How vehicles change lanes after encountering crashes: empirical analysis and modeling

Trans-local linkages are central mechanism for peripheral regions to sustain innovation systems, but their spatial configuration and mechanisms remain underexplored. This paper proposes four hypotheses based on the tri-polar framework integrating actor, network, and contextual dimensions, and tests them using co-invention patents from China's high-end equipment manufacturing industry covering 1985-2021. Employing Exponential Random Graph Models, the analysis reveals several key findings: (1) state-owned enterprises and large firms dominate peripheral innovation; (2) the spatial configuration of peripheral innovation is relatively stable; and (3) peripheral cities prioritize cross-hierarchy cooperation, resulting in open network configurations rather than locally closed structures. Overall, peripheral innovation in China is predominantly firm-led, cross-boundary, and selectively oriented toward higher-tier partners. By placing firms at the center of analysis and identifying differentiated types of peripheral cities, this study advances understanding of peripheral regional innovation systems in emerging economies. The findings contribute to the applied geography literature by providing spatially explicit evidence that informs place-based innovation and regional development policy. • Using co-invention patent data from China's high-end equipment manufacturing industry (1985–2021), this study examines the spatial configuration of innovation collaboration in peripheral regions from firm, network, and contextual perspectives. • Innovation activities in peripheral regions are predominantly led by state-owned enterprises and large firms, relying more on cross-regional and cross-hierarchical linkages than on localized clustering. • Peripheral innovation networks display strong relational stability but limited local closure, resulting in open and hierarchically oriented spatial network structures.

The background of this research is based on the condition of capital expenditure allocation management in the provinces of Sumatra Island, which has not yet reached an optimal level. The objective is to analyze the influence of the Regional Financial Ratio on capital expenditure allocation. Effective PAD management is important to reduce dependence on debt and ensure the sustainability of infrastructure financing. Excessive dependence on short-term debt has the potential to create fiscal risks, so efficient financial management is needed to maintain liquidity stability and long-term investment. The type of data used is secondary data in the form of financial reports. The analysis was conducted using a purposive sampling approach and panel data regression using Eviews 12 software. The results of the adjusted R2 analysis with a value of 0.540432 indicate that 54% of the variation in Capital Expenditure Allocation can be explained through the contribution of the Degree of Decentralization Ratio, Regional Financial Efficiency Ratio, and Liquidity Ratio. For future research, it is recommended to include additional independent variables and increase the number of research samples to expand the scope of the empirical analysis.

The integration of circular economy (CE) practices with Industry 4.0 (I4.0) technologies in industrial sectors remains limited, primarily due to a lack of practical knowledge about key drivers, barriers, and mitigation strategies. Despite their strong potential to enhance sustainable performance (SP), the adoption of CE–I4.0 faces several challenges across organizational levels. This study aims to empirically investigate the key drivers, barriers, and mitigation strategies for effective CE–I4.0 adoption, focusing on micro, meso, and macro-organizational contexts. Data were collected through an open-ended survey of 287 professionals, including sustainability leaders, managers, executives, and consultants, with 128 responses from micro level organizations, 87 from meso level organizations, and 71 from macro level organizations. The findings reveal level-specific drivers: “Technological innovation” at the micro level, “Regulatory pressure and sustainability standards” at the meso level, and “Regulatory and policy support” at the macro level. Across all levels, financial constraints emerged as the most critical barrier: “High initial investment costs” at the micro level, “High capital investment requirements” at the meso level, and “High upfront and ongoing costs” at the macro level. Mitigation strategies varied accordingly, including financial, skill, and change management at the micro level; education, collaboration, and technology adoption at the meso level; and awareness, stakeholder engagement, and infrastructure development at the macro level. These results underscore the need to tailor interventions to organizational scale while coordinating system-wide actions for enhanced sustainability. The study introduces a CE–I4.0 integration framework that consolidates these insights into an evidence-based roadmap linking drivers, barriers, and strategies across levels, providing actionable guidance for managers and policymakers. By offering a multi-level empirical analysis of CE–I4.0 integration, the study advances theory and practice, supporting coordinated adoption that enhances efficiency, sustainability, and organizational resilience. • First multi-level empirical study of CE–I4.0 integration across micro, meso, macro levels. • Identifies level-specific drivers, barriers, and mitigation strategies for CE–I4.0 adoption. • Introduces an evidence-based framework linking drivers, barriers, and strategies across levels. • Provides actionable insights for managers and policymakers to enhance sustainability outcomes. • Supports coordinated adoption to improve operational efficiency, resilience, and system-wide SP.

