Search Results (960)

High-risk antibiotics remain indispensable in contemporary infectious diseases practice, yet they account for a disproportionate share of preventable toxicity, therapeutic drug monitoring complexity, and antimicrobial stewardship workload. Vancomycin, aminoglycosides, colistin, linezolid, daptomycin, selected beta-lactams, and amphotericin B are particularly challenging because clinically relevant exposure-toxicity relationships coexist with marked inter-patient variability and fragmented post-marketing safety surveillance. Artificial intelligence and real-world evidence are increasingly proposed as complementary approaches to address these limitations, although the evidence base remains heterogeneous and predominantly retrospective. This narrative review synthesises literature from PubMed/MEDLINE, Scopus, and Web of Science published between 2019 and April 2026, supplemented by citation chaining and regulatory pharmacovigilance resources, with studies prioritised by implementation maturity, external validation status, and stewardship relevance. Current evidence indicates that artificial intelligence may improve safety monitoring when embedded within clinically rich data environments: machine learning models show promising discrimination for nephrotoxicity and haematological toxicity in vancomycin, colistin, and linezolid therapy; natural language processing may enhance adverse drug event extraction from clinical text; and Bayesian, model-informed tools already demonstrate clinical utility in vancomycin and aminoglycoside dosing. However, prospective implementation data remain sparse, external validation is uncommon, and evidence that these tools improve real-world antibiotic safety outcomes, as opposed to predictive discrimination alone, remains limited. Artificial intelligence-enabled antibiotic safety monitoring is therefore transitioning from methodological promise towards conditional clinical utility rather than proven benefit. Near-term value is most likely to arise from integration with therapeutic drug monitoring, antimicrobial stewardship, and pharmacology-led clinical review rather than autonomous decision-making, with clinical pharmacologists and stewardship teams leading local implementation, validation, and governance of these tools. Full article

Agritourism is increasingly intersecting with digital technologies to foster rural resilience, economic growth, and sustainable development. This study conducts a comprehensive systematic bibliometric review to map the intellectual structure, thematic evolution, and collaborative networks characterizing the digitalization of agritourism from 2010 to 2025. Guided by the PRISMA framework, data from the Scopus database were analyzed using scientific mapping techniques, including keyword co-occurrence, thematic evolution tracking, and spatial collaboration analysis. The findings reveal a paradigm shift categorized into three evolutionary phases: an incubation period of basic web adoption (2011–2017), a disruptive phase catalyzed by the COVID-19 pandemic (2018–2022), and an exponential maturation phase driven by Industry 4.0 technologies such as Artificial Intelligence (AI), Big Data, and Virtual Reality (2023–2025). Four primary thematic clusters emerged: digital marketing and connectivity, smart tourism and advanced analytics, immersive technologies for heritage preservation, and macro-level sustainability policies. Geopolitically, research is driven by two distinct networks: an Asian-centric hub led by China focusing on state-sponsored smart villages, and a Western hub anchored by the USA and Italy emphasizing entrepreneurial diversification. The study concludes that digitalization has transitioned from a reactive survival mechanism to a proactive strategic necessity. It highlights the critical need to bridge the digital divide through human capital investment and provides a future research agenda focusing on the ethical application of AI, the circular economy, and the preservation of rural authenticity in emerging ’phygital’ environments. Full article

Shared energy storage is emerging as a pivotal institutional and technological arrangement for increasing power-system flexibility, integrating renewable electricity, and improving the allocation of storage resources. Focusing on power generation enterprises, this study develops a Technology–Organization–Environment (TOE) model that incorporates perceived risk and perceived benefit to explain how participation intentions toward shared energy storage are formed. Structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) show that technological compatibility, technological maturity, top-management support, organizational slack, subjective norms, policy support, and market competition shape participation intentions by reducing perceived risk and strengthening perceived benefit. Perceived risk significantly suppresses participation intention, whereas perceived benefit significantly promotes it. The fsQCA results identify three configurational pathways to high participation intention: benefit–risk co-activation, market competition and benefit-driven participation, and market–policy dual activation. These findings show that participation in shared energy storage is generated by interdependent technological, organizational, and environmental conditions rather than by any single determinant. The study offers evidence for refining a shared-energy-storage policy and improving business models in the transition to a new power system. Full article

