Search Results (18,907)

Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitatively integrate subjective aesthetic evaluation with objective sustainability indicators such as energy consumption, carbon emissions, and material recyclability. To address this challenge, this study proposes a semantic-guided multi-objective optimization framework for sustainable design that integrates cross-modal aesthetic evaluation with life cycle environmental performance assessment. The proposed framework employs a Contrastive Language–Image Pre-training (CLIP)-based semantic evaluation mechanism to translate abstract sustainability and aesthetic concepts into quantifiable design features, enabling consistent assessment across diverse design solutions. These semantic features are further optimized using a multi-objective evolutionary optimization strategy to simultaneously minimize energy consumption and carbon emissions while maximizing material recovery and design quality. Life cycle environmental indicators derived from OpenLCA datasets are incorporated into the optimization process to ensure practical sustainability relevance. The experimental results demonstrate that the proposed framework achieves a superior performance compared with benchmark optimization methods. Specifically, carbon emission equivalents are reduced to as low as 12.3 kg CO₂e, material recovery rates exceed 92%, and total computational energy consumption is reduced by more than 40% relative to comparative models. In addition, the framework shows strong stability and convergence efficiency while maintaining a high aesthetic evaluation accuracy in high-quality design ranges. The findings indicate that the proposed approach provides an effective pathway for integrating aesthetic value with environmental responsibility in sustainable design practice. This framework supports low-carbon and resource-efficient product development and offers practical insights for sustainable manufacturing, circular design, and environmentally conscious innovation. Full article

Urban renewal is essentially a process of redefining land property rights, restructuring land use functions and redistributing land value increment, which is of great significance for improving the efficiency of land resource allocation and realizing sustainable land management. This study investigates the urban renewal practice of 21 pilot cities in China, and focuses on the policy frameworks, implementation models and financing mechanisms of urban renewal in four first-tier cities, Beijing, Shanghai, Guangzhou and Shenzhen, through comparative analysis of policy documents and typical case studies. The results show that: (1) the current system for revitalizing land value through urban renewal remains exploratory in China, and the top-level design of land-related systems requires improvement; (2) there are obvious differences in land value distribution mechanisms under different renewal models, and the multi-stakeholder collaborative value sharing mechanism is insufficient; (3) the single financing model leads to blocked land value realization paths, and it is difficult to balance investment and return. Based on the findings, this study proposes targeted optimization paths for sustainable land value revitalization in urban renewal, which provides empirical evidence for land policy innovation and land resource value realization. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

Technological innovation is changing virtually every aspect of business practices and operational procedures. The introduction of large language models and various types of intelligent processing, commonly referred to as artificial intelligence, presents significant change to cope with. In this paper, we suggest an estimation method, based on real options analysis (ROA), that improves the assessment and valuation of intelligent data processing’s impact on organizations. The presented approach can reflect direct and indirect effects from introducing artificial intelligence methods and is therefore better suited than traditional financial metrics for the assessment of contemporary intelligent tools and solutions. Using Monte Carlo simulation and American-style real options, we have estimated two sample use cases to compare the ROA results against other common valuation methods. Numerical experiments indicate that the suggested approach is capable of capturing both the direct and indirect impact of new technologies, which improves relevant financial and management decisions. Full article

Adaptive reuse of industrial heritage from a tourism perspective presents a complex design challenge requiring a balance between heritage preservation, functional innovation, and diverse stakeholder expectations. However, current practices often face issues such as ambiguous demand interpretation and a disconnect between design generation and systematic evaluation. Addressing these limitations, this paper proposes and illustrates a human–machine collaborative design paradigm that integrates generative AI into a closed-loop process of “demand analysis–intelligent generation–comprehensive evaluation.” The method first employs Kansei Engineering and the KANO model to qualitatively extract and quantitatively prioritise heterogeneous user needs, translating subjective perceptions into structured design constraints and optimisation objectives. Next, these needs are encoded as text prompts to drive targeted spatial exploration by the generative AI tool Nano Banana AI. Finally, the TOPSIS method is applied for multi-criteria performance evaluation and solution selection. A case study of Shanghai Libo Brewery suggests that this paradigm can enhance design efficiency and show potential to outperform traditional methods across dimensions such as historical preservation, public accessibility, ecological integration, social inclusivity, and formal innovation. The research offers a quantifiable and systematically documented intelligent design methodology for industrial heritage renewal, while acknowledging the exploratory nature of the generative phase. Furthermore, it provides a visitor-demand-driven innovation pathway for developing industrial heritage tourism destinations, thereby potentially enhancing cultural experiences and tourism appeal at heritage sites. This research illustrates a move from an experience-driven paradigm toward a data- and value-driven approach, contributing theoretical methodologies to the intersection of cultural tourism and artificial intelligence. Full article