This article presents an empirical analysis of the legislative output of the Legislative Assembly of the State of Santa Catarina (Alesc) in the years 2023 and 2024, focusing on the quantitative evolution of propositions, approval rates, normative typology, authorship and priority legislative themes. The data was obtained from statistical reports and institutional documents and analyzed using legislative effectiveness indicators. The results show a significant increase in the rate of approval of proposals, from 42% in 2023 to 66.38% in 2024, associated with a small increase in the total number of proposals presented, which suggests greater rationalization of the legislative process. There is a trend towards rationalization and a higher rate of legislative use. There was a predominance of ordinary billsand a centrality in parliamentary authorship. The legislative agenda has evolved from predominantly social issues in 2023 to social policies, health and education in 2024, demonstrating institutional responsiveness. The conclusion is that Alesc has been moving towards more effective and socially relevant normative production, although challenges related to impact assessment and normative implementation remain.

Characteristics of online medication consultation from home-based patients on a tertiary hospital WeChat platform: a cross-sectional study.

Empirical analysis of generative AI tool adoption in software development

Empirical analysis of the roles of dynamic sustainable capabilities and artificial intelligence in accelerating circular business model innovation: Insights from Chinese manufacturing firms

The aim of this study is to analyze how co-creation activities influence consumer behavior. The study is framed within the context of post-pandemic tourism recovery in emerging destinations, where traditional travel agencies face increasing challenges derived from digitalization and the growing tendency of tourists to independently plan their trips online. Specifically, it examines how these activities first improve perceived value and customer satisfaction, and how these improvements in turn strengthen customer loyalty, fostering long-term relationships between travel agencies and their customers. By addressing this chain of effects, the study responds to a relevant gap in the literature regarding the mechanisms through which value co-creation contributes to relationship sustainability in traditional tourism intermediaries. Drawing on the results of the conducted survey, based on primary data collected in Ecuador in 2023, the research examines the connection between co-creation, perceived value, customer satisfaction, and loyalty, while also identifying key motivationa l factors that influence co-creation in the tourism sector. The empirical analysis relies on a quantitative research design, using a structured questionnaire administered to 450 national and international tourists and analyzed through structural equation modeling. The findings show that co-creation plays a crucial role in increasing customer loyalty by directly impacting perceived value and satisfaction. The results further reveal that satisfaction acts as the main mediating variable in the relationship between cocreation and loyalty, while trust plays a complementary but less decisive role. Additionally, the study highlights the importance of customer involvement in service design to create personalized experiences. This effect is particularly relevant for a predominantly young and female customer profile, which characterizes the demand of travel agencies in the analyzed context. This study demonstrates that co-creation can give traditional travel agencies a competitive edge by enhancing customer loyalty, enabling personalized services, and fostering long-term relationships. From a theoretical perspective, the research contributes empirical evidence from an underexplored Latin American setting, while from a managerial standpoint it provides actionable insights for strengthening customer relationships in highly volatile, post-pandemic tourism markets. It also emphasizes the need for further research on the operational and economic impact of co-creation in the tourism industry.

