 21.7 mW·m⁻¹·K⁻¹ can effectively impede the transfer of high outdoor temperatures into the room, significantly reducing the energy consumption demands of heating and cooling systems. Coupled with its ability to en-hance the photovoltaic conversion efficiency of solar cells by 12.9%, this material can serve as the core encapsulation layer for BIPV facades. While enabling build-ing-integrated photovoltaic power generation, through the synergistic effect of light management and thermal insulation, it is expected to reduce comprehensive building energy consumption, providing a new solution for building energy efficiency under carbon neutrality goals. Full article

(This article belongs to the Section Thin Films)

34 pages, 3959 KB

Open AccessArticle

Multimodal Video Summarization Using Machine Learning: A Comprehensive Benchmark of Feature Selection and Classifier Performance

by Elmin Marevac, Esad Kadušić, Nataša Živić, Nevzudin Buzađija, Edin Tabak and Safet Velić

Algorithms 2025, 18(9), 572; https://doi.org/10.3390/a18090572 - 10 Sep 2025

Abstract

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education. Full article

(This article belongs to the Special Issue Visual Attributes in Computer Vision Applications)

20 pages, 18323 KB

Open AccessArticle

MambaRA-GAN: Underwater Image Enhancement via Mamba and Intra-Domain Reconstruction Autoencoder

by Jiangyan Wu, Guanghui Zhang and Yugang Fan

J. Mar. Sci. Eng. 2025, 13(9), 1745; https://doi.org/10.3390/jmse13091745 - 10 Sep 2025

Abstract

Underwater images frequently suffer from severe quality degradation due to light attenuation and scattering effects, manifesting as color distortion, low contrast, and detail blurring. These issues significantly impair the performance of downstream tasks. Therefore, underwater image enhancement (UIE) becomes a key technology to solve underwater image degradation. However, existing data-driven UIE methods typically rely on difficult-to-acquire paired data for training, severely limiting their practical applicability. To overcome this limitation, this study proposes MambaRA-GAN, a novel unpaired UIE framework built upon a CycleGAN architecture, which introduces a novel integration of Mamba and intra-domain reconstruction autoencoders. The key innovations of our work are twofold: (1) We design a generator architecture based on a Triple-Gated Mamba (TG-Mamba) block. This design dynamically allocates feature channels to three parallel branches via learnable weights, achieving optimal fusion of CNN’s local feature extraction capabilities and Mamba’s global modeling capabilities. (2) We construct an intra-domain reconstruction autoencoder, isomorphic to the generator, to quantitatively assess the quality of reconstructed images within the cycle consistency loss. This introduces more effective structural information constraints during training. The experimental results demonstrate that the proposed method achieves significant improvements across five objective performance metrics. Visually, it effectively restores natural colors, enhances contrast, and preserves rich detail information, robustly validating its efficacy for the UIE task. Full article

(This article belongs to the Section Ocean Engineering)

19 pages, 760 KB

Open AccessArticle

Design of a Sensor-Based Digital Product Passport for Low-Tech Manufacturing: Traceability and Environmental Monitoring in Bio-Block Production

by Alessandro Pracucci and Matteo Giovanardi

Sensors 2025, 25(18), 5653; https://doi.org/10.3390/s25185653 - 10 Sep 2025

Abstract

The Digital Product Passport (DPP) is an emergent strategic tool poised to significantly enhance traceability, circularity, and sustainability within industrial supply chains, aligning with evolving European Union regulatory frameworks. This paper introduces a conceptual sensor-based DPP architecture specifically designed for the construction industry, exemplified by a real case study for a bio-based manufacturing company. This framework facilitates a transparent and accessible data management approach, crucial for fostering circular practices and guiding stakeholders in decision-making along the value chain. The proposed architecture addresses critical challenges in product-related traceability and information accessibility across the entire product life cycle, spanning from raw material supply to the construction and installation process (A1–A5 stages). Data collected from the low-tech sensor network and digital tools can generate relevant environmental indicators for Life Cycle Assessment (LCA) and DPP creation, thereby offering a comprehensive, detailed, and certified overview of product attributes and their environmental impacts. The study clarifies the benefits and current barriers to implementing a sensor-based DPP architecture in low-tech construction manufacturing, underscoring the potential of lightweight, interoperable sensing solutions to advance compliance, transparency, and digitalization in traditionally under-digitized sectors like construction materials manufacturing. Full article

