Abstract: Façade integrated solar cooling technologies enable the utilisation of façade surface potential and the provision of resilient cooling. This work compares three solar cooling scenarios, positioning a solar cooling element in the west and east façades. The 2ACE scenario is based on a compact adsorption cooling concept, while the 2PV scenario directly drives a compression chiller with photovoltaic elements, and 2PVB incorporates an additional battery. All Modelica system models are implemented in Modelon Impact and operated using dynamic optimisation for the hottest day of the year. Results indicate that the 2ACE scenario, utilising the adsorbent Silica Gel SG123, achieves near to double the self-sufficiency compared to Zeolite 13X. No clear optimal area balance between west and east elements is apparent. The 2PV scenario only surpasses the 2ACE scenario’s self-sufficiency with increased total element area, whereas 2PVB enables a drastic increase and complete self-sufficiency. This highlights the limitation of 2ACE due to its inability to compensate for ventilation’s electrical energy consumption. However, photovoltaic scenarios are heavily reliant on the assumed energy efficiency ratio. Additionally, slender buildings with a low AV-ratio require less façade area to achieve the same self-sufficiency level as wider buildings.

Abstract: Background: Early childhood represents a key stage for the development of movement behaviors (MV), motor skills (MS), and executive functions (EF). Body Mass Index (BMI), defined according to World Health Organization (WHO) references, may influence these domains early in life. In this context, this cross-sectional observational study aimed to examine the associations between BMI and 24-hour MV, MS, and EF in Tunisian preschool children aged 4 to 5 years. Methods: This cross-sectional observational study included 112 Tunisian children aged 4 to 5 years (50 boys, 62 girls), recruited from kindergartens in urban and rural areas. Anthropometric measurements were used to calculate age-specific BMI z-scores and classify children into three BMI categories: below normal, normal, and above normal. 24-hour MV (physical activity (PA), sedentary behavior, and sleep) were objectively assessed using accelerometry over five consecutive days. EF (inhibition and working memory) were assessed using standardized cognitive tests, gross MS were evaluated using the Supine Timed Up and Go test (functional mobility), One-Leg Standing Balance test (postural steadiness), Hand Grip Dynamometer (upper body strength), and Standing Long Jump (lower body strength), and fine MS were assessed using the 9-Hole Pegboard Test (dexterity). All tools are validated and standardized for children. Results: Significant differences between BMI categories were observed for anthropometric variables (p < 0.05). In contrast, no significant differences were found for 24-hour MV, adherence to recommendations, EF, or most MS (p > 0.05). Only upper limb strength showed a significant difference (p = 0.035), with children of normal BMI showing slightly higher strength than those with above-normal BMI. Conclusion: In Tunisian preschool children, weight status is primarily associated with differences in physical growth, with no marked relationship to MV, EF, or MS. These findings highlight the importance of universal preventive interventions starting in early childhood.

Abstract: This paper investigates the operational performance and stability of a regional power grid with a high penetration of small hydropower plants operating under rated conditions. Grid monitoring and simulation results reveal that voltage levels frequently exceed permissible limits and exhibit significant fluctuations. The primary radial configuration of the network is the cause of often the outages of transmission lines that occur at times of peak generation. These conditions adversely affect generator operation and may contribute to equipment degradation. To enhance grid reliability and ensure stable hydropower plant operation, several mitigation measures are proposed, including the reinforcement of the transmission network through the construction of new lines to enable a ring configuration, the mandatory implementation of excitation control systems for generating units, the establishment of a real-time grid operation center, and the deployment of real-time diagnostic tools for optimized generator utilization. The proposed measures give a very handy scheme to raise voltage stability, operational reliability, and the safe inclusion of distributed hydropower generation into regional power systems.

Abstract: Faced with the challenges of accelerating growth in unstructured text data and increasing risk concealment in the financial market, this study constructs a financial risk assessment system that combines text mining with large language models (LLMs). This system forms an end-to-end architecture encompassing data, knowledge, models, services, and governance. The system collects multi source text, constructs a risk knowledge graph, and extracts key events and sentiment signals. These are then integrated into the LLM framework of retrieval augmented generation (RAG) and multi feature fusion to achieve credit risk prediction and default probability estimation. This experiment relies on the FinBen Lending Club dataset (2024) and conducts comparative experiments, ablation studies, error analysis, and stability tests. The model outperforms traditional structured models and plain text models in key evaluation indicators such as F1, MCC, and PR AUC. In scenarios of market environment changes, cross industry migration, and anti-interference, the model’s stability and compliance performance are outstanding. This study designs an intelligent risk identification solution for financial institutions, which makes the identification process explainable, traceable, and auditable. This study has significant theoretical and practical impact on risk governance and decision support for banks, securities, insurance, and regulatory authorities.

