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The paper investigates the design and operation of microgrid arrangements, with a focus on renewable power systems, system architectures, and storage solutions. The research evaluates stochastic and multi-objective optimization methods to show how demand response systems improve operational flexibility. The study evaluates 183 journal articles to select those that address microgrid design in conjunction with optimization models and demand response approaches. The articles are classified into three essential categories, which include microgrid design optimization methods and demand response integration. The review establishes that microgrid performance depends on three fundamental design parameters, which include energy generation systems, storage capabilities, and load demand control mechanisms. The review demonstrates that advanced optimization approaches, such as stochastic and multi-objective optimization methods, offer effective solutions for managing renewable energy variability. The paper demonstrates that demand response strategies are crucial for reducing costs and enhancing system flexibility. However, current published research falls short of establishing an integrated system that combines real-time demand response with stochastic optimization. This integration, while not yet fully realized, is suggested as a critical advancement for ensuring both system performance optimization and long-term sustainability. Therefore, this paper calls for further research to develop resilient hybrid renewable microgrids that integrate flexibility with sustainability through advanced optimization models and demand response strategies. Full article

(This article belongs to the Special Issue Advanced Grid Integration with Power Electronics: 2nd Edition)

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26 pages, 7761 KB

Open AccessArticle

Artificial Intelligence-Based Optimized Nonlinear Control for Multi-Source Direct Current Converters in Hybrid Electric Vehicle Energy Systems

by Atif Rehman, Rimsha Ghias and Hammad Iqbal Sherazi

Energies 2025, 18(19), 5152; https://doi.org/10.3390/en18195152 (registering DOI) - 28 Sep 2025

Abstract

The integration of multiple renewable and storage units in electric vehicle (EV) hybrid energy systems presents significant challenges in stability, dynamic response, and disturbance rejection, limitations often encountered with conventional sliding mode control (SMC) and super-twisting SMC (STSMC) schemes. This paper proposes a condition-based integral terminal super-twisting sliding mode control (CBITSTSMC) strategy, with gains optimally tuned using an improved gray wolf optimization (I-GWO) algorithm, for coordinated control of a multi-source DC–DC converter system comprising photovoltaic (PV) arrays, fuel cells (FCs), lithium-ion batteries, and supercapacitors. The CBITSTSMC ensures finite-time convergence, reduces chattering, and dynamically adapts to operating conditions, thereby achieving superior performance. Compared to SMC and STSMC, the proposed controller delivers substantial reductions in steady-state error, overshoot, and undershoot, while improving rise time and settling time by up to 50%. Transient stability and disturbance rejection are significantly enhanced across all subsystems. Controller-in-the-loop (CIL) validation on a Delfino C2000 platform confirms the real-time feasibility and robustness of the approach. These results establish the CBITSTSMC as a highly effective solution for next-generation EV hybrid energy management systems, enabling precise power-sharing, improved stability, and enhanced renewable energy utilization. Full article

(This article belongs to the Special Issue AC, DC and Hybrid Microgrids - Trends and Prospects with Distributed Generation)

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16 pages, 1477 KB

Open AccessArticle

Valorization of Oat Husk for the Production of Fermentable Sugars, Xylooligosaccharides, and Inulinase via Deep Eutectic Solvent and Microwave-Assisted Pretreatment

by Hatice Gözde Hosta Yavuz, Ibrahim Yavuz and Irfan Turhan

Fermentation 2025, 11(10), 561; https://doi.org/10.3390/fermentation11100561 (registering DOI) - 28 Sep 2025