(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)

16 pages, 1614 KB

Open AccessArticle

Tri-Band Inverted-F Antenna for Wi-Fi 7 Laptops with Reduced Ground Plane Support

by Yu-Kai Huang, Kuan-Hsueh Tseng and Yen-Sheng Chen

Electronics 2025, 14(18), 3601; https://doi.org/10.3390/electronics14183601 - 10 Sep 2025

Abstract

In modern laptops, antenna design for Wi-Fi 7 is constrained by limited space and reduced ground plane size, conditions under which many compact designs exhibit degraded bandwidth or efficiency or require large device grounds. This paper presents a miniaturized tri-band inverted-F antenna (IFA) that supports the 2.4, 5, and 6 GHz Wi-Fi 7 bands within a radiator area of 20 × 5 × 0.8 mm³ and a ground plane of 60 × 40 mm². The proposed design achieves wideband impedance matching and stable radiation efficiency under intentionally reduced grounding conditions, addressing a scenario rarely considered in prior studies where both radiator and ground plane miniaturization must be satisfied. Measurements confirm efficiencies of 74–81% at 2.4 GHz and 64–90% across 5–7 GHz, with performance in the lower band exceeding that of many compact designs and upper-band coverage comparable to structures requiring larger footprints. By demonstrating tri-band operation under simultaneous radiator and ground reduction, this work provides a practical antenna solution for next-generation Wi-Fi 7 laptop integration. Full article

(This article belongs to the Special Issue Future Prospects of Wireless Transmission Based on Engineering Electrical Electronic and Telecommunications)

26 pages, 3901 KB

Open AccessArticle

Towards Robotic Pruning: Automated Annotation and Prediction of Branches for Pruning on Trees Reconstructed Using RGB-D Images

by Jana Dukić, Petra Pejić, Ivan Vidović and Emmanuel Karlo Nyarko

Sensors 2025, 25(18), 5648; https://doi.org/10.3390/s25185648 - 10 Sep 2025

Abstract

This paper presents a comprehensive pipeline for automated prediction of branches to be pruned, integrating 3D reconstruction of fruit trees, automatic branch labeling, and pruning prediction. The workflow begins with capturing multi-view RGB-D images in orchard settings, followed by generating and preprocessing point clouds to reconstruct partial 3D models of pear trees using the TEASER++ algorithm. Differences between pre- and post-pruning models are used to automatically label branches to be pruned, creating a valuable dataset for both reconstruction methods and training machine learning models. A neural network based on PointNet++ is trained to predict branches to be pruned directly on point clouds, with performance evaluated through quantitative metrics and visual inspections. The pipeline demonstrates promising results, enabling real-time prediction suitable for robotic implementation. While some inaccuracies remain, this work lays a solid foundation for future advancements in autonomous orchard management, aiming to improve precision, speed, and practicality of robotic pruning systems. Full article

(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)

25 pages, 1218 KB

Open AccessArticle

Color and Texture of Wheat and Whole Grain Wheat Salty Crackers—Technological Aspects of Cricket Powder Addition

by Ivan Švec, Beverly Hradecká, Pavel Skřivan, Marcela Sluková, Jiří Štětina, Filip Beňo and Jana Hajšlová