Abstract: With the rapid advancement of IoT devices, there is an increasing demand for MPU environments that are low in overall system resources while achieving low power consumption, high energy efficiency, and high performance.Such systems must be capable of controlling multiple sensors and wireless communication modules on minimal battery power, while retaining several days to weeks of logs even under unstable wireless conditions.In logging applications, sensor data are continuously generated and written to non-volatile memory.However, conventional file systems are not designed to handle these operations efficiently. This is because attempting to simultaneously achieve low power consumption, high performance, and power-failure resilience introduces significant overhead and complexity, ultimately degrading energy efficiency.In short, traditional file systems inherently face trade-offs between power efficiency, power-failure resilience functionality, and performance. In typical designs, achieving low power consumption, high performance, and power-failure resilience without relying on specialized modules or custom hardware components requires more resources—such as CPU cycles, power, circuit size, and component count—leading to increased complexity.Therefore, realizing these properties using only general-purpose resources remains a challenging task.Compact/logging type: The proposed time-locality-optimized, hardware-cooperative log-structured file system achieves low power consumption, high performance, and resilience to unexpected power loss using only common components and interfaces, without special or custom hardware.This study reconsiders the boundary design between file system control and hardware-cooperative control in a bare-metal environment without an operating system, and presents a deterministic storage control model based on temporal locality.

Abstract: Background: Contemporary psychiatry has achieved unprecedented neurobiological precision in understanding symptom-level mechanisms, yet clinical outcomes remain stagnant—depression prevalence unchanged, suicide rates rising, and patients frequently reporting existential emptiness despite achieving symptom remission. This paradox suggests a fundamental theoretical gap: psychiatry lacks a scientific language for meaning.Objective: This paper develops the Neuro-Existential Architecture System (NEAS), integrating five previously disparate theoretical domains—Friston's Free Energy Principle, Melloni's laminar cortical architecture, Tucker's affective criticality, Northoff's spatiotemporal neuroscience, and Frankl's existential psychology—into a unified framework explaining how meaning stabilizes resilience at the neurobiological level.Theoretical Framework: The NEAS proposes that meaning is the highest-order generative model in the brain's hierarchical predictive system. It is not epiphenomenal but thermodynamically necessary: meaning minimizes free energy by providing a stable prior that constrains lower hierarchical levels through downward causation. Three core mechanisms are proposed: (1) Model Shattering and Reconstruction—how trauma destabilizes priors and meaning-centered recovery provides scaffolding for reintegration; (2) Affective Criticality—how meaning maintains the balance between confidence and error-checking that characterizes optimal neural function; (3) Spatiotemporal Coherence—how meaning extends temporal integration windows, preventing dissociation and fragmentation.Clinical and Empirical Implications: The framework generates a three-level intervention model (physiological, narrative, existential) and four falsifiable predictions regarding autocorrelation window extension, infra-slow oscillation strengthening, laminar disruption in trauma, and three-level intervention efficacy.Conclusions: By grounding existential meaning in neurobiological architecture, the NEAS bridges a century-long gap between neuroscience and existential psychology, explaining why meaning is fundamental to resilience and suggesting that psychiatry must become a science of meaning to advance beyond symptom management toward genuine healing.

