Search Results (116)

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (${\alpha }_3$ >${ \alpha }_1$ > ${ \alpha }_2$ > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

Background: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions. Full article

The innovation cooperation network (ICN) drives innovation. However, how its network diversity affects local innovation needs further exploration. This paper examines the effects of ICN’s scale and functional diversity on local innovation. Employing the township-level co-invention network in Beijing, we analyze the evolution of the scale and functional diversity from 2010 to 2020, and explore their impacts, as well as the effects of their interaction, on local innovation. Moreover, the relationship between network and Jacobs’ diversity is further discussed. The results show that the township-level scale and functional diversity of the ICN in Beijing have increased by over 40%, accompanied by a transformation in the core–periphery distribution pattern. Both scale and functional diversity significantly contribute to local innovation, but manifest as inverted-U relationships, and they substitute for each other in promoting innovation. Furthermore, a substitution effect also exists between network and Jacobs’ diversity, though not robustly. Research highlights the role of scale and functional diversity in the ICN. It emphasizes that local governments need to conduct more precise management and adjustments in light of the heterogeneity of network connections in different scales and sectors within the ICN, in order to boost local innovation and foster regional development. Full article

Geopolymers, as sustainable alternatives to conventional cement, face application limitations due to pronounced drying shrinkage. This study systematically investigates the effects of cement incorporation (0–40%) on the drying shrinkage mitigation and performance evolution of metakaolin-based geopolymers (MKBGs) through multi-scale characterization of mechanical properties, reaction kinetics, and pore structure refinement. Key findings reveal that 10% cement addition optimally reduces drying shrinkage through pore structure densification and elastic modulus enhancement. The cement–geopolymer hybrid system exhibited a distinctive dual-reaction mechanism: cement hydration produced C-S-H gels that refined the pore structure while simultaneously competing with and delaying the geopolymerization kinetics, as demonstrated by the extended duration of the reaction exotherm. However, cement contents exceeding 20% induce detrimental self-desiccation shrinkage, resulting in net shrinkage amplification. Microstructural analysis confirms that the optimal 10% cement dosage achieves synergistic phase evolution, with N-A-S-H and C-S-H gels co-operatively improving mechanical strength and dimensional stability. This work provides quantitative guidelines for designing shrinkage-resistant geopolymer composites through controlled cement hybridization. Full article

As the global ecological environment faces serious challenges and extreme climate change threatens the survival of humankind, the promotion of green development has become the focus for all countries in the world. As one of the world’s major greenhouse gas emitters, China has put forward the “twin goals” of achieving carbon peaking and carbon neutrality and is committed to promoting the green and low-carbon transformation of its cities. As the core of economic and social development, cities are the main source of carbon emissions. In response to the dual challenges of carbon emission control and traffic growth, it is particularly important to promote the development of green transportation. With the acceleration of urbanization, urban traffic pollution is becoming more and more serious. As a zero-emission transportation mode, electric vehicles have become a key way to achieve the carbon peak and carbon neutrality targets. In order to deeply analyze the research status of electric vehicles in the field of the green and low-carbon transformation of urban transportation in China and to explore the research hot spots, evolution trends, and their roles and strategies in the construction of green transportation networks, this paper uses the CiteSpace, VOSviewer, and Tableau analysis tools to review and analyze the 2460 articles and reviews in the Web of Science Core Collection (WOS) and 2650 articles and reviews in the China National Knowledge Infrastructure (CNKI), including the “publication volume and publication trend”, “subject citation path”, “countries cooperation and geographical distribution”, “author cooperation and institution cooperation”, “keyword co-occurrence and keywords clusters”, and the “evolution trend of research hot spots in timeline”. The results show that: (1) Since 2010, the research focus on electric vehicles has gradually increased, and especially in the past three years, the number of such publications has increased significantly. (2) China holds the lead in research output regarding electric vehicles and related fields, but its international cooperation needs to be strengthened. (3) In recent years, the research has focused on “energy transformation”, “energy-saving technology”, “carbon emissions”, “battery recycling”, and other relevant topics. The promotion and development of electric vehicles will continue to usher in new opportunities concerning technological innovation, policy support, and market expansion. Finally, based on the research hot spots and evolution trends of electric vehicles in the field of urban green transportation and low-carbon transportation in China, this paper discusses the key paths and strategies for electric vehicles to promote the transformation of urban transportation in China to green and low-carbon types and looks forward to future research directions. The research in this paper can provide theoretical support and practical guidance for China to promote electric vehicles, build low-carbon cities, and realize green transportation. It is expected to act as a useful reference for relevant policy formulation and academic research. Full article