With the continuous embedding of small- and medium-sized manufacturing enterprises (SMMEs) into manufacturing platform ecosystems, selecting the optimal resource reconstruction model has become a critical decision for both platforms and SMMEs. However, the existing literature provides limited insight into how different resource types compare in terms of their efficiency in enabling capability reconstruction. Addressing this gap, this paper constructs asymmetric Stackelberg game-theoretic models for the platform–SMME collaborative system and systematically investigates efficiency differentiation and strategy selection under three resource models: the fund resource model, the knowledge resource model, and the fund and knowledge hybrid model. Drawing on dynamic capability theory, the models formalize the transmission paths through which heterogeneous resources affect SMMEs’ capability reconstruction, and derive the equilibrium conditions governing resource input, pricing, revenue-sharing, and payoff outcomes. The findings reveal three key insights. (1) As dynamic capability matures and price sensitivity rises, the platform’s optimal strategy shifts systematically from fund resource model through hybrid model to knowledge resource model, requiring dynamic model switching aligned with the SMME’s capability path and market conditions. (2) Fund-driven improvement operates through two paths, while knowledge-driven improvement operates through four paths and the upstream paths, creating a win-win zone and downstream paths generating diverging interests. (3) Dynamic capability, particularly conversion efficiency of “information-management,” reduces resource input while increasing both parties’ payoffs, shifting the strategy from a fund resource model to a knowledge resource model. The findings point to a shift from resource provision to capability-driven collaboration, requiring dynamic model switching by platforms, precision matching by SMMEs, and shared investment in dynamic capabilities for sustainable mutual benefit. Full article

This article examines Liquid Equity Rewards (LERs), a proposed blockchain-enabled mechanism designed to provide shareholders with time-weighted, utility-only incentives as a potential tool for improving corporate governance. LERs employ a dual architecture comprising voucher-based rewards for off-chain equities and programmable on-chain units for tokenized stocks to encourage shareholder retention amid proxy battles, activist challenges, and corporate political complexities. Drawing on NASDAQ’s tokenized stock framework, stablecoin infrastructure, and DeFi liquid staking principles, this article develops a conceptual and normative framework for LERs and evaluates its potential effectiveness relative to conventional defenses such as poison pills. The analysis assesses LER’s plausible legal compatibility with Delaware corporation law, U.S. securities rules, and the EU’s MiCA framework, while acknowledging that definitive legal conclusions require case-specific adjudication and future regulatory interpretation. The article advances four testable hypotheses regarding LER’s potential to mitigate stock price volatility, reduce activist success rates, and address ESG, M&A, and political expenditure disputes in a market context shaped by shareholder activism. The proxy-fight context is the principal application; ESG, M&A, political-spending, and executive-compensation contexts are discussed as illustrative extensions of the framework rather than as equally mature use cases. A comparative evaluation against existing governance mechanisms and a cost–benefit analysis suggest that LER’s governance enhancements and market opportunities may outweigh implementation challenges, subject to empirical validation. This article contributes a structured analytical framework and identifies conditions under which LERs could offer a scalable, transparent alternative that fosters stakeholder alignment. Full article

