Search Results (641)

The rapid proliferation of Large Language Models (LLMs) is increasing electricity demand from data centers, creating new challenges for power-demand forecasting and grid planning. A key difficulty is that architecture- and deployment-related information that affects inference load is often private to LLM providers. This paper proposes a two-stage, mechanism-assisted forecasting framework under information asymmetry. In the first stage, a stylized incentive mechanism elicits verifiable reduced-form demand parameters from LLM providers at a chosen reporting precision. In the second stage, the elicited parameters are incorporated into forecasting models as architecture- and deployment-informed features. Using calibrated synthetic scenarios constructed from public data-center energy reports, open LLM-inference energy benchmarks, and secondary public estimates, we find that incorporating elicited parameters reduces the mean squared error (MSE) of the ResNet forecasting backbone by 65.1% relative to an architecture-agnostic ResNet baseline. Similar improvements are observed for a gradient-boosting model, indicating that the main empirical value comes from procuring informative provider-side demand features rather than from a specific neural architecture. The results should be interpreted as a proof-of-concept demonstration rather than a full operational model of LLM serving or power-system dispatch. Full article

Sustainability targets in contemporary construction projects are increasingly defined through embodied carbon limits, circular material obligations, waste diversion benchmarks, and energy performance requirements. However, a persistent gap remains between the establishment of these commitments during policy and design stages and their effective verification throughout project delivery and post-handover operation. Although Building Information Modelling (BIM), digital twins, and associated digital monitoring systems are widely discussed in sustainable construction research, their collective role in enabling continuous sustainability compliance assurance within construction management remains insufficiently synthesised. This study addresses this gap through a PRISMA-guided systematic review and structured comparative thematic synthesis of 117 peer-reviewed studies published between 2016 and 2026. A structured analytical coding matrix, MMAT-informed methodological quality appraisal, and descriptive evidence mapping were used to evaluate dominant digital technologies, sustainability compliance domains, lifecycle verification gaps, and study validation approaches. The findings indicate that current research remains concentrated around BIM-enabled design modelling and isolated operational analytics, with comparatively limited attention to integrated multi-stage sustainability verification during procurement, construction, commissioning, and operation. Four recurring sustainability compliance domains requiring stronger construction management control are identified, including embodied carbon verification, material reuse traceability, waste diversion monitoring, and energy performance validation. In response, the study proposes a Digital Sustainability Compliance Framework that conceptually integrates sustainability targets, PMBOK-aligned project control functions, BIM information models, digital twins, sensor systems, and centralised construction data platforms within a continuous lifecycle verification architecture. The study repositions digital technologies as governance-oriented infrastructures for more transparent, auditable, and continuously monitored sustainability compliance assurance while highlighting the need for future empirical validation. Full article

Background: Clinical artificial intelligence (AI) is rapidly moving from retrospective model development into prospective evaluation, implementation, and routine care. Existing reviews have addressed specific aspects of this transition, including monitoring, drift, implementation, governance, and human–AI interaction; however, these bodies of work remain methodologically and conceptually fragmented across different review traditions. Methods: We conducted a scoping review of review-level and review-oriented literature. We searched MEDLINE, Embase, Scopus, and Web of Science Core Collection from database inception to 28 February 2026. We charted review characteristics and conducted an inductive thematic synthesis of extracted review-level findings, while distinguishing operational, deployment-proximal, methodological, and conceptual/governance-oriented evidence. Results: We included 25 review-level publications spanning systematic, scoping, methodological, narrative, and governance-oriented reviews. Three major themes emerged. First, clinically important risks were consistently framed as socio-technical rather than purely algorithmic: trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness. Second, the included review literature consistently recommended post-deployment monitoring but showed limited operational maturity; monitoring methods, action thresholds, fairness surveillance, and corrective responses were weakly standardised, and mature evidence from activated systems in routine care remained sparse. Third, trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement. Discussion: The review literature suggests a persistent normative–operational gap, meaning that recommendations about what trustworthy clinical AI should require have advanced faster than evidence on how monitoring, updating, and governance are implemented in routine care. The strongest unresolved challenge is therefore not principal generation alone, but the translation of monitoring and governance expectations into actionable operational systems. Conclusions: Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement. Future work should prioritise stronger operational evidence, clearer reporting of deployment-proximal and post-deployment evaluation, methodological standardisation of monitoring metrics and thresholds, implementation research on feasible governance models, and evaluation frameworks for assessing post-deployment safety, fairness, accountability, and sustainability. Full article