Are ChatGPT and Midjourney tools or creators? Ownership of billions of AI-assisted creative outputs hangs in the balance. Copyright scholars have long debated whether an autonomous artificial intelligence could qualify as an author, but this remains a hypothetical question. Despite widespread application of the term “AI” to software products of the 2020s, autonomously creative artificial intelligence still does not exist. Today’s commercial AI products—such as ChatGPT, Midjourney, Dall-E, Copilot, Gemini, Claude, Suno, Perplexity, and Lumo—are simply the newest generation of computer software. They do not qualify as “artificial intelligence” in either the scientific or science fiction senses. They are mere computer programs, tools that act only upon instructions of human creators. Under basic principles of copyright law, long applied to computer software, the human user of the software program is entitled to protection of the output. Every “AI-generated” work has a human author. To prove that point, this Article’s centerpiece is a Socratic dialogue, presented as a conversation between myself and a large language model (LLM). Following long tradition, this dialogue is structured to lead readers to a logical conclusion; namely, that every “AI-generated” work has a human author. By design, the narrative presents as an encounter between two minds. In reality, there is only one intelligence at work—mine. This dialogue serves several purposes. Of interest to scholars in any discipline, it demonstrates how we can use digital dialectic to explore hard problems, identify related literature, develop theories, and achieve new insights. Thematically, it explains how LLM software works, contrasts this with common misconceptions about “artificial intelligence,” and reflects on the intellectual work we do when we use LLM software and what this means for the authorship question in copyright law. It also models how scholars—and lawyers—can leverage LLM AI tools to conduct more powerful research, thinking, and analysis. Finally, it offers copyright scholars a welldocumented specific instance of AI-assisted writing, so that we can move from hypothetical debate to empirical analysis of generative authorship. The dialogue thus serves as argument, methodology, and demonstration— piercing the illusion of artificial intelligence to offer a more realistic understanding of today’s generative software as tools, not creators. The Article also surveys over four decades of copyright scholarship about authorship and artificial intelligence, distinguishing between hypothetical and empirical AI scholarship. Copyright issues surrounding AI have long been explored hypothetically. In my view, there is now an urgent need for empirical AI scholarship. The new school of generative empiricists ground their legal analysis in real-world study of how artists, musicians, and writers use the new tools to explore, play, study, and create. The longhypothetical copyright debate presumed an autonomously-acting artificial intelligence. Empirical AI scholarship emphasizes that today’s generative technology does not fit this description. Midjourney and ChatGPT are software tools, subject to the same rules of copyright that govern Photoshop and Microsoft Word. Under the Supreme Court’s Feist standard of originality, software users are entitled to copyright ownership of the output whenever they contribute at least a minimal spark of creativity.

In recent years, as the Sino-US trade war has intensified, the economic and trade relationship between the two countries has undergone significant changes. Given this, this paper examines the impact of trade frictions on developing countries' trade flows from the perspectives of tariff barriers and trade policy uncertainty. First, it examines whether developing countries' exports to the United States change after the onset of trade frictions (i.e., the US import substitution effect). Second, it examines whether developing countries' exports to China change after the onset of trade frictions (i.e., the Chinese export deflection effect). An empirical analysis based on bilateral trade data from 2013 to 2023 shows that trade frictions significantly increase developing countries' exports to the United States, demonstrating a clear import substitution effect. However, imports from China do not increase significantly, indicating a nonsignificant Chinese export deflection effect. Finally, the study proposes policy recommendations for developing countries to address the changing international trade environment, such as promoting the diversification of export markets and products and enhancing supply chain autonomy.

ABSTRACT Martha Nussbaum's central human capabilities provide a crucial theoretical foundation within the capability approach, yet their empirical operationalisation often relies on predefined theoretical groupings. This research adopts an empirically driven strategy to explore the dimensional reduction of human capabilities based on Nussbaum’s list and the identification of respondent profiles through clustering, using primary data collected in a specific socio-economic context. Using Principal Component Analysis, the capabilities were aggregated into four empirical dimensions: relational autonomy, social respect, physical and mental health, and environment. Additionally, the sample was clustered into five groups based on their capability profiles: Capable, Dependent, Threatened, Vulnerable and Debilitated. This study contributes to the literature on capability measurement by empirically exploring how indicators derived from Nussbaum’s framework relate to one another within a specific socio-economic context. It demonstrates that empirical dimensions can deviate significantly from Nussbaum's theoretical proposal. Furthermore, the findings support the use of dimension reduction techniques to develop capability models better suited for structural equation modelling applications.

