Search Results (710)

Understanding public service performance remains a persistent challenge, particularly when services are delivered through complex interorganizational networks. This difficulty is amplified in contexts addressing wicked problems such as homelessness, where needs are multifaceted, solutions are interdependent, and outcomes are hard to measure. In the United States, the Continuum of Care (CoC) system represents a federally mandated and HUD-funded network model designed to coordinate local responses to homelessness through collaborative governance. Despite its standardized structure and federal oversight, CoC’s performance varies significantly across regions. This study investigates the conditions that influence the CoC network’s performance, focusing on the delivery of Permanent Supportive Housing (PSH) services, a critical intervention for addressing chronic homelessness. It applies to a theoretical framework that combines Ansell and Gash’s collaborative governance model with Emerson et al.’s integrative framework. This approach allows for a comprehensive assessment of internal network factors such as board size, nonprofit leadership, and federal funding, as well as external system contexts including political orientation, income levels, and rent affordability. Drawing on regression analysis of data from 343 CoCs across the United States, the study shows that federal funding, favorable political climates, and larger board size are significant predictors of PSH availability, while nonprofit leadership and income levels are not. Findings highlight the importance of aligning internal governance and external context to improve network outcomes. Full article

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes. Full article

This study presents a bibliometric and thematic analysis of economic convergence analysis from 1982 to 2025, based on a corpus of 2924 Scopus-indexed articles. Using VOSviewer and the bibliometrix R package, this research maps the field’s intellectual structure, identifying five main thematic clusters: (1) formal statistical models, (2) institutional-contextual approaches, (3) theoretical–statistical foundations, (4) nonlinear historical dynamics, and (5) normative and policy assessments. These reflect a shift from descriptive to explanatory and prescriptive frameworks, with growing integration of sustainability, spatial analysis, and institutional factors. The most productive journals include Journal of Econometrics (121 articles), Applied Economics (117), and Journal of Cleaner Production (81), while seminal contributions by Quah, Im et al., and Levin et al. anchor the co-citation network. International collaboration is significant, with 25.99% of publications involving cross-country co-authorship, particularly in European and North American networks. The field has grown at a compound annual rate of 14.4%, accelerating after 2000 and peaking in 2022–2024, indicating sustained academic interest. These findings highlight the maturation of convergence analysis as a multidisciplinary domain. Practically, this study underscores the value of composite indicators and spatial econometric models for monitoring regional, environmental, and technological convergence—offering policymakers tools for inclusive growth, climate resilience, and innovation strategies. Moreover, the emergence of clusters around sustainability and digital transformation reveals fertile ground for future research at the intersection of transitions in energy, digital, and institutional domains and sustainable development (a broader sense of structural change). Full article

Europe has experienced a growing divergence in trends of population change across the urban hierarchy. A key driver of this divergence is internal migration, which underpins the efficient functioning of the economy by enhancing labor market flexibility and allowing people to choose the most desired locations. Internal migration in Latvia is of increasing importance, as the propensity to change residence within national borders has become the primary mechanism of demographic change, shaping population redistribution across regions and the urban hierarchy. We used Latvia as a case study, exemplified by the monocentric urban system with Riga City at its center, as well as a relatively dense network of small towns spread across all regions. Small towns in Latvia, although not characterized by high levels of internal migration, exhibit notable changes in their demographic and socioeconomic composition. Our analysis uses administrative data on registered migration for each year from 2011 to 2021 to characterize migration patterns, as well as data from the 2011 and 2021 census rounds on 1-year migration to analyze the composition of the migrant population. The results showed sociodemographic variations in the characteristics of individuals migrating to small towns. Understanding the temporal and spatial dynamics of internal migration patterns and compositional effects is vital for effective local and regional development policies to plan essential services and infrastructure. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

The increasing integration of artificial intelligence agents (AIAs) based on large language models (LLMs) is transforming many spheres of society. These agents act as human assistants, forming Distributed Intelligent Systems (DISs) and engaging in opinion formation, consensus-building, and collective decision-making. However, complex DIS network topologies introduce significant uncertainty into these processes. We propose a quantum-inspired graph signal processing framework to model collective behavior in a DIS interacting with an external environment represented by an influence matrix (IM). System topology is captured using scale-free and Watts–Strogatz graphs. Two contrasting interaction regimes are considered. In the first case, the internal structure fully aligns with the external influence, as expressed by the commutativity between the adjacency matrix and the IM. Here, a renormalization-group-based scaling approach reveals minimal reservoir influence, characterized by full phase synchronization and coherent dynamics. In the second case, the IM includes heterogeneous negative (antagonistic) couplings that do not commute with the network, producing partial or complete spectral disorder. This disrupts phase coherence and may fragment opinions, except for the dominant collective (Perron) mode, which remains robust. Spectral entropy quantifies disorder and external influence. The proposed framework offers insights into designing LLM-participated DISs that can maintain coherence under environmental perturbations. Full article