DeFi perpetual futures have expanded from crypto-native instruments to tokenised equities and commodities, yet the economics of these instruments remain poorly understood. We study 17 assets—5 crypto coins, 8 tokenised equities, and 4 tokenised commodities—on three DeFi perpetual platforms (Hyperliquid, EdgeX, Lighter) over July 2025 to February 2026. Applying a rolling 3-day t-test to identify abnormal trading volume without a predetermined event calendar, we document 1797 statistically significant volume anomalies. DeFi perpetual volume is driven primarily by macroeconomic and policy shocks (ADA $t=+628$ on the U.S. Crypto Strategic Reserve announcement; 15 of 17 assets simultaneously anomalous during January 2026 mega-cap earnings), asset-class-specific catalysts, and a recurring 24/7 market-structure effect tied to weekends and U.S. holidays. Price tracking accuracy reveals a sharp maturity gradient: crypto coin perpetuals exhibit near-perfect price tracking ($\rho \ge 0.999$) and strong TradFi volume co-movement ($\rho \left(0\right)\in [0.72,0.83]$), while equity perpetuals show weaker integration and commodity perpetuals range from adequate (oil, gold) to unreliable (natural gas). We conclude that crypto DeFi perpetuals constitute credible synthetic economic claims on underlying assets, while equity and commodity perpetuals remain at an early developmental stage. Integration with traditional financial markets is well-established for crypto coin perpetuals; for equity and commodity perpetuals, the evidence is preliminary, given short observation windows, and further research with longer time series is needed before definitive conclusions can be drawn. Full article

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI capabilities onto the full digital twin lifecycle—from sensor-driven calibration through real-world deployment to district-scale operation. This review addresses this gap through six objectives: analyzing AI-enhanced modeling approaches for building digital twins; examining data infrastructure and interoperability requirements; evaluating validation, calibration, and uncertainty quantification practices; synthesizing real-world implementation evidence across diverse building typologies; assessing integration with renewable energy systems and smart grids; and identifying challenges, research gaps, and a strategic deployment roadmap. Physics-based, data-driven, and hybrid modeling strategies occupy distinct and complementary roles. Physics-informed surrogate models preserve thermodynamic interpretability while reducing computational overhead; deep learning architectures—including recurrent networks and reinforcement learning agents—deliver adaptive control; and federated learning frameworks enable privacy-preserving optimization across distributed building portfolios. Rigorous multi-metric validation aligned with established calibration standards proves essential for trustworthy deployment, while Bayesian and ensemble-based uncertainty quantification methods emerge as indispensable components of operationally credible digital twins. Evidence from real-world deployments in residential, commercial, healthcare, and industrial facilities confirms that AI-powered digital twins consistently deliver substantial energy savings and measurable improvements in occupant comfort. Scaling to district and urban levels introduces challenges in data governance, computational architecture, and multi-stakeholder coordination, yet federated digital twin frameworks are beginning to demonstrate viable pathways. The paper concludes with a decade-long strategic roadmap spanning technological maturation, market development, regulatory alignment, and decarbonization impact—positioning AI-enhanced digital twins not as incremental optimization tools, but as the foundational infrastructure for the coordinated transformation of the global building stock. Full article

Data has become a strategic production factor, but the institutional logic underlying data’s tradability, priceability, and governability remains insufficiently theorized. In response, this study develops a coevolutionary framework that connects conventional factor market theory with digital political economy, platform theory, and comparative institutional analysis. This study adopts a conceptual–analytical research design, integrating three research methods: theory synthesis, comparative institutional analysis, and policy-process interpretation. Through theoretical synthesis, institutional comparison, and policy-process interpretation, it analyzes the conditions under which data circulation becomes feasible, lawful, and economically sustainable. In addition, by combining transaction data, exchange listings, property rights registrations, network indicators, and regional policy variations, it formulates testable propositions and an empirical agenda. The study finds that data factor markets do not emerge automatically with digitalization; their formation requires three mutually reinforcing conditions: technologically reducing search, verification, privacy protection, and contract enforcement costs; institutionally realizing a modular definition of rights and establishing compliance boundaries; and market demand from firms, public agencies, and research organizations generating use-case-specific value. Meanwhile, this study revises the three-stage model of market evolution as a contingent and testable pathway—from administrative pilot allocation, through hybrid state–market professionalization, to ecosystem-based cross-domain circulation. It also clarifies a closed-loop dynamic mechanism consisting of external shocks, internal strategic feedback, and adaptive governance, which jointly shapes market boundaries, pricing rules, and competition patterns. Full article

