Search Results (7,102)

Additive manufacturing (AM) of polymers and polymer composites is changing how customized, lightweight, and complex parts are produced across various industries. However, predicting the final properties of printed parts remains challenging due to variations in material compositions, processing conditions, and microstructural characteristics. This review explores how machine learning (ML) is being used to address these challenges. It examines the application of various ML approaches in polymer and polymer composite design for AM, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning, for predicting key properties such as mechanical strength, thermal stability, and electrical performance. The review also highlights hybrid techniques that combine ML with physics-informed modeling, including the use of digital twins, to enhance AM process control. Challenges and future perspectives, such as data scarcity, model interpretability, and computational demands, are discussed. In summary, ML is showing strong potential to support faster, more reliable, and more sustainable development of advanced polymers and polymer composites for AM. Full article

Recent studies in the Romanian Western Carpathians have revealed increasing socio-demographic fragility in rural areas and small towns, driven by depopulation, population aging, and declining living standards. These trends stem from the legacy of forced collectivization and industrialization (1950–1990) and the post-1990 transition, which triggered extensive out-migration and the erosion of local socio-economic structures. This study examines the fragility of human communities in the Trascău Mountains in order to evaluate spatial, demographic, and economic recovery dynamics and to assess settlement vulnerability as a major obstacle to sustainable regional development. Fragility was measured using indicators of population density and change, age structure, accessibility, and socio-demographic dynamics, based on comparative data for the interval of 1977–2021. These variables were integrated into a composite development index (Id), derived from twelve indicators covering demography, economy, infrastructure, and living standards, enabling the hierarchical classification of settlements by degree of vulnerability. The methodological framework combines empirical and analytical methods, statistical, cartographic, bibliographic, and field-based analyses within evolutionary, structural–functional, and typological perspectives. The results identify the main drivers of decline, quantify their impacts, and outline development prospects and policy directions for reducing territorial disparities. Overall, fragile settlements emerge as critical pressure points that undermine sustainability, intensify regional instability, and increase risks related to migration and social cohesion. Full article

This study presents a method for extracting lignin and hemicellulose from black liquor using organic acids (citric, malic, and acetic) in comparison to the traditional sulfuric acid method. We investigated and compared the influence of the acid type on the structural properties of the resulting precipitates in the context of their potential applications. The lignin fractions were characterized for their chemical structure (ATR-FTIR, NMR), thermal stability (TGA), morphology and surface elemental composition (SEM-EDS), bulk elemental composition (C, H, N, S), and molecular weight distribution (GPC). The hemicellulose fractions were analyzed for their molecular weight (GPC), surface elemental composition (EDS), and chemical structure (ATR-FTIR). These analyses revealed subtle differences in the properties of the individual materials depending on the extraction method. We showed that organic acids, particularly citric acid, can effectively precipitate lignin with yields comparable to the sulfuric acid method (47–60 g/dm³ vs. 50 g/dm³). Simultaneously, this method produces lignin with higher purity (regarding sulfur content) and an increased content of carboxyl groups. This latter aspect is of particular interest due to the enhanced potential of lignin’s adsorption functions towards metal ions. AAS analysis confirmed that lignin precipitated with citric acid showed better adsorption efficiency towards heavy metals compared to lignin precipitated with sulfuric acid, especially for Cu²⁺ ions (80% vs. 20%) and Cr³⁺ ions (46% vs. 2%). This enhanced adsorption efficiency of the isolated lignins, combined with the environmental benefits of using organic acids, opens a promising perspective for their application in water treatment and environmental remediation. Furthermore, the presented research on the valorization and reuse of paper industry by-products fully aligns with the fundamental principles of the Circular Economy. Full article

