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17 pages, 816 KiB  
Article
Risk Stratification Using a Perioperative Nomogram for Predicting the Mortality of Bladder Cancer Patients Undergoing Radical Cystectomy
by Daniel-Vasile Dulf, Anamaria Larisa Burnar, Patricia-Lorena Dulf, Doina-Ramona Matei, Hendea Raluca Maria, Cătălina Bungărdean, Maximilian Buzoianu, Iulia Andraș, Tudor-Eliade Ciuleanu, Nicolae Crișan and Camelia Alexandra Coadă
J. Clin. Med. 2025, 14(16), 5810; https://doi.org/10.3390/jcm14165810 (registering DOI) - 16 Aug 2025
Abstract
Background: Perioperative factors significantly impact oncologic outcomes after radical cystectomy (RC) for bladder cancer. This study aimed to identify key perioperative predictors for overall (OS) and progression-free survival (PFS) and to develop a prognostic nomogram for the identification of high-risk patients adapted to [...] Read more.
Background: Perioperative factors significantly impact oncologic outcomes after radical cystectomy (RC) for bladder cancer. This study aimed to identify key perioperative predictors for overall (OS) and progression-free survival (PFS) and to develop a prognostic nomogram for the identification of high-risk patients adapted to the clinical routines and standard of care of our country. Methods: We retrospectively analyzed 121 patients undergoing RC (2014–2024). Data on patient demographics, comorbidities, tumor pathology, neoadjuvant treatments, extensive intraoperative factors, and postoperative events were assessed using COX models. A prognostic nomogram for 3-year OS was constructed. Results: Median follow-up was 44.33 months. Significant predictors for worse OS included lymphovascular invasion (LVI) (HR 2.22), higher T stage (HR 8.75), N+ status (HR 1.10), and intraoperative complications (HR 3.04). Similar predictors were noted for PFS. The developed nomogram incorporated T-, N-stages, sex, grade, intraoperative complications and early (12 months) recurrence, and was able to significantly identify patients with a higher mortality risk (p < 0.001) with a C-index of 0.74. Conclusions: Our nomogram for mortality prediction of BC patients offers a promising tool for individualized risk stratification. Further studies are required for its external validation. Full article
(This article belongs to the Special Issue Advances and Perspectives in Cancer Diagnostics and Treatment)
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22 pages, 5692 KiB  
Article
RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages
by Jianping Zhang, Tailai Chen, Yizhe Li, Qi Meng, Yanying Chen, Jie Deng and Enhong Sun
Remote Sens. 2025, 17(16), 2858; https://doi.org/10.3390/rs17162858 (registering DOI) - 16 Aug 2025
Abstract
The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers [...] Read more.
The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers complementary and enriched spectral–spatial information, providing novel pathways for crop growth stage recognition in complex agricultural scenarios. However, the lack of publicly available multimodal datasets specifically designed for rice growth stage identification remains a significant bottleneck that limits the development and evaluation of relevant methods. To address this gap, we present RiceStageSeg, a multimodal benchmark dataset captured by unmanned aerial vehicles (UAVs), designed to support the development and assessment of segmentation models for rice growth monitoring. RiceStageSeg contains paired centimeter-level RGB and 10-band multispectral (MS) images acquired during several critical rice growth stages, including jointing and heading. Each image is accompanied by fine-grained, pixel-level annotations that distinguish between the different growth stages. We establish baseline experiments using several state-of-the-art semantic segmentation models under both unimodal (RGB-only, MS-only) and multimodal (RGB + MS fusion) settings. The experimental results demonstrate that multimodal feature-level fusion outperforms unimodal approaches in segmentation accuracy. RiceStageSeg offers a standardized benchmark to advance future research in multimodal semantic segmentation for agricultural remote sensing. The dataset will be made publicly available on GitHub v0.11.0 (accessed on 1 August 2025). Full article
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20 pages, 410 KiB  
Article
Reduction and Efficient Solution of ILP Models of Mixed Hamming Packings Yielding Improved Upper Bounds
by Péter Naszvadi, Peter Adam and Mátyás Koniorczyk
Mathematics 2025, 13(16), 2633; https://doi.org/10.3390/math13162633 (registering DOI) - 16 Aug 2025
Abstract
We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard [...] Read more.
