Abstract: Objective: To characterize real-world distributions of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) across major rheumatic diagnoses, and to quantify concordance/discordance patterns and combined CRP-ESR inflammatory phenotypes. Methods: We retrospectively extracted all CRP and ESR tests performed in a tertiary university rheumatology hospital (January 2018-December 2023), including ICD-10-coded diagnoses. Analyses were conducted at measurement level and patient level (medians across repeated tests). CRP and ESR were expressed as raw values and multiples of ULN, and categorized into severity strata. CRP and ESR datasets were merged by patient identifier and calendar date to define same-day pairs; paired analyses used Spearman correlations and ULN-based phenotype classes. Sensitivity analyses tested alternative pairing windows, first-pair-only analyses, phenotype persistence rules, and tertile/quartile discordance definitions. Results: Among 16921 patients with ≥1 CRP and 17126 with ≥1 ESR, CRP was more disease-discriminative and only negligibly age-related, whereas ESR increased modestly with age and showed marked sex shifts across severity categories. Inflammatory burden was highest in gout and rheumatoid arthritis, intermediate in psoriatic arthritis and ankylosing spondylitis, and lower in connective tissue diseases and osteoarthritis; CRP distributions were more strongly right-tailed than ESR. Merging yielded 44427 same-day CRP-ESR pairs from 16824 patients (99.1% match). CRP and ESR were moderately correlated at measurement and patient levels, yet discordance was common: 27.3% of pairs showed isolated elevation of a single marker. Conclusions: In routine rheumatology care, CRP and ESR provide complementary information. Clinically relevant CRP-ESR dissociation is frequent, persists at the patient level, and follows diagnosis-dependent phenotype patterns.

Abstract: We revisit a hyperbolic reaction–diffusion wildfire model with relaxation effects and extend it by incorporating an advective transport term that accounts for wind-driven fire spread. After a planar two-dimensional reformulation and systematic nondimensionalization, the analysis is restricted to the minimal ignition regime characterized by the presence of a logistic reaction term governing the evolution of the fire-affected tree fraction. The principal focus of the study isto assess the influence of the effective wind velocity on the propagation dynamics of the fire-affected tree fraction. In particular, we examine how wind-induced advection modifies front morphology, internal thickness and local stability properties of travelling combustion fronts. To derive analytical solutions to the exteded forest fire model, we apply the Simple Equations Method (SEsM) in its (1,1) variant, using a Riccati-type ordinary differential equation as the simple equation. This approach yields several physically relevant families of real-valued exact travelling-wave solutions of the extended hyperbolic model. The obtained solutions describe transition fronts connecting fire-unaffected and fully fire-affected states or vise versa. Numerical simulations are performed to illustrate and validate the analytical solutions, demonstrating how the internal front thickness and profile morphology depend on the specific Riccati solution variant and on the magnitude of the effective wind velocity. A phase-plane stability and bifurcation analysis of the reduced travelling-wave system is carried out. The equilibrium states corresponding to fire-free and fully burned regimes are classified as nodes, foci, or saddles depending on the relaxation and reaction parameterd as well as the traveing wave speed and the effective wind velocity. Hopf bifurcation thresholds with respect to the effective wind velocity parameter are identified, revealing transitions between monotone front propagation and oscillatory regimes. The existence, admissibility, and qualitative structure of travelling wave fronts are interpreted in terms of invariant manifolds and heteroclinic connections between equilibrium points. Finally, a regime map in the parameter plane spanned by the effective wind velocity and the travelling-wave speed is constructed. This diagram delineates regions of qualitatively different propagation behavior, including monotone advancing fronts, oscillatory fronts, and regimes where travelling-wave solutions cease to exist. The regime map provides a compact dynamical characterization of wind-assisted wildfire spread in hyperbolic reaction–diffusion systems with relaxation.

