Search Results (689)

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article

Phase change materials (PCMs) offer potential for passive thermal regulation in building envelopes through latent heat storage; however, their effectiveness remains strongly climate-dependent, and configurations optimized for one region often underperform in others. Existing evaluation approaches rely largely on location-specific simulations or surrogate models with limited climate transferability. This study develops a physics-informed, climate-aware machine-learning framework to assess PCM-integrated wall assemblies across diverse climates. A structured dataset of 720 EnergyPlus simulations was generated across nine PCM materials, ten ASHRAE climate zones, two placement configurations, and four thickness levels using automated model generation and batch simulation through Eppy-based workflows. Ensemble-based models (XGBoost, LightGBM, CatBoost, Random Forest) were trained under climate-grouped validation to predict total annual energy consumption, peak cooling demand, and peak heating demand. The models achieved high predictive accuracy for total annual energy use (R² ≈ 0.98–0.99) and peak cooling demand (R² ≈ 0.93–0.96), outperforming statistical, climate-only, and PCM-agnostic baselines. In contrast, peak heating demand showed low predictability (R² ≤ 0.26), indicating limited sensitivity to PCM parameters under the studied configuration. These results demonstrate that climate-aware validation enables defensible cross-climate PCM assessment, supporting energy demand reduction and sustainable envelope design decisions aligned with global building decarbonization goals. Full article

The rapid growth of 5G necessitates wireless receivers capable of high-speed, low-latency communication under complex channel conditions. Traditional receivers struggle with the performance–complexity trade-off in massive MIMO systems, where linear detectors underperform and maximum likelihood (ML) detection becomes computationally prohibitive. Deep-learning-based model-driven approaches have demonstrated a favorable balance between detection performance and computational cost. However, despite their algorithmic promise, the transition of these learned detectors into practical, real-time systems is critically hampered by inefficient hardware mapping, resulting in suboptimal throughput, high resource overhead, and limited scalability. To bridge this gap, this paper presents LwAMP-Net, a dedicated FPGA accelerator for a lightweight learned AMP detector. We propose a modular and multi-mode hardware architecture for LwAMP-Net, featuring an outer-product-based dataflow that mitigates pipeline stalls and multi-mode processing elements that adapt to diverse computation patterns. These innovations jointly enhance computational parallelism and resource utilization on the FPGA. Implemented on a Xilinx XC7VX690T FPGA for a 128 × 8 MIMO system with 16QAM, the accelerator achieves a 49.2% higher normalized throughput per iteration, an 85.4% improvement in throughput per LUT slice, and a 12.7% improvement in throughput per DSP compared to the state-of-the-art methods. This work provides a complete architectural solution for deploying high-performance, hardware-efficient learned MIMO detectors in real-world systems. Full article

This paper examines the evolution of Sustainable Development Goal (SDG) performance among European Union (EU) countries from 2000 to 2024 using a taxonomy-based approach. It aims to identify changes in sustainability performance, investigate regional disparities between Western Europe (WE) and Eastern Europe (EE), and assess progress across the social, economic, and environmental dimensions. A panel dataset comprising multiple SDG indicators was employed, with variables aggregated into the Taxonomic Measure of Sustainable Development (TM_SD). Based on this measure, countries were classified into performance categories—pioneers, challengers, below-average performers, and underperformers—allowing for the analysis of long-term structural trends. The results indicate an overall improvement in SDG performance across the EU, reflected in an increasing share of countries classified as pioneers and a declining share of underperformers. WE countries more often occupy higher performance categories, although the gap with EE has recently narrowed. Progress is found to be uneven across SDG dimensions, with more pronounced improvements in the economic and environmental areas than in the social dimension. The study contributes by providing a comprehensive and longitudinal assessment of SDG implementation in the EU over a 25-year period, identifying persistent regional disparities, and supporting systematic monitoring and policy coordination at the European level. Full article