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape. Full article

The construction of artificial islands is a complex engineering challenge requiring precise scheduling to optimize resource utilization, manage costs, ensure safety, and minimize environmental impacts in dynamic marine settings. In this paper, we present a multi-objective artificial island construction scheduling optimization model. This model considers many crucial factors that influence artificial island construction from 5 aspects: construction time, construction cost, project quality, resource utilization efficiency, and environmental impact. To optimize the proposed model, we propose an algorithm called Multi-objective Cooperative Differential Evolution (MOCDE). MOCDE integrates Cooperative Co-evolutionary Algorithms, and Differential Evolution to efficiently obtain the optimal schedules. To explore the performance of this model and the algorithm, extensive experiments are conducted based on real-world project data. Comparing MOCDE with established algorithms, results indicate that MOCDE improvements over previous SOTA models, achieving a reduction of 0.56% in Total Time, a decrease of 0.43% in Total Cost, and an enhancement of 7.38% in Total Quality. Besides, it also could adhere to ensure the environmental requirements. Full article

This paper challenges the prevalent characterization of domesticated horses as prey species that inherently view humans as predators. Drawing on evolutionary, ethological, and cognitive evidence, we propose the “mutualistic coevolution hypothesis”, which posits that horses and humans have evolved a partnership marked by cooperation rather than fear. We critically assess the “prey hypothesis”, emphasizing a predator–prey model, which dominates equine training and the literature, and we argue that it inadequately explains horses’ morphology, behaviors, and cognitive capacities. Comparative studies on horses’ socio-cognitive skills suggest that domestication has fostered emotional, behavioral, and cognitive adaptations supporting a human–horse bond. This review examines evidence from archaeological findings and experimental research on horses’ responsiveness to human gestures, emotions, and social cues, underscoring their complex cognition and capacity for collaboration. Furthermore, morphological and behavioral analyses reveal inconsistencies in using orbital orientation or predation-related traits as evidence for categorizing horses as prey species. By emphasizing the coevolutionary dynamics underlying human–horse interactions, we advocate for replacing traditional training models centered on fear and submission with approaches that leverage horses’ mutualistic and social nature. This perspective offers insights for enhancing horse welfare and improving human–equine relationships. Full article

With the continuous development of information and communication technology, “software-defined vehicle” has become the trend of the times. The intelligent connected vehicle (ICV) is becoming a new direction for the development of the automotive industry. Nevertheless, the absence of cooperative innovation in the ICV sector, the dispersal of industrial chain resources, and the absence of enduring and consistent cooperation pose significant obstacles to value co-creation. Therefore, this paper constructs a value co-creation evolutionary game model of the innovation ecosystem of the ICV industry with the automotive enterprise, an intelligent automotive solution provider and the government as players, and applies prospect theory to optimize the tripartite evolutionary game. The payment matrix is established, the expected revenue is analyzed for each player’s strategies, and the replication dynamic equation and evolutionary stability strategy are analyzed. Finally, the theoretical research is validated through numerical simulation. The aim is to promote value co-creation by analyzing the co-evolution mechanism of various stakeholder strategies in the ICV innovation ecosystem. The results show the following: (1) The best evolutionary stability strategy is the positive cross-border cooperation between the automotive enterprise and the intelligent automotive solution provider, while the government gradually does not provide subsidies. (2) The government’s subsidy support should be controlled within an appropriate range. If the subsidy is too great, the marginal effect of incentives will gradually weaken. (3) The players’ willingness to integrate across borders can be enhanced by a higher level of trust and resource complementarity between the automotive enterprise and intelligent automotive solution provider. Also, liquidated damages and opportunity loss can effectively prevent the occurrence of negative integration behaviors. (4) The greater the risk attitude coefficient and risk aversion coefficient of the automotive enterprise and intelligent automotive solution provider, the more conducive they are to the occurrence of positive integration behavior. Full article

