Search Results (201)

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

The concurrent dissolution and early hydration of tricalcium aluminate (C₃A) and tetracalcium aluminoferrite (C₄AF) critically govern early-stage reaction dynamics in Portland cement systems. However, their mutual kinetic interactions during reaction, particularly sulfate-dependent modulation mechanisms, remain poorly understood. Using in-situ digital holographic microscopy (DHM), this study resolved their interaction mechanisms during co-dissolution in aqueous and sulfate-bearing environments. Results reveal asymmetric modulation: while C₄AF’s dissolution exhibited limited sensitivity to C₃A’s presence, C₃A’s kinetics were profoundly altered by C₄AF through sulfate-concentration-dependent pathways, which originated from two competing C₄AF-mediated mechanisms: (1) suppression via common-ion effects, and (2) acceleration through competitive sulfate species adsorption. These mechanistic insights would provide a roadmap for optimizing cementitious materials through optimized reaction pathways. Full article

With the rapid expansion of high-speed rail infrastructure, maintaining the structural integrity of insulators is critical to operational safety. However, conventional defect detection techniques typically rely on extensive labeled datasets, struggle with class imbalance, and often fail to capture large-scale structural anomalies. In this paper, we present an unsupervised insulator defect detection framework based on a masked autoencoder (MAE) architecture. Built upon a vision transformer (ViT), the model employs an asymmetric encoder-decoder structure and leverages a high-ratio random masking scheme during training to facilitate robust representation learning. At inference, a dual-pass interval masking strategy enhances defect localization accuracy. Benchmark experiments across multiple datasets demonstrate that our method delivers competitive image- and pixel-level performance while significantly reducing computational overhead compared to existing ViT-based approaches. By enabling high-precision defect detection through image reconstruction without requiring manual annotations, this approach offers a scalable and efficient solution for real-time industrial inspection under limited supervision. Full article

Platform markets are concentrating, even as their content suppliers fragment, yet this asymmetric evolution is poorly understood. Using panel data from 11–12 Korean home shopping firms (2015–2023), we employ Hansen threshold regression, instrumental variables, and panel fixed-effects models to examine its competitive impact. Our analysis of 104 firm-year observations reveals four key findings. First, platform concentration alone explains 94.4% of transmission fee variation, with fees rising from 41.15% to 68.72% as platform HHI increased from 1390 to 2154 while content HHI declined from 1797 to 1118. Second, we identify critical fee thresholds at 62.2% (p = 0.012) and 73% (p = 0.002) that divide markets into three distinct operating regimes. Third, the fee–profitability relationship reversed from negative (r = −0.145) to positive (r = 0.554), indicating fees’ evolution from cost burdens to selection mechanisms. Fourth, instrumental variable estimates (0.473) exceed OLS estimates (0.184) by 2.6 times, revealing severe selection bias. Simulations indicate a 60% fee cap would affect 25 firms (24%) while increasing total surplus by 15.1% and improving SME profitability by 2.9 percentage points. We propose the Asymmetry Ratio (Platform HHI/Content HHI) as a regulatory tool, with ratios exceeding 1.0 triggering enhanced scrutiny. Our findings demonstrate that asymmetric market evolution creates new sources of platform power requiring novel regulatory approaches. Full article

In recent years, online retailing platforms (ORPs) have increasingly introduced private brand (PB) products as a new profit source, reshaping market dynamics and affecting their commission revenues. This shift creates a strategic trade-off for the platform: maximizing PB product profits while maintaining commission income from national brand (NB) retailers. This paper examines the platform’s pricing and quality strategies for PB products, as well as its incentives to introduce them. We develop a game-theoretic model featuring a platform and a retailer, and derive results through equilibrium analysis and comparative statics. Special attention is given to the platform’s strategy when market power is asymmetric and the PB product is homogeneous. The analysis yields three key findings. Firstly, the platform is always incentivized to introduce a PB product, regardless of its brand value. Even when direct profit is limited, the platform can leverage the PB product to increase competitive pressure on the retailer and boost commission revenue. Secondly, when the PB product has low brand value, the platform adopts a cost-saving strategy with low quality for extremely low brand value, and a function-enhancing strategy with high quality for moderately low brand value. Thirdly, when the PB product has high brand value, the platform consistently prefers a function-enhancing strategy. This study contributes to the literature by systematically characterizing the platform’s strategic trade-offs in introducing PB products, highlighting its varied pricing and quality strategies across categories, and revealing the critical role of brand value in supply chain competition. Full article