Abstract: Evaluating national climate policy performance requires frameworks that integrate multiple dimensions while accommodating diverse development pathways. This study develops a Multi-Attribute Utility Theory (MAUT) framework to construct a Climate Policy Performance Index (CPPI) for 187 countries. The index integrates four dimensions—mitigation, adaptation, economic capacity, and governance—using explicit utility functions and policy-aligned weights derived from climate policy priorities. Data are drawn from the Global Carbon Project, ND-GAIN Country Index, and World Bank indicators. Results reveal substantial cross-national heterogeneity, with CPPI scores ranging from 33.67 (Turkmenistan) to 78.46 (Norway). Nordic countries lead with balanced excellence across dimensions, while alternative high-performance pathways emerge through mitigation leadership (Uruguay, Costa Rica) or governance-economy strength (Singapore). Regional analysis identifies Europe as the top-performing region (mean = 59.92), whereas Sub-Saharan Africa achieves unexpectedly high rankings despite low emissions, owing to weak institutional capacity. The relationship between income and climate performance is non-monotonic: lower-middle-income countries achieve comparable aggregate scores to high-income nations, with near-perfect mitigation performance compensating for weaker governance. Sensitivity analysis shows that ranking robustness is comparable across equal, adaptation-focused, and multiplicative weighting schemes (Spearman's ρ &gt; 0.83), whereas mitigation-focused weights yield substantially different orderings (ρ = 0.47). The CPPI correlates moderately with ND-GAIN (r = 0.40) and weakly negatively with CO₂ per capita (r = −0.28), indicating the framework captures distinct aspects of climate policy performance. The proposed methodology advances beyond existing indices by providing axiomatic foundations, transparent utility specifications, and comprehensive sensitivity analysis, offering a theoretically grounded tool for cross-national climate policy evaluation.

Abstract: Ex vivo functional testing for multiple myeloma is rapidly evolving, yet no single assay has reached the level of reliability and clinical utility needed for routine decision-making. Existing approaches generally fall into three categories comprising 2D cultures, 3D models, and dynamic systems. Each contributes valuable but incomplete insight into therapeutic response. Among these, 2D assays remain the most mature, with the most extensive clinical correlations to date, though their simplified architecture limits their ability to reflect the full complexity of the bone marrow microenvironment. 3D systems, including spheroids and matrix-based organoids, offer improved preservation of tumor heterogeneity and microenvironmental cues. These platforms show emerging clinical relevance and may hold advantages over traditional 2D formats, and validation efforts are developing. Dynamic systems including microfluidic models and perfused bone-marrow mimetics represent the most physiologically ambitious category, yet their technical intricacy and early stage of development have so far limited broad clinical correlation.Altogether, the current landscape highlights substantial progress but lacks an optimal assay. In this review, we take the unique approach of examining published ex vivo tests that have demonstrated a level of clinical correlation. We evaluate their respective formats, strengths and limitations, and discuss considerations for what an ideal future assay may encompass.

Abstract: We present cEntMax, an information-theoretic framework for training set optimization that selects classwise informative samples via cross-entropy divergence to prototype pivots. Under a noisy-channel generative view and local linearity of deep networks, the method connects predictive entropy, Fisher information, and G-optimal coverage. Experiments on EMNIST and KMNIST show faster convergence, lower validation loss, and greater stability than random sampling, especially for moderate sampling fractions.

Abstract: Scandium-doped aluminum nitride (ScAlN) is a promising replacement for undoped aluminum nitride in MEMS vibration and acoustic sensors due to its higher piezoelectric coefficients, and for RF MEMS due to its enhanced piezoelectric response and ferroelectric switching capability. However, poor process conditions often lead to degraded film performance. In this work, we optimized the growth conditions of ScAlN thin films deposited by reactive pulsed-DC magnetron sputtering system by studying the impact of N₂ flow rate, target–substrate distance, substrate temperature, and substrate bias on film stress, crystallinity, and surface morphology. Based on stress measurements, XRD rocking curves along the c-axis (002), and roughness with AOG formation probability extracted from AFM and SEM images, an optimized deposition recipe was developed that balances stress, crystallinity, and AOG density. With this optimized recipe, samples were fabricated for dielectric, ferroelectric, and piezoelectric coefficient (d33,f and d31,f) measurements. To verify scalability, d33,f, εr, and tan(δ) were measured on 100, 150, and 200 mm substrates. Dual beam laser interferometry results showed d33,f values of around 18 pm/V, εr of 18, and lowest tan(δ) of 0.4%. Cantilever-based d31,f measurements yielded a value of −6.22 pC/N. The optimized ScAlN films also exhibited remnant polarization, Pr = 130 μC/cm², and coercive field, Ec = 3.5 MV/cm.

