Search Results (5,055)

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

The aim of this study was to evaluate the effects of graded replacement of soybean meal (SBM) with black soldier fly larvae meal (BSFLM) on growth performance, growth dynamics, carcass characteristics, and economic efficiency in Japanese quails (Coturnix japonica). A total of 300 one-day-old quail chicks were randomly allocated to five dietary treatments in which SBM was replaced with BSFLM at 0, 25, 50, 75, or 100% using isocaloric and isonitrogenous diets. Body weight was recorded weekly, feed intake was measured per cage, and growth dynamics were assessed using the Gompertz growth model. At 42 d of age, 150 quails were slaughtered to determine carcass yield and major carcass components, and economic evaluation was performed using scenario-based analyses to compare feed cost efficiency under contrasting ingredient price conditions. Dietary inclusion of BSFLM had no significant effects on body weight at any measured age, mortality rate, or carcass yield and composition. Feed intake and feed conversion ratio were significantly improved at the 50% BSFLM inclusion level, indicating improved feed efficiency at moderate replacement. Gompertz growth parameters, including mature weight, growth rate, and inflection point traits, were not affected by dietary treatment, confirming that intrinsic growth patterns were maintained. Economic analyses showed that partial replacement of SBM with BSFLM was associated with improved or stabilized feed cost efficiency depending on relative ingredient prices, whereas higher inclusion levels were more sensitive to unfavorable price conditions. In conclusion, BSFLM can be incorporated into Japanese quail diets without detrimental effects on growth performance or carcass traits, with moderate inclusion levels providing the most consistent balance between biological efficiency and economic robustness, thereby supporting risk-aware and sustainable poultry feeding strategies under variable market conditions. Full article

In this study, we describe a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional self-reports, physiological monitoring, and neuroimaging remain limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers an advantageous alternative, reflecting rapid neuroendocrine changes linked to emotional states. We combined four key salivary biomarkers, cortisol, alpha-amylase, dehydroepiandrosterone, and oxytocin, to capture multidimensional emotional responses. Two clinical studies (n = 30, n = 63) and one user study (n = 80) exposed volunteers to six fragrances, with saliva collected before and 5 and 20 min after olfactory stimulation. Subjective emotional ratings were also obtained through questionnaires or an implicit approach. Rigorous analytical validation accounted for circadian variation and sample stability. Biomarker patterns revealed fragrance-induced emotional profiles, highlighting subgroups of participants whose biomarker dynamics correlated with particular emotional states. Increased oxytocin and decreased cortisol levels aligned with happiness and relaxation; in comparison, distinct biomarker combinations were associated with confidence or dynamism. Classification and Regression Trees (CART) analysis results demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. Our findings establish salivary biomarker profiling as an objective tool for decoding real-time emotional responses. Beyond advancing affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders. Full article

As a key component of sustainable economic development, digital transformation has become a fundamental driver for developing and upgrading the modern economic system. While existing research has identified resources and dynamic capabilities as foundational elements, a critical yet underexplored factor lies in the cognitive foundations that enable firms to strategically direct and leverage these assets. Based on 19,062 observation samples of more than 3000 listed companies in Shanghai and Shenzhen stock markets from 2010 to 2023, this paper constructs a theoretical framework of entrepreneurship, organizational attention and digital transformation from the Attention-Based View, and examines a moderated mediation model of the relationship between entrepreneurship and digital transformation. The results show that entrepreneurship significantly promotes digital transformation; organizational attention to “cooperation orientation” and “future orientation” plays a mediating role in it; and the regional innovation atmosphere positively strengthens the “cooperation orientation” path, facilitating the diffusion of innovative knowledge and technologies within the region. Meanwhile, online media reports negatively regulate the “future orientation” path, reflecting that short-term public pressure may weaken enterprises’ attention to long-term sustainable technology investment. In addition, different dimensions of entrepreneurship have varied effects on digital transformation. Heterogeneity analysis revealed significant variations across ownership type, scale, region, industry competition intensity, and technological intensity. This study expands the theoretical mechanism of entrepreneurship and digital transformation from the perspective of attention allocation, and provides theoretical and empirical foundation for fostering a strategic cognitive orientation and advancing digital transformation. Full article

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products. Full article

Digitalization is increasingly central to economic growth strategies, yet robust macro-level evidence on the role of SME-led e-commerce remains limited. Drawing on the Resource-Based View, this study examines how SME digitalization, internet finance, and platform-based activities influence regional economic growth in China, and how these effects depend on digital infrastructure readiness (DIR). We construct an annual panel of 30 provincial-level regions in China over 2015–2024 and estimate dynamic relationships using two-step system GMM to address endogeneity and growth persistence. The results show that SME digitalization, supply-chain efficiency, mobile payment penetration, tech-driven employment growth, platform-economy contribution, and DIR all exert statistically significant positive effects on GDP growth. Quantitatively, a 10-percentage-point increase in SME digitalization is associated with approximately 0.3-percentage-point higher regional GDP growth, while a 10-point increase in DIR corresponds to about 0.4-percentage-point higher growth. Moderation analyses reveal that DIR significantly amplifies the growth effects of e-commerce expansion, mobile payments, and digital marketing, whereas its moderating role is weaker or insignificant for cross-border payments and supply-chain efficiency. These findings reconceptualize digitalization as a coordinated bundle of complementary resources and position DIR as a critical enabling capability for translating SME digital transformation into macroeconomic growth. The study offers policy-relevant evidence for targeting infrastructure investment and digital-economy strategies in emerging platform economies. Full article