Abstract

This study presents an integrated valorization strategy for oat husks through microwave-assisted pretreatment using a deep eutectic solvent (DES) composed of choline chloride and glycerol (1:2). The process was designed to enhance the release of fermentable sugars, enable xylooligosaccharide (XOS) production, and support inulinase production by Aspergillus niger A42 via submerged fermentation of the hydrolysate and solid-state fermentation of the residual biomass. Response surface methodology (RSM) was applied to evaluate the effects of microwave power, treatment time, and liquid-to-solid ratio (LSR) on fermentable sugar content (FSC) and total phenolic compounds (TPCs). Following pretreatment, the biomass was hydrolyzed using 1.99% sulfuric acid for 1 min. Optimal pretreatment conditions (350 W, 30 s, LSR 4 w/w) yielded an FSC of 51.14 g/L. Additionally, 230.78 mg/L xylohexaose and 6.47 mg/L xylotetraose were detected. Submerged fermentation of the liquid fraction with A. niger A42 resulted in inulinase and invertase activities of 60.45 U/mL and 21.83 U/mL, respectively. Solid-state fermentation of the pretreated solids produced 37.03 U/mL inulinase and 17.64 U/mL invertase. The integration of microwave-assisted DES pretreatment, dilute acid hydrolysis, and fungal fermentation established a robust strategy for the sequential production of XOS, fermentable sugars, and inulinase from oat husks, supporting their comprehensive utilization within a sustainable biorefinery framework. Full article

(This article belongs to the Special Issue Valorisation of Agro-Industrial By-Products Through Fermentation or Eco-Friendly Techniques)

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15 pages, 361 KB

Open AccessFeature PaperArticle

Natural Additives for Sustainable Meat Preservation: Salicornia ramosissima and Acerola Extract in Mertolenga D.O.P. Meat

by Gonçalo Melo, Joana Paiva, Carla Gonçalves, Sónia Saraiva, Madalena Faria, Tânia Silva-Santos, Márcio Moura-Alves, Juan García-Díez, José M. M. M. de Almeida, Humberto Rocha and Cristina Saraiva

Resources 2025, 14(10), 153; https://doi.org/10.3390/resources14100153 (registering DOI) - 28 Sep 2025

Abstract

The search for natural additives from underutilized halophytes and fruit by-products aligns with circular economy principles, addressing consumer demand for healthier and more sustainable alternatives to salt and synthetic antioxidants in foods. Salicornia ramosissima, a halophytic plant rich in minerals, and Malpighia emarginata (acerola), a fruit rich in bioactive compounds, were selected for their potential to enhance meat preservation while reducing reliance on conventional salt and chemical additives. This study evaluated the effects of replacing salt with S. ramosissima powder (1% and 2%) and adding acerola extract (0.3%) in Mertolenga D.O.P. beef hamburgers. Control, 1% salt, acerola, and salicornia formulations were analyzed over 10 days for the following: (1) microbial counts (mesophiles, psychrotrophics, Enterobacteriaceae, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria, fungi, Salmonella spp., and E. coli); (2) physicochemical parameters (pH, a_w, and CIE-Lab color); and (3) sensory attributes (odor, color, and freshness). Higher Salicornia concentrations negatively affected color (lower a* values) and sensory perception (darker appearance). Acerola extract improved color stability and delayed the development of off-odors, contributing to higher freshness scores throughout storage. No significant differences in microbial counts were observed between treatments. Overall, acerola and low-dose Salicornia showed potential as natural ingredients for meat preservation, with minimal impact on physicochemical and microbiological quality. These findings support the use of halophytes and fruit extracts in sustainable meat preservation strategies. Full article

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15 pages, 2082 KB

Open AccessArticle

Comparative Transcriptomics Unveils Pathogen-Specific mTOR Pathway Modulation in Monochamus alternatus Infected with Entomopathogenic Fungi

by Haoran Guan, Jinghong He, Chuanyu Zhang, Ruiyang Shan, Haoyuan Chen, Tong Wu, Qin Sun, Liqiong Zeng, Fangfang Zhan, Yu Fang, Gaoping Qu, Chentao Lin, Shouping Cai and Jun Su

Insects 2025, 16(10), 1006; https://doi.org/10.3390/insects16101006 (registering DOI) - 28 Sep 2025