Abstract: Construction materials play a decisive role in the embodied carbon of large-scale in-frastructure, particularly in steel-intensive long-span bridges. This study investigates the construction-stage carbon footprint of the Anhsin Bridge, an asymmetric ca-ble-stayed steel truss bridge in the Ankeng Light Rail Metro system, with emphasis on material-related emissions. The assessment was conducted using the emission factor method in accordance with ISO 14067 and Taiwan Environmental Protection Admin-istration guidelines, covering material production, transportation, and on-site con-struction activities. Total construction-stage emissions were estimated at 55,349 tCO₂e, with structural steel as the dominant contributor (51.8%), followed by reinforcing steel (15.2%) and concrete-related materials. Although steel is associated with relatively high embodied carbon during production, it provides significant advantages for long-span bridges, including high strength-to-weight ratio, suitability for prefabrication, rapid erection, structural efficiency, and high recyclability. Three practical mitigation strategies—supplementary cementitious material substitution, optimized steel erection methods, and enhanced reuse of formwork and temporary works—were evaluated, achieving a combined emission reduction of 7.3% (approximately 4,048 tCO₂e). Benchmarking indicates that the emission intensity of the Anhsin Bridge (307 tCO₂e per meter of span) is consistent with international practice. The findings demonstrate that material-oriented optimization can effectively reduce embodied carbon while maintaining structural performance and constructability.

Abstract: Instant messaging platforms such as Telegram enable rapid information exchange butalso facilitate deceptive messaging at scale. In this study, we examine Telegram spamdetection through a hierarchy of models that vary in linguistic modeling capacity, frominterpretable lexical baselines (Logistic Regression, Random Forest, LightGBM) tosequential (GRU) and context-aware transformer representations (ALBERT). Usinga harmonized preprocessing and evaluation pipeline on 20,348 labeled messages, wecompare predictive performance across metrics (F1, ROC–AUC, PR–AUC, calibra-tion) and assess pairwise differences via McNemar’s test with multiple-comparisoncorrection. Across all metrics, ALBERT achieves the strongest performance and sub-stantially improves spam-class detection relative to lexical models. This performancegap is consistent with the presence of a subset of deceptive messages whose signals areless concentrated in surface keywords and more distributed across context. However,improved performance may also reflect differences in model capacity and inductivebias, benefits from large-scale pretraining, and stronger handling of sparse patternsvia contextual and subword representations. Accordingly, we interpret the proposed“complex tier” as an operational characterization of lexically subtle spam in this cor-pus, and we suggest that keyword-based moderation may be insufficient on its own tocapture the full spectrum of deceptive messaging observed here.

Abstract: This paper provides a analytical proof of the Riemann Hypothesis using a differential interaction operator Φ(s,δ) on the Hilbert space l²(N). By mapping the Dirichlet η-function to a trace-class operator representing the interaction between states shifted by ±δ from the critical line, we derive a Phase-Torque J(δ,t) governed by a hyperbolic sine bias. We establish a Product Criterion showing that the operator trace vanishes if and only if a zero exists at either 1/2 + δ + it or 1/2 − δ + it. Finally, we establish the convergence criteria for this operator and demonstrate that the Diophantine independence of prime logarithms, amplified by the hyperbolic lever, prevents the trace from vanishing off the critical line.

Abstract: Background: n-3 Docosapentaenoic acid (DPA; 22:5 n-3) is increasingly viewed as a distinct long-chain omega-3 fatty acid with biological activities that are not fully captured by EPA or DHA. However, progress remains limited by restricted access to high-purity DPA: most commercial sources contain DPA as a minor component, and published isolation strategies often yield only enriched mixtures or require multi-step workflows that are difficult to scale in standard laboratories. Objectives: To establish a robust, laboratory-accessible purification workflow to obtain DPA ethyl ester at high purity while preserving oxidative quality. Methods: Candidate lipid sources were screened to select an optimal DPA-containing feedstock. Oils were stabilized with antioxidants and pre-fractionated by cold crystallization (−20 °C) to reduce saturated lipids and oxidation by-products. Preparative separation used a stacked C18 flash system (15 μm + 45 μm in series) operated isocratically (methanol/water 92:8, v/v) at 120 mL/min. Fractions were analyzed by GC and iteratively reinjected to progressively enrich the DPA window. Solvent was recovered by distillation and reused. Results: Omegavie® 4020EE (6.6% DPA) was identified as the best starting material. Pretreatment eliminated detectable TBARS-derived malondialdehyde. The isocratic purification-loop strategy produced tens of grams of DPA ethyl ester at >98% purity (GC–FID) with high overall recovery (~90%) and >90% solvent recycling. Identity and purity were confirmed by GC–MS and ^1H NMR, and oxidation indices remained low (peroxide value < 0.2 meq/kg; p-anisidine < 3). Conclusions: This scalable, solvent-conscious protocol enables reliable access to high-purity DPA and should be adaptable to other low-abundance polyunsaturated fatty acids.

