Search Results (3,065)

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards. The methodology comprises three stages, starting with the acquisition of data from laboratory deliverables and 3D models, followed by data standardisation and relational structuring in the software Power BI Desktop (version 2.157.879.0, Microsoft Corporation, Redmond, WA, USA), and finally the publishing of generated dashboards embedded in a web application environment. The methodology is assessed through a real case study of a railway infrastructure asset, showing how laboratory records can be accessed and interpreted within a 3D model context, while preserving stakeholder-specific visibility through access control. The proposed approach supports element-level navigation of quality control and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment. Full article

Wide-area monitoring and accurate assessment of potential geohazards (PGHs) based on remote sensing will provide a crucial foundation for geohazard prevention and mitigation. Current remote sensing methods for PGH identification and evaluation require extensive manual effort and lack intelligence throughout the process. To effectively integrate multisource remote sensing data, we propose an automated method for identifying and assessing PGHs across a wide area. This approach integrates InSAR deformation, high-resolution optical remote sensing, terrain, and vector data of ground features to enable automated delineation of unstable zones, automatic identification of potentially threatened objects (PTOs), automatic screening of PGHs, and risk assessment. The proposed method is tested in the Hequ–Baode–Pianguan (HBP) region of Shanxi province. Using the DS-InSAR technique, we process 94 Sentinel-1 SAR images covering the HBP region from 2020 to 2024 to estimate surface stability. We automatically detect the boundaries of 161 active deformation areas (ADAs) in HBP. A deep learning model based on DeepLabV3+ processes optical remote sensing images of the study area at 0.5 m resolution to automatically identify all PTOs. By integrating terrain data and spatial relationships among PTOs and ADAs, we develop an algorithmic model to identify 90 PGHs and classify them into external-threat, internal-threat, and internal-external-threat geohazard zones. Finally, a risk matrix is created for an automatic geohazard risk assessment, producing results for all PGHs in the study area. This developed method will support wide-area screening and prioritization of potential geohazards on the Loess Plateau and improve PGH investigation capabilities. Full article

Mediterranean extensive green roofs are increasingly recognized as Nature-based Solutions for climate adaptation and urban sustainability, yet plant selection remains largely based on a limited set of drought-tolerant species. This review critically evaluates the biodiversity, biogeographical composition, and performance of plant species tested on Mediterranean extensive green roofs. A systematic analysis of 118 studies published between 2010 and 2025 identified 340 taxa belonging to 48 families, while species-level performance data were extracted from 92 studies. Research effort was strongly concentrated in Italy, Greece, and Spain, revealing significant geographical biases across the Mediterranean Basin. The species pool was dominated by chamaephytes and succulent taxa, particularly Crassulaceae, whereas Mediterranean endemics were underrepresented. Although native species accounted for most occurrence records, more than half of the tested taxa belonged to extra-Mediterranean chorotypes, indicating only partial geographical coherence. Performance assessments focused primarily on survival, growth, and drought tolerance, whereas reproduction, biodiversity outcomes, and ecosystem services were rarely evaluated. Several Mediterranean native species, including Helichrysum italicum, Salvia officinalis, Origanum dictamnus, and Crithmum maritimum, showed performance comparable to that of commonly used Sedum species, highlighting the potential of underutilized regional flora. The review supports a shift from establishment-based plant selection toward ecologically coherent approaches that integrate biodiversity, biogeographical affinity, ecosystem functioning, and long-term resilience. Full article

Background/Objectives: This study examines gender differences in the relationship between multidimensional poverty and depression among single-parent families, drawing on the concepts of the feminization of poverty and dual vulnerability to inform gender-sensitive policy. Methods: Using a cross-sectional design, this study analyzed data from the 2024 Korean National Survey on Single-Parent Families, with a sample size of 3315 single-parent families. Multidimensional poverty was assessed across five domains: income, employment, housing, health, and social relations. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9). Results: Poverty levels were highest in income, followed by housing, health, social relations, and employment. Female single parents experienced significantly higher poverty in income, employment, and social domains compared to males, while male single parents showed higher asset poverty. The proportion of respondents in the depressed group did not differ significantly by gender, though male and female single parents reached this similar overall level through partly different configurations of risk. Employment, health, and discrimination were common predictors of depression across genders, while lower education was an additional risk factor specific to female single parents. Depression scores rose in a linear, dose-dependent manner with the accumulation of multidimensional poverty for both genders, with a formal test indicating no significant overall difference in this pattern by gender. Conclusions: The findings suggest a critical need to shift policy from simple cash transfers to an integrated, multidimensional paradigm encompassing housing, employment, health, and stigma reduction. Policy interventions must prioritize gender-sensitive approaches tailored to the distinct risk configurations identified for male and female single parents. Full article