This study examines how venture capital investors in Kazakhstan evaluate women-led enterprises within a frontier-emerging market context characterized by institutional transition and evolving entrepreneurial ecosystems. Addressing a gap in the literature on gendered investment behavior beyond mature markets, the research adopts an exploratory mixed-methods design combining survey data (n = 21) with open-ended qualitative responses from VC professionals. Descriptive and bivariate analyses are used to examine associations between investor characteristics and evaluation criteria, while exploratory factor analysis is employed as a heuristic tool to assess whether survey items cluster around broadly interpretable evaluative orientations shaping investment judgments. The findings suggest the presence of two indicative evaluative orientations—market orientation and impact orientation—that appear to structure how respondents in this sample prioritize dimensions such as scalability, innovation, teamwork, and social value. These orientations are interpreted as context-specific and exploratory rather than statistically generalizable. The study contributes to entrepreneurial finance and institutional theory by developing an exploratory evaluative framework that captures the coexistence of commercial and developmental considerations in venture investment decision-making within a transitional economy. The findings further highlight how gendered investment dynamics are shaped by both market criteria and institutional environments, offering implications for policymakers, investors, and scholars seeking to understand capital allocation processes in underexplored venture ecosystems. Full article

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article

Italy is among the world’s leading producers and exporters of chestnut. Over the past two decades, however, the sector has undergone significant structural changes driven by phytosanitary shocks and evolving market conditions. This study examines the structural and economic heterogeneity of Italian commercial chestnut farms over the period 2019–2023, aiming to identify recurrent production configurations and assess their economic performance and territorial distribution within the Farm Sustainability Data Network (FSDN) field of observation. The analysis is based on a balanced panel of 96 farms, from which a subsample of 77 inliers was identified through robust multivariate diagnostic tests. Farm-level indicators were aggregated over five years to capture medium-term positioning. Principal Component Analysis (PCA) was used to identify the main latent dimensions of variability, and fuzzy k-means clustering was subsequently performed on the resulting component scores. A five-cluster configuration was selected on the basis of internal validity indices, bootstrap stability, fuzzifier sensitivity and leave-one-variable-out robustness checks. The results reveal pronounced multidimensional differentiation within the observed sample. High economic intensity does not necessarily translate into greater margin stability, the effects of structural maturity vary according to cost exposure and labor organization. Territorial differentiation is statistically significant but not deterministic. Overall, the analysis provides an empirical characterization of structural profiles and their associated trade-offs within the observed commercial segment, offering insights into differentiated policy responses for perennial Mediterranean farming systems. Full article

This paper investigates the interaction and price discovery mechanisms among the day-ahead, intraday, and balancing segments of the Greek wholesale electricity market under the European Target Model, emphasizing their contribution to a sustainable and flexible energy transition. Using a Vector Error Correction Model with exogenous variables (VECMX), hourly data from 2023 to September 2025 are analyzed, incorporating key system fundamentals and regime-dependent dynamics. The results reveal a hierarchical market structure in which the day-ahead market dominates long-run price discovery, the intraday market acts as a short-run adjustment mechanism, and the balancing market reflects real-time system conditions associated with renewable energy variability and system reliability. Forecast Error Variance Decomposition shows that day-ahead shocks explain most long-run price variation, while balancing market effects are mainly transitory. Cointegration analysis confirms stable long-run relationships among market segments, with imbalance prices anchored to forward market outcomes and moderated by intraday adjustments. Robustness tests based on alternative recursive orderings and Generalized Impulse Response Functions (GIRFs) confirm the stability of the results and the dominant role of the day-ahead market in price discovery. The findings have important policy implications for market design and sustainability, highlighting the role of integrated day-ahead, intraday, and balancing markets in supporting renewable energy integration, system flexibility, and the transition toward a resilient low-carbon electricity system. The Greek electricity market is gradually evolving toward a mature and resilient Target Model structure capable of supporting higher renewable energy penetration, improved operational flexibility, and enhanced market efficiency within the European decarbonization framework. Full article