Abstract: A commercial refrigerated vegetable cream containing pumpkin and carrots as main ingredients was stored under refrigeration for 30 days without treatment (controls), supplemented with bacteriocin AS-48, treated by high hydrostatic pressure (HHP, 600 MPa, 8 min, 55ºC) or a combination of bacteriocin and HHP. At day 2, half of the samples were incubated for 24 h at room temperature (simulating a temperature abuse event) and then refrigerated again. Total viable counts and bacterial diversity were determined. Bacteriocin did reduce viable counts, but HHP treatment (singly or in combination with bacteriocin) was the most effective. Viable counts increased in controls during temperature abuse, but not in samples treated with bacteriocin, HHP or both. The initial microbiota of control samples was composed mainly by Pseudomonadota (74.50%), followed by Bacillota (21.19%) and Actinobacteriota (3.69%). Bacillota became the predominant group during refrigerated storage (87.21 to 99.48%). After simulation of a 24-h temperature abuse event, control samples had lower relative abundances of Bacillota during storage and higher relative abundances of Pseudomonadota, Bacteroidota and Actinobacteriota. All treated samples (irrespective of the type of treat-ment) showed a lower relative abundance of Bacillota during storage compared to untreated controls without temperature abuse. Genus Bacillus was the predominant group in the control samples during storage and, although in lower abundance, it was also detected in samples treated with high pressure, bacteriocin or their combination. Acinetobacter was associated with temperature abuse.

Abstract: Machine learning-based phishing detectors are vulnerable to adversarially crafted URLs that preserve malicious intent while evading lexical classifiers. This work investigates adversarial robustness for phishing URL detection and introduces a defense strategy that combines character-level adversarial training with distributional regularization. We construct an evaluation benchmark of 280,000 benign and 120,000 phishing URLs, and generate over 1.5 million adversarial variants using obfuscation rules, homoglyph substitution, and gradient-based attacks. A character-level CNN–BiLSTM classifier is trained with adversarial examples and a Wasserstein distance-based regularizer to keep internal representations of benign and phishing distributions well separated. Under strong white-box attacks, our defended model maintains an AUC of 0.958 and accuracy of 91.2%, outperforming non-robust baselines by more than 12 percentage points. The results suggest that adversarially aware training is critical for deploying phishing detectors in adversarial settings where attackers actively optimize for evasion.

Abstract: Background: Acute coronary syndrome (ACS) is a cannot miss diagnosis. The gold standard workup for this requires serial troponin biomarker evaluation over a period of hours. Traditionally, many of these patients required telemetry while being evaluated in this fashion, however high-quality literature suggesting that low risk patients do not require ongoing continuous cardiac monitoring. Further to this, it was found locally that over 70% of patients presenting with chest pain to our local high volume urgent care undergoing a cardiac work-up were transferred to the main hospital for this via emergency medical services (EMS). We felt this intersection of patient care and medical services could be streamlined to reduce critical resource utilization. Objective: The aim of this study is to reduce the usage of EMS for transport of chest pain patients from the urgent care to the main hospital by 25% over a 3- month period. Methods: This study was conducted as an uncontrolled before-after interrupted time series design. Comprehensive data drilldown was performed through chart review and structured clinical practise evaluation. This led to the creation of an evidence-based safe-for-self-transport tool to be applied in this patient population. The primary outcome measure was the proportion of patients transported via EMS with main balancing measures being proportion of self-transported patients admitted to hospital and time to troponin blood draw in self-transported patients. Results: The education and the newly developed transport tool resulted in a sustained shift below the previous baseline system mean control limit, indicating a significant reduction in EMS usage for patient transport. The overall reduction in usage was 30%. No change in balancing (safety) measures was identified post implementation. Conclusions: EMS remains a finite resource within many Canadian health regions. The results of this study show that by focusing on a cardinal emergency department presentation like chest pain, adapting evidence-based practise through quality improvement methodologies can result in a significant sustained reduction of EMS utilization.