We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article
22 pages, 1330 KiB  
Article
Internet Governance in the Context of Global Digital Contracts: Integrating SAR Data Processing and AI Techniques for Standards, Rules, and Practical Paths
by Xiaoying Fu, Wenyi Zhang and Zhi Li
Information 2025, 16(8), 697; https://doi.org/10.3390/info16080697 (registering DOI) - 16 Aug 2025
Abstract
With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. [...] Read more.
With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article
(This article belongs to the Special Issue Text Mining: Challenges, Algorithms, Tools and Applications)
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21 pages, 4322 KiB  
Article
Daylighting Performance Simulation and Optimization Design of a “Campus Living Room” Based on BIM Technology—A Case Study in a Region with Hot Summers and Cold Winters
by Qing Zeng and Guangyu Ou
Buildings 2025, 15(16), 2904; https://doi.org/10.3390/buildings15162904 (registering DOI) - 16 Aug 2025
Abstract
In the context of green building development, the lighting design of campus living rooms in hot summer and cold winter areas faces the dual challenges of glare control in summer and insufficient daylight in winter. Based on BIM technology, this study uses Revit [...] Read more.
In the context of green building development, the lighting design of campus living rooms in hot summer and cold winter areas faces the dual challenges of glare control in summer and insufficient daylight in winter. Based on BIM technology, this study uses Revit 2016 modeling and the HYBPA 2024 performance analysis platform to simulate and optimize the daylighting performance of the campus activity center of Hunan City College in multiple rounds of iterations. It is found that the traditional single large-area external window design leads to uneven lighting in 70% of the area, and the average value of the lighting coefficient is only 2.1%, which is lower than the national standard requirement of 3.3%. Through the introduction of the hybrid system of “side lighting + top light guide”, combined with adjustable inner louver shading, the optimized average value of the lighting coefficient is increased to 4.8%, the uniformity of indoor illuminance is increased from 0.35 to 0.68, the proportion of annual standard sunshine hours (≥300 lx) reaches 68.7%, and the energy consumption of the artificial lighting is reduced by 27.3%. Dynamic simulation shows that the uncomfortable glare index at noon on the summer solstice is reduced from 30.2 to 22.7, which meets the visual comfort requirements. The study confirms that the BIM-driven “static-dynamic” simulation coupling method can effectively address climate adaptability issues. However, it has limitations such as insufficient integration with international healthy building standards, insufficient accuracy of meteorological data, and simplification of indoor dynamic shading factors. Future research can focus on improving meteorological data accuracy, incorporating indoor dynamic factors, and exploring intelligent daylighting systems to deepen and expand the method, promote the integration of cross-standard evaluation systems, and provide a technical pathway for healthy lighting environment design in summer-hot and winter-cold regions. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
32 pages, 2119 KiB  
Article
Dynamic Calibration of Quartz Flexure Accelerometers
by Xuan Sheng, Xizhe Wang, Wenying Chen, Yang Shu and Kai Zhang
Sensors 2025, 25(16), 5096; https://doi.org/10.3390/s25165096 (registering DOI) - 16 Aug 2025
Abstract
The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration [...] Read more.
The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration excitation, a dynamic calibration model is proposed. A mathematical model is first developed based on the physical mechanism of the accelerometer, characterizing its intrinsic dynamic response. Simulation-based analysis demonstrates that the proposed dynamic model offers significantly improved accuracy compared to traditional static approaches. Furthermore, a dynamic calibration method leveraging a dual-axis precision centrifuge is designed and validated. The results confirm that the proposed approach enables the precise calibration of quartz flexure accelerometers in accordance with the dynamic model. The calibration of the dynamic parameter yields a relative standard deviation of −0.048%. Full article
(This article belongs to the Section Electronic Sensors)
24 pages, 2115 KiB  
Article
MHD-Protonet: Margin-Aware Hard Example Mining for SAR Few-Shot Learning via Dual-Loss Optimization
by Marii Zayani, Abdelmalek Toumi and Ali Khalfallah
Algorithms 2025, 18(8), 519; https://doi.org/10.3390/a18080519 (registering DOI) - 16 Aug 2025
Abstract
Synthetic aperture radar (SAR) image classification under limited data conditions faces two major challenges: inter-class similarity, where distinct radar targets (e.g., tanks and armored trucks) have nearly identical scattering characteristics, and intra-class variability, caused by speckle noise, pose changes, and differences in depression [...] Read more.
