Search Results (550)

Degradable polymers, such as poly(L-lactide) (PLLA), are widely investigated for biomedical applications, including drug delivery systems and temporary implants. Their functionality can be expanded by incorporating degradable metal microparticles that may influence degradation behaviour and enable additional surface modification strategies. In this study, the feasibility of composites consisting of PLLA and biodegradable iron microparticles was investigated. Composites were fabricated by solvent casting, providing a gentle alternative to thermal processing methods, which often compromise polymer integrity. Composites were evaluated by thermogravimetric analysis, differential scanning calorimetry, scanning electron microscopy (SEM), tensile testing, dynamic mechanical analysis, and X-ray photoelectron spectroscopy (XPS). Incorporation of iron altered thermal behaviour and crystallinity of PLLA, indicating interactions between polymer matrix and dispersed metal phase that may affect degradation kinetics and material stability. While iron addition reduced Young’s modulus, tensile strength, and elongation at break, composites maintained sufficient structural integrity for potential biomedical applications. XPS and SEM confirmed the embedding of particles within the polymer matrix, enabling potential post-processing approaches. In vitro direct contact and eluate tests demonstrated good cell viability, whereas exposure to free iron particles resulted in dose- and time-dependent cytotoxic effects. Overall, the results demonstrate the feasibility of solvent-cast PLLA–iron composites for resorbable biomedical applications. Full article

Sustainable nanotechnologies derived from renewable resources are increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics. Over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication. This work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are evidenced (feedstock origin vs. process sustainability) and how frequently translational readiness factors (scalability, quality control, regulatory alignment) are addressed. We critically compare renewable and conventional nanomaterial platforms across key translational dimensions, including carbon footprint, batch consistency, biodegradability, functional tunability, safety/persistence, and scale-up maturity. Finally, we delineate a practical translational pathway—from biomass sourcing and fractionation to nanoformulation, characterization/stability, and GMP scale-up—highlighting cross-cutting enablers such as lifecycle assessment, EHS/toxicology risk assessment, quality-by-design, and regulatory alignment. Collectively, the evidence supports renewable nanomaterials as viable, scalable candidates for next-generation therapeutics, provided that variability control, standardized characterization, and safety-by-design principles are embedded early in development. Full article

Remote sensing object detection (RSOD) models play an increasingly important role in modern remote sensing systems. However, during model delivery, sharing, and deployment, RSOD models face increasing risks of unauthorized redistribution, illegal replication, and intellectual property infringement. To mitigate these threats, this paper proposes a white-box watermarking framework for RSOD models that enables reliable copyright verification while preserving the performance of the primary detection task. Specifically, a gradient-based sensitivity analysis of the detection loss is first performed to adaptively identify model parameters that minimally affect detection performance, which are then selected as watermark carriers. Subsequently, a parameter-ranking-based watermark encoding scheme is developed, where watermark bits are embedded by enforcing relative ordering constraints between parameter pairs. To further improve robustness under practical deployment conditions, an attack-simulation-driven training strategy is introduced, in which common perturbations and watermark removal attacks are simulated during the embedding process. In addition, a stealthiness enhancement strategy based on statistical distribution constraints is designed to maintain consistency between the distribution of watermarked parameters and those of the original model, thereby reducing the risk of watermark exposure and localization. Extensive experiments across multiple RSOD datasets and detection architectures demonstrate that the proposed method achieves a high copyright verification success rate with negligible impact on detection accuracy and exhibits strong robustness and stealthiness against a variety of watermark removal attacks. Full article

Poor aqueous solubility remains a major limitation for the oral delivery of many active pharmaceutical ingredients (APIs). Deep eutectic solvents (DESs) exhibit remarkable drug-solubilization capacity, yet rapid precipitation upon aqueous dilution can compromise their ability to sustain supersaturation. This study investigates polymer-embedded DES (PEDES) systems as liquid supersaturating drug delivery platforms in which hydration and polymer chemistry jointly govern thermodynamic solubilization and kinetic stabilization. A choline chloride/DL-malic acid DES was prepared with 5% or 15% (w/w) water and combined with polyvinylpyrrolidone (PVP) or polyacrylic acid (PAA). Griseofulvin (GRF) was used as a precipitation-prone model drug. Structural characterization (ATR-FTIR, ¹H-NMR), equilibrium solubility measurements, storage stability studies, and non-sink dissolution testing were conducted to elucidate formulation behavior. The DES systems enhanced GRF solubility by up to ~59-fold relative to phosphate buffer (PBS, pH 6.8). Polymer incorporation produced hydration- and concentration-dependent effects. These results suggest the presence of competitive or cooperative interaction regimes. At 5% water, PEDES formulations failed to prevent recrystallization and showed limited supersaturation maintenance. In contrast, PEDES systems containing 15% water exhibited improved stability, with the formulation containing 4% PAA sustaining elevated drug concentrations for 120 min under non-sink conditions. Low-frequency solution-state ¹H-NMR confirmed stronger GRF–PAA interactions relative to PVP, supporting the role of polymer–drug association in supersaturation stabilization. These findings demonstrate that PEDES performance emerges from a hydration-dependent balance between solvent structuring and drug–polymer interactions, highlighting hydration and polymer functionality as key parameters for the rational design of liquid supersaturating systems. Full article

