Bio-based nanogenerators have emerged as promising power sources for next-generation self-powered wearable and biomedical systems; however, existing reviews largely focus on material catalogs or device demonstrations, with limited integration of structure-property relationships and sustainability metrics. This review presents an integrated and materials-centric analysis of biodegradable nanogenerators, with emphasis on the interdependence between molecular structure, crystallinity, dielectric properties, surface chemistry, and device-level performance. Advanced fabrication strategies, including electrospinning, interface modulation, additive manufacturing, and bio-waste valorization, are evaluated in terms of both performance enhancement and scalability. Sustainability is treated as a core design criterion rather than an afterthought, with integrated discussions on degradation kinetics, recyclability, life-cycle assessment, carbon footprint, and green synthesis routes using biomass-derived precursors and low-energy processing. The applicability of these principles is illustrated across a wide range of multidisciplinary domains, including smart textiles, wearable sensing platforms, implantable and therapeutic biomedical systems, and emerging intelligent technologies. By bridging materials design, device engineering, and circular-economy considerations, this review establishes a unified structure-property-performance-sustainability roadmap, providing actionable guidelines for the rational development of high-performance, eco-conscious bio-based nanogenerators.
A limit order book (LOB) queueing system is considered in which limit orders are generated by Markov modulated Poisson processes (MMPP) to capture the clustered nature of order arrivals. The queueing dynamics are represented by a multidimensional birth-death type Markov chain, and the probability distributions of the state variables are obtained through matrix computing procedures for Markov chains. By modifying the Markov chain structure and assigning selected states as absorbing states, two conditional probabilities relevant to high-frequency trading are computed: the probability of a midprice increase and the probability of order execution before a midprice change. Numerical results show that clustered MMPP arrivals substantially influence both probabilities, indicating that clustering in the limit order arrival process is an important factor in limit order book queueing models.
This paper investigates overfitting caused by overly idealized datasets that fail to capture real-world variability. Four common installation defects in underground cable terminations—improper overlapping of the stress control tube, voids within the insulation layer, carbon tracking on the insulation surface, and irregular edges of the outer semiconductive layer—were physically simulated. Three samples of each defect were fabricated to preserve inherent physical variations. Partial discharge (PD) signals were acquired and transformed into unipolar and bipolar phase-resolved PD (PRPD) patterns. A Residual Neural Network (ResNet-18) was employed for feature extraction and classification. By performing image similarity analysis to strategically allocate training datasets, the model effectively overcomes high intra-class variability, particularly in irregular edge defects. The results demonstrate that bipolar PRPD patterns provide superior diagnostic features compared to unipolar patterns, enabling the optimized ResNet model to improve classification accuracy from 69.81% to 96.50%. This study validates a highly robust, non-intrusive diagnostic framework suitable for practical condition monitoring of field-installed cable terminations.
High-performance concrete (HPC) is a low-carbon construction material that aligns with global sustainability goals focusing on climate change mitigation and reducing the carbon footprint of the construction industry. Because this industry contributes significantly to global CO2 emissions, developing sustainable alternative materials that balance environmental, economic, and performance requirements is critical to advancing green construction practices. However, existing studies lack comprehensive prediction models that effectively balance key decision-making factors, including cost, strength, and carbon emissions. To address this gap, this study develops an evolutionary deep learning model, ASOS-NN-BiGRU, which integrates Neural Networks (NN) and Bidirectional Gated Recurrent Units (BiGRU) to process independent and sequential data in HPC mixtures. The model is optimized using the Auto-tuning Symbiotic Organisms Search (ASOS) algorithm to enhance compressive strength prediction accuracy. The developed model is further deployed to optimize HPC mixture designs under three key scenarios: minimizing overall carbon emissions, identifying the most cost-effective mixture without carbon fees, and determining the most cost-effective mixture considering potential carbon fees. Additionally, the Multi-Objective Auto-tuning Symbiotic Organisms Search (MOASOS) algorithm is employed to identify optimal low-carbon HPC mixtures. By integrating carbon pricing mechanisms and multi-objective optimization, this research provides a practical framework for sustainable concrete production that supports both the transition of the construction industry toward low-carbon materials and the development and implementation of effective carbon taxation policies. Experimental results confirm the model’s robustness and reliability, enabling decision-makers to design HPC mixtures tailored to specific sustainability and cost preferences while ensuring structural performance.
While humor in leadership is typically beneficial, aggressive humor can have harmful effects. Despite the well-known negative impacts of leader aggressive humor (LAH), little is known about the antecedents of such behavior in the workplace. This study addresses this gap by utilizing self-enhancement theory to explain how excessive self-esteem may lead to negative outcomes. We propose that leader narcissism contributes to LAH and explore how target characteristics—specifically employee neuroticism—and workplace norms moderate this relationship. A two-wave survey was conducted with 375 full-time employees in Vietnam. The results indicated a positive correlation between leader narcissism and LAH. Furthermore, the relationship between leader narcissism and LAH was strengthened for employees with higher levels of neuroticism and a stronger perception that their leader accepts norm violations. Our findings contribute to the literature by identifying leader narcissism as a key antecedent of LAH, extending the application of self-enhancement theory to negative outcomes, and leveraging victim precipitation theory to explore the roles of target and contextual factors.
