Search Results (308)

The escalating climate crisis and global disruptions have prompted a critical re-evaluation of operations management within manufacturing and supply systems. This conceptual article addresses the theoretical and strategic gap in aligning resilience and sustainability by proposing an Integrated Sustainable Operational Strategy (ISOS) framework. Drawing on systems theory, circular economy principles, and sustainability science, the framework synthesizes multiple operational domains—circularity, localization, digital adaptation, and workforce flexibility—across macro (policy), meso (organizational), and micro (process) levels. This study constructs a conceptual model that explains the interdependencies and trade-offs among strategic operational responses in the Anthropocene era. Supported by multi-level logic and a synthesis of domain constructs, the model provides a foundation for empirical investigation and strategic planning. Key propositions for future research are developed, focusing on causal relationships and boundary conditions. The novelty of ISOS lies in its simultaneous integration of three strategic pillars—circularity, localization, and digital resilience—within a unified, multi-scalar architecture that bridges fragmented operational theories. The article advances theory by redefining operational excellence through regenerative logic and adaptive capacity, responding directly to SDG 9 (industry innovation), SDG 12 (responsible consumption and production), and SDG 13 (climate action). This integrative framework offers both theoretical insight and practical guidance for transforming operations into catalysts of sustainable transition. Full article

High-speed machines are attractive to many industries due to their small size and light weight, but present unique cooling challenges due to their increased loss and reduced surface area. Cooling system advancements are central to the development of faster, smaller machines, and as such, are constantly evolving. This paper presents a review of classical and state-of-the-art cooling systems. Each cooling method—air cooling, indirect liquid cooling, and direct liquid cooling—has potential use in cooling high-speed machines, but each comes with unique considerations, which are discussed. An example design process highlights the interdependence of the electromagnetic and thermal design choices, illustrating the necessity of integrating the electromagnetic and thermal designs in a holistic approach. Full article

In the gas industry, accurate forecasting of gas production is critical for optimizing well operating conditions. Although traditional hydrodynamic models offer high accuracy, they are often computationally intensive and time-consuming, prompting a growing interest in proxy-based alternatives. This study proposes a hybrid methodology based on Spatio-Temporal Graph Neural Networks (ST-GNNs) for gas production forecasting. The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. The model was validated using an extensive set of hydrodynamic simulation calculations and real-world field data. On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. The ST-GNN framework demonstrates promising potential as a tool for operational and strategic planning. Full article

Alkalihalophilus marmarensis is an obligate alkaliphile with exceptional tolerance to high-pH environments, making it a promising candidate for industrial bioprocesses that require contamination-resistant and extremophilic production platforms. However, its practical deployment is hindered by limited biomass formation under extreme conditions, which constrains overall productivity. This study presents a model-driven investigation of how pH (8.8 and 10.5), culture duration (24 and 48 h), and nitrogen source composition (peptone and meat extract) affect cell dry mass, lactate, and protease synthesis. Using the response surface methodology and multi-objective optimization, we established predictive models (R² up to 0.92) and uncovered key trade-offs in biomass and metabolite yields. Our findings reveal that peptone concentration critically shapes the metabolic output, with low levels inhibiting growth and high levels suppressing protease activity. Maximum cell dry mass (4.5 g/L), lactate (19.3 g/L), and protease activity (43.5 U/mL) were achieved under distinct conditions, highlighting the potential for targeted process tuning. While the model validation confirmed predictions for lactate, deviations in cell dry mass and protease outputs underscore the complexity of growth–product interdependencies under nutrient-limited regimes. This work delivers a foundational framework for developing fermentations with A. marmarensis and advancing its application in sustainable, high-pH industrial bioprocesses. The insights gained here can be further leveraged through synthetic biology and bioprocess engineering to fully exploit the metabolic potential of obligate alkaliphiles like A. marmarensis. Full article