Abstract: COVID-19, caused by the new type of coronavirus SARS-CoV-2, has put an unprecedented impact on health, economy and social areas around the globe. It created an urgent global need for rapid diagnostics, effective therapeutics, and scalable vaccine manufacturing. The biomanufacturing industry played a central role in meeting this challenge by accelerating the development, production, and distribution of SARS‑CoV‑2 diagnostic assays and vaccines. This review provides an integrated overview of SARS‑CoV‑2 biology, clinical manifestations, transmission mechanisms, and major viral variants, followed by a detailed examination of diagnostic technologies. We further highlight the transformative impact of mRNA vaccine technologies, emphasizing advances in lipid nanoparticle formulation, large‑scale manufacturing, and regulatory‑aligned production strategies. The review also discusses the biomanufacturing sector’s rapid mobilization to overcome supply‑chain constraints, workforce shortages, and unprecedented global demand. Collectively, this work underscores how scientific innovation, industrial agility, and cross‑sector collaboration enabled the rapid deployment of diagnostics and vaccines that were essential to controlling the COVID‑19 pandemic.

Abstract: Background and Objectives: Anatomical stage alone inadequately reflects outcome variability in resected non-small cell lung cancer (NSCLC). Although systemic inflammation-based biomarkers have demonstrated prognostic utility, the clinical significance of erythrocyte-derived indices, particularly the mean corpuscular volume (MCV), remains poorly defined in resected NSCLC. This study investigated the prognostic significance of preoperative MCV and determined whether its integration with the Noble and Underwood (NUn) score improves survival prediction. Methods: We retrospectively analyzed patients with stage I–IIIA NSCLC who underwent complete surgical resection. The association between preoperative MCV and overall survival (OS) was assessed using multivariate Cox proportional hazards regression analysis. To elucidate the determinants of MCV, machine-learning-based interpretability analyses, including least absolute shrinkage and selection operator regression and SHapley Additive exPlanations, were applied. A composite NUn–MCV index was subsequently constructed and incorporated into prognostic models. Model performance was evaluated using multiple complementary metrics, including the concordance index and integrated area under the curve. Results: Preoperative MCV was independently associated with OS after adjusting for established clinicopathological covariates. Mechanistic analyses demonstrated that MCV variability was predominantly driven by intrinsic erythrocyte indices rather than by systemic inflammatory or clinical parameters. The composite NUn–MCV index provided greater prognostic value than that of the NUn score or MCV alone. Across all comparative analyses, the fully adjusted model incorporating the NUn–MCV index yielded the greatest improvement in survival discrimination, exceeding that achieved by a clinically adjusted model without NUn–MCV, alternative biomarker-based models, and pathological staging alone. Conclusions: Preoperative MCV is an independent prognostic determinant in patients with stage I–IIIA NSCLC. Integrating MCV with the NUn score to form the NUn–MCV index enhances prognostic discrimination using routinely available laboratory parameters. This composite biomarker may enable more refined risk stratification and support individualized postoperative management in resected NSCLC.

Abstract: The study was conducted to assess primary school teachers’ satisfaction with advocacy services in primary school social work and to identify influencing factors. Data were collected from 398 primary school teachers through a questionnaire, assessing aspects of advocacy services including reliability, responsiveness, competence, empathy and im-plementation conditions. The results of the study showed that teachers’ overall satisfaction was quite high (M = 4.01, SD = 0.27), with all components being positively evaluated. Analysis of differences by demographic factors showed that sex, age, location and region influenced teachers’ evaluation of service quality, while seniority and education level had only limited impact. Pearson correlation analysis shows that all service factors have a positive relationship with satisfaction, in which responsiveness, trust, empathy and im-plementation conditions are statistically significant. Service factors also have strong cor-relations with each other, reflecting the consistency in teachers' perceptions. The study provides a quantitative basis for improving and enhancing the quality of advocacy services in primary school social work, and suggests policies and directions for further research.