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R² = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions. Full article

Background/Objectives: Large-scale bibliometric assessments of psoriasis pharmacotherapy research remain limited despite significant research output in this rapidly evolving field. This study aimed to map the evolution of systemic psoriasis therapy research over five decades and demonstrate how systematic analysis of research trajectories can illuminate the transformation of specialized medical fields into central components of precision medicine. Methods: A comprehensive bibliometric analysis was conducted using Web of Science Core Collection as the single data source, examining 19,284 publications spanning 1975–2025. The methodology employed AI-enhanced terminology normalization for standardizing pharmaceutical nomenclature, VOSviewer version 1.6.20 for network visualization, and Bibliometrix package for temporal trend analysis and thematic evolution mapping. International collaboration networks, thematic evolution across three distinct periods (1975–2000, 2001–2010, 2011–2025), and citation impact patterns were systematically analyzed. Results: Four distinct developmental phases were identified, with publications growing from 9 articles in 1975 to 1638 in 2024. The United States dominated research output with 5959 documents, while Canada achieved the highest citation efficiency at 62.65 citations per document. Global collaboration encompassed 70 countries organized into four regional clusters, with a 28-nation Asia–Pacific–Africa–Middle East alliance representing the largest collaborative group. Citation impact peaked during 2001–2008, coinciding with revolutionary biological therapy introduction. Thematic evolution demonstrated systematic transformation from two foundational themes to nine specialized domains, ultimately consolidating into four core areas focused on targeted therapeutics and evidence-based methodologies. Keyword analysis demonstrated progression from basic immunological studies to sophisticated targeted interventions, evolving from tumor necrosis factor alpha inhibitors to contemporary interleukin-17/interleukin-23 pathway targeting and Janus kinase inhibitors. Conclusions: Over five decades, psoriasis therapeutics research has shifted from a niche dermatological discipline to a central model for innovation in immune-mediated diseases. This evolution illustrates how bibliometric approaches can capture the dynamics of scientific transformation, offering strategic insights for guiding pharmaceutical innovation, shaping research priorities, and informing precision medicine strategies across inflammatory conditions. Full article

This paper examines the internationalization dynamics of the Turkish HVAC industry in Germany through a qualitative design based on 24 semi-structured interviews with senior executives. The analysis demonstrates that conformity with EU and German standards, product quality, and continuous innovation are decisive drivers of international expansion. At the same time, economic volatility and regulatory complexity constitute major constraints. Organizational capabilities—particularly internationally experienced managers, R&D capacity, and strategic partnerships—are shown to enhance firms’ competitiveness. Furthermore, diaspora networks provide relational capital that facilitates trust and market embeddedness. The study contributes to international business literature by identifying critical success factors for Turkish HVAC firms in a highly competitive European context. Full article

Although giant viruses have introduced new perspectives on the definition and evolution of viruses and are increasingly recognized for their significant biological roles within marine ecosystems, systematic evaluations of development trends and scientific contributions in this research field remain limited. This study conducted a bibliometric analysis of the global academic literature on marine giant viruses (MGVs), focusing on nucleocytoplasmic large DNA viruses (NCLDVs), from 1996 to 2024. Using the Web of Science Core Collection, 1544 publications related to giant viruses were identified. After filtering using marine-related keywords and manual review, 300 studies specifically addressing marine giant viruses were selected for the final analysis. This study comprehensively examined the structural characteristics and evolutionary trends in this field by analyzing annual publication productivity, citation patterns, contributions by countries and institutions, author collaboration networks, and keyword co-occurrence patterns. The results show that research on MGVs has steadily increased since the mid-2000s, with a notable surge after 2018 driven by advancements in metagenomics, next-generation sequencing technologies, and global ocean exploration initiatives. The United States and France have taken leading positions in terms of research productivity and impact, with key institutions such as the CNRS (Centre National de la Recherche Scientifique) and Aix-Marseille Université playing central roles. A multipolar network of international collaborations between countries and institutions has been formed. Research topics have evolved from an early focus on virus classification and genome analysis to more diverse themes, including interactions with marine microbiota, viral ecological functions, infection dynamics, virophage research, and metagenome-based ecosystem-level studies. This study provides an overview of the chronological and structural evolution of the marine giant virus research field by systematically presenting key research themes and collaborative networks. The results provide a valuable foundation for determining future academic directions and planning strategic research initiatives. Furthermore, it is expected to facilitate interdisciplinary research in marine biology, environmental science, systems biology, and artificial intelligence-based functional predictions. Full article