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models. Full article

Mapping vegetation alliances is essential for understanding ecological patterns and supporting sustainable land management in arid and semi-arid areas. However, traditional remote sensing typically only distinguishes grassland boundaries or broad subclasses, failing to differentiate specific vegetation alliances. Furthermore, while traditional vegetation mapping relies heavily on field surveys, manual interpretation, and expert knowledge, this labor-intensive approach hinders efficient large-scale mapping. This study proposes an efficient method for vegetation mapping by integrating field survey data with multi-source and multi-temporal remote sensing variables using deep learning. A series of deep neural network models was designed to systematically characterize and leverage spectral signatures, climatic factors, and topographic habitat features for fine-grained classification of 32 vegetation alliances in Xinjiang, a typical arid to semi-arid region. The resulting vegetation map achieved an alliance-level classification accuracy of 0.5125 on an independent test set, with the five most dominant alliances: Stipa spp. desert steppe, Stipa spp. steppe, Poa spp. meadow, and Anabasis spp. desert, accounting for over 10.45% of the total area of Xinjiang. Compared with traditional approaches, this method significantly improves mapping efficiency and offers a scalable solution for large-area, updatable vegetation classification. The approach provides a valuable reference for ecological assessment and dynamic vegetation monitoring in arid and semi-arid regions. Full article

Background/Objectives: The development of multifunctional biomaterials for bone regeneration remains a key challenge in additive manufacturing. Although polylactic acid (PLA) is widely used in fused deposition modeling (FDM), its limited bioactivity and lack of intrinsic antibacterial functionality restrict its application in implantable constructs. This study aimed to develop a PLA-based composite filament combining ion-mediated bioactive potential and local antibacterial functionality using a Quality by Design (QbD) approach. Methods: PLA-based composite filaments incorporating a mollusk shell-derived biogenic calcium-containing filler (20 wt.%) and gentamicin (5 wt.%) were fabricated by solvent-free melt extrusion. A QbD framework was applied to define the Quality Target Product Profile (QTPP), identify critical quality attributes (CQAs), and assess critical material attributes (CMAs) and critical process parameters (CPPs). The material was characterized by SEM–EDS combined with ImageJ-based quantitative image analysis, TGA/DSC, mechanical testing, ICP-AES analysis of aqueous extracts, agar diffusion antibacterial assays, FDM printability assessment, and in vivo biocompatibility testing in a rat subcutaneous implantation model. Results: The developed PLA–Gen–MS material was obtained as a continuous filament with a diameter of 1.75 ± 0.05 mm and was successfully used for FDM printing of model scaffold structures. SEM–EDS confirmed matrix continuity and distribution of the calcium-containing mineral phase. ICP-AES revealed a calcium-dominant multicomponent ion release profile, with Ca as the predominant element and measurable levels of Sr, Mg, P, Mn, and Fe. TGA/DSC confirmed thermal compatibility of the components under melt-processing conditions. PLA–Gen–MS demonstrated antibacterial activity against all tested strains, with inhibition zones of approximately 20–21 mm. In vivo, the material showed a favorable preliminary tissue response compared with TiLOOP^®, including faster reduction of inflammatory infiltration and absence of foreign body giant cells by day 14. Conclusions: The QbD-guided strategy enabled the development of a multifunctional PLA-based filament integrating melt processability, structural integrity, ion-mediated bioactive potential, antibacterial functionality, printability, and favorable preliminary biocompatibility. PLA–Gen–MS can be considered a promising platform for further development of personalized bioactive and antibacterial scaffold constructs for bone regeneration. Full article

The moisture regime is one of the principal determinants of plant distribution; however, traditional phytoindication systems generally treat it as a single ecological gradient, despite its complex environmental controls. This study examined whether vegetation responses could be used to integrate climatic, edaphic, and topographic determinants of moisture availability into ecologically meaningful indicator scales. European GBIF occurrence records for the regional flora were combined with climatic, edaphic, and topographic predictors, and the resulting moisture indicators were evaluated using vegetation plots, Sentinel-2 NDWI data, field soil-moisture measurements, and permutation-based null-model analyses. Constrained canonical correspondence analysis, using annual precipitation, topographic wetness index (TWI), and soil texture as environmental predictors, identified complementary moisture components that were used to derive empirically calibrated Edaphic Moisture Index (EMI) and Topographic Moisture Index (TMI). The edaphic component explained 32.7% of constrained variation and showed strong agreement with traditional moisture scales (R² = 0.75–0.80), whereas the topographic component explained an additional 24.1% of constrained variation and represented a complementary dimension of moisture variation. Species-level EMI and TMI values were subsequently evaluated in relation to traditional Didukh and Ellenberg phytoindication systems using multiple complementary approaches, including vegetation plot analyses, landscape-scale assessment of spatial agreement with environmental moisture gradients, and permutation-based null-model testing. Both EMI and TMI captured vegetation-associated moisture variation and enabled spatial characterization of moisture conditions, although improvements over traditional indicators were scale-dependent rather than universally greater. These findings indicate that vegetation-based moisture assessment benefits from considering complementary edaphic and topographic components of the moisture regime rather than representing moisture conditions as a single undifferentiated gradient. The framework extends traditional phytoindication by providing empirically calibrated indicators that integrate vegetation responses with environmental predictors for ecological assessment. Full article