Background/Objectives: The origin of genes and genetics is the story of the coevolution of translation systems and the genetic code. Remarkably, the history of the origin of life on Earth was inscribed and preserved in the sequences of tRNAs. Methods: Sequence logos demonstrate the patterning of pre-life tRNA sequences. Results: The pre-life type I and type II tRNA sequences are known to the last nucleotide with only a few ambiguities. Type I and type II tRNAs evolved from ligation of three 31 nt minihelices of highly patterned and known sequence followed by closely related 9 nt internal deletion(s) within ligated acceptor stems. The D loop 17 nt core was a truncated UAGCC repeat. The anticodon and T 17 nt stem-loop-stems are homologous sequences with 5 nt stems and 7 nt U-turn loops that were selected in pre-life to resist ribozyme nucleases and to present a 3 nt anticodon with a single wobble position. The 7 nt T loop in tRNA was selected to interact with the D loop at the “elbow”. The 5′-acceptor stem was based on a 7 nt truncated GCG repeat. The 3′-acceptor stem was based on a complementary 7 nt CGC repeat. In pre-life, ACCA-Gly was a primitive adapter molecule ligated to many RNAs, including tRNAs, to synthesize polyglycine. Conclusions: Analysis of sequence logos of tRNAs from an ancient Archaeon substantiates how the pre-life to life transition occurred on Earth. Polyglycine is posited to have aggregated complex molecular assemblies, including minihelices, tRNAs, cooperating molecules, and protocells, leading to the first life on Earth. Full article

Graphs offer a generic abstraction for modeling entities and the interactions and relationships between them. Most real-world graphs, such as social and cooperation networks, evolve over time, and exploring their evolution may reveal important information. In this paper, we present TempoGRAPHer, a system for analyzing and visualizing the evolution of temporal attributed graphs. TempoGRAPHer supports both temporal and attribute aggregation. It also allows graph exploration by identifying periods of significant growth, shrinkage, or stability. Temporal exploration is supported by two complementary strategies, namely skyline- and interaction-based exploration. Skyline-based exploration provides insights into the overall trends in the evolution, while interaction-based exploration offers a closer look at specific parts of the graph evolution history where significant changes occurred. We present experimental results demonstrating the efficiency of TempoGRAPHer. Additionally, we showcase the usefulness of our system in understanding graph evolution by presenting detailed scenarios, including exploring the evolution of a real contact network between primary school students and analyzing the collaborations in a co-authorship network between authors of the same gender over time. Full article

In the context of globalization and rapid technological advancement, the introduction of Artificial Intelligence (AI) has brought new opportunities and challenges to Human Resource Management (HRM). This study constructs an evolutionary game model to explore the strategy choices and evolutionary paths of enterprises and employees in HRM value co-creation with AI involvement. We numerically simulated the dynamic evolution of strategies under different scenarios, revealing the equilibrium characteristics of strategic interactions between enterprises and employees in the AI context. The study finds that, first, the evolutionary game system between enterprises and employees converges to two equilibrium points: {cooperation, active} and {non-cooperation, passive}. Overall, the probability of the former is 2.39 times greater than that of the latter. Second, higher initial probabilities of cooperation and active involvement, along with lower costs for cooperation and active involvement, facilitate the system’s evolution towards the {cooperation, active} equilibrium. Third, enterprises are more sensitive to the benefit distribution ratio than employees. This study provides theoretical support for effectively conducting HRM practices in the AI era through systematic analysis of HRM value co-creation behavior, along with practical policy recommendations. Full article