The rise in antimicrobial resistance and the resilience of microbial biofilms demand innovative strategies that combine, for instance, membrane-active agents with marketed drugs. Arginine-based surfactants are promising alternatives to conventional quaternary ammonium compounds, but comparative data on their antimicrobial, antibiofilm and modulatory activities remain limited. Five arginine-derived surfactants, the single-chain Nα-lauroyl-L-arginine methyl ester (LAM) and ethyl ester (LAE), together with their double-chain homologues LANHC₃, LANHC₅ and LANHC₈ were evaluated against Gram-positive and Gram-negative bacteria and four Candida spp. Minimum inhibitory (MIC) and lethal (MLC) concentrations were determined by broth microdilution method. Antibiofilm activity was assessed through minimum biofilm inhibitory (MBIC) and eradication (MBEC) concentrations. Checkerboard assays were used to evaluate the synergism between the surfactants and conventional therapeutic antibacterial and antifungal agents. LANHC₃ and LANHC₈ exhibited uniform antibacterial MICs of 19.53 µg/mL, while LAM and LANHC₅ showed MICs of 19.53 µg/mL for most strains, with Enterococcus faecalis requiring 39.06 µg/mL. LANHC₃ was the most potent surfactant over Candida spp. With MICs of 9.76 µg/mL for all species, and similarly to LAM, both were fungicidal at 39.06 µg/mL. LAM and LANHC₃ also showed the lowest MBIC and MBEC values, inhibiting the Candida biofilm formation at 39.06 µg/mL and eradicating mature biofilms at 78.12 µg/mL, while the other surfactants required higher concentrations to disrupt the microbial biofilms. Synergic or additive interactions were found between the surfactants and selected β-lactam and macrolide antibiotics, as well as azole antifungals, with no antagonism observed. LAM and particularly LANHC₃ combined broad-spectrum antimicrobial activity, relevant antibiofilm effects and the ability to potentiate the activity of conventional agents, supporting their choice as alternative or complementary antimicrobial adjuvants over resistant microorganisms and their biofilms. Full article

Offshore wind is expanding globally, driven by energy security and decarbonization goals. Brazil’s world-class potential for this resource is challenged by its unique context: an already clean electricity matrix and abundant, low-cost onshore alternatives, which reduce the immediate urgency for deployment. This paper starts with a global offshore wind market analysis, understanding why the main countries pursue this technology, in contrast with Brazil’s already high share of renewable generation. The following examination focuses on Brazil’s recently approved new offshore wind framework and the governance-related issues, revealing that the legislative process was distorted by unrelated riders mandating costly, non-competitive energy procurement. These riders threatened to absorb future market growth, undermining competition and jeopardizing the emergence of the entire offshore wind industry. While presidential vetoes of these riders were essential to preserve this opportunity, remaining market distortions still favor mature technologies. The study concludes that Brazil’s primary barrier to offshore wind is not technical or resource-based but institutional: the need for stable, transparent governance to foster a truly competitive and predictable policy environment. Full article

The paper investigates the evolution of earnings management across corporate life-cycle stages and evaluates the moderating role of leverage within an emerging market context. Utilising a dataset of 273 non-financial firms listed on Vietnamese stock exchanges over the 2012–2022 period (3003 firm-year observations), we delineate life-cycle phases—introduction, growth, maturity, and decline—using net cash flow patterns. Earnings quality is proxied via diverse discretionary accrual models. Methodologically, the study employs fixed-effects regressions with firm-clustered standard errors, incorporating interaction terms to capture stage-specific leverage dynamics. The empirical evidence reveals a non-linear and stage-dependent trajectory of earnings management, with introduction- and decline-stage firms exhibiting higher discretionary accruals compared to benchmarks. Crucially, the institutional impact of debt financing is contingent upon corporate maturity; while leverage exhibits a baseline positive association with earnings management, this relationship diminishes or reverses during the introduction and decline phases. These insights withstand rigorous robustness checks, including different discretionary accrual models and alternative life-cycle classifications. This study advances current literature by integrating capital structure into the corporate life-cycle framework, demonstrating that leverage effects are dynamic and shaped by shifting financial constraints and monitoring environments. Ultimately, the findings offer valuable insights into financial reporting incentives in emerging markets characterised by concentrated ownership and transitional corporate governance, yielding critical implications for regulators, investors, and auditors. Full article