Synthetic aperture radar (SAR) image classification under limited data conditions faces two major challenges: inter-class similarity, where distinct radar targets (e.g., tanks and armored trucks) have nearly identical scattering characteristics, and intra-class variability, caused by speckle noise, pose changes, and differences in depression angle. To address these challenges, we propose MHD-ProtoNet, a meta-learning framework that extends prototypical networks with two key innovations: margin-aware hard example mining to better separate confusable classes by enforcing prototype distance margins, and dual-loss optimization to refine embeddings and improve robustness to noise-induced variations. Evaluated on the MSTAR dataset in a five-way one-shot task, MHD-ProtoNet achieves 76.80% accuracy, outperforming the Hybrid Inference Network (HIN) (74.70%), as well as standard few-shot methods such as prototypical networks (69.38%), ST-PN (72.54%), and graph-based models like ADMM-GCN (61.79%) and DGP-NET (68.60%). By explicitly mitigating inter-class ambiguity and intra-class noise, the proposed model enables robust SAR target recognition with minimal labeled data. Full article
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23 pages, 4479 KiB  
Article
Optimizing Texture and Drying Behavior of Squid (Todarodes pacificus) for Elder-Friendly Applications Using Alkaline Pretreatment and Intermittent Drying: An Experimental and Numerical Study
by Timilehin Martins Oyinloye and Won Byong Yoon
Processes 2025, 13(8), 2592; https://doi.org/10.3390/pr13082592 (registering DOI) - 16 Aug 2025
Abstract
This study addresses the increasing demand for texture-modified seafood products suitable for elderly consumers by focusing on dried squid, a popular protein source. The aim was to optimize the softening and drying procedures to produce a dried squid product with improved chewability and [...] Read more.
This study addresses the increasing demand for texture-modified seafood products suitable for elderly consumers by focusing on dried squid, a popular protein source. The aim was to optimize the softening and drying procedures to produce a dried squid product with improved chewability and quality. Fresh squid was pretreated using sodium bicarbonate or potassium carbonate solutions (0, 0.3, 0.6, and 0.9 mol/kg) and dried at 40 °C using either continuous (CD) or intermittent drying (ID) until the final moisture content reached 18.34 ± 0.44%. Hardness generally increased with higher alkaline concentrations, with the potassium carbonate-treated samples showing better softening effects. Based on standards for elderly-friendly foods targeting chewable hardness (10,000–50,000 N/m2), low water activity (<0.58), and limited color change (ΔE = 14.32), the optimal result was achieved with 0.3 mol/kg potassium carbonate and ID. Among the thin-layer drying models, the Midilli–Kucuk model showed the best fit, with the highest average R2 (0.9974), and lowest SSE (0.0481) and RMSE (0.1688), effectively capturing the drying kinetics. Scanning electron microscopy (SEM) revealed smoother surfaces and consistent porosity in samples dried intermittently, indicating less structural degradation. Finite element analysis showed that ID improved internal moisture distribution, reduced surface crusting, and alleviated internal stresses. These results support mild alkaline soaking combined with ID as an effective strategy for enhancing dried squid quality for elderly individuals. Full article
(This article belongs to the Special Issue Feature Papers in the "Food Process Engineering" Section)
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34 pages, 13529 KiB  
Article
Cannabidiol Enhances the Therapeutic Efficacy of Olsalazine and Cyclosporine in a Murine Model of Colitis
by Dinesh Thapa, Mohan Patil, Leon N. Warne, Rodrigo Carlessi and Marco Falasca
Int. J. Mol. Sci. 2025, 26(16), 7913; https://doi.org/10.3390/ijms26167913 (registering DOI) - 16 Aug 2025
Abstract
Current therapies for inflammatory bowel disease (IBD), such as olsalazine and cyclosporine, often exhibit limited long-term efficacy and are associated with adverse effects. Cannabidiol (CBD), a non-psychoactive phytocannabinoid, shows promise for its anti-inflammatory properties, though its effectiveness as a monotherapy remains inconclusive. This [...] Read more.