Abstract

Pine wilt disease (PWD), transmitted by Monochamus alternatus (JPS), poses a severe threat to global pine forests. Although the entomopathogenic fungi Beauveria bassiana (Bb) and Metarhizium anisopliae (Ma) represent environmentally friendly biocontrol alternatives, their practical application is limited by inconsistent field performance and an incomplete understanding of host–pathogen interactions. We employed dual RNA-seq at the critical 48 h infection time point to systematically compare the transcriptional responses between JPS and Bb/Ma during infection. Key findings revealed distinct infection strategies: Bb preferentially induced autophagy pathways and modulated host carbohydrate metabolism to facilitate nutrient acquisition, triggering corresponding tissue degradation responses in JPS. In contrast, Ma primarily co-opted host amino acid and sugar metabolic pathways for biosynthetic processes, eliciting a stronger immune defense activation in JPS. Notably, the mTOR signaling pathway was identified as a key regulator of the differential host responses to various entomopathogenic fungi. Further functional validation-specifically, the application of a chemical inhibitor and RNAi targeting mTOR in JPS-confirmed that mTOR inhibition selectively enhanced Bb-induced mortality in JPS without affecting Ma virulence. Our findings reveal the molecular determinants of host–pathogen specificity in PWD biological control and indicate that mTOR regulation could serve as an effective strategy to improve fungal pesticide performance. Full article

(This article belongs to the Special Issue Insect Transcriptomics)

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24 pages, 4696 KB

Open AccessFeature PaperArticle

Hp-Adaptive Dynamic Trust Region Sequential Convex Programming: An Enhanced Strategy for Trajectory Optimization

by Zhengpeng Yang, Zhichao An, Chao Ming, Xiaoming Wang and Guangbao Wen

Symmetry 2025, 17(10), 1607; https://doi.org/10.3390/sym17101607 (registering DOI) - 28 Sep 2025

Abstract

Under stringent flight constraint conditions, trajectory optimization of Hypersonic Vehicles (HV) is crucial for guidance. Therefore, this paper proposes an hp-adaptive dynamic trust region SCP method to address the problems of initial value sensitivity and low computational efficiency that arise in traditional Sequential Convex Programming (SCP) methods during HV trajectory optimization. First, the discrete symmetry principle is utilized to simplify the boundary conditions and constraint handling of the trajectory model while combining scale transformation invariance to achieve nondimensionalization of the model’s physical quantities, thereby constructing the trajectory optimization model. On this basis, the hp-adaptive method is adopted to dynamically adjust the discretization step size and polynomial approximation order, and combined with an adaptive adjustment strategy for the trust region radius, to improve computational efficiency while ensuring optimization accuracy. Finally, the effectiveness of the algorithm is validated through optimizing the gliding phase of HV, and compared with GPOPS-II and fixed trust region SCP methods. The experimental results show that the algorithm has superiority in improving convergence speed, computational efficiency and solution accuracy, with its performance significantly outperforming traditional trajectory optimization schemes. Full article

(This article belongs to the Section Engineering and Materials)

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14 pages, 537 KB

Open AccessArticle

Enhancing Tetradesmus sp. Biomass Recovery: The Influence of Culture Media on Surface Physicochemical Properties

by Ana Carolina Anzures-Mendoza, Ulises Páramo-García, Nohra Violeta Gallardo-Rivas, Luciano Aguilera-Vázquez and Ana María Mendoza-Martínez

Processes 2025, 13(10), 3099; https://doi.org/10.3390/pr13103099 (registering DOI) - 27 Sep 2025