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article

This paper presents the implementation of a modern approach for automatic measurement, data acquisition, and processing using custom-developed software based on the ARDUINO version: 2.3.6 platform. State-of-the-art sensing elements are employed for enhanced precision and reliability. The obtained results are graphically visualized for comprehensive analysis. The ARDUINO microcontroller and its associated open-source programming environment are primarily designed for general-purpose users rather than specialized industrial applications. The study focuses on the experimental investigation of the characteristics of a Peltier element. The interdependence between current, voltage, internal resistance, and temperature differential is examined in detail. The findings concerning efficiency analysis are intended to support students across various engineering disciplines during their educational process. Full article

In response to the growing prevalence of tariffs as instruments of economic policy and strategic competition, this paper introduces a formal mathematical framework for optimizing counter-tariff strategies. We model the global trade ecosystem as a multi-layered, directed, weighted hypergraph, where vertices represent countries, industries, and subindustries, and hyperedges capture complex trade relationships and supply chain dependencies. The proposed framework employs bilevel optimization techniques to maximize strategic impact on target economies while minimizing self-inflicted economic costs. Through integration of graph theory, spectral analysis, and multilevel optimization methods, we develop a rigorous formalism that enables policymakers to identify optimal counter-tariff portfolios under various constraints. Our model explicitly accounts for industrial interdependencies, where export competitiveness depends on imported inputs, thus providing a more realistic representation of global value chains. Case studies applying our model to historical trade disputes demonstrate its capacity to generate superior strategic outcomes compared to conventional approaches. The framework’s axiomatic foundation allows for rapid recalibration in response to shifting economic conditions and policy objectives. Full article

With the increasing frequency of extreme weather events, global concerns regarding climate change have intensified, with carbon dioxide widely recognized as the primary driver of global warming and climate disruption. It is necessary to investigate how to develop industries to meet the constant GDP growth and minimum carbon emissions. This study investigates the optimization of industrial structure under China’s ‘Dual Carbon’ Goal in Guangdong Province from 2012 to 2017, employing a multi-objective programming model. Using the input–output table, carbon emissions across 42 industries are calculated based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission factor method. According to Hirschman’s theory of industrial interdependence, the economic and carbon emission linkage coefficients between these industries are obtained by calculating the Ghosh inverse matrix and the Leontief inverse matrix to analyze the economic forward and backward linkage of the industries, as well as the carbon emission forward and backward linkage. The impact of industry input and output on the urban economy and the resulting carbon emission problems are discussed, and industries are divided into encouraged and restricted industries. Using a multi-objective programming model, the expected final demand, changes in final demand, and expected carbon emissions of these industries under the ‘Dual Carbon’ Goal, with the target of maintaining the same economic growth rate and promoting carbon reduction, are analyzed. The results show that most industries in Guangdong Province need to reduce final demand, including the highest carbon-emitting industries and industries that are relatively restricted by scale in development. The policy implications of optimizing the industrial structure to reduce carbon emissions are provided. Full article

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

Understanding how sectoral CO₂ emissions shape sustainable development outcomes is essential for designing effective energy and economic strategies within the European Union (EU). This study presents a multidimensional analysis of CO₂ emissions, the contributions of individual sectors, and their connections to the Sustainable Development Goals (SDGs). Using Bayesian network analysis, the research identifies significant interdependencies between emission reductions and progress in sustainable development, highlighting the complex relationship between energy transition, economic growth, and social justice. The findings show that total CO₂ emissions in the EU have decreased since 1990; however, the rate of reduction varies across sectors and member states. The most substantial decreases have been recorded in the energy sector, while industrial processes and agriculture show slower progress. Economic crises, such as the 2008 financial collapse and the COVID-19 pandemic, have led to temporary declines in emissions; however, lasting achievements in sustainability require structural transformations rather than short-term disruptions. The Bayesian model reveals strong connections between emission reductions and progress on clean energy (SDG 7), responsible consumption (SDG 12), and climate action (SDG 13), while also indicating indirect impacts on economic growth (SDG 8) and social equity. This highlights the importance of integrated policymaking to maximise the benefits of sustainable development. This study provides a data-driven foundation for enhancing EU climate strategies, ensuring that emission reductions support environmental goals, economic resilience, and social well-being. Full article