Cryptic biodiversity presents an ongoing challenge to the accuracy and effectiveness of biodiversity assessment and conservation. To address this, we developed a non-lethal genetic monitoring framework for the subterranean rodent, European lesser blind mole rat (BMR), Nannospalax leucodon species complex, which encompasses multiple chromosomally diversified and reproductively isolated cryptic-species and subspecies. Our workflow combines species and sex detection through: karyotyping, Inter-Simple Sequence Repeat (ISSR) PCR profiling, and Sry-based sex determination of 33 individual samples from five BMR cryptic-species collected at 25 localities in Serbia. As conventional karyotyping protocols for BMR required animal sacrifice, we developed the first non-lethal fibroblast culture-based karyotyping approach from BMR skin biopsy, producing high-quality metaphase chromosomes across five cryptic-species. Among twelve tested ISSR primers, three yielded reproducible, species-specific DNA profiles that resolved four of the five cryptic-species. The Sry assay accurately determined the sex of all examined individuals. Our findings demonstrate that integrating fibroblast culture-based karyotyping, with fast, cost-effective ISSR-PCR species identification, and Sry-based sex determination, provides a reliable approach for identifying and monitoring cryptic BMR species without sacrificing individuals. This framework has potential applications in conservation programmes and contributes to an integrative taxonomy approach essential for the study and protection of other cryptic taxa. Full article

Forest fires are among the most critical disturbance agents reshaping Mediterranean ecosystems under accelerating climate change. This study employs a multi-scale remote sensing approach to examine the relationship between post-fire vegetation dynamics and climate variability in high-fire-risk areas of southern İzmir, Türkiye. Burned areas were delineated using the Burned Area Index (BAI) and differenced Normalized Burn Ratio (dNBR) applied to Landsat imagery (1990–2024) and Sentinel-2 imagery (2017–2024). Post-fire vegetation recovery was quantified through the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Leaf Area Index (LAI), and Land Surface Temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Climate variables, including soil moisture, precipitation, and maximum, minimum, and mean air temperature, were derived from the TerraClimate dataset. Long-term spatiotemporal trends were assessed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator for the 2000–2023 period. Results indicate a statistically significant increase in mean temperature (p < 0.05) and a concurrent decline in soil moisture over the past three decades, consistent with progressive atmospheric aridification. Vegetation indices exhibited marked seasonal asymmetry: significant declines in NDVI, SAVI, and LAI were recorded during summer months, whereas partial recovery was confined to the winter–spring wet season. A pronounced warm-dry shift was identified in the post-2015 period, characterized by positive Land Surface Temperature anomalies and compressed vegetation recovery windows. These findings highlight that increasing thermal stress and diminishing soil moisture collectively constrain post-fire ecosystem resilience in the Mediterranean climatic zone (MCZ). The integrated remote sensing framework developed here provides a robust and transferable basis for fire ecosystem monitoring and the formulation of climate adaptation strategies in fire-prone dryland regions. Full article

Chronic kidney disease (CKD) is sustained by a network of sterile inflammation, oxidative and metabolic stress, uremic toxin retention, gut barrier dysfunction, and maladaptive immune activation. These processes contribute to kidney fibrosis, cardiovascular injury, wasting, and excess mortality, but inflammatory biomarkers do not by themselves establish therapeutic causality. This narrative review integrates mechanistic and therapeutic evidence using an explicit three-layer translational hierarchy. Renin–angiotensin system inhibitors, sodium–glucose cotransporter-2 inhibitors, finerenone, and glucagon-like peptide-1 receptor agonists improve cardiorenal outcomes and have plausible anti-inflammatory actions, although inflammatory mediation remains unproven. Interleukin-1 blockade provides cardiovascular proof of principle and small dialysis feasibility data. Interleukin-6 ligand inhibition produces marked human target engagement; however, headline results from the completed phase 3 ZEUS trial showed no reduction in three-point major adverse cardiovascular events with ziltivekimab despite biomarker suppression, while serious infections were more frequent. POSIBIL6ESKD continues to test clazakizumab in inflamed dialysis patients. Direct NLRP3 inhibition has entered early human CKD development, whereas senescence-directed and microbiota-based approaches remain less mature. Future progress requires inflammatory endotyping, repeated biomarker assessment, mechanistically aligned outcomes, and rigorous infection surveillance. ZEUS underscores that pathway suppression must deliver clinical benefit beyond contemporary standard therapy. Full article