Current therapies for inflammatory bowel disease (IBD), such as olsalazine and cyclosporine, often exhibit limited long-term efficacy and are associated with adverse effects. Cannabidiol (CBD), a non-psychoactive phytocannabinoid, shows promise for its anti-inflammatory properties, though its effectiveness as a monotherapy remains inconclusive. This study investigates the therapeutic potential of combining low-dose CBD (10 mg/kg) with olsalazine (50 mg/kg) or cyclosporine (2.5, 5 mg/kg) in dextran sulphate sodium (DSS)-induced acute and chronic colitis models in mice. Disease severity was assessed via disease activity index (DAI), colon morphology, cytokine and chemokine expression, myeloperoxidase (MPO) activity, systemic inflammatory markers, and glucagon-like peptide-1 (GLP-1) regulation. Safety evaluations included haematology and plasma biochemistry. DSS-treated mice showed elevated DAI scores, colon shortening, heightened inflammation, and organ enlargement. Combination therapies significantly ameliorated colitis, reducing DAI, MPO activity, and inflammatory cytokines, while restoring colon length and GLP-1 levels—without inducing liver or kidney toxicity. These findings demonstrate that combining a low dose of CBD with standard IBD drugs enhances therapeutic efficacy while minimizing side effects, supporting its integration into future combination strategies for more effective and safer IBD management. Full article
47 pages, 1730 KiB  
Systematic Review
Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities
by Alfonso Ramírez-Pedraza, Juan Terven, José-Joel González-Barbosa, Juan-Bautista Hurtado-Ramos, Diana-Margarita Córdova-Esparza, Francisco-Javier Ornelas-Rodríguez, Raymundo Ramirez-Pedraza, Julio-Alejandro Romero-González and Sebastián Salazar-Colores
Agriculture 2025, 15(16), 1758; https://doi.org/10.3390/agriculture15161758 (registering DOI) - 16 Aug 2025
Abstract
Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that [...] Read more.
Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that meet the inclusion criteria. This review adopts a holistic framework aligned with three priority areas in agriculture—resource and climate management, crop productivity and quality, and sustainability—to explore how AI addresses key challenges in the cultivation and post-harvest processing of Hibiscus sabdariffa. The results show a predominance of classical machine learning techniques, with limited implementation of deep learning models. The most common applications include image classification, yield prediction, and analysis of bioactive compounds. However, limitations remain in the availability of open data, reproducible code, and standardized metrics. The narrative synthesis identified clear opportunities to integrate emerging technologies, such as deep neural networks and the Internet of Things (IoT), particularly in water management and stress monitoring. The review concludes that strengthening interdisciplinary research and promoting data openness is key to achieving a more resilient, sustainable, and technologically advanced crop. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
26 pages, 1420 KiB  
Article
Fuzzy Logic-Based Expert Evaluation of Tram Driver’s Console Fidelity in a Universal Simulator
by Łukasz Wolniewicz and Ewa Mardeusz
Appl. Sci. 2025, 15(16), 9048; https://doi.org/10.3390/app15169048 (registering DOI) - 16 Aug 2025
Abstract
Simulators are an effective tool for improving tram driver training. In urban rail transportation, the fidelity of reproducing the driver’s working environment is crucial due to the high diversity of vehicle models. This study presents a structured assessment model for evaluating the mapping [...] Read more.
Simulators are an effective tool for improving tram driver training. In urban rail transportation, the fidelity of reproducing the driver’s working environment is crucial due to the high diversity of vehicle models. This study presents a structured assessment model for evaluating the mapping of a tram driver’s console in a universal simulator. The model is based on expert judgment and utilizes fuzzy logic to evaluate four key criteria: perspective, button placement, functionality, and time required to locate safety buttons. A group of 30 experts, including experienced tram drivers and technical specialists, assessed the fidelity of the simulated consoles for three tram types: Solaris Tramino S105p, Moderus Gamma LF 06 AC, and Škoda 16T RK. The results enable the classification of console fidelity levels (low, moderate, high) and support the identification of design inconsistencies. The proposed model provides a standardized tool for assessing simulator realism, which can be applied by transport operators, manufacturers, and training centers to improve simulator configurations. Researchers may also use the model as a methodological framework for further evaluation studies involving human–machine interface fidelity. Full article
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15 pages, 899 KiB  
Review
Liquid Biopsy and Single-Cell Technologies in Maternal–Fetal Medicine: A Scoping Review of Non-Invasive Molecular Approaches
by Irma Eloisa Monroy-Muñoz, Johnatan Torres-Torres, Lourdes Rojas-Zepeda, Jose Rafael Villafan-Bernal, Salvador Espino-y-Sosa, Deyanira Baca, Zaira Alexi Camacho-Martinez, Javier Perez-Duran, Juan Mario Solis-Paredes, Guadalupe Estrada-Gutierrez, Elsa Romelia Moreno-Verduzco and Raigam Martinez-Portilla
Diagnostics 2025, 15(16), 2056; https://doi.org/10.3390/diagnostics15162056 (registering DOI) - 16 Aug 2025
Abstract
Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. [...] Read more.
Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. This scoping review explores the current applications of these technologies in placental development and the diagnosis of pregnancy complications, identifying research gaps and providing recommendations for future studies. Methods: This review adhered to PRISMA-ScR guidelines. Studies were selected based on their focus on liquid biopsy or single-cell analysis in perinatal research, particularly related to placental development and pregnancy complications such as preeclampsia, preterm birth, and fetal growth restriction. A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published in the last ten years. Data extraction and thematic synthesis were performed to identify diagnostic applications, monitoring strategies, and biomarker identification. Results: Twelve studies were included, highlighting the transformative potential of liquid biopsy and single-cell analysis in perinatal research. Liquid biopsy technologies, such as cfDNA and cfRNA analysis, provided non-invasive methods for real-time monitoring of placental function and early identification of complications. Extracellular vesicles (EVs) emerged as biomarkers for conditions like preeclampsia. Single-cell RNA sequencing (scRNA-seq) revealed cellular diversity and pathways critical to placental health, offering insights into processes such as vascular remodeling and trophoblast invasion. While promising, challenges such as high costs, technical complexity, and the need for standardization limit their clinical integration. Conclusion: Liquid biopsy and single-cell analysis are revolutionizing perinatal research, offering non-invasive tools to understand and manage complications like preeclampsia. Overcoming challenges in accessibility and standardization will be key to unlocking their potential for personalized care, enabling better outcomes for mothers and children worldwide. Full article
(This article belongs to the Special Issue Advancements in Maternal–Fetal Medicine: 2nd Edition)
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19 pages, 2520 KiB  
Article
Research on a Blockchain-Based Quality and Safety Traceability System for Hymenopellis raphanipes
by Wei Xu, Hongyan Guo, Xingguo Zhang, Mingxia Lin and Pingzeng Liu
Sustainability 2025, 17(16), 7413; https://doi.org/10.3390/su17167413 (registering DOI) - 16 Aug 2025
Abstract
Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting [...] Read more.
Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article
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37 pages, 2287 KiB  
Article
Parameterised Quantum SVM with Data-Driven Entanglement for Zero-Day Exploit Detection
by Steven Jabulani Nhlapo, Elodie Ngoie Mutombo and Mike Nkongolo Wa Nkongolo
Computers 2025, 14(8), 331; https://doi.org/10.3390/computers14080331 - 15 Aug 2025
Abstract
Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. [...] Read more.
Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article
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32 pages, 425 KiB  
Article
Asymptotic Analysis of a Kernel-Type Estimator for Parabolic Stochastic Partial Differential Equations Driven by Cylindrical Sub-Fractional Brownian Motion
by Abdelmalik Keddi, Salim Bouzebda and Fethi Madani
Mathematics 2025, 13(16), 2627; https://doi.org/10.3390/math13162627 - 15 Aug 2025
Abstract
The main purpose of the present paper is to investigate the problem of estimating the time-varying coefficient in a stochastic parabolic equation driven by a sub-fractional Brownian motion. More precisely, we introduce a kernel-type estimator for the time-varying coefficient θ(t) [...] Read more.
The main purpose of the present paper is to investigate the problem of estimating the time-varying coefficient in a stochastic parabolic equation driven by a sub-fractional Brownian motion. More precisely, we introduce a kernel-type estimator for the time-varying coefficient θ(t) in the following evolution equation:du(t,x)=(A0+θ(t)A1)u(t,x)dt+dξH(t,x),x[0,1],t(0,T],u(0,x)=u0(x), where ξH(t,x) is a cylindrical sub-fractional Brownian motion in L2[0,T]×[0,1], and A0+θ(t)A1 is a strongly elliptic differential operator. We obtain the asymptotic mean square error and the limiting distribution of the proposed estimator. These results are proved under some standard conditions on the kernel and some mild conditions on the model. Finally, we give an application for the confidence interval construction. Full article
(This article belongs to the Special Issue Partial Differential Equations in Applied Mathematics)
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