Abstract

Efficient biomass harvesting remains one of the primary barriers to the commercial feasibility of large-scale microalgal production. This study investigates the effect of different culture media on the surface physicochemical properties of Tetradesmus sp., with emphasis on their role in natural aggregation. Cultures were grown for 30 days under controlled light and temperature conditions using Blue Green 11 (BG11), Tris–acetate–phosphate (TAP), and deionized water supplemented with Bayfolan^® fertilizer. Surface hydrophobicity was assessed through microbial adhesion to solvents (MATS) and contact angle analysis, electrokinetic properties were evaluated by zeta potential measurements, and cell surface chemistry was characterized by attenuated total reflectance (ATR) sampling methodology for Fourier Transform Infrared (FTIR) spectroscopy. Across all treatments, Tetradesmus sp. exhibited inherent hydrophobicity, but Bayfolan^® supplementation yielded the highest contact angle (49.0 ± 0.9°) and the least negative free energy of interaction (ΔGsws = −26.36 mJ·m⁻²), indicating a stronger tendency toward self-aggregation. Zeta potential values remained consistently negative (−10 to −14 mV), with no significant variation among media, suggesting that hydrophobic interactions rather than electrostatic forces govern aggregation. ATR-FTIR spectra confirmed the presence of lipids, proteins, and carbohydrates, with changes in peak intensities reflecting metabolic adjustments to media composition. These results demonstrate that low-cost Bayfolan^® supplementation enhances surface hydrophobicity and aggregation, providing a sustainable strategy to facilitate biomass recovery and reduce harvesting costs in microalgal biorefineries. Full article

(This article belongs to the Special Issue Advances in Bioprocess Technology, 2nd Edition)

16 pages, 4987 KB

Open AccessArticle

Nitrogen Transformation Survival Strategies of Ammonia-Oxidizing Bacterium N.eA1 Under High Nitrite Stress

by Zhiyao Yan, Kai Li, Yuhang Liu, Zhijun Ren, Xueying Li and Haobin Yang

Sustainability 2025, 17(19), 8708; https://doi.org/10.3390/su17198708 (registering DOI) - 27 Sep 2025

Abstract

Ammonia-oxidizing bacteria (AOB) are key to the nitrogen cycle, but their resistance to nitrite (NO₂⁻-N) accumulation is unclear. This study examined N.eA1, an AOB from the completely autotrophic nitrogen removal over nitrite (CANON) process, assessing its adaptive responses to NO₂⁻-N. The ammonia oxidation and N₂O emission were evaluated at varying NO₂⁻-N levels, and 3D fluorescence, extracellular polymeric substances (EPS), and soluble microbial products (SMP) analysis were used to probe stress responses. Cellular respiration and key enzyme activities were measured, and proteomics was applied to study protein expression changes. Results showed that higher NO₂⁻-N levels boosted N₂O production, inhibited nitrification, and stimulated denitrification in N.eA1. At 100 mg·L⁻¹ NO₂⁻-N, EPS rose and SMP fell, with ammonia monooxygenase (AMO) suppressed and nitrite reductase (NIR) as well as nitric oxide reductase (NOR) enhanced. Gene expression analysis revealed decreased AMO, hydroxylamine oxidoreductase (HAO), and energy transport-related enzymes, but increased NIR and NOR genes. The downregulation of electron transport complex genes offered insights into molecular adaptation to nitrite stress of N.eA1, highlighting the interplay between metabolic and genetic responses, which is essential for developing sustainable and efficient nitrogen management strategies. Full article

(This article belongs to the Special Issue Sustainability and Advanced Research on Microbiology)

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18 pages, 476 KB

Open AccessReview

Nutrition and Physical Activity in the University Population: A Scoping Review of Combined Impacts on Psychological Well-Being, Cognitive Performance, and Quality of Life

by Paride Vasco, Salvatore Allocca, Claudia Casella, Francesco Paolo Colecchia, Maria Ruberto, Nicola Mancini, Maria Casillo, Antonietta Messina, Marcellino Monda, Giovanni Messina, Vincenzo Monda, Antonietta Monda, Fiorenzo Moscatelli and Rita Polito