High noise levels in industrial workplaces pose significant challenges to occupational safety, particularly with hearing protection and effective communication. Traditional hearing protection devices, while effectively attenuating harmful noise, often compromise situational awareness by excessively isolating workers from the acoustic environment and preventing the perception of critical auditory cues (e.g., emergency alarms), thereby introducing additional safety risks. This paper presents a smart industrial safety system that integrates Internet of Things (IoT) and artificial intelligence (AI) and is based on intelligent hearing protection devices to (a) selectively attenuate hazardous industrial noise while (b) preserving human speech and (c) reproduce targeted audio notifications to workers near malfunctioning or hazardous machinery. A real-time voice activity detection (VAD) model is employed to distinguish vocal components from background noise to adaptively control digital signal processing filters. Furthermore, indoor localization enables the delivery of targeted audio messages to workers in proximity to relevant events. Experimental evaluations on embedded hardware demonstrate that the selected VAD model operates well within real-time constraints and effectively supports dynamic noise filtering. Objective evaluation of the filtering stage using Mean Opinion Score (MOS), signal-to-noise ratio (SNR), and Harmonics-to-Noise Ratio (HNR) shows consistent quality improvements across all tested conditions, with MOS gains up to +118%, SNR increases between +10.4 and +29.0 dB, and HNR improvements up to +6.22 dB, indicating enhanced speech intelligibility and preservation of voice harmonic structure even under high-noise scenarios. Robustness validation of the VAD module across varying acoustic conditions confirms reliable speech detection performance, achieving perfect classification at +10 dB SNR, very high accuracy at 0 dB (98.3%, ROC AUC 0.998), and stable operation even at $-7$ dB SNR (79.8% accuracy, ROC AUC 0.878). The proposed architecture achieves a balanced trade-off between hearing protection and speech intelligibility while enhancing the effectiveness of safety communications in noisy industrial environments. Full article

Urban last-mile logistics systems must improve service responsiveness while reducing environmental impact. While micromobility-based delivery fleets offer significant emission advantages compared to conventional vans, their operational efficiency depends on adaptive, data-driven capacity allocation. We develop and analyze a real-time optimization framework that explicitly integrates sustainability considerations into zone-level fleet allocation decisions. The continuous-time backlog dynamics admit a closed-form discrete-time prediction, enabling computationally efficient rolling-horizon fleet reallocation. Sustainability is explicitly embedded through zone-specific emission factors and a multi-criteria objective function balancing backlog reduction, environmental impact, and operational stability. In a ten-zone numerical case study with a fleet of 40 vehicles, the proposed method reduced backlog in all zones within a 15-min interval while preserving strict feasibility and stability (spectral radius is less than 1). The framework also demonstrated a controllable emission–service trade-off via sensitivity analysis. These results suggest the practical applicability and real-time suitability of the proposed Industry 4.0-aligned optimization approach. Full article

Any risks arising during the construction phase of nuclear power projects become permanently embedded in the power station’s lifecycle, evolving into inherent and difficult-to-alter potential hazards. Consequently, identifying behavioral risks in this phase is critical to the successful delivery of nuclear power engineering projects. This paper proposes a behavior risk identification and early warning methodology for nuclear power construction operations based on artificial intelligence algorithms. The research employs text mining techniques to construct a risk indicator system for nuclear power construction operations; based on the You Only Look Once (YOLOv8) algorithm, it incorporates modules such as Deformable Convolutional Network (DCN), Generalized Lightweight Attention Network (GELAN), Efficient Channel Attention (ECA), and Atrous Spatial Pyramid Pooling (ASPP) to develop the DCN -GELAN-ECA- ASPP-YOLO for Nuclear Power Engineering (DGEAYoLo-NPE) model, and designs and develops a supporting behavior risk identification and early warning methodology. Results show that the precision of nuclear power construction behavioral risk detection reaches 94.3%, with a 2.2% improvement in precision. This study confirms that artificial intelligence technology can effectively enhance the behavior risk prevention and control capabilities of nuclear power construction operations. Full article