Appl. Sci. 2025, 15(18), 9914; https://doi.org/10.3390/app15189914 - 10 Sep 2025

Abstract

Salty wheat crackers prepared from wheat white (WF) and whole grain flour (WG) were enriched with 5, 10, and 15% cricket powder (CRPW). According to the content of dietary fiber and fat, two types of wheat flour and CRPW differed in terms of darkness “100 − L*” and redness a*. The color of the baked products reflected these differences, but the darkening of the whole grain crackers was less intense; the shades of wheat–cricket 90:10 and whole grain 100:0 cracker variants were comparable. Within the WF subset, the hardness diminished insignificantly, with the reverse occurring in the WG group (from 25 to 22 N and from 31 to 35 N, respectively). The flexibility of the crackers was independent on type of wheat flour and the proportion of CRPW, as shown by a 90% confidence interval of 0.97–1.06 mm. By Principal Component Analysis, the primary role of wheat flour type in distinguishing the crackers was confirmed. As expected, the darkness “100 − L*” and the redness a* of the cracker surface could be used to predict the results of the texture breaking test and fragility in general (P = 95%). The 90:10 WF–cricket crackers and 95:5 WG–cricket crackers had similar properties, and both could be adopted in baking practice without modification. Full article

(This article belongs to the Special Issue Cereal-Based Food Processing and Nutritional Quality: Latest Advances and Prospects)

12 pages, 536 KB

Open AccessArticle

The Association Between Schizophrenia and Cardiovascular Diseases: A Retrospective Cohort Study of Primary Care Routine Data in Germany

by Ira Rodemer, Marcel Konrad, Mark Luedde and Karel Kostev

Brain Sci. 2025, 15(9), 974; https://doi.org/10.3390/brainsci15090974 - 10 Sep 2025

Abstract

Background: This novel study addresses the question of whether schizophrenia is associated with an increased risk of cardiovascular diseases (CVDs) by controlling for metabolic syndrome-related conditions through propensity score matching, using real-world primary care data from Germany. Methods: This retrospective cohort study analyzed 12,527 patients aged 18 or older with schizophrenia from 1209 general practices (GPs) in Germany between 2005 and 2023 from the IQVIA Disease Analyzer database. Patients were matched 1:5 with individuals without schizophrenia based on sex, age, index year, consultation frequency, and chronic conditions. CVDs cumulative incidence was assessed using Kaplan–Meier curves and hazard ratios (HRs) were calculated using univariable Cox regression analysis. Results: Over a 10-year follow-up, schizophrenia was associated with a higher risk of heart failure (HR: 1.33, 95% CI: 1.20–1.48) and a lower risk of atrial fibrillation and flutter (HR: 0.77, 95% CI: 0.67–0.89). No significant associations were observed for acute myocardial infarction (HR: 0.97, 95% CI: 0.76–1.25), angina pectoris (HR: 0.78, 95% CI: 0.63–0.96), or chronic ischaemic heart disease (HR: 0.91, 95% CI: 0.82–1.02). Stratified analyses showed that schizophrenia was most strongly associated with heart failure in women aged 41–50 years (HR: 3.34, 95% CI: 2.11–5.31), followed by women aged 61–70 years (HR: 1.88, 95% CI: 1.45–2.44) and men aged 51–60 years (HR: 1.81, 95% CI: 1.34–2.45). Conclusions: This study highlights significant differences in the 10-year cumulative incidence of CVDs between individuals with and without schizophrenia. While patients with schizophrenia appear less likely to be diagnosed with milder or asymptomatic CVDs, they are at increased risk for severe outcomes. The study’s findings underscore the need for sex-specific and symptom-sensitive public health strategies to improve early detection and prevention of CVDs in patients with schizophrenia. Full article

(This article belongs to the Section Neuropsychiatry)

