Search Results (162)

Precast concrete frames integrating ultra-high-performance concrete (UHPC) beams and high-strength concrete (HSC) columns offer exceptional seismic resilience and construction efficiency. However, a performance-based seismic design methodology tailored for this hybrid structural system remains underdeveloped. This study aims to develop and validate a direct displacement-based design (DDBD) procedure specifically for precast UHPC-HSC frames. A novel six-tier performance classification scheme (from no damage to severe damage) was established, with quantitative limit values of interstory drift ratio proposed based on experimental data and code calibration. The DDBD methodology incorporates determining the target displacement profile, converting the multi-degree-of-freedom system to an equivalent single-degree-of-freedom system, and utilizing a displacement response spectrum. A ten-story case study frame was designed using this procedure and rigorously evaluated through pushover analysis. The results demonstrate that the designed frame consistently met the predefined performance objectives under various seismic intensity levels, confirming the effectiveness and reliability of the proposed DDBD method. This work contributes a performance oriented seismic design framework that enhances the applicability and reliability of UHPC-HSC structures in earthquake regions, offering both theoretical insight and procedural guidance for engineering practice. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

The National Cookstove Program has been launched by the Federal Government of Mexico, attempting to reach one million rural homes by the year 2030. Voluntary ISO emission standards for fine particulate matter (PM_2.5) and carbon monoxide (CO) relate emission rates from stoves to indoor air concentrations using a single zone box model (SZM) to derive performance tiers. Region-specific emission benchmarks for cookstove performance that are linked to estimated benefits in reduced indoor air concentrations and resultant health impacts will be important in product selection. Here we compare the SZM to measured indoor PM_2.5 and CO concentrations for five stove stacking combinations using controlled cooking tests of typical foods from the Purepecha region of Mexico to derive region-specific benchmarks. The results demonstrate that the SZM systematically overpredicted PM_2.5 emissions based on thermal plume effects and ventilation which can be adjusted based on strong relationships (Adjusted r² = 0.96, p < 0.001) with emission rates and air changes per hour. Adjustment of PM_2.5 ISO voluntary standards for systematic bias caused by plume buoyancy and ventilation is important in ensuring that the ISO benchmarks reflect the actual indoor concentrations measured in homes. The ISO benchmarks for CO should be revisited as the indoor concentrations from traditional stoves met the most stringent benchmarks but were in the range of concentrations associated with adverse health impacts in adults and psychosocial impacts in children. Full article

Digital contact-tracing (CT) systems differ in how they process risk and expose data, and the centralized–decentralized dichotomy obscures these choices. We propose a modular six-model classification and evaluate 18 platforms across 12 countries (July 2020–April 2021) using a 24-indicator rubric spanning privacy, security, functionality, and governance. Methods include double-coding with Cohen’s $\mathsf{\kappa }$ for inter-rater agreement and a 1000-draw weight-sensitivity check; assumptions and adversaries are stated in a concise threat model. Results: No single model dominates; Bulletin Board and Custodian consistently form the top tier on privacy goals, while Fully Centralized eases verification/notification workflows. Timelines show rapid GAEN uptake and near-contemporaneous open-source releases, with one late outlier. Contributions: (i) A practical, generalizable classification that makes compute-locus and data addressability explicit; (ii) a transparent indicator rubric with an evidence index enabling traceable scoring; and (iii) empirically grounded guidance aligning deployments with goals G1–G3 (PII secrecy, notification authenticity, unlinkability). Limitations include reliance on public documentation and architecture-level (not mechanized) verification; future work targets formal proofs and expanded double-coding. The framework and findings generalize beyond COVID-19 to privacy-preserving digital-health workflows. Full article

Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, particularly data leakage, systematically inflates performance metrics, and examines the broader landscape of validation practices that impact clinical readiness. Methods: We conducted a scoping review following PRISMA-ScR guidelines, with protocol pre-registered in the Open Science Framework (OSF osf.io/2s6e9). We searched PubMed, Scopus, and CINAHL databases through May 2025 for studies employing deep learning for AD diagnosis. We developed a novel three-tier risk stratification framework to assess data leakage potential and systematically extracted data on validation practices, interpretability methods, and performance metrics. Results: From 2368 identified records, 44 studies met inclusion criteria, with 90.9% published between 2020–2023. We identified a striking inverse relationship between methodological rigor and reported accuracy. Studies with confirmed subject-wise data splitting reported accuracies of 66–90%, while those with high data leakage risk claimed 95–99% accuracy. Direct comparison within a single study demonstrated a 28-percentage point accuracy drop (from 94% to 66%) when proper validation was implemented. Only 15.9% of studies performed external validation, and 79.5% failed to control for confounders. While interpretability methods like Gradient-weighted Class Activation Mapping (Grad-CAM) were used in 18.2% of studies, clinical validation of these explanations remained largely absent. Encouragingly, high-risk methodologies decreased from 66.7% (2016–2019) to 9.5% (2022–2023). Conclusions: Data leakage and associated methodological flaws create a pervasive illusion of near-perfect performance in AD deep-learning research. True accuracy ranges from 66–90% when properly validated—comparable to existing clinical methods but far from revolutionary. The disconnect between technical implementation of interpretability methods and their clinical validation represents an additional barrier. These findings reveal fundamental challenges that must be addressed through adoption of a “methodological triad”: proper data splitting, external validation, and confounder control. Full article

Background/Objectives: Early neonatal hypocalcemia is a common metabolic disorder in premature neonates with various risk factors, including perinatal asphyxia and fetal growth restriction (FGR). We aimed to investigate the incidence of early neonatal hypocalcemia in preterm neonates with and without FGR and to explore several maternal and neonatal risk factors for early neonatal hypocalcemia. Cardiotocography (three-tiered fetal heart rate categorization) was a novel risk factor. Materials and methods: This was a secondary analysis of the retrospective, single-center, case-control study of neonates admitted to a neonatal intensive care unit (NICU) between January 2021 and December 2023. The study included 24 neonates with FGR and 124 control neonates without FGR born at 33 to 36 6/7 gestational weeks. Results: Total serum Ca was significantly lower in control neonates (2.042 (SD 0.208)) compared to neonates with FGR (2.178 (SD 0.180)) (p = 0.004), and early neonatal hypocalcemia was significantly higher in control neonates (42.75%) compared to neonates with FGR (4.35%) (p < 0.001). There was no statistical difference in acid base and blood gas analysis between FGR and control (p > 0.05). Logistic regression with the backward method showed that FGR reduces the probability of early neonatal hypocalcemia by 96.3% (t = 9.679, p = 0.001), and cesarean delivery increases it by 2.702 times (t = 6.963, p = 0.004). Conclusions: In this observational study, FGR was found to reduce and cesarean delivery was found to increase the probability of early neonatal hypocalcemia in moderate and late neonates. Clinicians should consider screening neonates born by cesarean delivery for early neonatal hypocalcemia. Three-tiered fetal heart rate categorization and acid base and blood gas analysis upon NICU admission cannot alert neonatologists to early neonatal hypocalcemia. Full article

Societal challenges are inherently complex and multi-tiered, arising from the interplay of diverse stakeholders with a spectrum of purposes and different perceptions and expectations, interdependent systems, and dynamic contextual factors that transcend single domains or disciplines. This paper presents a novel approach to developing a Reference Model of Governance tailored to a specific complex, multi-organisational enterprise facing socially complex challenges. Drawing on Angyal’s systems framework, the model introduces a three-dimensional structure with vertical, progression, and transverse dimensions, integrated within a holistic contextual whole. By mapping selected systems methodologies, including Soft Systems Methodology (SSM), Viable System Model (VSM), System Dynamics (SD), and dependency modelling, to these dimensions, the model offers a pragmatic, structured way to explore and regulate complex organisational behaviour. It enables collaborative inquiry, supports adaptive governance, and enhances the enterprise’s ability to address dynamic societal problems such as health, education, and public service delivery. The result is a governance reference model that captures both the operational and contextual realities of the enterprise, providing actionable insight for strategic design or diagnostic intervention. The novel approach is grounded in systemic and critical systems thinking and emphasises the use of methods for understanding to develop a common and shared understanding of the enterprise context and to surface multiple stakeholder perspectives. Full article

