Search Results (549)

Wound care in military and combat environments poses distinct challenges that set it apart from conventional medical practice in civilian settings. The nature of injuries sustained on the battlefield—often complex, contaminated, and involving extensive tissue damage—combined with limited access to immediate medical intervention, significantly increases the risk of infection, delayed healing, and adverse outcomes. Traditional wound dressings frequently prove inadequate under such extreme conditions, as they have not been designed to address the specific physiological and logistical constraints present during armed conflicts. This review provides a comprehensive overview of recent progress in the development of advanced wound dressings tailored for use in military scenarios. Special attention has been given to multifunctional dressings that go beyond basic wound coverage by incorporating biologically active macromolecules such as collagen, chitosan, thrombin, alginate, therapeutic peptides, and growth factors. These compounds contribute to properties including moisture balance control, exudate absorption, microbial entrapment, and protection against secondary infection. This review highlights the critical role of advanced wound dressings in improving medical outcomes for injured military personnel. The potential of these technologies to reduce complications, enhance healing rates, and ultimately save lives underscores their growing importance in modern battlefield medicine. Full article

Achieving carbon emission reductions in the residential building sector while maintaining economic growth represents a global challenge, particularly in rapidly developing regions with internal disparities. This study examines Jiangsu Province in eastern China—a economic hub with north-south development gradients—to develop an integrated framework for differentiated carbon reduction pathways. The methodology combines spatial autocorrelation analysis, logarithmic mean Divisia index (LMDI) decomposition, system dynamics modeling, and Tapio decoupling analysis to examine urban residential building emissions across three regions from 2016–2022. Results reveal significant spatial clustering of emissions (Moran’s I peaking at 0.735), with energy consumption per unit area as the dominant driver across all regions (contributing 147.61%, 131.82%, and 147.57% respectively). Scenario analysis demonstrates that energy efficiency policies can reduce emissions by 10.1% while maintaining 99.2% of economic performance, enabling carbon peak achievement by 2030. However, less developed northern regions emerge as binding constraints, requiring technology investments. Decoupling analysis identifies region-specific optimal pathways: conventional development for advanced regions, balanced approaches for transitional areas, and subsidies for lagging regions. These findings challenge assumptions about environment-economy trade-offs and provide a replicable framework for designing differentiated climate policies in heterogeneous territories, offering insights for similar regions worldwide navigating the transition to sustainable development. Full article

Lactic acid (LA) is a high-value chemical with growing demand for the production of polymers and plastics and in the food and pharmaceutical industries. However, production costs remain a significant constraint when using conventional food-grade substrates. This study investigates Ivorian sugarcane molasses, an abundant agro-industrial by-product, as a low-cost carbon source for LA production via batch fermentation with Limosilactobacillus fermentum ATCC 9338. Molasses was pretreated by acid hydrolysis to improve fermentability, increasing glucose and fructose concentrations. Comparative fermentations using raw and pretreated molasses showed a 75% increase in LA production (32.4 ± 0.03 g/L) after pretreatment. Optimisation using Box–Behnken design revealed that the initial sugar concentration, inoculation rate, and stirring speed significantly influenced lactic acid production. Under optimal conditions, a maximum LA concentration of 52.4 ± 0.49 g/L was achieved with a yield of 0.95 g/g and productivity of 0.73 g/L·h. Kinetic analysis confirmed efficient sugar utilisation under the optimised conditions, and polarimetry revealed a near-racemic lactic acid. A simplified cost analysis showed that molasses could reduce carbon source costs by over 70% compared to refined sugars, supporting its economic viability. This work demonstrates the potential of pretreated molasses under robust fermentation conditions as a sustainable and cost-effective substrate for LA production in resource-limited contexts. The approach aligns with circular bioeconomy principles and presents a replicable model for decentralised bioproduction in a developing country like Côte d’Ivoire. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

Government support has long been viewed as a key driver of sustainable transformation and green technological progress. However, the underlying mechanisms (“how”) through which preferential policies influence green innovation, as well as the contextual conditions (“when”) that shape their effectiveness, remain insufficiently understood. Drawing on resource dependence theory, this study develops a dual-mediation framework to investigate how preferential tax policies promote both the quantity and quality of green innovation—by enhancing R&D investment as an internal mechanism and alleviating financing constraints as an external mechanism. These effects are especially salient among non-state-owned enterprises, firms in resource-constrained industries, and those situated in environmentally challenged regions—contexts that entail higher dependence on external support for sustainable development. Leveraging China’s 2017 R&D tax reduction policy as a quasi-natural experiment, this study uses a sample of high-tech small- and medium-sized enterprises (SMEs) to test the hypotheses. The findings provide robust evidence on how preferential policies contribute to corporate sustainability through green innovation and identify the conditions under which policy tools are most effective. This research offers important implications for designing targeted, sustainability-oriented innovation policies that support SMEs in transitioning toward more sustainable practices. Full article

Oyster aquaculture in the U.S. faces severe inefficiencies due to the absence of precise path planning tools, resulting in environmental degradation and resource waste. Current dredging techniques lack trajectory planning, often leading to redundant seabed disturbance and suboptimal shell distribution. Existing vessel models—such as the Nomoto or Dubins models—are not designed to map steering inputs directly to spatial coordinates, presenting a research gap in maneuver planning for underactuated boats. This research fills that gap by introducing a novel hybrid vessel kinetics model that integrates the Nomoto model with Dubins motion primitives. Our approach links steering inputs directly to the vessel motion, enabling Cartesian coordinate path generation without relying on intermediate variables like yaw velocity. Field trials in the Chesapeake Bay demonstrate consistent trajectory following performance across varied path complexities, with average offsets of 0.01 m, 1.35 m, and 0.42 m. This work represents a scalable, efficient step toward real-time, constraint-aware automation in oyster harvesting, with broader implications for sustainable aquaculture operations. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