This study evaluated the profitability and efficiency of honey production in Southwest Nigeria, with emphasis on technical, economic, and allocative efficiencies. A multistage sampling technique was used to select 114 honey producers. Data were analyzed using descriptive statistics, budgetary analysis, the Stochastic Frontier Model, Ordinary Least Squares (OLS), and the Relative Importance Index (RII). The results demonstrate that honey production is male-dominated, with an average household size of five persons. Budgetary analysis confirms that honey production is economically viable, generating a gross margin of ₦452,201.65, a net profit of ₦371,428.58, and a capital turnover ratio of 3.70, indicating a return of ₦3.70 for every ₦1.00 invested. Stochastic frontier estimates reveal that honey price and depreciation costs significantly increased output at the 1% level, while labor, herbicides, and transportation costs significantly reduced output at the same level of significance. The mean technical efficiency of 0.92 suggests that producers are operating close to the production frontier. However, the mean economic and allocative efficiencies (0.43 each) indicate substantial cost inefficiencies and suboptimal resource allocation. The RII identifies the major constraints affecting productivity and profitability as limited access to modern technology, outdated honey extraction methods, human disturbances, and high beehive costs. The findings imply that while honey production is profitable and technically efficient, improving resource allocation and adopting modern production technologies would significantly enhance overall economic performance.

Export-based growth strategies have led to increasing economic competition between countries. A country's global competitiveness is shaped by the influence of various factors, and determining these factors is of great importance. This study, which examines the determinants of competitiveness, is based on Porter's (1990) Diamond Model. In addition, three different models were developed in line with Dunning's (1992) contributions to Porter's Diamond Model and an alternative perspective. In this research, which covers Türkiye and 11 European Union member countries, annual data between 1995-2019 were used. In the study, the competitiveness index developed by Balassa was calculated for two different groups of goods that require high technology and are included in the Harmonized System. To explore the relationships among the variables, the Seemingly Unrelated Regression (SUR) method was applied. The empirical findings revealed that determinants such as market demand, resource conditions, industry support structures, competitive strategies, regulatory policies, and productivity levels have statistically significant both positive and negative effects on international competitiveness. In addition, it has been determined that the effects of these variables differ both across the examined commodity groups and countries.

ABSTRACT This paper introduces a novel approach to graph neural networks (GNNs) that incorporates topological data analysis (TDA) to enhance the representational power of traditional GNN architectures. We propose Topological Graph Neural Networks (TopGNNs), which leverage persistent homology and simplicial complexes to capture multi‐scale structural information that conventional GNNs often overlook. Our experimental results on benchmark datasets demonstrate that TopGNNs achieve competitive performance compared to state‐of‐the‐art methods, particularly for tasks requiring sensitivity to global graph structure. We provide a comprehensive empirical analysis of TopGNNs across various domains including molecular property prediction, social network analysis, and citation networks. The proposed framework bridges the gap between algebraic topology and deep learning on graphs, offering a promising direction for future research in geometric deep learning.

This study explores the sustainability bottlenecks such as “data islands”, lack of user feedback mechanisms, and service homogeneity that are common when artificial intelligence is applied in community service information systems. This study conducts empirical analysis based on the 2023 operation data of community service platforms in three typical Chinese cities and usage logs from over 1,200 residents. A “multi-loop branched closed loop mechanism” collaborative optimization mechanism was proposed and piloted, and the service performance was significantly improved through institutionalized data fusion architecture, dynamic classification of service nodes, and human-machine collaborative feedback closed-loop (user satisfaction increased by 15.7%, and the average response time was shortened by 34.1%). The approach is not only applicable to smart community scenarios, but its core logic-embedding technology into institutionalized collaborative processes to enable sustainable smart services-provides a migratable methodological contribution to a wider range of service information systems.