Licorice is an important specialty crop that links agricultural production, processing and trade, and rural livelihoods in the arid and semi-arid regions of China. Using UN Comtrade data for HS 130212 from 1990 to 2024, this study evaluates the international Trade Competitiveness of China’s licorice exports and identifies the sources of export growth. A multi-indicator static framework is constructed, combining International Market Share (IMS), the Trade Competitiveness Index (TC), the Revealed Symmetric Comparative Advantage index (RSCA) and the Revealed Competitive Advantage index (CA). The results show that China maintains a relatively large and stable global market share and a persistent net export position, but its comparative and net Competitive Advantages are weaker than those of high-end suppliers such as France and Israel, revealing a pattern of “large scale but weak competitiveness”. To capture dynamic drivers, an extended Constant Market Share (CMS) model is applied to decompose China’s licorice exports into world demand, structural and competitiveness effects. The decomposition indicates that export growth has gradually shifted from being mainly driven by global demand expansion to relying more on improvements in product competitiveness and market reconfiguration, particularly in emerging markets. These findings suggest that upgrading product quality and processing, strengthening standards and branding, and promoting more inclusive value-chain development are essential for transforming China’s licorice exports from scale expansion to high-quality growth and for enhancing rural incomes in producing regions. Full article

This study critically examines how Artificial Intelligence for Social Responsibility (AI for SR) is enacted within Thai education, using this Global South context to expose the universal dynamics of educational marketization. Drawing on Freire’s critical pedagogy and Habermas’s theory of lifeworld, the research employs a qualitative design grounded in critical phenomenology. Analysis of interviews, observations, and policy documents reveals that AI for SR is driven less by ethical participation than by policy compliance, funding agendas, and portfolio-driven competition. This dynamic transform responsibility from a moral practice into symbolic capital. Students become producers of symbolic output, and educators act as image managers for institutional displays. The study concludes by proposing a critical pedagogical framework that reclaims AI for SR as a public good, emphasizing dialog and social justice to resist this commodification. Full article

Against the backdrop of global energy transition and sustainable development, advancing the new energy industry has become a critical pathway for optimizing energy structures and achieving the dual carbon goals. However, while China’s new energy sector has experienced rapid growth, it has also exposed a series of challenges, including insufficient innovation momentum, irrational resource allocation, and low conversion rates of R&D outcomes. To delve into the root causes and propose improvement pathways, this study selected 76 listed new energy enterprises from 2021 to 2023 as samples. It comprehensively employed the DEA-BCC model, Malmquist productivity index, and Tobit regression model to conduct empirical analysis across three dimensions: static, dynamic, and influencing factors. The findings revealed: firstly, during the study period, overall static efficiency remained low, with only about 32.90% of enterprises operating efficiently. Efficiency decomposition indicated that low and unstable pure technical efficiency constrained overall efficiency gains. In contrast, while scale efficiency was relatively high, its growth was sluggish, and some enterprises exhibited significant scale irrelevance. Secondly, dynamic total factor productivity exhibited fluctuating growth primarily driven by technological progress. However, declining technical efficiency—particularly the deterioration of scale efficiency—indicated that while the new energy industry advanced technologically and expanded in scale, its management capabilities had not kept pace. This mismatch among the three factors trapped the industry in a “high investment, low efficiency” dilemma. Thirdly, regression analysis of influencing factors indicated that corporate governance and market competitiveness were pivotal to innovation efficiency: the proportion of independent directors and revenue growth rate exerted significant positive impacts, while equity concentration showed a significant negative effect. Firm size had a weaker influence, and government support did not demonstrate a significant positive impact. Accordingly, this paper proposes pathways to enhance innovation efficiency in new energy enterprises, including optimizing corporate governance structures, formulating differentiated subsidy policies, and improving the innovation ecosystem. The findings of this study not only provide empirical references for the innovative development of the new energy industry but also offer theoretical support for relevant policy formulation. Full article

