Search Results (20,816)

This study discusses the role of organizational pro-environmental behavior in driving sustainable development. Studies of green practices highlight their capacity to achieve ecological goals while delivering economic sustainability with business strategies for sustainable businesses and advancing environmental sustainability law. It also considers how the development of artificial intelligence, resource management, big data analysis, blockchain, and the Internet of Things enables companies to maximize supply efficiency and address evolving environmental regulations and sustainable decision-making. Through digital technology, businesses can facilitate supply chain transparency, adopt circular economy practices, and produce in an equitable and environmentally friendly manner. Additionally, intelligent business management practices, such as effective decision-making and sustainability reporting, enhance compliance with authorities while ensuring long-term profitability from a legal perspective. Integrating business innovation and digital technology within legal entities enhances economic efficiency, reduces operational costs, improves environmental sustainability, reduces paper usage, and lowers the carbon footprint, creating a double-benefit model of long-term resilience. The policymakers’ role in formulating policy structures that lead to green digital innovation is also to ensure that economic development worldwide is harmonized with environmental protection and international governance. Using example studies and empirical research raises awareness about best practices in technology-based sustainability initiatives across industries and nations, aligning with the United Nations Sustainable Development Goals. Full article

With the gradual development of digital twin technology from theory to practice, the importance of the efficient reuse of existing digital twin models has become increasingly prominent in order to reduce the waste of resources and additional costs caused by repeated modeling. To address the difficulty of reusing multi-dimensional model information (MMI) in existing digital twin models during the conversion process from geometric models to digital twin models, this paper proposes a method for reusing MMI in discrete workshops. First, MMI and its representations are defined and constructed. Subsequently, a model-matching approach is introduced to identify appropriate MMIs for geometric models. Following this, a reuse strategy for workshop MMIs is thoroughly explained. Finally, the effectiveness of the proposed method is validated through case studies in the arc-welding workshop. The accuracy of single-model matching remains consistently at 1 across all model tests, and the proposed method reduces the total number of operations by 126 (94.7%) compared to existing methods in multi-device model construction. The results show that this method can effectively organize the workshop digital twin model, compensate for the shortage of digital twin model reuse, and help engineers reuse the existing MMI to build a digital twin model. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

The energy sector uses artificial intelligence (AI) as a crucial instrument to achieve environmental sustainability targets by improving resource efficiency and decreasing emissions while minimizing waste production. This paper establishes an industry-specific executive playbook that guides energy sector leaders by implementing AI technologies for sustainability management with approaches suitable for industrial needs. The playbook provides an industry-specific framework along with strategies and AI-based solutions to help organizations overcome their sustainability challenges. Predictive analytics combined with smart grid management implemented through AI applications produced 15% less energy waste and reduced carbon emissions by 20% according to industry pilot project data. AI has proven its transformative capabilities by optimizing energy consumption while detecting inefficiencies to create both operational improvements and cost savings. The real-time monitoring capabilities of AI systems help companies meet strict environmental regulations and international climate goals by optimizing resource use and waste reduction, supporting circular economy practices for sustainable operations and enduring profitability. Leaders can establish impactful technology-based sustainability initiatives through the playbook which addresses the energy sector requirements for corporate goals and regulatory standards. Full article

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital twins can enhance progress toward the goals the circular economy sets to identify inefficiencies, predict maintenance needs, and optimize the use of resources. This integration is a promising industry approach that will introduce more sustainable operations and maintain economic viability. Full article

The urgent demand for sustainable energy solutions in the face of climate change and resource depletion has catalyzed a global shift toward cleaner energy production and more efficient storage technologies. Lithium-ion batteries (LIBs), as the cornerstone of modern portable electronics, electric vehicles, and grid-scale storage systems, are continually evolving to meet the growing performance requirements. In this dynamic context, two-dimensional (2D) materials have emerged as highly promising candidates for use in electrodes due to their layered structure, tunable electronic properties, and high theoretical capacity. Among 2D materials, molybdenum disulfide (MoS₂) has gained increasing attention as a promising low-dimensional candidate for LIB anode applications. This review provides a comprehensive yet concise overview of recent advances in the application of MoS₂ in LIB electrodes, with particular attention to its unique electrochemical behavior at the nanoscale. We critically examine the interplay between structural features, charge-storage mechanisms, and performance metrics—chiefly the specific capacity, rate capability, and cycling stability. Furthermore, we discuss current challenges, primarily poor intrinsic conductivity and volume fluctuations, and highlight innovative strategies aimed at overcoming these limitations, such as through nanostructuring, composite formation, and surface engineering. By shedding light on the opportunities and hurdles in this rapidly progressing field, this work offers a forward-looking perspective on the role of MoS₂ in the next generation of high-performance LIBs. Full article