Abstract: The global urgency to mitigate climate change has intensified the development of Carbon Capture, Utilization and Storage (CCUS) technologies. A critical step in CCUS is the safe and efficient pipeline transport of supercritical CO₂ (sCO₂), where flow dynamics are strongly influenced by phase change phenomena under transient heat transfer or depressurization conditions. Indeed, pressure disturbances, such as leaks or rapid decompression events, can induce vaporization and condensation, processes further complicated by the inevitable presence of impurities (e.g., N₂,CH₄,Ar) originating from different conditions at sources. These impurities not only shift thermodynamic boundaries but also alter the kinetics of phase transitions, directly impacting pipeline safety and design. In this study, we investigate the effect of impurities on leakage mass flow rate, and decompression waves in sCO₂ pipeline transport through computational fluid dynamics (CFD) simulations, benchmarked against experimental data. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed for these two-phase simulations, where a tabulation-based approach ensures accurate representation of thermodynamic and transport properties across multiphase regimes. Simulations are performed for varying impurity concentrations, enabling systematic evaluation of their influence on flow rate, and decompression wave propagation and associated flow variables, such as temperature. The results demonstrate strong agreement with experimental observations while providing insights into impurity-driven phase change behavior. The study investigates the effect of outlet geometry, dimensions, and role of Equation of State as well. CPA shows a better fit to the experimental results compared to PR and PC-SAFT for the simulations of supercritical CO₂. It is found that for nozzle geometry where there is smooth change in cross-section area, the simulations prediction were quite close to experiment. However, for the case of orifice venting where there is sharp change in cross-section area, the simulations under predict the leakage mass flow rate, implying the influence of head loss due to geometry. Finally, the feasibility of simulating a 50 km industrial pipeline transporting sCO₂ was investigated. The role of venting towers and gravity prove to be predominant in this specific case.

Abstract: Hypertension and heart failure are leading causes of morbidity and mortality worldwide, with disproportionately worse outcomes among culturally diverse and socioeconomically marginalized populations. Cultural determinants—including health beliefs, dietary traditions, language barriers, trust in healthcare systems, family dynamics, and perceptions of chronic illness—significantly influence disease recognition, treatment adherence, and long-term cardiovascular outcomes. This narrative review synthesizes current evidence on how cultural, structural, and social factors shape the management of hypertension and heart failure across diverse groups such as immigrants, racial and ethnic minorities, and individuals experiencing housing instability. Existing literature demonstrates that culturally discordant care, limited health literacy, and structural inequities are associated with poor blood pressure control, delayed diagnosis, reduced medication adherence, and higher rates of hospitalization and mortality, whereas culturally tailored interventions—including community health worker engagement, linguistically concordant education, faith-based partnerships, and culturally adapted dietary counseling—improve self-management behaviors and clinical outcomes. The findings underscore the need to integrate cultural competence and structural awareness into cardiovascular care delivery, clinical training, and health policy, emphasizing that addressing cultural determinants is both an ethical obligation and a clinically necessary strategy for improving long-term outcomes in chronic cardiovascular disease.

Abstract: Colorectal cancer (CRC) represents a major global health burden, accounting for roughly 10% of all newly diagnosed cancers and cancer-related deaths worldwide. It is the third most diagnosed malignancy and the second leading cause of cancer mortality according to the World Health Organization. Postoperative complications remain a significant concern after CRC resection, occurring in up to 50% of patients and contributing to increased morbidity, mortality, prolonged hospitalization, and substantial healthcare expenditure. Artificial intelligence (AI) has emerged as a transformative tool in modern healthcare, offering advanced capabilities in predictive analytics, clinical decision support, and personalized perioperative management. The present review systematically evaluates the application of AI, specifically machine learning (ML) and deep learning (DL) algorithms, in predicting anastomotic leak (AL) and other major postoperative complications. AI models aim to refine risk stratification and enhance surgical decision-making. A total of 13 studies were included, encompassing 15,105 patients. Across these studies, ML and DL algorithms consistently outperformed conventional statistical models in forecasting postoperative outcomes. Current evidence suggests that AI has substantial potential to improve perioperative risk prediction, support intraoperative decision-making, and personalize postoperative surveillance in CRC surgery. Methodological limitations including high risk of bias, limited external validation, heterogeneous outcome definitions, and inconsistent reporting necessitate more robust, prospective, multicenter research before widespread clinical adoption can be realized.

Abstract: Background: Fatigue is a common and distressing symptom in inflammatory bowel disease (IBD), yet it is rarely addressed in routine care. Most available evidence comes from Western and East Asian populations, with limited data from the Middle East. Objectives: To estimate the prevalence of fatigue in Saudi patients with IBD, using the Arabic‑validated Brief Fatigue Inventory (BFI‑A), and to examine associations with demographic, clinical, treatment, and laboratory factors. Methods: This cross-sectional study was conducted at King Abdulaziz University Hospital, Saudi Arabia, between March and December 2025. Patients aged ≥12 years with histologically-confirmed IBD completed a structured telephone interview. Demographic characteristics, comorbidities, IBD control scores, Montreal classification, medication history, and laboratory results were collected. Patients experiencing severe flares, hospitalization, or another primary condition likely to explain fatigue were excluded. Fatigue severity was classified as none, mild, moderate, or severe. Associations were tested using chi-square and Kruskal–Wallis tests. Results: Among 286 patients (mean age, 30.8 ± 9.1 years; 57.7% male), 23.1% reported mild fatigue, 36.4% moderate fatigue, and 19.2% severe fatigue on the BFI‑A. Fatigue severity was not associated with demographic factors, IBD type or phenotype, treatment exposure, or most laboratory parameters. Only serum iron (p = 0.011) and erythrocyte sedimentation rate (p = 0.023) differed across fatigue categories, without a clear dose–response pattern. Conclusions: Fatigue affects more than half of Saudi patients with IBD and is not explained by routine clinical or laboratory factors. Routine fatigue assessment and attention to biopsychosocial contributors may improve IBD care.

