This paper proposes an adaptive Overcurrent (OC) relay protection scheme that combines load forecasting and clustering-based operating state identification to dynamically adjust relay settings. A Long Short-Term Memory (LSTM) model predicts feeder current, allowing the system to proactively select appropriate relay setting groups for different load conditions. The method is validated on the IEEE 33-bus distribution network. Results show that the forecasting model achieves an Mean Absolute Percentage Error (MAPE) of 2.94 %, while the relay settings derived from predicted loads closely match those obtained from actual data. The difference in operating time is negligible between forecast-based and actual coordination and a safety protection mechanism is employed to deal with the forecast or classification errors by switching to a safe backup protection group. These results demonstrate the effectiveness of the proposed approach in maintaining reliable protection performance under changing operating conditions through SCADA, and are activated using IEC 61,850 protocols and GOOSE-based group switching mechanisms. The time-domain analysis shows a worst-case timing deviation below 41 ms, well within the 300 ms Coordination Time Interval (CTI) requirement.
Seafood spoilage produces volatile basic compounds that can be detected from package headspace, but practical colorimetric freshness labels must remain stable and quantitative under the high-humidity conditions typical of seafood packaging. The purpose of this study was to develop a humidity-tolerant headspace freshness indicator and to evaluate whether its color response could be quantitatively translated into seafood shelf-life decisions. Pitaya-peel betacyanin was immobilized in a pectin–chitosan polyelectrolyte complex film to improve pigment retention, reduce humidity-induced baseline drift, and maintain sensitivity to volatile bases. The resulting label showed a monotonic color response to NH3 over the tested concentration range and retained a strong signal under near-saturated relative humidity. Compared with single-polymer films, the pectin–chitosan matrix reduced pigment leaching and improved color stability in wet headspace conditions. A two-step calibration workflow was established by converting color difference, ΔE, first to predicted headspace NH3 and then to total volatile basic nitrogen, TVB-N. In packaged whiteleg shrimp stored under refrigerated and retail-mimic temperature-abuse conditions, the label response closely followed the increases in TVB-N, total viable count, and pH during spoilage. Across 45 package–time points, TVB-N prediction achieved RMSE values of 2.13–2.24 mg N/100 g and R2 values of 0.947–0.960. Freshness classification into Fresh, Warning, and Spoiled categories reached an overall accuracy of 0.867 with Cohen's κ = 0.792. These results indicate that the PEC–betacyanin label can provide a humidity-resilient and quantitative tool for non-destructive seafood freshness monitoring and shelf-life decision support.
This paper presents an advanced stress-based topology optimization framework for designing multi-material auxetic periodic microstructures with consideration of local volume constraints. The recently developed partition-of-unity mapping scheme is utilized for description of multi-material interpolation. Based on homogenization, the effective components of elastic tensor are computed, which are necessary to impose the requirement on the (effective) negative Poisson's ratio. Controllable porosity of the microstructure is conducted by enforcement of local volume constraint for each material. For this purpose, either i) the control radius of the local zone (the zone for determination of local volume fraction surrounding an element e) is variable while the local volume fraction limit is fixed, or ii) the control radius is fixed while the local volume fraction limit is variable. A significant innovation of this work is the introduction of designable porosity, achieved through variable local volume constraints that adjust either the control radius of a local zone or the local volume fraction limit. Numerical investigations reveal that while controllable porosity can naturally reduce peak stress, it often results in a lower effective bulk modulus, making the structure softer. To overcome this trade-off, the authors propose a comprehensive optimization model that enforces separate stress constraints for each material phase-allowing for the use of distinct yield criteria like von Mises or Drucker-Prager-while simultaneously preserving structural stiffness through a bulk modulus constraint. In multi-material design, the stress measures are computed for each material, including the case of distinct yield criteria, and thus stress constraints are enforced separately. The performance of the developed approach is demonstrated via several numerical examples under different settings of the microstructures (e.g., initial configurations, requirement on the amount of materials, and requirement on variable local volume).
Quantum Neural Networks (QNNs) are emerging as promising tools for complex biological data analysis due to their ability to exploit quantum mechanics for enhanced computational power. In Sec. 2, we provide readers with a comprehensive overview of proteins, DNA sequencing, and recent updates on classical approaches to PTM prediction, protein function analysis and DNA analysis. Furthermore, we present recent findings that underscore the limitations of traditional methods and justify the need for quantum approaches. This section also highlights the unique advantages and potential benefits that quantum-based methods offer over classical techniques, paving the way for more powerful and accurate solutions in biological data analysis. These tasks are critical for understanding protein regulation, cellular processes and genetic information. In Sec. 3, we provide a comprehensive review of various QNN architectures that have been proposed to date, categorized based on current theoretical and practical developments. The models discussed include Quantum McCulloch-Pitts (M-P) Neural Networks, Quantum Competitive Neural Networks, Quantum-Inspired Neural Networks, Quantum Dot Neural Networks, Quantum Cellular Neural Networks and Quantum Associative Neural Networks. Each of these models offers unique computational mechanisms rooted in quantum principles such as superposition, entanglement and interference, enabling them to capture complex patterns in high-dimensional biological data. We further highlight their potential and emerging applications in bioinformatics, particularly in the prediction of protein Post-translational Modifications (PTMs). Given the critical role of PTMs in regulating protein activity and cellular functions, leveraging QNNs provides a promising avenue for improving prediction accuracy and interpretability in large-scale proteomic datasets. In Sec. 4, we present the latest advancements in the application of quantum machine learning in genomics, quantum computing for protein structure and function prediction, as well as quantum machine learning in drug discovery and molecular modeling. Section 5 discusses the current limitations of quantum hardware and the challenges in training quantum models. In Sec. 6, we propose potential improvements for quantum machine learning models in bioinformatics, including hybrid quantum-classical architectures and more efficient quantum circuit designs. Finally, Sec. 7 concludes the review by summarizing the key insights and outlining specific future research directions, particularly focusing on extending our recent findings into the broader field of quantum machine learning. Together with a system of illustrative figures presented in Secs. 2-4, as well as concrete examples of algorithms discussed in Sec. 4, this review provides a solid foundation for future applications of quantum machine learning in bioinformatics and highlights the transformative potential of quantum models in revolutionizing biological data analysis.
A multifunctional advanced material that integrates antibacterial activity, anti-inflammatory effects, and tissue regenerative capability represents a highly effective platform for the treatment of infected wounds. Among various candidates, in situ–forming hydrogels have gained significant attention owing to their favorable physicochemical characteristics and biological performance. Chromolaena odorata (CO) leaves, traditionally used for their antibacterial and wound healing effects, contain abundant phenolic and polyphenolic compounds that also enable them to function as effective reducing and capping agents for silver nanoparticle (AgNPs) synthesis. In this study, we developed a multifunctional thermoresponsive nanocomposite hydrogel by incorporating AgNPs and CO extract into a chitosan-based polymer matrix. This integration improved the mechanical robustness of the system and conferred multiple bioactivities, including antioxidant, antimicrobial, anti-inflammatory, and cell proliferative effects, while preserving its injectability and temperature-triggered gelation behavior. The hydrogel demonstrated enhanced proliferation of human fibroblasts, strong free-radical scavenging capacity, and potent antibacterial activity against both Staphylococcus aureus and Pseudomonas aeruginosa. These findings support the potential of this multifunctional hydrogel platform for further development and application in the treatment of infected skin wounds.