In Europe, corporate sustainability reporting through the double materiality assessment was formally introduced with the Corporate Sustainability Reporting Directive in response to the European Sustainability Reporting Standards. The double materiality assessment is essential not only to determine the scope of corporate sustainability reporting but also to guide companies toward an efficient allocation of resources and shape corporate sustainability strategies. However, although EFRAG represents the technical adviser of the European Commission, there are numerous “interoperable” standards related to the assessment of double materiality, including the Global Reporting Initiative (GRI), or UNI 11919-1:2023. This research intends to systematically analyze similarities and divergences between the most widespread double materiality assessment standards at the global scale, highlighting their strengths and weaknesses and trying to identify a comparable path toward the creation of a set of common guidelines. This analysis is carried out through the systematic study of seven standards and by answering nine questions ranging from generic ones, such as “what is the concept of double materiality?”, to more technical questions like “does the standard identify thresholds?”, but adding original prospects such as “does the standard refer to different types of capital?”. Findings highlight that EFRAG, UNI 11919-1:2023, and GRI represent the most complete and least-discretionary standards, but some methodological aspects need to be enhanced. In the double materiality assessment, companies must identify key stakeholders, material topics and material risks, and must develop the double materiality matrix, promoting transparent disclosure, continuous monitoring, and stakeholders’ engagement. While comparability is principally required among companies operating within the same sector and of similar size, this does not preclude the possibility of comparing firms across different sectors with respect to specific indicators, when appropriate or necessary. Full article

(1) Background: The provision of monofunctional or inadequately distributed services in urban park green spaces often constrains residents’ opportunities and diversity for outdoor activities, particularly limiting access and participation for specific age groups or activity preferences. However, functional nodes with temporal and spatial flexibility demonstrate high-quality characteristics of resilient and shared services through integrated development. Accurately identifying user demand provides a solid basis for optimizing the functional configuration of urban parks. (2) Methods: This study took the old city area of Zhengzhou, Henan Province, China, as a case study. By collecting and integrating various types of data, such as geographic spatial data, field investigation data, and behavioral observations, we developed a population demand quantification method and a modular analysis approach for park service functions. This framework enabled correlation analysis between diverse user needs and park services. The study further classified and combined park functions into modular units, quantifying their elastic and shared service capabilities—namely, the adaptive flexibility and shared utilization capacity of park services. Additionally, we established a demand-responsive evaluation system for identifying and diagnosing problem areas in park services based on multi-source data. (3) Results: The demand response index and diagnostic results indicate that the supply of fitness facilities—particularly equipment-based installations—is insufficient within the old urban district of Zhengzhou. Among the three user groups—children, young and middle-aged adults, and the elderly—the elderly population exhibited the lowest demand response index, revealing a significant gap in meeting their specific needs. (4) Conclusions: Based on the research findings, a three-tier optimization strategy is proposed: A. improve green space connectivity to expand the service coverage of parks; B. implement multifunctional overlay and coordinated integration in spatial design based on site characteristics and demand diagnostics; and C. increase the total supply of facilities to enhance spatial efficiency in parks. By integrating the demand assessment data and diagnostic results, this approach enabled a data-driven reorganization of service types and targeted allocation of resources within existing park infrastructure, offering a practical tool and reference for the planning of urban outdoor activity spaces. Full article

Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Soilless cultivation technology is a key means of overcoming traditional agricultural resource limits, providing an important path to efficient and sustainable modern agriculture by precisely regulating crop rhizospheric environments. This paper systematically reviews the technical system of soilless cultivation, nutrient solution management strategies, the interaction mechanism of rhizosphere microorganisms, and future development directions, aiming to reveal its technical advantages and innovation potential. This review shows that solid and non-solid substrate cultivation improves resource utilization efficiency and yield, but substrate sustainability and technical cost need urgent attention. The dynamic regulation of nutrient solution and intelligent management can significantly enhance nutrient absorption efficiency. Rhizosphere microorganisms directly regulate crop health through nitrogen fixation, phosphorus solubilization, and pathogen antagonism. However, the community structure and functional stability of rhizosphere microorganisms in organic systems are prone to imbalance, requiring targeted optimization via synthetic biology methods. Future research should focus on the development of environmentally friendly substrates, the construction of intelligent environmental control systems, and microbiome engineering to promote the expansion of soilless cultivation towards low-carbon, precise, and spatial directions. This paper systematically references the theoretical improvements and practical innovations in soilless cultivation technology, facilitating its large-scale application in food security, ecological protection, and resource recycling. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

The 5G network’s commercialization has revealed challenges in providing customized and personalized deployment and services for diverse vertical industrial use cases, leading to high cost, low resource efficiency and management efficiency, and long time to market. Although the 5G core network (CN) has adopted a service-based architecture (SBA) to enhance agility and elasticity, the radio access network (RAN) keeps the traditional integrated and rigid architecture and suffers the difficulties of customizing and personalizing the functions and capabilities. Open RAN attempted to introduce cloudification, openness, and intelligence to RAN but faced limitations due to 5G RAN specifications. To address this, this paper analyzes the experience and insights from 5G SBA and conducts a systematic study on the service-based RAN, including service definition, interface protocol stacks, impact analysis on the air interface, radio capability exposure, and joint optimization with CN. Performance verification shows significant improvements of service-based user plane design in resource utilization and scalability. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. Full article