Abstract: Land degradation assessments for SDG 15.3.1 often misinterpret rainfall-driven vegetation fluctuations as human-induced decline, particularly in dryland environments where vegetation productivity responds strongly to precipitation variability. This study addresses this challenge by presenting a national-scale land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow that integrates 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core contribution is a precipitation-conditioned hybrid productivity indicator that adaptively selects among NDVI trends, Rain Use Efficiency (RUE), and Residual Trends (RESTREND) according to local precipitation dynamics. This framework operationalizes a climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1 and enables systematic comparison among productivity metrics under varying rainfall conditions. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that 18% of land showed declining productivity, 75% remained stable, and 6% showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions.

Abstract: The European Green Deal places environmental taxation at the centre of decarbonisation policies. Nevertheless, the empirical evidence for its effectiveness as an incentive for capital eco-investments remains limited, particularly at the sectoral level. The present study analyses this relationship through a country-sector panel of seven EU member states and four sectors under NACE Rev.2 for the period 2014-2023. A five-step empirical strategy is employed, comprising: descriptive statistics, correlation analysis with relative indicators, fixed-effects panel regressions, the Granger causality test, and robustness checks. The results demonstrate a clear scale effect - the correlation between the absolute values of environmental taxes and eco-investments is very high, yet following normalisation against the scale of the economy it becomes practically zero and statistically insignificant. The panel regressions likewise establish no statistically significant relationship, and the Granger test does not confirm causality in either direction. The robustness checks confirm this finding. On this basis, the study concludes that environmental taxation in isolation does not stimulate sectoral eco-investments and functions rather as a fiscal instrument without a discernible investment effect. The findings suggest the need for a policy rethink through more targeted revenue use, sectoral differentiation, and the combining of tax instruments with non-fiscal mechanisms for more effective management of transition financial risk.

Abstract:

In the Philippine agricultural setup, pre-harvest cacao (Theobroma cacao) fruits are wrapped with low-density polyethylene (LDPE) for moisture retention and damage protection. Responding to the growing concern for its waste volume and scarcity of treatment, this research explores the co-hydrothermal carbonization (co-HTC) of cacao shells (CS) and LDPE as a method to convert agricultural waste with plastic into hydrochar of potential energy applications. Thus, observations on the thermal, physicochemical, and morphological changes from feedstocks to hydrochar are carried out. Optimal conditions of 200 °C for 60 minutes resulted in hydrochar with 21.11 MJ/kg and appreciable thermal properties. SEM micrographs show rough and porous structures of hydrochar powder and presence of cracks on oversized LDPE film, while EDX analysis reveals C, K, Ca, and Zn metals that affects chemical pathway. FTIR analysis further supports chemical synergy by preservation of functional groups innate from both parent materials, as well as relative LDPE degradation due to chain scissoring and oxidative reactions. Kinetic and thermal evolutions are also investigated to reveal influence of pretreatment to the stability of cacao shells-dominated hydrochar and the effectivity of biomass integration to facilitate relatively easier degradation of LDPE. The findings support co-HTC as a viable technology to enhance the circular economy by valorizing LDPE and cacao shells while promoting energy recovery.