Generative artificial intelligence (AI) has demonstrated significant potential in education, yet empirical research on its application in “hardware-driven” interdisciplinary design courses remains scarce. This study employed a randomized crossover experimental design in an IoT Hardware and Design Innovation course at Hunan University. Twelve industrial design undergraduates with no prior IoT background alternated between AI-assisted (ChatGPT-4o) and traditional learning resource conditions across six short-cycle tasks. The crossover design enabled each participant to serve as both experimental and control subjects, yielding 72 observation-level data points. Grounded in Cognitive Load Theory, the study examined three dimensions: process efficacy, difficulty structure, and switching adaptation costs. Results indicated that AI significantly improved perceived task completion efficiency, self-reported goal attainment, and learning experience, yet self-assessed knowledge transfer did not differ significantly between conditions. AI reduced the total number of reported difficulties but altered the difficulty-type distribution: resource-retrieval difficulties decreased while information-verification difficulties increased—a phenomenon we term “difficulty restructuring”. Furthermore, switching from AI back to traditional resources incurred significantly higher adaptation costs than the reverse transition, revealing emerging dependency risks. These findings suggest that generative AI may function more as a “difficulty restructurer” than a “difficulty eliminator” in hardware-driven design education, providing exploratory empirical evidence for incorporating verification literacy into future course design and calling for calibrated scaffold fading that may help mitigate emerging dependency risks. Full article

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article

Understanding the sustainable management of the complex adaptive tourism systems requires an integrated research approach that combines environmental processes with stakeholder behaviors. Agent-based modelling (ABM) has emerged as a pivotal tool for decoding the resilience, adaptability, and sustainability of tourism systems. However, the current application landscape, methodological limitations, and future research directions of ABM remain insufficiently synthesized, thereby constraining its full potential in advancing sustainable tourism management. This study examines 137 publications on the application of ABM in tourism research between 1989 and 2025, aiming to clarify the application characteristics and evolutionary trajectories. The results show the following: (1) ABM applications in tourism have become increasingly comprehensive and refined, evolving from simplistic simulations based on simplex agents and static spatial representations toward integrated models incorporating heterogeneous agents, fine-grained spatial environments, and multiple contextual factors. (2) Behavioral modeling has progressed from basic human–space interactions to complex, co-evolutionary dynamics among human, social, and ecological systems. (3) ABM applications exhibit context specificity: climate-sensitive scenarios emphasize resource dynamics and adaptation strategies; disaster-prone contexts focus on multi-agent responses and emergency management; conservation-oriented systems support sustainable policy development; and management-centric scenarios prioritize technological innovation and macro-level regulation. Future research should prioritize refining agent interactions through dynamic social network integration, incorporating cross-scale and long-distance system linkages, and strengthening the connection between theoretical modeling and real-world applications. This study would provide a comprehensive knowledge base for advancing the innovative application of ABM in sustainable tourism research and contribute to strengthening resilience, adaptive governance, and long-term sustainability within complex tourism systems. Full article

Arsenic contamination in groundwater is a severe and widespread environmental and public health challenge. Recent years have witnessed rapid advances in catalytic remediation technologies, particularly those integrating advanced oxidation processes (AOPs), bifunctional materials, and field-scale applications. This comprehensive review synthesizes recent developments, emphasizing the synergy between catalytic oxidation and adsorption, the design of innovative and recyclable materials, and the practical translation of laboratory findings to real-world remediation scenarios. Key breakthroughs include dual-function catalysts for combined contaminant removal, scalable systems compatible with renewable energy, and hybrid strategies integrating conventional and catalytic routes. Case studies from arsenic hotspots worldwide demonstrate not only technological feasibility but also highlight knowledge gaps and sustainability challenges. By evaluating catalytic mechanisms, operational performance, and environmental impact, this review identifies promising directions for the next generation of arsenic remediation and offers a critical roadmap to guide future research and practice. Full article