Student employment has become an increasingly common feature of higher education, yet research on international students has predominantly approached paid work as an economic necessity. This article examines student employment as a culturally embedded social practice among international students in Hungary, focusing on employment patterns and cultural value orientations. The study applies a mixed-methods design, combining a focus group interview with an online questionnaire survey conducted among international students at a Hungarian university (N = 61). Cultural value orientations were measured using Hofstede’s Values Survey Module, and differences between working and non-working students were analyzed using inferential statistical methods. The results show that international students’ employment is dominated by flexible, low-entry-threshold jobs, particularly platform-based delivery work, while study-related or professional positions remain less common and are associated with higher income levels. Employment participation was significantly related to gender and academic year, with male students and those in higher years of study being more likely to work. Regarding cultural value orientations, a statistically significant difference between working and non-working students emerged only along the masculinity–femininity dimension, with working students displaying more performance-oriented values. The findings highlight that international student employment is associated with both structural constraints and culturally grounded value orientations. Full article

Urban short-haul cold-chain distribution operates under strict service constraints while facing increasing pressure to reduce carbon emissions under the dual-carbon goals. Existing emission-aware routing studies often treat carbon emissions as external constraints or ex post evaluation indicators, limiting their influence on operational decision making. This study addresses this gap by developing a cold-chain distribution network optimization model that integrates internal carbon pricing (ICP), enabling carbon emissions to be internalized as economic costs within routing and scheduling decisions. Using the student milk cold-chain distribution system serving 54 primary and secondary schools in Fuzhou as an empirical case, the model incorporates multiple cost components, including energy consumption, warehouse operation, carbon emissions, and low-load penalties, while embedding operational constraints such as vehicle capacity, delivery time windows, and minimum economic loading requirements. An improved genetic algorithm is applied to solve the model. Scenario analyses are conducted across carbon price variation and demand fluctuation. Results show that when the internal carbon price increases from 97.49 RMB/t to 2000 RMB/t, the total distribution cost rises from 3531.2 RMB to 4082.842 RMB, indicating that carbon costs become an increasingly important factor in operational decision making. The distribution network exhibits a core-route-dominated structure, with key routes remaining stable across carbon price scenarios, suggesting that the influence of ICP is primarily reflected through cost internalization rather than route substitution. Demand analysis further shows that a 10% demand reduction reduces costs through route consolidation, while a 20% reduction weakens load efficiency and reduces vehicle utilization without triggering low-load penalty costs. These findings demonstrate that integrating ICP into routing optimization provides an effective pathway for aligning operational decisions with low-carbon transition objectives in rigid-demand cold-chain distribution systems. Full article

Deaf and Hard-of-Hearing (DHH) students face persistent barriers in geometry education due to instructional approaches that inadequately support visual communication and embodied learning. This study examined DHH students’ experiences with GeoMETriA, a metaverse-based geometry learning platform integrating sign language instruction, three-dimensional visualization, and avatar-mediated interaction. Guided by the Cognitive Affective Model of Immersive Learning (CAMIL), a multi-phase qualitative design was employed, including pre-workshop interviews with four special education teachers and post-workshop focus group discussions with seven DHH secondary students following a four-session learning workshop. The findings indicate that gamified activities and peer collaboration enhanced interest and sustained engagement, while avatar customization supported embodiment and a sense of presence. Students described progression from initial uncertainty to greater confidence through practice and scaffolded support. However, cognitive and usability challenges emerged, particularly concerning sign language video pacing, navigation complexity, and limited instructional scaffolding. The study contributes theoretically by extending CAMIL-informed interpretations to sign-supported metaverse learning, empirically by documenting how engagement, embodiment, and self-efficacy develop during immersive geometry learning, and practically by offering design implications including adjustable sign language delivery, structured scaffolding, and culturally responsive avatar options. These findings suggest that metaverse-based platforms hold promise for supporting DHH learners when accessibility and learner-centered principles are embedded as foundational design considerations. Full article