Strategic financial decision-making is critical for organizational sustainability and competitive advantage. However, traditional approaches that rely solely on human expertise or isolated machine learning (ML) models often fall short in capturing the complex, multifaceted, and often asymmetrical nature of financial data, leading to suboptimal predictions and limited interpretability. This study addresses these challenges by developing an innovative, symmetry-aware integrated ML framework that synergizes decision trees, advanced ensemble techniques, and human expertise to enhance both predictive accuracy and model transparency. The proposed framework employs a symmetrical dual-feature selection process, combining automated methods based on decision trees with expert-guided selections, ensuring the inclusion of both statistically significant and domain-relevant features. Furthermore, the integration of human expertise facilitates rule-based adjustments and iterative feedback loops, refining model performance and aligning it with practical financial insights. Empirical evaluation shows a significant improvement in ROC-AUC by 2% and F1-score by 1.5% compared to baseline and advanced ML models alone. The inclusion of expert-driven rules, such as thresholds for debt-to-equity ratios and profitability margins, enables the model to account for real-world asymmetries that automated methods may overlook. Visualizations of the decision trees offer clear interpretability, providing decision-makers with symmetrical insight into how financial metrics influence bankruptcy predictions. This research demonstrates the effectiveness of combining machine learning with expert knowledge in bankruptcy prediction, offering a more robust, accurate, and interpretable decision-making tool. By incorporating both algorithmic precision and human reasoning, the study presents a balanced and symmetrical hybrid approach, bridging the gap between data-driven analytics and domain expertise. The findings underscore the potential of symmetry-driven integration of ML techniques and expert knowledge to enhance strategic financial decision-making. Full article

Digital transformation is crucial for businesses to thrive in today’s rapidly evolving marketplace. It is a strategic choice that enables organizations to improve customer service, strengthen supplier relationships, and boost sales and business growth, ultimately enhancing their competitive stance. The Internet of Things (IoT) has become a transformative force across various domains, leveraging interconnected devices and sensors to gather and analyse data, thus enhancing decision making, efficiency, and innovation. This paper analyses the strategic competition between two 3PL firms integrating IoT technologies. Based on a game-theoretic model, the study uses Monte Carlo simulation and K-means clustering to identify distinct strategic groups and optimal adoption ranges. The findings highlight risks of over- or under-investments as well as asymmetric outcomes. Also, a set of recommendations and managerial insights are provided for better decision making in a tech-competitive setting. Full article

Molecular property prediction, as one of the important tasks in cheminformatics, is attracting more and more attention. The structure of a molecule is closely related to its properties, and a symmetrical molecular structure may differ significantly from an asymmetrical structure in terms of properties, such as the melting point, boiling point, water solubility, and so on. However, a single molecular representation does not provide a better overall representation of the molecule. And, it is also a challenge to better use graph neural networks to aggregate the information of neighboring nodes in the molecular graph. So, in this paper, we constructed a novel graph neural network with additive attention (termed Add-GNN) for molecular property prediction, which fuses the molecular graph and molecular descriptors to jointly represent molecular features in order to make the molecular representations more comprehensive. Then, in the message-passing stage, we designed an additive attention mechanism that can effectively fuse the features of neighboring nodes and the features of edges to better capture the intrinsic information of molecules. In addition, we applied L2-norm to calculate the importance of each atom to the predicted results and visualized it, providing interpretability to the model. We validated the proposed model on public datasets and showed that the model outperforms graph-based baseline methods and some graph neural network variants, proving that our proposed method is feasible and competitive. Full article

The fundamental principles of “sustainable development” and “green” promoted by ESG align with the concept of “green and sustainable” development. Enhancing enterprise ESG is a methodical endeavor that necessitates enterprises to possess ESG investment capabilities, coordinate many stakeholders, and leverage the influence of prominent market players. State-owned enterprises (SOEs) possess a specific level of support within a nation’s economy. SOEs serve as a fundamental pillar of China’s socialist economic system with distinctive characteristics, significantly influencing business conduct and reinforcing corporate value orientation. Consequently, the capacity of SOEs to assume a strategic leadership role in enhancing supply chain ESG performance is of paramount importance for the general elevation of ESG standards among Chinese enterprises. Limited research has investigated the transmission effect of the ESG performance among chain enterprises from a supply chain viewpoint, particularly regarding the pivotal role of SOEs in enhancing the ESG performance of these entities. This article examines the influence of SOEs’ ESG performance on the ESG performance of supply chain enterprises, focusing on the spillover effects of SOEs’ ESG performance within the supply chain context. It investigates how SOEs lead upstream and downstream enterprises in enhancing their ESG performance, aiming to address the existing cognitive gap in this area and provide substantial evidence for pertinent theories and practices. This article, employing an empirical research methodology, discovers that the ESG performance of state-owned supply chain core enterprises significantly enhances the ESG performance of enterprises in a supply chain, while non-state-owned supply chain core enterprises do not exhibit this effect. Furthermore, research indicates that this effect is asymmetric: when the supply chain core enterprise is a SOE and the enterprises in the supply chain are non-state-owned, the leading effect is more pronounced, and this effect is more powerful for upstream enterprises. The heterogeneity test reveals that the impact of the ESG performance is more pronounced in larger state-owned supply chain core enterprises that have been publicly listed for an extended duration and operate in highly competitive markets. The conclusions of this essay address the deficiencies of current research and provide significant practical implications for the development of green supply chains in the contemporary era. Full article