Polyurethanes (PUs) are indispensable polymeric materials widely employed across diverse industrial sectors due to their excellent thermal stability, chemical resistance, adhesion, and mechanical durability. However, the intrinsic three-dimensional crosslinked network that underpins their performance also presents a fundamental barrier to reprocessing and recycling. Consequently, most end-of-life PU waste is currently managed through landfilling or incineration, resulting in significant resource loss and environmental impact. To address these challenges, this review presents an integrated perspective on sustainable PU systems by unifying green synthesis strategies with closed-loop recovery approaches. First, recent advances in bio-based polyols and phosgene-free isocyanate synthesis derived from renewable resources—such as plant oils, carbohydrates, and lignin—are discussed as viable means to reduce dependence on petrochemical feedstocks and mitigate toxicity concerns. Next, emerging chemical recycling methodologies, including acidolysis and aminolysis, are reviewed with a focus on the selective recovery of high-purity monomers. Finally, PU vitrimers and dynamic covalent polymer networks (DCPNs) based on urethane bond exchange reactions are examined as reprocessable architectures that combine thermoplastic-like processability with the mechanical robustness of thermosets. By integrating synthesis, recovery, and reuse within a unified framework, this review aims to outline a coherent pathway toward establishing a sustainable circular economy for PU materials. Full article

Developing a deep understanding of the evolutionary driving mechanisms of university–industry collaborative innovation networks among Chinese cities is of great significance for advancing sustainable urban development. Based on university–industry collaborative patent data from 275 prefecture-level and above cities in China during the period 2004–2020, this study constructs an intercity university–industry collaborative innovation network and employs the temporal exponential random graph model to analyze its evolutionary driving mechanisms. The results indicate that the network structure has become increasingly complex over time and exhibits pronounced small-world characteristics in the later stages. Network formation is distinctly non-random and is jointly shaped by endogenous structural effects and exogenous factors. Diffusion, connectivity, and closure effects are all significant, while intercity collaborative ties are influenced by multidimensional proximity, including economic, geographic, and organizational proximity. Moreover, the network structure demonstrates strong temporal stability. In the context of high-intensity collaboration, cities place greater emphasis on economic and organizational proximity, and cities with higher levels of economic development and prior experience in high-intensity collaboration are more likely to establish collaborative ties. Furthermore, eastern cities tend to collaborate with partners at similar levels of economic development, whereas cities in central and western regions display a more pronounced core–periphery pattern. Overall, from the perspective of intercity university–industry collaborative innovation networks, this study provides new empirical evidence and insights for promoting coordinated regional innovation capacity and sustainable urban development. Full article

Effective communication in English is a critical behavioral competency for seafarers in a multilingual maritime environment, directly impacting operational safety. However, a gap exists between current Maritime English (ME) training in China and the actual communication demands of global seafaring. This study aims to identify the specific ME skills including linguistic, behavioral, and sociolinguistic dimensions that are most important for on-board performance and safety management from the perspective of pre-service maritime students at Shanghai Maritime University. A mixed-methods approach was used, combining structured questionnaires (n = 313) with in-depth follow-up interviews (n = 10). The results identified 24 highly needed ME skills, particularly focused on areas governing safety-critical behaviors, such as wireless communication, security protocols, and emergency procedures. In addition, based on learner profiling, the study depicts two different learner characteristics: exam-focused and work-focused students, each with different views on the importance of skills. Work-focused students place greater emphasis on the practicality of their skills. The interview data confirms and enriches these quantitative research results. The research findings emphasize that ME courses must be more closely aligned with real-world communicative scenarios and behaviors, prioritize scenario based teaching and practical operations, and tailor differentiated teaching based on learner psychology and behavioral preference. This study offers references for maritime education institutions with similar learner profiles to optimize ME curricula, prioritize secure communication skills, and strengthen industry-education collaboration, thereby enhancing pre-service maritime students’ safety behavior and professional competitiveness in China. Full article