Abstract: This paper targets key challenges in enterprise financial anomaly monitoring, including coupled multi-source heterogeneous features, concealed anomaly signals, and cost-sensitive alerting. It proposes a unified monitoring framework based on representation-driven data modeling. The approach first normalizes and encodes transaction and identity-related fields within a unified feature space. A learnable representation mapping function is then used to obtain low-dimensional latent representations. A reconstruction consistency mechanism is introduced to suppress noise while preserving essential behavioral information. In the latent space, normal behavior structures are statistically characterized. Structural anomaly signals are derived by measuring the deviation between representation centers and sample representations. These signals are combined with reconstruction errors from the original inputs to form a unified anomaly score. This enables continuous ranking of samples and threshold-based anomaly decisions. To ensure controllability in training and deployment, the framework is systematically analyzed from the perspectives of hyperparameter sensitivity, including learning rate, optimizer choice, and network depth, as well as environmental sensitivity under input noise injection. The analysis delineates the impact boundaries of key configurations on overall detection performance and stability. The results demonstrate that the framework maintains consistent risk identification characteristics under different configurations and perturbations. It provides sortable and configurable anomaly signals for enterprise risk alerts, audit sampling, and compliance inspection.

Abstract: Based on a spatial clustering and partitioned stacking ensemble model, this study addresses the limitations of traditional geoweighting regression in capturing nonlinear location premiums and submarket heterogeneity within urban real estate markets. It proposes a two - stage modeling framework: “spatial clustering → partitioned differentiated stacking ensemble.” Using long - term multi - source transaction data for Beijing's secondary housing market, the study divides the market into three spatially heterogeneous submarkets: core, near - suburban, and far - suburban. Stacked ensemble models based on ElasticNet, XGBoost, LightGBM, and Random Forest are constructed within each submarket.Factor analysis extracts interpretable common factors, which are combined with Lasso and SHAP for feature selection and impact mechanism analysis.Results indicate that the zoned stacking model performs exceptionally well across all three submarkets, achieving an R² of 0.916 in the core urban area. Significant nonlinear location premiums exist within the core urban area.The multi - level interpretability framework reveals the differentiated effects of location and scale factors across different submarkets.This study advances from “global modeling” to “spatial zoning + adaptive ensemble,” providing a viable tool for refined valuation and risk management in highly heterogeneous markets.

Abstract: Institutional trust plays a critical role in shaping organizational responses to risk, particularly in emerging market financial systems. This study examines the psychosocial mechanisms through which institutional trust influences preparedness for social responsibility (SR) implementation in Ecuadorian savings and credit cooperatives. We used covariance-based structural equation modeling (CB-SEM) with 5000 bootstrap resamples (n = 2,116) to assess four competing structural models. These models compared direct, sequential, and parallel mediation requirements. The findings demonstrate that institutional trust has a significant direct impact on preparation (β = 0.626, p < 0.001), accounting for 42.3% of its variance. Statistical rejection of full mediation models validates that readiness cannot be exclusively elucidated through cognitive or affective risk perception pathways. Trust exhibited a minor positive correlation with anxiety (β = 0.100, p < 0.001). Affective mechanism: This link is statistically significant, but its size is little, which means it doesn't have a big effect. These results show that being ready for SR implementation in cooperative finance is more about governance than about threats. The study enhances sustainability research by recognizing institutional trust as a fundamental factor influencing organizational resilience in emerging market financial cooperatives.

Abstract: Background: Emotional aspects of early dental experiences have long-lasting effects. This study aimed to assess parents’ childhood dental memories and their impact on current attitudes toward dental treatment and to evaluate the perceived usefulness of educational material focused on psychological management of children’s dental visits. Methods: An educational booklet was developed and distributed to parents, who were encouraged to read it and complete a short questionnaire. Responses were analysed using IBM SPSS Statistics 25. Results: In the first month, 142 parents (88% mothers) participated. Negative childhood dental experiences were reported by 44.4% (more frequent among mothers, p

Abstract: This study describes the development and mechanistic analysis of a coaxial jet antisolvent process for the continuous production of nanocarriers. A single experimental platform was used to generate both curcumin-based nanoparticles and nanoliposomes, enabling direct comparison of how mixing regime and formulation variables influence product characteristics. Fluid-dynamic behavior was first characterized using tracer and micromixing experiments, revealing a strong dependence of mixing time and composition gradients on flow conditions. Nanoparticles and liposomes obtained under optimized conditions exhibited submicron sizes and controlled polydispersity. To rationalize these observations, a preliminary computational framework was implemented, combining Reynolds-averaged computational fluid dynamics with a population balance formulation solved by the method of moments. The model provided spatially resolved insight into solvent exchange, supersaturation development, and nucleation–growth dynamics, offering qualitative agreement with experimental trends. Although simplified, the modeling approach establishes the basis for future extensions toward full population-balance distribution simulations capable of predicting complete particle size distributions. Overall, the coaxial jet mixer emerges as a versatile and informative tool for continuous nanocarrier production and for advancing a rational, model-assisted design of pharmaceutical nano-systems.