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article

Antrodia cinnamomea is a rare medicinal and edible macrofungus, and its triterpenoids (ACT, A. cinnamomea triterpenoids) exhibit notable hepatoprotective, antioxidant, anticancer, and immunomodulatory activities. However, their poor aqueous solubility and low dispersibility in aqueous media have limited their practical applications. In this study, the conditions for ultrasonic treatment and xylo-oligosaccharide (XOS)-mediated glycation for soy protein isolate (SPI) were optimized; ACT was then encapsulated into the modified SPI carrier to prepare XOS-SPI-ACT nanoparticles. The delivery system was systematically characterized in terms of encapsulation efficiency (74.22 ± 2.15)%, drug-loading capacity (71.19 ± 4.67)%, storage stability, thermal stability, Fourier transform infrared (FTIR) spectroscopy, UV fluorescence spectroscopy, circular dichroism (CD) spectroscopy, and surface morphological features. The results showed that ACT was effectively embedded in XOS-SPI to form a stable complex with excellent thermal stability and favorable storage stability over a 28-day period. The in vitro antioxidant activities of XOS-SPI-ACT, XOS-SPI, and free ACT were comparatively evaluated. XOS-SPI-ACT exhibited significantly higher scavenging capacities against DPPH radicals, ABTS radicals, hydroxyl radicals, and superoxide anions, as well as higher FRAP values (94%, 74%, 75%, 68%, and 2 mmol/g), compared with free ACT (48%, 17%, 21%, 32%, and 1 mmol/g). Furthermore, XOS-SPI-ACT effectively inhibited lipid peroxidation in the β-carotene/linoleic acid oxidation model, with an overall antioxidant performance of 72%, markedly higher than the 20% of free ACT. This study effectively improves the aqueous solubility and dispersibility of ACT, expands their application potential, and provides a foundation for developing ACT-based natural antioxidants and functional foods. Full article

The COVID-19 pandemic exposed large cross-country differences in mortality that cannot be explained by short-run policy responses alone. This study investigates how pre-pandemic electrification and human capital jointly shaped pandemic outcomes, emphasizing their potential non-linear complementarity. Using cross-country data and pre-pandemic averages of electricity access and schooling, we examine how long-run development conditions influenced COVID-19 mortality during 2020–2021. We estimate fixed-effects models and a threshold regression framework that allows the interaction between electrification and human capital to vary across infrastructure regimes. The results identify a sharp electrification threshold at approximately 96 percent. Below this threshold, higher levels of schooling are not associated with lower pandemic mortality and may even coincide with increased vulnerability, consistent with binding infrastructure constraints that prevent human capital from being effectively deployed during a health crisis. Above the threshold, the interaction between electrification and schooling becomes statistically insignificant, indicating that in highly electrified economies, the benefits of human capital are already embedded within integrated systems of healthcare delivery, communication, and public health governance. These findings reveal a non-linear complementarity between infrastructure and human capital. Education alone does not enhance pandemic resilience when basic infrastructure remains incomplete, while in near-universally electrified societies its protective role is largely internalized. The results highlight the importance of long-run infrastructure completion as a structural prerequisite for translating human capital into effective pandemic preparedness and resilience. Full article

Ideological and political education (IPE) is a cornerstone of higher education in China. As IPE-related book collections expand rapidly, university libraries face a growing challenge of information overload, which hinders the accurate characterization of student reading preferences and the efficient matching of resources to demand. To address these issues, this study proposes GDNN, a practical hybrid recommendation system designed for both warm-start and cold-start scenarios. For warm-start users with historical borrowing records, we develop the PPSM-GCN framework. This framework enhances the classical graph convolutional collaborative filtering model LightGCN by integrating a novel potential positive sample mining (PPSM) strategy, which effectively mitigates data sparsity and improves the modeling of latent interests. For cold-start users without interaction history, we introduce an embedding and MLP architecture. This deep neural network learns implicit reader–book associations from reader attributes and book metadata, enabling personalized recommendations even in the absence of historical data. Experimental results demonstrate that PPSM-GCN and the embedding and MLP method achieve significant performance gains in their respective scenarios. This research provides both technical support and practical insights for the precise delivery of IPE resources and the overall enhancement of educational effectiveness in higher education. Full article

Digitalization is increasingly reshaping business models, yet the mechanisms through which specific digital technologies influence business model transformation in renewable energy remain insufficiently understood. Unlike prior research that treats digitalization and business models separately or focuses on macro-level impacts, this study examines how digital technologies affect business model components—value creation, value delivery, and value capture—in renewable energy firms and the extent to which they drive business model adaptation, evolution, or innovation. It aims to combine insights from the literature on digitalization, sustainability, and business models. Through a systematic literature review following the four-phase PRISMA methodology, 32 peer-reviewed studies were analyzed using a combination of descriptive, bibliometric, and Gioia-based thematic coding analyses to identify structures and patterns across the dataset. The analysis introduces a functional grouping perspective, linking digital technologies to business model components, and business model changes. Findings reveal that the same technology can enable multiple, overlapping transformation pathways and that outcomes vary depending on how technologies are implemented and embedded within firm operations. This study contributes theoretically by integrating a functional technology lens and sustainability lens with business model change typologies—a novel integrative framework absent from the prior literature. It practically provides a framework to help renewable energy firms move toward sustainability-oriented reconfiguration of business models by prioritizing and integrating digital tools effectively, thereby enhancing competitive advantage and accelerating value capture from digitalization. This paper closes with directions for future research on technology-enabled business model change. Full article