The rapid expansion of artificial intelligence (AI) and digitalization in contemporary education has intensified global debates on sustainable education, frequently framed around efficiency, personalization, and technological innovation. At the same time, these developments have accelerated processes of technologization and commodification, raising concerns about the erosion of educational values and human-centered purposes. This tension calls for a critical reassessment of what sustainability should mean in AI-mediated educational contexts. The objective of this study is to examine under what conditions AI contributes to sustainable education as a value-based and human-centered project, and under what conditions it undermines it. Methodologically, the article adopts a qualitative, value-critical analysis of contemporary scholarly literature and policy-oriented debates, employing the distinction between sustainable education, sustainability in education, and education for sustainable development as a heuristic entry point within a broader theoretical dialogue. The analysis demonstrates that AI does not exert a uniform or inherently progressive influence on education. While AI can enhance access, personalization, and instructional support in ethically grounded and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, weaken academic integrity, and marginalize the formative and human dimensions of education under market-driven and weakly regulated conditions. These dynamics are particularly visible in culturally and religiously grounded educational contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on subordinating AI and digitalization to coherent normative, ethical, and governance frameworks that prioritize educational purpose, social justice, and human dignity. Full article

The technological attributes of urban digital infrastructure (UDI) are transforming labor skill structures in the market, thereby altering changes in skill premiums. This study investigates the relationship between UDI and skill premiums by developing a theoretical model that incorporates both digital and material capital. Using data from the China Labor Force Dynamic Survey and urban statistics, we examine the underlying mechanisms. The findings indicate that UDI exhibits skill-biased technological attributes, thereby increasing the skill premium. UDI development raises the demand for high-skilled labor across both skill-intensive and non-skill-intensive industries, altering the labor skill structure and consequently elevating the skill premium. This effect stems from the complementarity between UDI-related digital capital and high-skilled labor. Compared to material capital, deepening digital capital enables high-skilled labor to contribute more significantly to output. Full article

Understanding how housing attributes are capitalized into prices is central to addressing urban affordability challenges. Using 2799 second-hand housing transactions from Wenzhou, China, this study examines residential price formation under pronounced spatial and structural heterogeneity. Multiple predictive models are evaluated within a unified 10-fold cross-validation framework. Results indicate that Random Forest delivers the strongest predictive performance, achieving a normalized mean squared error below 0.10 and explaining over 90% of out-of-sample price variation, substantially outperforming hedonic regression, regression trees, bagging, boosting, and support vector models. Permutation-based importance analysis identifies district location, building scale, and floor area as the dominant price determinants, while the influence of renovation quality, transportation access, and educational amenities varies across districts and dwelling types. These findings reveal strong nonlinearities and heterogeneous valuation mechanisms in rapidly urbanizing housing markets. Methodologically, the study demonstrates how interpretable machine learning complements traditional hedonic analysis, while providing policy-relevant insights into housing affordability dynamics in medium-sized Chinese cities. Full article

This study presents a novel methodology that integrates Transformer-based textual analysis from professional networking platforms with traditional demographic variables within a survival analysis framework to predict turnover. Using a dataset comprising 4087 work events from Maimai (a leading professional networking platform in China) spanning 2020 to 2022, our approach combines sentiment analysis and deep learning semantic representations to enhance predictive accuracy and interpretability for HR decision-making. Methodologically, we adopt a hybrid feature-extraction strategy combining theory-driven methods (sentiment analysis and TF-IDF) with a data-driven Transformer-based technique. Survival analysis is then applied to model time-dependent turnover risks, and we compare multiple models to identify the most predictive feature sets. Results demonstrate that integrating textual and demographic features improves prediction performance, specifically increasing the C-index by 3.38% and the cumulative/dynamic AUC by 3.43%. The Transformer-based method outperformed traditional approaches in capturing nuanced employee sentiments. Survival analysis further boosts model adaptability by incorporating temporal dynamics and also provides interpretable risk factors for turnover, supporting data-driven HR strategy formulation. This research advances turnover prediction methodology by combining text analysis with survival modeling, offering small and medium-sized enterprises a practical, data-informed approach to workforce planning. The findings contribute to broader labor market insights and can inform both organizational talent retention strategies and related policy-making. Full article

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets. Full article

This paper examines how colonial and neoliberal logics have influenced the ideas of self-management, democracy, and participation and how a decolonial perspective might reinterpret them. Although democracy and participation are celebrated in mainstream development discourse, they frequently serve as technologies of control that uphold market rationalities and dependency. The paper presents a conceptual model for comprehending how political and organisational practices in the Global South are both resisted by and limited by these dynamics, drawing on the framework of the colonial matrix of power. With reference to grassroots movements like Abahlali base Mjondolo, which represent alternative democratic logics based on collective self-management and epistemic justice, South Africa is used as a focal case. How gaps in the global architecture of dominance create opportunities for pluriversal futures is further demonstrated by comparative observations from Latin America and other Global South contexts. By (i) exposing the limitations of institutionalised participatory frameworks, (ii) highlighting radical democracy at the grassroots level, and (iii) describing the structural and epistemic prerequisites for significant change, the paper adds to discussions on decolonial political economy. By doing this, it reinterprets participation as a fight for liberating alternatives outside of colonial modernity rather than as inclusion within the status quo. Full article