Regional science and technology cooperation networks are pivotal for fostering sustainable global innovation. The China–ASEAN science and technology cooperation network integrates regional innovation resources, thereby promoting the sustainable flow of innovation elements and complementing technological strengths among countries, which significantly enhances cooperation efficiency and outcomes. This study employs a Social Network Analysis (SNA) and the Temporal Exponential Random Graph Model (TERGM) to analyze co-authored publications between China and ASEAN countries from 2003 to 2022, constructing a cooperation network that integrates both endogenous network structures and exogenous driving factors. This study explores the distinct mechanisms through which these factors influence the formation of cooperative relationships and highlights the key features and determinants of the network. The findings reveal the following: first, the China–ASEAN science and technology cooperation network has evolved from an initial “star-shaped structure” with China and Singapore as central nodes to a more interconnected network exhibiting “small world” and “high clustering” characteristics. Second, endogenous network structures, including the number of edges, node centrality, and closed triadic structures, significantly shape the network’s evolution, with some structures inhibiting the formation of new partnerships, while an increase in shared collaborators promotes new connections. Third, the evolution of the network demonstrates both stability and variability. Fourth, human capital is a key driver of partnership formation, while higher per-capita GDP countries show less inclination to form new partnerships. Fifth, proximity factors have heterogeneous effects: linguistic proximity positively impacts the formation of partnerships, while institutional proximity negatively affects the establishment of new collaborations. Based on these findings, this paper suggests improving international cooperation mechanisms, optimizing resource allocation, and enhancing the development of cross-border scientific talent. These measures aim to enhance the connectivity within the China–ASEAN science and technology cooperation network, effectively improve the utilization efficiency of regional innovation resources and technological capabilities, and promote the sharing and long-term collaboration of innovation resources within the region, thereby advancing sustainable development at both regional and global levels. Full article

Artificial island construction is a multifaceted engineering endeavor that demands precise scheduling to optimize resource allocation, control costs, ensure safety, and minimize environmental impact within dynamic marine environments. This study introduces a comprehensive multi-objective optimization model that integrates critical factors such as resource limitations, task dependencies, environmental variability, safety risks, and regulatory compliance. To effectively address the complexities of this model, we develop and employ the Multi-Objective Adaptive Cooperative Evolutionary Marine Genetic Algorithm (MACEMGA). MACEMGA combines cooperative coevolution, adaptive dynamic weighting, dynamic penalty functions, and advanced genetic operators to navigate the solution space efficiently and identify Pareto optimal schedules. Through extensive computational experiments using data from the Dalian Bay Cross-Sea Traffic Engineering project, MACEMGA is benchmarked against algorithms such as NSGA-II, SPEA2, and MOEA/D. The results demonstrate that MACEMGA achieves a reduction in construction time from 32.8 to 23.5 months and cost savings from CNY 4105.3 million to CNY 3650.0 million while maintaining high-quality outcomes and compliance with environmental standards. Additionally, MACEMGA shows improvements in hypervolume by up to 15% over existing methods and a Convergence Rate that is 8% faster than MOEA/D. Full article

Mesophase pitch is regarded as a profoundly promising candidate for the production of advanced carbon-based multifunctional materials such as carbon fibers, carbon microspheres, and carbon foams owing to its excellent intrinsic properties. Consequently, a deeper understanding of pyrolytic chemistry is indispensable for the efficient and environmentally friendly utilization of mesophase pitch. In this study, details about the structure compositions and microscopic morphologies of petroleum-driven mesophase pitch (pMP) were investigated through ultimate, FTIR, XPS, and ¹³C-NMR analyses. Furthermore, a large-scale molecular model of typical pMP with 11,835 atoms was constructed to unveil the comprehensive pyrolysis behaviors and the underlying reactions. Significantly, the evolution of specific chemical bonds and the decomposition of crucial molecular fragments were elucidated within an amalgamation of experimental TG-FTIR/MS and ReaxFF MD simulation. Accordingly, three fundamental reaction stages were artificially divided, including the low-temperature reaction, rapid thermal decomposition, and the molecular condensation reaction. During the rapid thermal decomposition stage, the cleavages of C–C and C–O bonds cooperatively contributed to the formation of C₂H₄ and H₂O gaseous products. As the temperature escalated to the molecular condensation stage, the pyrolysis process was governed by the dehydrogenation condensation, accompanied by an augmentation of C–C and H–H bonds and a diminution of C–O and C–H bonds. Additionally, the rare graphitization phenomenon was observed, suggesting a remarkable degree of structural organization in pMP. Overall, the results of ReaxFF MD simulations complement experimental observations, successfully reproducing the microstructure of pMP and atomic-scale pyrolysis behavior, thereby providing invaluable insights for the rational guidance of efficient utilization of pMP and other related carbonaceous precursors. Full article