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding. Full article

Background: Neurocysticercosis (NCC) and HIV co-infection frequently occur in sub-Saharan Africa, yet the accuracy of available serological tests for NCC in immunosuppressed patients is uncertain. Methodology: We performed a cross-sectional diagnostic study on 101 people living with HIV from two endemic districts in Tanzania. Participants provided serum for cysticercosis antigen ELISA and Western Blot IgG; any positive result prompted neuroimaging investigation with cerebral computed tomography. NCC was diagnosed according to the 2017 revised Del Brutto criteria based on cCT according to Del Brutto criteria modified to exclude serology. Sensitivity, specificity, and area under the receiver–operating–characteristic curve (AUC) were calculated and adjusted for CD4 count and HIV stage. Two algorithms were compared: parallel testing (“either-test-positive”) and sequential screening (Ag ELISA screen, western blot IgG confirm). Results: NCC prevalence was 23%. Western Blot IgG outperformed Ag ELISA (sensitivity 57% vs. 30%; specificity 87% vs. 86%; AUC 0.73 vs. 0.57). Western blot IgG sensitivity declined to 54% when CD4 < 500 cells µL⁻¹, while Ag ELISA remained low. Western blot IgG positivity independently predicted NCC (adjusted odds ratio 4.1, 95% CI 1.4–11.9); Ag ELISA did not. When we counted a positive if either test was positive (parallel rule), sensitivity rose to 78% and NPV to 87%. When we ran Ag ELISA only if IgG was negative (sequential rule), we saved 70% of IgG strips, kept specificity at 95%, and PPV at 69%, but sensitivity fell to 39%. Conclusions: Western blot IgG is the most reliable single serological test for NCC in PLHIV. Parallel testing increased sensitivity and NPV and may suit better primary-level facilities without routine imaging. Sequential testing achieved high specificity, PPV, and conserved test kits, making it ideal for centers with limited reagents or scanner access. Tiered use of these assays can streamline NCC diagnosis in T. solium endemic, resource-limited settings. Full article

Prior economic research emphasized land, labor and physical capital as the primary drivers of growth, but contemporary work highlights the pivotal role of human capital. Investments in education, health and governance are now regarded as central to sustainable development; yet important questions remain regarding their effectiveness and context-specific impact. This study investigates how human capital investment influences labor force participation and income growth within the ASEAN nine economies for the period from 2000 to 2022 which provides a rich example of contrast in economic and governance outcomes within a single geographic region. Impacted units of measurement of labor force participation and income growth are evaluated using the Bayesian Additive Regression Trees model to select the most important variables, the Bayesian Dynamic Nonlinear Multivariate panel model to estimate regional effects, and the Time-varying Seemingly Unrelated Regression Equations model to evaluate country-specific dynamics, which considers not just the influence of investments in health and education but also the context of rule, law, and governance. The findings indicate that human capital investments exhibit heterogenous effects across economic tiers and the need for strategies and future study of preconditions to improve returns particularly in low-tier economies. Accordingly, mid-tier, emerging economies exhibit the greatest benefit from human capital investments while top-tier exhibit the probable impact of the law of diminishing returns as their human capital development is already well underway. Despite the limited scope, this study still has the potential to draw constructive theoretical and practical implications. Full article