J. Funct. Morphol. Kinesiol. 2025, 10(4), 374; https://doi.org/10.3390/jfmk10040374 (registering DOI) - 27 Sep 2025

Abstract

Background: University students are particularly vulnerable to psychological distress due to the transitional nature of this life phase and increasing academic, social, and financial pressures. Accumulating evidence indicates that lifestyle behaviors—especially nutrition and physical activity—play a critical role in shaping mental health, cognitive functioning, and overall well-being in this population. Methods: The objective of this scoping review was to systematically map the literature on the combined impacts of diet and physical activity on psychological well-being among university students. Following PRISMA-ScR guidelines, an initial search of three major databases (PubMed, Sciencedirect, and Wiley) yielded 718 articles. After a multi-stage screening process, 39 articles of various designs (including cross-sectional, interventional, and review studies) focusing on non-clinical student populations were included. The studies were then thematically analyzed. Results: While most research explored isolated behaviors, a smaller set of integrated studies revealed synergistic effects, reporting enhanced outcomes in mental health and quality of life. Notably, several articles proposed practical strategies—such as app-based tools, structured wellness initiatives, and interdisciplinary educational programs—as effective means to support healthier habits. Conclusions: The evidence strongly suggests that universities should prioritize holistic, multi-component wellness strategies over siloed, single-behavior initiatives. Developing integrated programs that combine nutritional education and physical activity support represents a practical and effective approach to enhance student well-being. Full article

(This article belongs to the Topic Exercise and Nutrition for Physical Performance, Mental Health and General Well-Being)

15 pages, 5911 KB

Open AccessArticle

Integrative Bioinformatics-Guided Analysis of Glomerular Transcriptome Implicates Potential Therapeutic Targets and Pathogenesis Mechanisms in IgA Nephropathy

by Tiange Yang, Mengde Dai, Fen Zhang and Weijie Wen

Bioengineering 2025, 12(10), 1040; https://doi.org/10.3390/bioengineering12101040 (registering DOI) - 27 Sep 2025

Abstract

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms. Full article

(This article belongs to the Special Issue Advanced Biomedical Signal Communication Technology)

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24 pages, 17328 KB

Open AccessArticle

Integrating Multi-Temporal Sentinel-1/2 Vegetation Signatures with Machine Learning for Enhanced Soil Salinity Mapping Accuracy in Coastal Irrigation Zones: A Case Study of the Yellow River Delta

by Junyong Zhang, Tao Liu, Wenjie Feng, Lijing Han, Rui Gao, Fei Wang, Shuang Ma, Dongrui Han, Zhuoran Zhang, Shuai Yan, Jie Yang, Jianfei Wang and Meng Wang

Agronomy 2025, 15(10), 2292; https://doi.org/10.3390/agronomy15102292 (registering DOI) - 27 Sep 2025

Abstract

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions. Full article

(This article belongs to the Special Issue In-Field Detection and Monitoring Technology in Precision Agriculture—2nd Edition)

24 pages, 4242 KB

Open AccessArticle

Causal Matrix Long Short-Term Memory Network for Interpretable Significant Wave Height Forecasting

by Mingshen Xie, Wenjin Sun, Ying Han, Shuo Ren, Chunhui Li, Jinlin Ji, Yang Yu, Shuyi Zhou and Changming Dong

J. Mar. Sci. Eng. 2025, 13(10), 1872; https://doi.org/10.3390/jmse13101872 (registering DOI) - 27 Sep 2025

Abstract

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

(This article belongs to the Special Issue AI-Empowered Marine Energy)

27 pages, 1365 KB

Open AccessSystematic Review

Enhancing Osseointegration of Zirconia Implants Using Calcium Phosphate Coatings: A Systematic Review

by Jacek Matys, Ryszard Rygus, Julia Kensy, Krystyna Okoniewska, Wojciech Zakrzewski, Agnieszka Kotela, Natalia Struzik, Hanna Gerber, Magdalena Fast and Maciej Dobrzyński

Materials 2025, 18(19), 4501; https://doi.org/10.3390/ma18194501 (registering DOI) - 27 Sep 2025