The evolving macroeconomic landscape, shaped by the global financial crisis and the COVID-19 pandemic, poses significant challenges for economies worldwide. However, Central, Eastern, and Southeastern European (CESEE) countries have demonstrated resilience and rapid recovery during crises, driven by a surge in consumption fueled by domestic credit and robust export growth supported by flexible exchange rates and adaptive monetary policies. Prior to EU accession, substantial foreign direct investment (FDI) during privatization and restructuring facilitated knowledge and technology transfers in CESEE economies. This study examines the interplay of exports, real exchange rates, GDP growth, FDI, inflation, domestic credit, and the human development index (HDI) in the CESEE region from 1995 to 2022, covering the transition period, EU accession, and major crises. Employing a panel ARDL model, we account for asymmetric effects of these variables on exports. The results reveal that GDP, FDI, inflation, domestic credit, and HDI significantly and positively influence exports, with HDI and GDP exerting the strongest effects, underscoring the pivotal roles of human capital and economic growth in enhancing export competitiveness. Conversely, real exchange rate depreciation negatively impacts exports, though non-price factors, such as product quality, mitigate this effect. These findings provide a robust basis for targeted policy measures to strengthen economic resilience and export performance in the CESEE region. Full article

As critical strategic mineral resources underpinning high-tech industries and national defense security, rare earth elements have become a central focus of international competition, with their global industrial chain configuration deeply intertwined with geopolitical dynamics. Leveraging a novel multilateral database encompassing 140 countries’ geopolitical relationships and rare earth trade flows (2001–2023), this study employs social network analysis and temporal exponential random graph models (TERGMs) to decode structural interdependencies across upstream mineral concentrates, midstream smelting, and downstream permanent magnet sectors. Empirical results show that topological density trajectories reveal intensified network coupling, with upstream/downstream sectors demonstrating strong clustering. Geopolitical cooperation and conflict exert differential impacts along the value chain: downstream trade exhibits heightened sensitivity to cooperative effects, whereas midstream trade suffers the most pronounced obstruction from conflicts. Cooperation fosters long-term trade relationships, whereas conflicts primarily impose short-term suppression. In addition, centrality metrics reveal asymmetric mechanisms. Each unit increase in cooperation degree centrality amplifies the mid/downstream trade by 3.29 times, whereas conflict centrality depresses the midstream trade by 4.76%. The eigenvector centrality of cooperation hub nations enhances the midstream trade probability by 5.37-fold per unit gain, in contrast with the 25.09% midstream trade erosion from conflict-prone nations’ centrality increments. These insights provide implications for mitigating geopolitical risks and achieving sustainable governance in key mineral resource supply chains. Full article

Hyperspectral imagery (HSI), with its rich spectral information across continuous wavelength bands, has become indispensable for fine-grained land cover classification in remote sensing applications. Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. Experimental results from four common hyperspectral data sets validate the effectiveness of the proposed method. Specifically, when 10, 10, 10 and 25 samples from each class are selected for training, ASLNN achieves the highest overall accuracy (OA) of 81.12%, 85.88%, 80.62%, and 97.97% on the four data sets, outperforming other methods with increases of more than 1.70%, 3.21%, 3.78%, and 2.70% in OA, respectively. Full article

This study examines the asymmetric effects of foreign worker employment and low educational attainment on labor productivity across Malaysia’s three main economic sectors—agriculture, industry, and services—from 1991 to 2019 using the nonlinear autoregressive distributed lag (NARDL) model. Three sectoral models are estimated to capture how overdependence on foreign workers and low-skilled local labor influences productivity. Model 1 for agriculture underscores positive variations vis-à-vis how foreign worker employment boosts agricultural productivity in both the short and long term. However, negative variations lead to diminished productivity in the long run. Primary education negatively affects long-term agricultural productivity. In Model 2 for industry, neither foreign worker employment nor low educational attainment significantly affects productivity. Model 3 for services reveals a short-term boost in productivity with increased foreign workers’ employment, whereas reduced employment enhances long-term productivity. The absence of formal education is detrimental to long-term service productivity, while primary education affects it negatively in the short term. NARDL multiplier graphs and Wald tests confirm significant long-run asymmetric effects of foreign labor in the agriculture and services sectors. The findings highlight the need for Malaysia to reduce reliance on low-skilled labor and accelerate its transition toward a high-skilled workforce to sustain productivity growth and economic competitiveness. Full article

Sensors generate extensive time series data across various domains, and effective methods for detecting anomalies in such data are still in high demand. Unsupervised time series anomaly detection provides practical approaches to addressing the challenges of collecting anomalous data. For effective anomaly detection, a range of deep-learning-based models have been explored to handle temporal patterns inherent in time series data. In particular, Transformer encoders have gained significant attention due to their ability to efficiently capture temporal dependencies. Various studies have attempted the architectural improvements of Transformer encoders to address the inherent complexity of time series data analysis. Unlike the previous studies, this work demonstrates that a vanilla Transformer encoder-based framework remains yet a competitive model for time series anomaly detection. Instead of architectural modification of the Transformer encoder, we identify key design choices and propose an asymmetric autoencoder-based framework incorporating those design choices with a vanilla Transformer encoder and a linear layer decoder. The proposed framework has been evaluated on a range of unsupervised time series anomaly detection benchmarks, and the experimental results show that it achieves performance that is either superior or competitive compared to state-of-the-art models. Full article