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27 pages, 1002 KB

Open AccessArticle

Exergy Efficiency of Closed and Unsteady-Flow Systems

by Yunus A. Çengel and Mehmet Kanoğlu

Entropy 2025, 27(9), 943; https://doi.org/10.3390/e27090943 - 10 Sep 2025

Abstract

Exergy efficiency is viewed as the degree of approaching reversible operation, with a value of 100 percent for a reversible process characterized by zero entropy generation or equivalently zero exergy destruction since X_destroyed = T₀S_gen. As such, exergy efficiency becomes a measure of thermodynamic perfection. There are different conceptual definitions of exergy efficiency, the most common ones being (1) the ratio of exergy output to exergy input η_ex = X_output/X_input = 1 − (X_destroyed + X_loss)/X_input, (2) the ratio of the product exergy to fuel exergy η_ex = X_product/X_fuel = 1 − (X_destroyed + X_loss)/X_fuel, and (3) the ratio of exergy recovered to exergy expended η_ex = X_recovered/X_expended = 1 − X_destroyed/X_expended. Most exergy efficiency definitions are formulated with steady-flow systems in mind, and they are generally applied to systems in steady operation such as power plants and refrigeration systems whose exergy content remains constant. If these definitions are to be used for closed and unsteady-flow systems, the terms need to be interpreted broadly to account for the exergy change of the systems as exergy input or output, as appropriate. In this paper, general exergy efficiency relations are developed for closed and unsteady-flow systems and their use is demonstrated with applications. Also, the practicality of the use of the term exergy loss X_loss is questioned, and limitations on the definition η_ex = W_act,out/W_rev,out are discussed. Full article

(This article belongs to the Special Issue Thermodynamic Optimization of Energy Systems)

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30 pages, 6577 KB

Open AccessArticle

Private 5G and AIoT in Smart Agriculture: A Case Study of Black Fungus Cultivation

by Cheng-Hui Chen, Wei-Han Kuo and Hsiao-Yu Wang

Electronics 2025, 14(18), 3594; https://doi.org/10.3390/electronics14183594 - 10 Sep 2025

Abstract

Black fungus cultivation in bagged form requires frequent inspection of mycelial growth, a process that is labor-intensive and susceptible to subjective judgment. In addition, timely detection of contamination in low-light and high-humidity environments remains a significant challenge. To address these issues, this paper proposed an intelligent agriculture system for black fungus cultivation, with emphasis on practical deployment under real farming conditions. The system integrates a private 5G network with a YOLOv8-based deep learning model for real-time object detection and growth monitoring. Continuous image acquisition and data feedback are achieved through a multi-parameter environmental sensing module and an autonomous ground vehicle (AGV) equipped with IP cameras. To improve model robustness, more than 42,000 labeled training images were generated through data augmentation, and a modified C2f network architecture was employed. Experimental results show that the model achieved a detection accuracy of 93.7% with an average confidence score of 0.96 under live testing conditions. The deployed 5G network provided a downlink throughput of 645.2 Mbps and an uplink throughput of 147.5 Mbps, ensuring sufficient bandwidth and low latency for real-time inference and transmission. Field trials conducted over five cultivation batches demonstrated improvements in disease detection, reductions in labor requirements, and an increase in the average yield success rate to 80%. These findings indicate that the proposed method offers a scalable and practical solution for precision agriculture, integrating next-generation communication technologies with deep learning to enhance cultivation management. Full article

(This article belongs to the Collection Electronics for Agriculture)

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21 pages, 4538 KB

Open AccessArticle

Estimation of Downlink Signal Transmitting Antenna PCO and Equipment Delays for LEO Navigation Constellations with Limited Regional Stations

by Ziqiang Li, Wanke Liu and Jie Hu

Remote Sens. 2025, 17(18), 3138; https://doi.org/10.3390/rs17183138 - 10 Sep 2025