Efficient scheduling in industrial prefabrication environments—such as Prefabricated Bathroom Unit (PBU) production—faces increasing challenges due to resource limitations, overlapping projects, and complex task dependencies. To address these challenges, this paper presents a Learning-Enhanced Differential Evolution (LEDE) framework for solving the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The MRCMPSP models the operational difficulty of coordinating interdependent activities across multiple PBU projects under limited resource availability. To address the computational intractability of this NP-hard problem, we first formulate a mixed-integer linear programming (MILP) model, and then develop an adaptive DE-based metaheuristic. The proposed LEDE method co-evolves activity sequencing and mode assignment using floating-point encodings, incorporating strategy switching, parameter adaptation, elitism, stagnation handling, and rank-based crossover control. Evaluated on real-world production data from the PBU industry, the algorithm produces high-quality solutions with strong scalability. These results demonstrate its practical potential as a decision-support tool for dynamic, resource-constrained industrial scheduling. Full article

This research investigates the spillover effects between assets of the Fourth Industrial Revolution (4IR), focusing on the role of climate policy uncertainty in shaping these interactions. Using a time-varying parameter vector autoregressive (TVP-VAR) approach and a joint connectedness method, the analysis incorporates five global indices representing key 4IR domains: the internet, cybersecurity, artificial intelligence and robotics, fintech, and blockchain. The findings reveal significant interdependencies among 4IR assets and evaluate the effect of risk factors, including climate policy uncertainty, as a critical driver of the determinants of returns. The results indicate the growing impact of climate-related risks on the structure of connectedness between 4IR assets, highlighting their implications for portfolio diversification and risk management. These insights are vital for investors and policymakers navigating the intersection of technological innovation and environmental challenges in a rapidly changing global economy. Full article

Urbanization in African countries entails substantial growth in the urban population and economic development. The interdependent progress of the population and economy significantly impacts the sustainable development of these nations. By constructing an evaluation framework, this paper assesses the urban population growth and economic development systems in African countries. Building upon the coupling coordination model, it quantitatively investigates the relationship between the two and utilizes a geographical detector model to analyze the driving factors of the coordination of evolution. The findings reveal a continuous improvement in the quality of urban population growth and economic development between 2001 and 2020. Nevertheless, their overall quality remains relatively low, exhibiting considerable variation across different countries. Many African countries struggle with a low level of development coordination, with economic progress often trailing behind the pace of urban population growth. The average coupling coordination degree increased from 0.464 to 0.526 over 20 years, with 48.08% of countries still in uncoordinated development by 2020. Factors such as industrialization, foreign trade dependence, government spending, international aid, and political stability are all influential factors affecting the degree of coordination. The absence of industrialization in conjunction with urbanization poses a major impediment to effectively harnessing urban population growth for economic development. Ultimately, this study provides a targeted framework for integrating urban population growth and economic development to address low coupling coordination. Full article

The optimum choice of safety measures (SMs) within constraints is necessary for effective risk management in occupational health and safety (OHS). The stochastic nature of safety interventions is frequently overlooked by traditional approaches such as deterministic models and risk matrices. This study presents a novel stochastic knapsack model that maximizes the overall expected benefit during a risk assessment period considering budgetary constraints and the interdependencies between risks and safety measures. Two models are developed as follows: a one-to-one relationship model assuming independent risks and a multiple-relationship model accounting for interdependent safety measures. The suggested model’s real-world implementation is illustrated through a case study in the retail industry. The results demonstrate the model’s ability to efficiently prioritize SMs, showing an 18% reduction in objective function value and an average risk reduction of 29.5 per monetary unit invested, compared to 26.2 for the deterministic model. A more realistic and flexible framework for safety investment planning is offered by the analysis, which emphasizes the benefits of including stochastic components and interdependencies in decision-making. By addressing the significant drawbacks of deterministic models and providing a flexible, data-driven framework for safety optimization, this study adds to the body of literature. The suggested model is in line with the United Nations Sustainable Development Goals (SDGs), specifically SDGs 3, 8, 9, and 12. Its adaptability contributes to achieving SDG 13, emphasizing possible uses in risk management for climate change. This study shows how decision-making that is structured and aware of uncertainty can support safer, more sustainable industrial processes. Full article