Background: While large language models (LLMs) have demonstrated proficiency in medical examinations, their comparative performance on European gastroenterology assessments remains underexplored, particularly regarding architectural differences between reasoning and non-reasoning models. This study benchmarks five state-of-the-art LLMs—DeepSeek-R1, ChatGPT-o1, ChatGPT-4o, Gemini-1.5-Pro, and Llama-3.1-405B (All versions January 2025)—using 203 board-style questions from validated ESEGH preparation materials. Methods: Questions from two commercial ESEGH preparation banks were administered five times per model using standardized prompts. Accuracy, consistency, and domain-specific performance across clinical, diagnostic, and therapeutic questions were analyzed. Four practicing gastroenterologists validated human performance under uniform conditions. Results: ChatGPT-o1 achieved the highest overall accuracy at 84.0% (95% CI: 81.8–86.3), followed closely by ChatGPT-4o (81.7%), DeepSeek-R1 (79.0%), and Llama-3.1-405B (77.2%), while Gemini-1.5-Pro significantly underperformed with 68.5% accuracy (difference vs. ChatGPT-o1: 15.5 percentage points, 95% CI: 11.9 to 19.1, p < 0.01). Although all models exhibited high internal consistency ≥98.4% average agreement across repeated attempts, with 94.6–98.0% of questions answered identically in all five attempts), greater consistency did not necessarily correspond to higher accuracy. Domain-specific analysis revealed that diagnostic questions were answered most accurately, whereas clinical examination questions posed considerable challenges. Topic analysis demonstrated that questions on small intestine disorders were answered with the highest accuracy, in contrast to the lower performance observed in bariatric and pancreatic disorders. Notably, reasoning models, which employed explicit chain-of-thought strategies, outperformed non-reasoning counterparts (81.5% vs. 75.8%, difference: 5.7 percentage points, 95% CI: 3.4 to 8.0, p < 0.001), particularly on therapy questions and complex bait-and-switch formats. Practicing gastroenterologists achieved substantially lower accuracy (mean: 50.9%, range: 37.9–69.0%) compared to all LLMs. All models exceeded the current ESEGH passing threshold of 61.5%, with the top four models surpassing this benchmark by 15.7–22.5 percentage points. Conclusions: This benchmarking study demonstrates that current LLMs, particularly those with reasoning architectures, achieve high accuracy on European gastroenterology board-style questions. However, significant performance gaps in specific domains highlight limitations that must be addressed before clinical application. These findings provide a baseline for evaluating LLM capabilities in European medical contexts. Full article

This paper presents Diff-GTISR, a novel diffusion-based model for achieving super-resolution in thermal images guided by a high-resolution visible image. Thermal sensors are widely used in surveillance, safety, and industrial inspection; however, their limited spatial resolution constrains thermal image quality because of the low resolution. Thermal image super-resolution is thus critical to compensate for this limitation. The increasing prevalence of multisensor platforms has resulted in the availability of high-resolution visible images, providing effective guidance to enhance thermal image resolution. Recently, diffusion-based super-resolution has demonstrated strong capability in recovering perceptually plausible details; however, such models often underperform in distortion-oriented metrics compared with transformer-based approaches. To address this gap, the proposed Diff-GTISR method employs a modality-specific dual encoder to extract multiscale features and a cross-modal guidance attention module to transfer structural information from visible images into low-resolution thermal images. Also, a refinement network is employed to improve the method further. The experimental results indicate that Diff-GTISR consistently enhances perceptual quality in comparison to state-of-the-art diffusion-based methods. Furthermore, it is superior to transformer-based methods in terms of distortion performance. Full article

Although Spain has formally aligned with the 2030 Agenda, the implementation of the Sustainable Development Goals (SDGs) remains uneven and fragmented. This study proposes a multi-criteria framework for evaluating national SDG performance between 2015 and 2024. The approach combines trend analysis, statistical coverage, and distance-to-target metrics. The results reveal clear structural asymmetries. Spain has made significant progress in terms of health, education, and access to energy. However, the country continues to underperform in terms of inequality, climate action, biodiversity, and institutional effectiveness. These disparities are exacerbated by data limitations, as several SDGs lack updated, disaggregated information. The findings highlight a systemic disconnect between political commitment and implementation capacity. Key weaknesses include limited policy coherence, poor coordination between government bodies, and inadequate fiscal alignment with sustainability goals. This study contributes to shifting the focus from reporting on indicators to governance for sustainability by integrating quantitative analysis with structural interpretation. The proposed framework is transparent and adaptable, offering relevant insights for countries facing similar challenges in decentralised governance contexts. Full article