Sulfur-mediated autotrophic denitrification (SAD) is an innovative and sustainable water treatment technology, which operates without an external carbon source and achieves lower sludge production. Firstly, this review provides a detailed examination of sulfur-based fillers, encompassing their respective types, preparation methods, advantages and drawbacks. Subsequently, it reviews the mainstream functional microbial communities across various process stages, such as Thiobacillus, Sulfurimonas, and Ignavibacterium. Moreover, the process characteristics of mainstream SAD reactor types, such as fluidized bed, fixed bed, and moving bed biofilm reactors, are reviewed, and the effects of key process parameters like pH, temperature, and dissolved oxygen on treatment efficiencies are further analyzed. Additionally, the applications cases of SAD in advanced wastewater treatment, river remediation, wetland restoration, and groundwater purification are summarized, demonstrating its broad and diverse application potential in environmental engineering. Finally, key challenges of SAD are identified, including the complexity of microbial metabolic interactions, the accumulation of intermediate products, and the need for improved fillers and reactor configurations. Future research priorities are discussed in three areas: microbial community regulation, control and utilization of intermediate products, and development of advanced fillers and reactor configurations. Overall, this review integrates key technical parameters and operational experience of SAD, providing a consolidated reference for researchers and practitioners interested in the development and application of this technology. Full article

This study reveals the internal mechanism of the synergetic evolution of financial support, technological innovation, and industrial upgrading from the perspective of system synergy. It aims to provide a theoretical basis and reference for promoting benign interactions among these elements, thereby driving high-quality economic development. During the research process, an evaluation indicator system was constructed based on China’s industrial development data, utilizing the entropy method to determine indicator weights and the Haken model to analyze synergy effects. In a methodological innovation, this study identifies the system’s order parameters to derive the potential function. Through this approach, it systematically analyzes the dynamic evolution characteristics and synergetic mechanisms of the composite system. The research results indicate that the three systems have formed a mutually promoting and closely coupled compound synergetic mechanism, rather than following a single linear transmission path. The overall synergy level presents a medium-to-low development trend, following an asymmetric U-shaped evolution trajectory that first decreases and then slowly recovers. Furthermore, the degree of synergy exhibits an inverse relationship with the volatility of the subsystems, suggesting that the stability of synergy is highly susceptible to external forces and remains in a state of dynamic flux. Full article

Count time series often exhibit extremal dependence that may not be adequately captured by Gaussian copula models. We develop a likelihood-based framework for count-valued time series using Student-t copulas with latent ARMA dependence. The latent process is constructed through a scale-mixture representation of a Gaussian ARMA process, preserving the second-order dependence structure while introducing tail dependence and greater persistence of extreme events. Likelihood inference requires evaluating high-dimensional truncated multivariate t probabilities, which is computationally demanding under heavy tails and strong serial dependence. To address this challenge, we develop scalable likelihood approximations tailored to the time series structure. In particular, we formulate a time series version of minimax exponential tilting for multivariate t probabilities, termed Time Series Minimax Exponential Tilting (TMET), which exploits the exact conditional representation of the latent ARMA process. The resulting algorithm reduces computational complexity from cubic to near-linear in the series length while retaining the high accuracy of minimax exponential tilting. For comparison, we also extend two widely used Gaussian copula approximations—the continuous extension (CE) method and the Geweke–Hajivassiliou–Keane (GHK) simulator—to the Student-t copula setting. Simulation studies show that TMET outperforms CE and GHK, particularly under strong dependence, heavy tails, and low-count regimes. The framework also supports predictive inference and residual diagnostics. An application to weekly rotavirus counts illustrates how the Student-t copula provides a flexible extension of the Gaussian copula while retaining stable inference even when tail dependence is weak or absent. Full article

Carbon monoxide (CO) is a poisonous gas because it disrupts functional oxygen transport of red blood cell (RBC) by binding heme of hemoglobin with high affinity. Contrarily, endogenous CO, which is constantly generated in the process of heme degradation by heme oxygenase, functions as a gaseous mediator necessary for maintaining physiological homeostasis. This toxicological (Yin) and physiological (Yang) duality presents a distinctive problem in medical and pharmaceutical applications, prompting the central question of this review: How can strict control over CO’s exposure dynamics, magnitude, kinetics, and tissue context be achieved to enable its safe therapeutic use? Here, we integrate the Yin and Yang of CO through an innovative exposure-engineering framework, leveraging the inherent RBC characteristics to offer a novel conceptualization for therapeutic development. We highlight the role of native RBCs as a biologically grounded platform that can convert hemoglobin binding—classically viewed as the basis of CO toxicity—into a measurable and controllable buffering mechanism. Then, reconciling the Yin and Yang of CO based on RBCs enables medical and pharmaceutical modulation that is attractive for clinical situations, therapeutics and diagnostics. Finally, we discuss key translational challenges—local concentration control, patient-specific risk stratification, manufacturability and critical quality attributes, and regulatory positioning—and outline how quantifiable exposure control can enable the safe clinical development of RBC-based CO therapy. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article