This study examines the transformative role of digital and artificial intelligence (AI)-enabled public procurement systems in enhancing governance efficiency within smart city environments, with a specific focus on Yerevan, Armenia. As urban administrations increasingly adopt data-driven governance models and digital infrastructures, public procurement remains a critical yet underexplored domain for innovation in transition economies. Despite ongoing e-government reforms in Armenia, procurement systems continue to face challenges related to procedural inefficiencies, limited transparency, and institutional constraints. To address these challenges, the paper develops a Governance Efficiency Framework that integrates digitalization, AI capabilities, and multi-criteria decision-making principles to assess and optimize public procurement processes in urban settings. The framework incorporates key dimensions such as transparency, operational efficiency, accountability, and data integration, enabling a comprehensive evaluation of procurement performance. The empirical application of the framework to the case of Yerevan provides insights into the structural and technological determinants of procurement efficiency in a transition economy context. The findings indicate that while digitalization has contributed to improvements in transparency, significant limitations remain in efficiency and system integration. A scenario-based analysis further suggests that AI-enabled analytics, process automation, and digital procurement platforms have the potential to reduce administrative delays, enhance transparency, and support more strategic and evidence-based decision-making under assumed implementation conditions. By bridging the fields of public procurement, digital governance, and smart city research, this study contributes both theoretically and practically. It offers a structured and adaptable framework for policymakers and urban administrators seeking to modernize procurement systems and strengthen governance efficiency in evolving digital environments. Full article

Decarbonizing the built environment depends not only on improving operational efficiency but also on how supply is formed along the construction chain. Carbon pricing may reshape that process through upstream material costs, financing conditions, and project timing, yet evidence on the timing and stability remains limited. This study examines how carbon-price shocks are transmitted to construction activity in China and whether this transmission changed after the launch of the national emissions trading system (ETS) in July 2021. Using monthly data from January 2014 to October 2025, the analysis first applies additive Bayesian network (ABN) structure learning to identify links among carbon-market conditions, material costs, finance, and construction activity and then estimates a time-varying structural vector autoregression (TVP-SVAR) to trace dynamic responses across regimes. The results show that carbon-price shocks mainly depress housing starts and area under construction at medium horizons, especially around 6–12 months, with stronger contraction around the 2021 transition and easing later. Allowance trading volume responds positively on impact, but this sensitivity weakens in the post-2021 period. Forecast error variance decompositions further show that carbon-price shocks become an important source of medium- and long-horizon fluctuations. At the 12-month horizon, they account for 18.7% and 18.4% of the forecast-error variance of housing starts in the pre- and post-2021 regimes, and 13.7% and 10.8% of that of trading volume. Overall, the findings point to a project-cycle channel through which carbon pricing reshapes built-environment supply formation, with implications for procurement, transition finance, and the evaluation of carbon-market effectiveness in construction. Full article

Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly generation and hydro-meteorological records from eight hydropower plants in Kazakhstan. Two tasks are addressed: deterministic multi-step forecasting for D+1–D+7 and uncertainty-aware envelope forecasting for D+8–D+14 using MIN and Q90 targets. The benchmark uses Persistence as the primary baseline, against which RIDGE, SARIMAX, Random Forest, HistGradientBoosting, MLP, and LSTM are compared using Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). For D+1–D+7, the results reveal strong cross-station heterogeneity and the expected decline in skill with increasing lead time. In the aggregated comparison, SARIMAX achieves the highest mean NSE at D+1 (0.903), while RIDGE becomes strongest by D+7 (0.625), both outperforming Persistence (0.534 at D+7). At the station level, SARIMAX performs best for Kapch, Kask, Moin, Bukh, and Ustk, RIDGE is best for Shar and Lenin, and LSTM is best for Shulb. The strongest stations, Kapch and Kask, reach mean NSE values of 0.941 and 0.933, respectively, whereas Ustk and Bukh remain the most difficult cases. A central methodological contribution is a flood-sensitive switched hybrid strategy for Ust-Kamenogorsk based on an observed-generation high-flow window selected by a regime-score procedure. This strategy improves robustness at medium lead times: for SARIMAX, NSE increases from 0.587 to 0.739 at D+2 and from 0.161 to 0.559 at D+7, while for RIDGE, NSE increases from 0.549 to 0.701 at D+2 and from 0.109 to 0.435 at D+7, together with substantial RMSE and MAE reductions. For D+8–D+14, envelope forecasting remains informative, but model ranking becomes target-dependent: SARIMAX and RIDGE provide the strongest mean performance for MIN (0.664 and 0.658), whereas LSTM and RIDGE are strongest for Q90 (0.746 and 0.743). Overall, the results show that hydropower forecasting in Kazakhstan is best approached as a station-wise, regime-aware, and horizon-specific problem. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all $\pm 1\sigma$ parameter variations and across two combined-scenario stress tests. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. Full article