Anxiety and depression are among the most prevalent mental health conditions worldwide. Early and accurate automated detection from naturalistic conversations (e.g., those recorded with a remote chatbot) could eventually improve screening and, in turn, access to timely care. As a first step towards this goal, we aim to evaluate the efficacy of both traditional machine learning and large language models (LLMs) in classifying anxiety and depression from psychotherapy sessions using labels derived from clinician-annotated session metadata reflecting the primary presenting psychiatric concerns. While psychotherapy transcripts do not reflect the real-world domain of remote naturalistic conversation, we conduct this analysis as an “easy” starting point towards the eventual goal of building generalizable, clinician-assistive models that can infer mental health status from unstructured, non-directive conversations captured in the home setting as part of a remote digital assessment process. LLM underperformance on a psychotherapy benchmark would indicate that LLMs are most likely not yet ready to advance towards mental health classifications in more complex and less structured contexts, such as from remote conversations with a chatbot or family member. To study whether LLMs can classify anxiety and depression from psychotherapy transcripts, we fine-tuned both established transformer models (BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained a Support Vector Machine using engineered features, and assessed prompting GPT chatbots. We observe that (1) all machine learning approaches perform poorly and (2) state-of-the-art models fail to improve multi-label classification performance relative to traditional machine learning methods, indicating the current limitations of using LLMs for classification of psychiatric diagnoses from unstructured patient text as of 2026. Full article

Small-molecule immunomodulators have become important components of modern immunotherapy by targeting immune checkpoints, cytokine signaling pathways, metabolic enzymes, and intracellular kinases. Despite pharmacological rationale, many of these agents underperform clinically due to unfavorable physicochemical properties, rapid systemic clearance, limited target accumulation, and dose-limiting toxicities, reflecting inadequate exposure control rather than a lack of target validity. Polymeric micelles, formed through the self-assembly of amphiphilic block copolymers, offer a versatile delivery platform to address these challenges by enhancing solubility, modulating pharmacokinetics, enabling stimuli-responsive release, and facilitating targeted or synchronized co-delivery. In this review, we classify representative small-molecule immunomodulators according to their immunological targets and examine the delivery constraints that shape their therapeutic performance. We then discuss design principles of polymeric micelle systems, including solubilization-driven formulations, microenvironment-responsive architectures, spatial targeting strategies, and co-delivery approaches that align cytotoxic and immunomodulatory mechanisms. Attention is given to the distinction between direct immunomodulators and cytotoxic agents that induce immunogenic cell death, highlighting how micelle-based delivery can enhance efficacy through improved exposure control. By integrating immunopharmacology with formulation science, this review outlines how polymeric micelles may advance the efficacy and safety of small-molecule immunomodulators and identifies key considerations for future translational development. Full article