Modern utility systems are being heavily strained by rising energy consumption and dynamic load variations, which have an impact on the quality and reliability of the supply. Harmonic injection and reactive power imbalance are caused by the widespread divergence. Power quality (PQ) issues are mostly caused by renewable energy powered by power electronic converters that are integrated into the utility grid, despite the fact that a range of industries require high-quality power to function properly at all times. Several solutions have been created, but continuing efforts and newly improved solutions are needed to solve these problems by operating according to various international standards. Distributed Static Compensator (DSTATCOM) was created in the proposed model to enhance PQ in a standard bus system. A standard bus system using the DSTATCOM model was initially developed. A real-time dataset was gathered while applying various PQ disturbance conditions. A deep learning controller was created using this generated dataset, which examined the bus voltages to generate the DSTATCOM pulse signal. Two case studies, the IEEE 13 bus and the IEEE 33 bus system, were used to analyze the proposed work. Performance of the proposed deep learning controller was verified in various situations, including interruption, swell, harmonics, and sag. The outcome of THD in the IEEE 13 bus is 0.09% at the sag period, 0.08% at the swell period, 0.01% at the interruption period, and in the IEEE 33 bus was 1.99% at the sag period, 0.44% at the swell period, and 0.01% at the interruption period. Also, the effectiveness of the proposed deep learning controller was examined and contrasted with current methods like K-Nearest Neighbor (KNN) and Feed Forward Neural Network (FFNN). The validated results show that the suggested method provides an efficient mitigation mechanism, making it suitable for all cases involving PQ issues. Full article

The assessment of ship dynamic stability in waves is crucial for navigation safety. To mitigate accidents, the International Maritime Organization (IMO) has formulated corresponding technical standards. However, evaluating the dynamic stability performance of ships involves complex numerical simulation or model experiments based on hydrodynamic methods, which demands professionalism, substantial time, and significant financial cost. This paper analyzes the feasibility of using the Generalized Regression Neural Network (GRNN) method to build a surrogate model for ship dynamic stability performance calculation. Comparisons with hydrodynamics-based simulations reveal that the surrogate model matches the trends well, yet the root-mean-square error (RMSE) remains non-negligible. Therefore, an improved GRNN surrogate model is proposed to solve this problem. By incorporating enhanced feature preprocessing and clustering techniques, the improved model not only increases predictive accuracy but also achieves significant efficiency gains, reducing the computational time from days or weeks for numerical simulations to seconds or minutes. Experimental results show that the improved surrogate model outperforms the baseline GRNN model, and this framework can serve as a practical surrogate for hydrodynamics-based numerical models to rapidly assess pre-voyage dynamic stability. Full article

The silkworm (Bombyx mori) is rich in germplasm resources, including thermotolerant strains that live in tropical/subtropical humid climates. In this study, two thermotolerant strains and one sensitive strain were used as materials, with the former exhibiting higher critical thermal maximum (CTmax) values. Under different temperature and humidity stresses, physiological and transcriptomic responses of the fifth instar larvae were compared. It was confirmed that high humidity exacerbates harmful effects only under high temperature conditions. Based on transcriptome and co-expression network analysis, 88 evolved thermoplastic genes (Evo_TPGs) and 1338 evolved non-plastic genes (Evo_non-PGs) were identified, which exhibited specific responses or expressions in the two thermotolerant strains. Eighteen of the Evo_TPGs encode cuticular proteins, 17 of which were specifically downregulated in the two thermotolerant strains after short-term exposure to 35 °C. This may promote cuticular transpiration to dissipate internal heat, thus compensating for the suppression of tracheal ventilation in hot and humid climates. For the Evo_non-PGs, most of the metabolic genes showed lower expression at background levels in the thermotolerant strains, while oxidative stress genes showed the opposite trend, suggesting that silkworms can enhance heat tolerance by suppressing metabolic rates and allocating more resources to overcome heat-induced oxidative damage. Furthermore, the heat resistance-related genes showed higher single nucleotide polymorphisms (SNPs) between resistant and sensitive strains compared to randomly selected genes, suggesting that they may have been subjected to natural selection. Through long-term adaptive evolution, thermotolerant silkworms may reduce their internal temperature by dynamically regulating cuticle respiration in response to high temperature and humidity, while allocating more energy to cope with and repair heat-induced damage. Overall, these findings provide insights into the evolution of heat-resistant adaptations to climate change in insects. Full article

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance. Full article