Abstract

In LEO constellation–augmented navigation, the transmitting antenna phase center offset (PCO) and the equipment delay associated with the downlink signals of LEO satellites constitute major error sources that must be precisely characterized. Previous studies primarily focused on single or small-scale satellite scenarios, lacking comprehensive evaluations regarding the influence of constellation scale, orbital altitude, ground station configuration, and various error sources. To address this gap, we propose a joint estimation method utilizing observations from a limited number of regional ground stations in China that simultaneously track GNSS and LEO satellites. The method is specifically designed to accommodate practical constraints on ground station distribution within China. Initially, a batch least-squares estimation strategy is employed to simultaneously determine the ionosphere-free PCO and initial equipment delays for all LEO satellites in a constellation-wide solution. Subsequently, the estimated PCO parameters are fixed, and the equipment delays are further refined using a precise point positioning (PPP) approach. To systematically evaluate the method’s performance under realistic conditions, we analyze the impact of orbital altitude, constellation size, ground station number, data processing duration, and orbit/clock biases through comprehensive simulations. The results indicate: (1) the Z-direction component of the PCO (pointing toward the Earth’s center) and equipment delay is more sensitive to orbit and clock errors; (2) Increasing the number of LEO satellites generally improves the estimation accuracy of equipment delays, but the marginal gain diminishes as the constellation size expands; (3) sub-centimeter PCO accuracy and equipment delay accuracies better than 3 cm can still be achieved using only 3–4 regionally distributed ground stations over an observation period of 5–7 days. Full article

(This article belongs to the Special Issue Advanced Multi-GNSS Positioning and Its Applications in Geoscience)

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17 pages, 2525 KB

Open AccessArticle

A Non-Destructive Deep Learning–Based Method for Shrimp Freshness Assessment in Food Processing

by Dongyu Hao, Cunxi Zhang, Rui Wang, Qian Qiao, Linsong Gao, Jin Liu and Rongsheng Lin

Processes 2025, 13(9), 2895; https://doi.org/10.3390/pr13092895 - 10 Sep 2025

Abstract

Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to propose a non-destructive approach for shrimp freshness assessment based on imaging and deep learning, enabling efficient and reliable freshness classification. The core innovation of the method lies in constructing an improved GoogLeNet architecture. By incorporating the ELU activation function, L2 regularization, and the RMSProp optimizer, combined with a transfer learning strategy, the model effectively enhances generalization capability and stability under limited sample conditions. Evaluated on a shrimp image dataset rigorously annotated based on TVB-N reference values, the proposed model achieved an accuracy of 93% with a test loss of only 0.2. Ablation studies further confirmed the contribution of architectural and training strategy modifications to performance improvement. The results demonstrate that the method enables rapid, non-contact freshness discrimination, making it suitable for real-time sorting and quality monitoring in shrimp processing lines, and providing a feasible pathway for deployment on edge computing devices. This study offers a practical solution for intelligent non-destructive detection in aquatic products, with strong potential for engineering applications. Full article

(This article belongs to the Section Food Process Engineering)

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21 pages, 1372 KB

Open AccessReview

Creative Airs: Using Art to Raise Awareness About Particulate Matter Pollution

by Jeiser Rendón Giraldo, Henry Alonso Colorado Lopera, David Aguiar Gil and Mauricio Andrés Correa Ochoa

Sustainability 2025, 17(18), 8143; https://doi.org/10.3390/su17188143 - 10 Sep 2025

Abstract

This scoping review examines how art has been used as an educational and awareness-raising strategy against particulate matter (PM) pollution. PRISMA-ScR guidelines and the SPIDER framework were applied to structure a search of the Scopus and ScienceDirect databases, identifying 19 studies exploring diverse forms of artistic expression linked to environmental awareness. The documented interventions include immersive installations, participatory theater, murals, music, photography, eco-art design, poetry, and self-published publications (zines), encompassing experiences in urban contexts in Europe, the Americas, Asia, Africa, and Oceania. These artistic practices were aimed at diverse audiences—from vulnerable communities and schoolchildren to citizens in public spaces—and acted as mediators between scientific knowledge and social perceptions of environmental risk. The results show that art enhances ecological literacy, stimulates citizen participation, and contributes to the construction of effective and collective responses to the invisible effects of PM. This review highlights the value of art as a channel for integrating knowledge, generating critical awareness, and supporting innovative educational strategies in the face of complex environmental challenges. Full article

(This article belongs to the Special Issue Particle Matter Diminishing Solutions and Strategies for Air Polluted Cities)

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