Abstract

Objective: Yttria-stabilized tetragonal zirconia polycrystal (Y-TZP), a variant of zirconia (ZrO₂), has attracted interest as a substitute for titanium in dental and orthopedic implants, valued for its biocompatibility and aesthetics that resemble natural teeth. However, its bioinert surface limits osseointegration, making surface modifications such as calcium phosphate (CaP) coatings highly relevant. Materials and methods: The review process adhered to the PRISMA guidelines. Electronic searches of PubMed, Scopus, Web of Science, Embase, and Cochrane Library (July 2025) identified studies evaluating CaP-coated zirconia implants. Eligible studies included in vitro, in vivo, and preclinical investigations with a control group. Data on coating type, deposition method, and biological outcomes were extracted and analyzed. Results: A total of 27 studies were analyzed, featuring different calcium phosphate (CaP) coatings including β-tricalcium phosphate (β-TCP), hydroxyapatite (HA), octacalcium phosphate (OCP), and various composites. These coatings were applied using diverse techniques such as RF magnetron sputtering, sol–gel processing, biomimetic methods, and laser-based approaches. In multiple investigations, calcium phosphate coatings enhanced osteoblast attachment, proliferation, alkaline phosphatase (ALP) expression, and bone-to-implant contact (BIC) relative to unmodified zirconia surfaces. Multifunctional coatings incorporating growth factors, antibiotics, or nanoparticles showed additional benefits. Conclusion: CaP coatings enhance the bioactivity of zirconia implants and represent a promising strategy to overcome their inertness. Further standardized approaches and long-term studies are essential to verify their translational relevance. Full article

(This article belongs to the Special Issue Calcium Phosphate Biomaterials with Medical Applications)

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21 pages, 2380 KB

Open AccessArticle

Edge-Embedded Multi-Feature Fusion Network for Automatic Checkout

by Jicai Li, Meng Zhu and Honge Ren

J. Imaging 2025, 11(10), 337; https://doi.org/10.3390/jimaging11100337 (registering DOI) - 27 Sep 2025

Abstract

The Automatic Checkout (ACO) task aims to accurately generate complete shopping lists from checkout images. Severe product occlusions, numerous categories, and cluttered layouts impose high demands on detection models’ robustness and generalization. To address these challenges, we propose the Edge-Embedded Multi-Feature Fusion Network (E2MF2Net), which jointly optimizes synthetic image generation and feature modeling. We introduce the Hierarchical Mask-Guided Composition (HMGC) strategy to select natural product poses based on mask compactness, incorporating geometric priors and occlusion tolerance to produce photorealistic, structurally coherent synthetic images. Mask-structure supervision further enhances boundary and spatial awareness. Architecturally, the Edge-Embedded Enhancement Module (E3) embeds salient structural cues to explicitly capture boundary details and facilitate cross-layer edge propagation, while the Multi-Feature Fusion Module (MFF) integrates multi-scale semantic cues, improving feature discriminability. Experiments on the RPC dataset demonstrate that E2MF2Net outperforms state-of-the-art methods, achieving checkout accuracy (cAcc) of 98.52%, 97.95%, 96.52%, and 97.62% on Easy, Medium, Hard, and Average mode, respectively. Notably, it improves by 3.63 percentage points in the heavily occluded Hard mode and exhibits strong robustness and adaptability in incremental learning and domain generalization scenarios. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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34 pages, 1658 KB

Open AccessArticle

A Potential Outlier Detection Model for Structural Crack Variation Using Big Data-Based Periodic Analysis

by Jaemin Kim, Seong Woong Shin, Seulki Lee and Jungho Yu

Buildings 2025, 15(19), 3492; https://doi.org/10.3390/buildings15193492 (registering DOI) - 27 Sep 2025

Abstract

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies. Full article

(This article belongs to the Section Construction Management, and Computers & Digitization)

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