Background: Type 2 diabetes mellitus (T2DM) is a major global public health challenge. Aerobic exercise (AE) can be a key strategy for non-pharmacological intervention in T2DM through multi-targeted modulation of glucose and lipid metabolism, inhibition of chronic inflammation, and reduction of oxidative stress. This study aims to investigate the current status of AE intervention in T2DM research and analyze its future evolution. Methods: Using the R-based bibliometric software package and the Java-based visualization software CiteSpace and VOSviewer, we analyzed the literature and cited references related to AE intervention in T2DM research included in the Web of Science Core Collection (WOSCC) and China National Knowledge Infrastructure (CNKI) from 2014 to 2024. Results: This study included a total of 882 relevant literature sources (488 of which were indexed in WOSCC and 394 in CNKI). From the perspective of research trends, the number of literature sources on AE interventions for T2DM has shown fluctuating changes over time. In terms of research output, the United States, China, and Canada are at the forefront. It is worth noting that, although China has a relatively high number of published papers, there is still a significant gap in terms of the depth of international collaboration and the presentation of results in top-tier journals. Among researchers, Dai Xia (China) and Riddell MC (Canada) are the scholars with the highest number of published articles in this field. Keyword analysis indicates that mechanisms such as oxidative stress, insulin resistance, inflammatory responses, and glucose metabolism disorders remain core research hotspots. Time-series analysis reveals that the research paradigm in this field has evolved from single exercise methods to comprehensive exercise prescription studies, and multi-dimensional intervention studies combining exercise, diet, and pharmacological interventions are emerging as new research frontiers. Conclusions: This study uses bibliometric methods to visualize and analyze the progress of AE in T2DM intervention research from a broader perspective, providing a scientific overview and macro-level predictions for the research landscape in this field. Full article

As an integral component of humanity’s cultural heritage, traditional villages universally confront challenges such as population loss and cultural discontinuity amid rapid urbanization. Cluster-based protection models have increasingly become the international consensus for addressing the survival crisis of such settlements. This study selects the Central Yunnan region of Southwest China—characterized by its complex geography and multi-ethnic habitation—as the research area. Employing ArcGIS spatial analysis techniques alongside clustering algorithms, we examine the spatial distribution characteristics and clustering patterns of 251 traditional villages within this region. The findings are as follows. In terms of spatial distribution, traditional villages in Central Yunnan are unevenly dispersed, predominantly aggregating on mid-elevation gentle slopes; their locations are chiefly influenced by rivers and historical courier routes, albeit with only indirect dependence on waterways. Regarding single-cluster attributes, the spatial and geomorphological features exhibit a composite “band-and-group” pattern shaped by river valleys; culturally, two dominant modes emerge—“ancient-route-dependent” and “ethnic-symbiosis”—reflecting an economy-driven cultural mechanism alongside latent marginalization risks. Concerning construction characteristics, the “Qionglong-Ganlan” and Han-style “One-seal” residential features stand out, illustrating both adaptation to mountainous environments and the cumulative effects of historical culture. Based on these insights, we propose a three-tiered clustering classification framework—“comprehensive-element coordination”, “feature-led”, and “potential-cultivation”—to inform the development of contiguous and typological protection strategies for traditional villages in highland, multi-ethnic regions. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Resettlement communities in Qinghai are located in cold, high-altitude regions with dry climates and strong solar radiation. Although not extremely cold, the moderate heating demand aligns well with high solar availability, making passive design highly effective for reducing energy use. This study investigates solar-optimized spatial configurations that enhance passive energy performance while addressing functional settlement needs. Through parametric modeling and climate-responsive simulations, four key spatial parameters are examined: building spacing, courtyard depth, density, and volumetric ratio. The findings highlight the dominant role of front–rear spacing in solar access, with optimal values at 3–4 m for single-story and 5–10 m for two-story buildings, balancing radiation gain and land use efficiency. Courtyard depths under 2.7 m significantly limit south façade exposure due to shading from the opposite courtyard wall under low-angle winter sun. This reduction results in the south façade attaining only 55.7–79.6% of the solar radiation acquisition by an unobstructed south façade (the baseline). Meanwhile, clustered orientations reduce inter-building shading losses by 38–42% compared to dispersed layouts. A three-tiered design framework is proposed: (1) macro-scale solar orientation zoning, (2) meso-scale spacing tailored to building height, and (3) micro-scale courtyard modulation for low-angle winter radiation. Together, these strategies provide practical, scalable guidelines for energy-efficient, climate-responsive settlement design in the alpine regions of Qinghai. Full article

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article