As semiconductor process nodes shrink to 3 nm and beyond, traditional optical proximity correction (OPC) and resolution enhancement technologies (RETs) can no longer meet the high patterning precision needs of advanced chip manufacturing due to the sub-wavelength lithography limits. Inverse lithography technology (ILT), a key part of computational lithography, has become a critical solution for these issues. From an EDA industry perspective, this review provides an original and systematic summary of ILT’s development and applications, which helps integrate the scattered research into a clear framework for both academic and industrial use. Compared with traditional OPC, the latest ILT has three main advantages: (1) better patterning accuracy, as a result of the precise optical models that fix complex optical issues (like diffraction and interference) in advanced lithography systems; (2) a wider process window, as it optimizes mask designs by working backwards from the target wafer patterns, making lithography more stable against process changes; and (3) stronger adaptability to new lithography scenarios, such as High-NA EUV and extended DUV nodes. This review first explains ILT’s working principles (the basic concepts, mathematical formulae, and main methods like level-set and pixelated approaches) and its development history, highlighting key events that boosted its progress. It then analyzes ILT’s current application status in the industry (such as hotspot fixing, full-chip trials, and EUV-era use) and its main bottlenecks: a high computational complexity leading to long runtime, difficulties in mask manufacturing, challenges in model calibration, and a conservative market that slows large-scale adoption. Finally, it discusses promising future directions, including hybrid ILT-OPC-SMO strategies, improving model accuracy, AI/ML-driven design, GPU acceleration, multi-beam mask writer improvements, and open-source data to solve data shortage problems. By combining the latest research and industry practices, this review fills the gap of comprehensive ILT summaries that cover the principles, progress, applications, and prospects. It helps readers fully understand ILT’s technical landscape and offers practical insights for solving the key challenges, thus promoting ILT’s industrial use in advanced chip manufacturing. Full article

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive industry. To overcome these analytical limitations, this study implements a multilayer network framework for examining global automotive supply chains spanning 2017 to 2023. The research particularly emphasizes the identification of critical risk sources through both static and dynamic analytical perspectives. The static analysis employs multilayer degree and strength centralities to illuminate the pivotal roles that countries such as China, the United States, and Germany play within these multilayer automotive supply networks. Conversely, the dynamic risk propagation model uncovers significant cascade effects; a disruption in a major upstream supplier can propagate through intermediary layers, ultimately impacting over 85% of countries in the finished automotive layer within a short temporal threshold. Furthermore, this study investigates how individual nations’ anti-risk capabilities influence the overall resilience of multilayer automotive supply networks. These insights offer valuable guidance for policymakers, enabling strategic topological modifications during disruption events and enhanced protection of the most vulnerable risk sources. Full article

Functional food plays an increasingly important role in shaping healthy dietary habits by integrating health-promoting components into consumers’ everyday diets and helping reduce food waste. Products developed using by-products and raw material streams from food processing can support the principles of the circular economy (CE) by improving resource efficiency and lowering the environmental footprint of the food system. In Poland, as in many countries worldwide, functional food is a rapidly growing category that can contribute to public health and serve as a driver of economic development. However, the excessive and scientifically unsupported use of the term “functional food” for marketing purposes may mislead consumers and weaken trust in this product group. From a CE perspective, transparent communication and informed consumer choices are essential, as they promote environmentally responsible behaviors and support sustainable production models. The aim of this study was to assess the level of knowledge about functional food among residents of Eastern Poland, which is a less industrialized and predominantly agricultural region. The analysis focused on awareness of health benefits, consumption frequency, consumer attitudes, and interest in educational initiatives promoting functional food within CE principles. The study also examined whether consumers associate functional food with sustainability-oriented practices. The findings offer insights for educational and marketing strategies grounded in scientific evidence and highlight the potential of functional food in building a sustainable, resource-efficient food system. Full article

This study aims to review the energy management of microgrids with a structured focus on residential, commercial, and industrial applications. Building on early optimization and control strategies, this study synthesizes advances in forecasting, uncertainty management, computational intelligence, and digital twin integration. Particular attention is given to multi-energy coupling through storage technologies, including hydrogen and thermal pathways, along with life cycle, trilemma, and sustainability considerations. Sector-specific energy management system (EMS) strategies are compared in terms of objectives, methods, and implementation challenges, highlighting both converging and unique requirements across application domains. Cross-sectoral challenges, such as interoperability, cyber-security, resilience valuation, and policy gaps, are analyzed, and emerging research directions, including artificial intelligence (AI)-driven optimization, hierarchical and multi-agent frameworks, and hydrogen-enabled autonomy, are outlined. This review aims to equip researchers, practitioners, and policymakers with a consolidated reference on microgrid EMS, bridging technical innovation with sustainable and resilient energy transitions. Full article