Abstract: Contemporary organizations function as complex networks, yet leadership cognition remains dominated by linear metaphors that assume sequential causality and hierarchical control. This article introduces Graph Thinking as a multi-dimensional leadership capability comprising cognitive, analytical, and behavioral components that enable leaders to perceive, analyze, and deliberately shape organizational network structures. We position Graph Thinking at the intersection of systems thinking, social network analysis, and ecosystem strategy, arguing that it synthesizes these traditions while extending them to address the specific challenges of artificial intelligence deployment. Drawing on network science and strategic management theory, we develop a multi-level framework specifying how Graph Thinking manifests at individual, organizational, and ecosystem levels, with explicit attention to network dynamics and temporal evolution. Through illustrative thought experiments spanning diverse organizational contexts, we demonstrate how network properties function as diagnostic instruments for strategic decision-making. We argue that AI integration creates conditions that may reward explicit network mapping, while acknowledging this relationship is contingent and politically contested. The article contributes to strategic management literature by specifying measurement approaches for future empirical research, addressing power dynamics inherent in network legibility efforts, and providing actionable developmental frameworks. We conclude with boundary conditions, limitations, and directions for empirical validation.

Abstract: Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University provide controlled environments for algorithm evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of four band-selection strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)–based selector. These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement-learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band-selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing.

Abstract: Traditional search engines primarily rely on keyword matching and ranking algorithms, which often fail to capture users’ implicit intents and contextual needs. This paper presents an LLM-based search framework that integrates user memory and behavioral modeling to enable proactive, context-aware retrieval. By continuously analyzing user interaction patterns such as past queries, click behavior, and temporal preferences the system builds dynamic user profiles that guide the generation of adaptive query embeddings. This approach allows the model to infer what users intend to search, rather than what they type, resulting in faster response times and significantly higher relevance in returned results. Experimental evaluations demonstrate that the proposed LLM-memory framework reduces query latency by 21.8% and improves top-1 precision by 15.6% compared to traditional retrieval systems. The study highlights the potential of user memory augmented LLMs to reshape search paradigms, bridging the gap between explicit queries and latent human intentions.

Abstract: This study examines structural changes in regulatory risk disclosure using a semantic modeling framework that integrates sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research typically relies on dictionary-based word frequencies, tone indicators, or topic proportions to quantify risk disclosure. While these measures capture disclosure intensity, they do not directly assess whether the internal semantic organization of risk narratives has shifted relative to historical patterns. We propose a structural semantic deviation framework that represents each company-year disclosure using thematic shares and embedding-based dispersion statistics, and evaluates deviations from a historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase surrounding the 2016 regulatory shock, we demonstrate that traditional lexical metrics fail to isolate structural breaks, whereas embedding-based semantic trajectories reveal substantial narrative reconfiguration. Isolation-based modeling provides stable and discriminative anomaly scores, and SHAP decomposition identifies semantic distance, litigation emphasis, and disclosure contraction as key drivers of deviation in 2025 out-of-sample disclosures. The results suggest that structural semantic modeling captures risk narrative transformation beyond word accumulation, offering an interpretable and scalable framework for regulatory risk assessment.

Abstract: This paper explores the cross-domain application of AI-driven personalization in structured search scenarios that combine intent understanding with spatial and categorical constraints across dining, lodging, and leisure experiences. By integrating LLM-based coordination with reinforcement learning and user memory modules, the system continuously learns from users’ long-term preferences and interaction history to support complex, context-rich needs. Experimental evaluations show that memory-enhanced personalization improved result helpfulness by 17.25% and increased transactional referrals by 4.16% in lodging-related searches, while also achieving measurable satisfaction gains in dining and leisure domains. The study demonstrates that crossdomain LLM personalization frameworks with user memory can effectively capture evolving user intents within local categorical contexts, enhance contextual reasoning, and advance the design of adaptive information service systems in the digital economy

Abstract: This review paper examines innovative urban design strategies for sustainable noise management through a structured analysis framed by ten guiding questions. It begins with an overview of conventional noise assessment technologies and progresses to advanced mitigation approaches. Core principles of sustainable urban design are explored, alongside evaluations of urban and transportation planning, traffic-reduction measures, green infrastructure, and resilient architectural strategies. Material innovations and modern noise-control technologies are presented as complementary solutions. Community-based methods, including citizen science and participatory planning, are highlighted for fostering inclusive governance. The discussion concludes by addressing key challenges and future directions, underscoring interdisciplinary collaboration to transform urban noise pollution into opportunities for healthier, more livable cities.