Despite pressure to scale green building (GB) adoption in Indonesia, many government building projects underperform against their initial intended design, creating a compliance gap between the design and construction phases and reducing the GB rating and its potential benefits. This study investigated the barriers and drivers affecting the Indonesian government’s GB projects’ compliance using a systems thinking (ST) approach. A causal loop diagram (CLD) was constructed from stakeholder interviews and literature scoping, followed by semi-qualitative analysis, combining systems archetype identification, eigenvector centrality (EC), and influence mapping to propose potential leverage points as a basis for policy analysis of the current regulatory scenario. Key findings show that knowledge development, sustained stakeholder integration, project documentation readiness, and government support reinforce GB compliance, but are undermined by financial constraints. CLD analysis identified that the more sustainable factors, including regulation alignment, capacity building, and enhancing collaboration, should become a focus of interventions in the system, instead of focusing solely on the provision of funding. This study presents a novel exploration of the GB adoption problem in an Indonesian governmental context through a comprehensive and systems approach. Further research might require narrowing the system boundaries, broadening the literature and stakeholder validation, and performing quantitative modelling to test intervention scenarios to support rigorous decision-making processes. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident survey in China, this study assesses how city economic level relates to perceived urban well-being, proxied by city-level overall satisfaction. The survey was conducted in April–June 2024 in the main urban districts of 47 cities, using 499,500 valid questionnaires. We aggregate satisfaction to the city level, match it with GDP and key city characteristics, and estimate the GDP–satisfaction association using restricted cubic splines (RCS) to test for potential non-linearity. Across unadjusted and covariate-adjusted models (accounting for population scale and density, industrial structure, fiscal capacity, and regional effects), results show a robust positive association between economic level and satisfaction, while nested-model tests provide no evidence that spline terms improve fit over a linear specification within the observed GDP range. Substantial dispersion around the fitted curve indicates that GDP is an enabling capacity rather than a sufficient condition, pointing to cross-city differences in how effectively resources are converted into lived urban quality. We propose using GDP-adjusted satisfaction benchmarking within the UPE cycle to identify underperforming cities and prioritize targeted governance and renewal actions. Full article

Accurate surface defect classification is critical for industrial quality control. Although Deep Learning achieves strong results on individual datasets, most prior studies benchmark only a narrow set of models under inconsistent pipelines, limiting comparability and industrial relevance. This work introduces the first systematic benchmark of ten architectures—CNNs (CNN, ResNet18/50), lightweight models (MobileNetV2, SuperSimpleNet, GhostNet, EfficientNetV2), Vision Transformers (Swin Transformer), a hybrid CNN–Transformer (CoAtNet), and a one-stage detector (YOLOv12)—across five public defect datasets (NEU-DET, X-SDD, KolektorSDD2, DAGM, MTDD) under a unified pipeline. Results show that Swin Transformer and CoAtNet achieve the best performance (mean F1-scores 90.8% and 85.5%), while EfficientNetV2 underperformed (41.9%), underscoring the need for domain-specific benchmarks. Lightweight models such as MobileNetV2, GhostNet, and SuperSimpleNet deliver competitive accuracy at much lower cost, offering practical solutions for edge deployment. By bridging the gap between academic benchmarks and manufacturing requirements, this study provides actionable guidance for selecting defect detection models in automated inspection. Full article

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance. Full article

Natural ventilation remains a key strategy for improving indoor environmental quality (IEQ), lowering energy demand, and increasing resilience in low-rise residential buildings, especially in warm climates where mechanical ventilation is costly or unreliable. Classical ventilation studies are very often performed on computational fluid dynamics (CFD) or simplified thermal models, but they are computationally resource-heavy, data-dependent, or at odds with early design scenarios. Thus, this study proposes a Multi-Scale BIM-Driven Framework for Predictive Ventilation Opportunity Mapping (PVOM), presenting a geometry-based, data-light approach for investigating ventilation potential over micro-, meso-, and macro-scale spatial dimensions. Based on BIM models of two single-story residential buildings (Building A—author-developed and Building B—public reference model), the framework combines LOD 300 spatial modeling, multi-scale ventilation morphometrics, pathway prediction, and design optimization via opening repositioning, resizing, and envelope porosity adjustments. The outcomes indicate that PVOM correctly detects airflow constraints, stagnation pockets, and underperforming spaces, while simultaneously identifying geometrical areas for improvement on cross-ventilation. Performance for optimization scenarios indicated enhanced air change potential (ACH-P), cross-ventilation score (CVS), and spatial airflow continuity (SAC), thereby indicating the framework is adequate in facilitating early-stage sustainable design. This study presents a reproducible BIM-based method on natural ventilation assessment without CFD or advanced sensing systems, indicating PVOM as a scalable approach toward architects, engineers, and sustainability practitioners. BIM; natural ventilation; PVOM; ventilation morphometrics; low-rise buildings; sustainable design; performance optimization. Full article