(1) Background. The rapid development of the digital economy provides a new perspective for enhancing industrial chain resilience. This study examines how manufacturing firms’ digitalization affects their industrial chain resilience, drawing on resource dependence and dynamic capability theories, and explores spillover effects on upstream and downstream enterprises. (2) Data and Methods. Using panel data from Chinese listed manufacturing firms (2011–2023), we employ ordinary least squares (OLS) models to analyze the relationship, its mechanisms, and heterogeneity. We further match firms with their suppliers and customers to identify spillover effects. (3) Results. Digitalization significantly improves resilience, particularly by enhancing supply–demand matching and competitive capabilities. Effects are stronger for small, labor-intensive, and high-environment, social and governance (ESG) firms. Bargaining power and governance capability are key channels. Spillover effects are heterogeneous, with a stronger impact on downstream customers. (4) Discussion. The positive impact of digitalization varies by firm characteristics, and spillovers differ across the chain. These findings offer precise insights and policy implications for leveraging digitalization to strengthen industrial chain resilience. Full article

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility. Full article

As the global transition toward carbon neutrality accelerates, the sustainability of power electronics has received growing attention from both academia and industry. Nevertheless, standardized methodologies for evaluating the sustainability of power electronic systems—particularly traction inverters—remain limited, largely due to the absence of comprehensive databases and unified assessment frameworks. Leveraging industrial extensive design experience, this paper presents an enhanced methodology for sustainability evaluation of traction inverters. The proposed framework combines advanced component-level modelling with multi-physics-based analysis to more accurately quantify the environmental impacts associated with different power semiconductor technologies. A Random Forest (RF)-based algorithm is employed for junction temperature (T_J) estimation, offering reliable thermal data crucial for sustainability assessment. Experimental validation on a prototype automotive inverter confirms the accuracy and robustness of the RF-based T_J estimation approach, ensuring realistic thermal–environmental coupling within the evaluation workflow. From a thermal perspective, the sizing of power electronics key components (PEKCs) is performed with high precision, enabling a more accurate estimation of power electronics-related material (PERM) usage. Combined with a preliminary CO₂-equivalent (CO₂e) emissions database, this allows sustainability assessment to be integrated directly into the design stage of the traction inverter. The effectiveness of the proposed approach is demonstrated through a comparative evaluation of three representative inverter topologies. Full article

Driven by the energy transition within the framework of the United Nations Framework Convention on Climate Change, second-generation (2G) ethanol stands out as a technical and sustainable alternative to fossil fuels. Although first-generation ethanol, produced from saccharine and starchy feedstocks, represents an advance in mitigating emissions, its expansion is limited by competition with areas destined for food production. In this context, 2G ethanol, obtained from residual lignocellulosic biomass, emerges as a strategic route for diversifying and expanding the renewable energy matrix. Thus, this work discusses the current state of 2G ethanol technology based on the gradual growth in production and the consolidation of this route over the last few years. Industrial second-generation ethanol plants operating around the world demonstrate the high potential of agricultural waste as a raw material, particularly corn straw in the United States, which offers a lower cost and significant yield in the production of this biofuel. Similarly, in Brazil, sugarcane by-products, especially bagasse and straw, are consolidating as the main sources for 2G ethanol, integrated into the biorefinery concept and the valorization of by-products obtained during the 2G ethanol production process. However, despite the wide availability of lignocellulosic biomass and its high productive potential, the consolidation of 2G ethanol is still conditioned by technical and economic challenges, especially the high costs associated with pretreatment stages and enzymatic cocktails, as well as the formation of inhibitory compounds that compromise the efficiency of the process. Genetic engineering plays a particularly important role in the development of microorganisms to produce more efficient enzymatic cocktails and to ferment hexoses and pentoses (C₆ and C₅ sugars) into ethanol. In this scenario, not only are technological limitations important but also public policies and tax incentives, combined with the integration of the biorefinery concept and the valorization of (by)products, which prove fundamental to reducing costs, increasing process efficiency, and ensuring the economic viability and sustainability of second-generation ethanol. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article