The large-scale rapid deployment of renewable generation and energy storage is transforming traditional power system dynamics through intermittency, reduced inertia, and pronounced long-range temporal dependence. Existing power system modeling frameworks are primarily based on short-memory assumptions and integer-order dynamics, which are unable to capture the persistence and oscillatory behavior of emerging renewable-dominant power systems. This structural mismatch leads to inaccurate system representation and degraded long-horizon prediction performance. Although fractional calculus has been applied to specific control and forecasting tasks in power systems, the joint system-level modeling of renewable generation and load demand using real-world data remains largely unexplored. In this paper, we develop a data-driven fractional-order dynamic modeling framework that explicitly incorporates long-memory effects into the governing equations through fractional differential equations based on the Caputo formulation. Using publicly available high-resolution datasets of load and renewable generation, empirical analysis reveals power-law decaying autocorrelations and dominant low-frequency spectral characteristics that motivate the use of fractional-order dynamics. Fractional orders and model parameters are jointly identified through prediction-error minimization to ensure consistency between modeled trajectories and observed persistence. The numerical results demonstrate that the proposed approach achieves a root–mean–square error of 3.12, compared to 5.64 and 4.98 for integer-order and finite-memory models, respectively, and reduces the normalized root–mean–square error from 0.156 and 0.132 to 0.087. Residual and spectral analyses further confirm that long-memory behavior is effectively captured by the proposed dynamics. The framework provides a scalable and physically interpretable foundation for the data-driven modeling of renewable-dominant power systems.

International inequality grew during history. 200 years ago, rich countries were only 3 times richer than poor countries. By the end of colonialism in the 1960s they were 35 times richer. Today, they are about 80 times richer. Around the world, many developing and emerging countries have been praised for their economic success during the last two decades. Those results were often ascribed to the neoliberal policies of financial and trade liberalization that enabled the countries to attract foreign lending, especially Foreign Direct Investment (FDI), while increasing exports of the richest natural resources those countries have been endowed with. This work analyses how, despite their historical, geographical, and cultural distinctions, many Global South countries share important structural characteristics that constrain them to the same path of dependency and subordination. A productive and economic structure mainly devoted to extractive activities is matched with substantial financial inflows that increase foreign ownership in the strategic key industries of those countries. Focusing on Latin American countries and supported by a deep empirical analysis of balance of payments dynamics and international investment position statistics, this paper highlights the adverse dynamics of development strategies reliant on natural resource extraction and export activities. It questions the sustainability of such strategies and their capacity to reduce inequality at the international level.

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study (N=25, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is β=0.0036 (p=0.495) and the Alphabet Rank coefficient is β=−0.0027 (p=0.642), with the results robust to all tested parameterizations (λ∈[0.05,0.20]; p>0.50 throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower.

Enhancing the carrying capacity of the ecological environment serves as a pivotal pathway to achieving sustainable development and also constitutes a concrete response to the UN SDGs. Based on a provincial panel dataset covering 30 Chinese provinces spanning 2011–2023, the present work examines how digital finance shapes EECC and explores the corresponding transmission mechanisms. Findings from the empirical analysis confirm that digital finance exerts a significant positive effect in boosting ecological environmental carrying capacity. Heterogeneity tests further show that this catalytic influence is most salient in eastern China, while it lacks statistical significance or even turns negative in the central and western areas. Meanwhile, the catalytic function of digital finance becomes more distinct in highly urbanized areas. Mechanism analysis verifies that digital finance assumes a partial mediating function by cutting down energy consumption intensity and boosting human capital accumulation. Further analysis reveals that as digital finance matures, the above impact exhibits increasing marginal returns. Our spatial spillover assessment further indicates that digital finance contributes to stronger EECC within host provinces, while also facilitating coordinated improvements in this key indicator across neighboring jurisdictions. Accordingly, we propose that economies speed up the building of digital-related infrastructure, expand the outreach of digital finance, and properly steer the orderly movement of population, thus facilitating the eco-friendly sustainable advancement of the natural environment.