In alignment with global educational trends, Australia has adopted a pluralistic approach to initial teacher education (ITE), encompassing traditional university-based programs, employment-integrated models and vocational training routes. This diversification of pathways has emerged as a strategic response to persistent workforce challenges, including chronic shortages, uneven distribution of qualified educators, and limited demographic diversity within the profession. Rather than supplanting conventional ITE models, these alternative pathways serve as complementary options, broadening access and enhancing system responsiveness to evolving societal and educational needs. The rise in non-traditional routes represents a deliberate response to the well-documented global teacher shortage, frequently examined in comparative educational research. Central to their design is a restructuring of traditional program elements, particularly duration and delivery methods, to facilitate more flexible and context-sensitive forms of teacher preparation. Such approaches often create opportunities for individuals who may be excluded from conventional pathways due to socioeconomic constraints, geographic isolation, or non-linear career trajectories. Significantly, the diversity introduced by alternative entry candidates has the potential to enrich school learning environments. These educators often bring a wide range of prior experiences, disciplinary knowledge, and cultural perspectives, contributing to more inclusive and representative teaching practices. The implications for student learning are substantial, particularly in disadvantaged communities where culturally and professionally diverse teachers may enhance engagement and academic outcomes. From a policy perspective, the development of flexible, multifaceted teacher education pathways constitutes a critical component of a sustainable workforce strategy. As demand for qualified teachers intensifies, especially in STEM disciplines and in rural, regional and remote areas, the role of alternative pathways is likely to become increasingly pivotal in achieving broader goals of equity, quality and innovation in teacher preparation. Full article

In response to growing environmental pressures and material constraints, circular economy principles are gaining traction across manufacturing sectors. However, most existing frameworks emphasize design and supply chain considerations, with limited focus on how circularity can be operationalized within internal manufacturing systems. This paper proposes a conceptual model that embeds circular operations at the core of production strategy. Grounded in circular economy theory, operations management, and socio-technical systems thinking, the model identifies four key operational pillars: circular input management, looping process and waste valorization, product-life extension, and reverse logistics. These are supported by enabling factors—digital infrastructure, organizational culture, and leadership—and mediated by operational flexibility, which facilitates adaptive, closed-loop performance. The model aims to align internal processes with long-term sustainability outcomes, specifically resource efficiency and operational resilience. Practical implications are outlined for resource-intensive industries such as automotive, electronics, and FMCG, along with a readiness assessment framework for guiding implementation. This study offers a pathway for future empirical research and policy development by integrating circular logic into the structural and behavioral dimensions of operations. The model contributes to advancing the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, by positioning circularity as a regenerative operational strategy rather than a peripheral initiative. Full article

This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. Recognizing that large row-crop tractors represent a significant capital investment—ranging from USD 0.4 to over 0.8 million for current-generation models—and that machinery costs constitute a substantial share of farm production expenses, this study addresses the urgent need for data-driven decision-making in agricultural enterprises. Utilizing consolidated OECD Code 2 tractor test data for all large row-crop John Deere tractors from the MFWD era to the latest generation, the study evaluates tractor performance across multiple productivity and efficiency indicators. The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. By enabling the optimization of productivity and efficiency while accounting for agronomic and timeliness constraints, this model supports sustainable and profitable management practices in modern agriculture. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

Water scarcity is a key constraint for commercial Eucalyptus plantations, particularly given the increasing frequency of droughts driven by climate change. This study assessed annual transpiration (Tr) and water use efficiency (WUE) across eight genotypes subjected to contrasting irrigation regimes (WR). A split-plot design was implemented, comprising two irrigation levels: high (maintained above 75% of field capacity) and low (approximately 25% above the permanent wilting point). The genotypes included Eucalyptus globulus (EgH, EgL), E. nitens × globulus (EngH, EngL), E. nitens (En), E. camaldulensis × globulus (Ecg), E. badjensis (Eb), and E. smithii (Es). Between stand ages of 7 and 9 years (2020–2023), we measured current annual increment (CAI), leaf area index (LAI), Tr, and WUE. Under high WR, CAI ranged from 8 to 36 m³ ha⁻¹ yr⁻¹, Tr from 520 to 910 mm yr⁻¹, and WUE from 0.7 to 2.9 kg m⁻³. Low irrigation reduced CAI by 5–25% and Tr by 10–35%, while WUE responses varied across genotypes, ranging from a 12% decrease to a 48% increase. Based on their functional responses, genotypes were grouped as follows: (i) stable performers (Es, Ecg, Eb) exhibited high WUE and consistent Tr under both WR; (ii) partially plastic genotypes (EgH, EngH) combined moderate reductions in Tr with improved WUE; and (iii) water-sensitive genotypes (EgL, EngL, En) showed substantial declines in Tr alongside variable WUE gains. These findings underscore the importance of selecting genotypes with adaptive water-use traits to improve the resilience and long-term sustainability of Eucalyptus plantations in Mediterranean environments. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article