Background/Objectives: Vaccine development pipelines are forward-looking indicators of public health preparedness, reflecting the capacity to address unmet medical needs and emerging threats. This descriptive analysis aims to characterise the 2025 clinical-stage pipeline of infectious disease vaccines and prophylactic monoclonal antibody candidates developed by Vaccines Europe member companies, and to describe how pipeline characteristics address evolving public health priorities. Methods: A descriptive analysis was conducted using publicly available data compiled in the Vaccines Europe Pipeline Review 2025, with validation by participating companies. Candidates in clinical development or regulatory review were classified using a standardised framework by pathogen/disease, target population, public health priority, and technologies. Results: The Vaccines Europe member company pipeline comprises 91 candidates across clinical development phases, 19% of which are in Phase III and 7% undergoing regulatory review. This pipeline is predominantly targeting respiratory pathogens (75%), with a strong life-course focus (85% evaluated in adults and/or older adults), and sustained activity in bacterial pathogens relevant to antimicrobial resistance. Notably, 41% of candidates were classified as addressing diseases, disease combinations, or indications for which no licenced preventive product exists. This category includes candidates targeting diseases without a preventive solution, as well as novel combination vaccines and therapeutic approaches in areas where individual components or preventive vaccines are already available. This captures vaccines candidates in different stages of development, not necessarily first-in-disease innovation. The pipeline shows broad technological diversity (12 technologies), dominated by RNA approaches and multivalent candidates, with growing focus on climate-sensitive, zoonotic, and pandemic-prone pathogens. Conclusions: Within the pipeline of Vaccines Europe member companies, this analysis describes development activity oriented toward broader prevention, platform-based approaches, and preparedness-relevant targets. As a structured and recurring annual assessment, the Vaccines Europe Pipeline Review supports horizon scanning and evidence-based dialogue between industry and vaccine ecosystem stakeholders. In order to maximise the impact of vaccine development pipelines to public health, predictable investment, streamlined trial and regulatory pathways, strong surveillance, and real-world data systems, coordinated decision-making is required to enable timely and equitable access, and complementary incentive and procurement reforms. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

The undesired prognosis of biliary tract cancer is mainly attributed to the difficulty in detecting cancer lesions, including intraepithelial neoplasia, and other hurdles in procuring sufficient pathological samples by forceps biopsy and brushing, or even their combination. However, the transpapillary approach under endoscopic retrograde cholangiopancreatography (ERCP) is the mainstream approach for the work-up and treatment of biliary tract diseases, especially biliary tract cancers, because the ERCP-guided approach efficiently enables simultaneous biliary drainage for the treatment of cholangitis/jaundice and specimen acquisition for the diagnosis of biliary tract lesions. To improve diagnostic accuracy, several studies have been conducted on the feasibility and efficacy of genomic analysis of endoscopic specimens, namely, brushing samples, forceps biopsy samples, and aspiration samples such as bile with sensitivities ranging from 47 to 100%, with specificities ranging from 69 to 100%. Clinical use of genomic analysis remains heterogeneous due to the panel and next-generation sequencing system. For the efficient and precise treatment of patients with biliary tract cancer, future diagnosis and treatment should be based on molecular and genetic analyses. In this article, we review and summarize the comprehensive gene panel analyses of transpapillary brushing/biopsy/aspiration specimens for biliary tract cancer using next-generation sequencing, promoting effective clinical practice and providing a basis for future studies. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

The energy transition is rapidly increasing the penetration of inverter-based resources (IBRs), thereby reducing the share of conventional directly grid-connected synchronous generation in modern power systems. In scenarios with very high shares of IBRs, potentially reaching 100% inverter-based operation, key features that have traditionally guaranteed power system stability and security, such as inertia, short circuit strength, fault response, and damping of oscillations, are significantly changing. This review paper examines the main challenges of operating and planning power systems with a high penetration of inverter-based resources. These challenges are grouped into three main areas: (i) technical issues, including frequency and voltage stability, system strength, fault behavior, control interactions and oscillations; (ii) regulatory issues, such as the evolution of grid codes, ride-through requirements, grid-forming specifications and testing, compliance assessment, and model validation; and (iii) market issues, focusing on how non energy services, like synthetic inertia, damping, voltage support, and stability services, are defined, measured, and procured. The paper discusses the compromises between system performance, implementation costs, and overall system robustness, relying on lessons learned from existing specifications and international standards. Finally, it outlines key research needs and provides recommendations for developing coherent technical requirements and market mechanisms to support the reliable operation of inverter-dominated power systems. Full article