This review examines the clinical and practical implications of embedding molecular profiles directly into the 2023 FIGO staging system for endometrial carcinoma, in the context of the 2025 ESGO/ESTRO/ESP guidelines. The primary purpose is to navigate a central conflict in modern oncology: how to deliver increasingly personalized care while maintaining a globally accessible, equitable, and standardized cancer classification system. The 2023 FIGO update represents a paradigm shift from the traditional dualistic model (Type I versus Type II) by allowing molecular findings to redefine stage itself. While this integration offers clear benefits, it introduces significant challenges. First, the system depends on advanced molecular testing, creating a “rich-poor” divide where patients in resource-limited settings are systematically overtreated because testing is unavailable. Second, stage becomes unstable, changing with sequential histologic and molecular re-review, which causes confusion for patients and clinicians. Third, the system lumps prognostically distinct histotypes (Serous, Clear Cell, Carcinosarcoma, and Grade 3 Endometrioid) into a single aggressive stage, obscuring meaningful differences in survival. Fourth, it relies on subjective parameters such as “substantial” lymphovascular space invasion, for which no standardized definition exists, leading to high inter-observer variability. After analyzing these controversies, the review proposes a pragmatic solution: decouple anatomical staging from molecular risk stratification. Staging should remain a purely anatomical, universally applicable descriptor of tumor extent, while molecular and histologic data are used separately within a dynamic risk assessment model, as suggested by the European guidelines. This dual-track approach preserves global comparability, reduces inequity, and maintains diagnostic stability, while still enabling personalized treatment where advanced diagnostics are available. Full article

(1) Background/Objectives: Blood-based biomarkers have emerged as practical tools for identifying Alzheimer’s disease (AD) pathology in patients with mild cognitive impairment (MCI). Among them, plasma phosphorylated Tau217 (p-Tau217) demonstrates strong associations with cerebral amyloid deposition. In parallel, the easy Z-score Imaging System (eZIS), a quantitative brain perfusion SPECT analysis tool, has been widely used to detect characteristic AD-related hypoperfusion patterns. Although both measures reflect distinct AD processes, the relationship between plasma p-Tau217 and eZIS in MCI remains unclear. (2) Methods: This retrospective study included 62 patients with MCI who underwent plasma p-Tau217 testing and brain perfusion SPECT with eZIS analysis. Associations between plasma p-Tau217 and the three eZIS indices (severity, extent, and ratio) were evaluated. Exploratory subgroup analyses were performed using a previously reported plasma p-Tau217 threshold of 0.63 pg/mL. In addition, a validation sub-cohort of 21 participants who underwent plasma p-Tau217 testing, eZIS, and amyloid PET was analyzed to assess concordance with cerebral amyloid pathology. (3) Results: Among the three eZIS indices, severity demonstrated the highest sensitivity relative to elevated plasma p-Tau217 levels. However, all eZIS indices showed limited discriminative performance. Optimal eZIS cutoff values derived from the present cohort were higher than previously reported thresholds. In the amyloid PET-validated sub-cohort, plasma p-Tau217 demonstrated closer concordance with amyloid positivity than any individual eZIS parameter. The reduced performance of eZIS appeared to be associated with advanced age, substantial vascular burden, white matter lesions, and cerebral atrophy. (4) Conclusions: Plasma p-Tau217 showed a stronger association with cerebral amyloid pathology than eZIS indices in this elderly MCI cohort. Nevertheless, eZIS may provide complementary information regarding downstream neurodegenerative and cerebrovascular processes that are not directly captured by plasma biomarkers. This integrated approach highlights plasma p-Tau217 as a primary screening tool for amyloid pathology to guide disease-modifying therapies (DMTs), alongside eZIS for tracking follow-up mixed co-pathologies. Full article

Leishmaniasis is an emerging zoonotic disease recognized by WHO as a priority neglected tropical disease (NTD) targeted for elimination by 2030. The disease is transmitted through bites of infected female phlebotomine sand flies and presents in three main forms: cutaneous, mucocutaneous, and visceral leishmaniasis. Visceral leishmaniasis (VL) has two distinct epidemiological cycles: anthroponotic visceral leishmaniasis (AVL) cycle, caused by L. donovani and transmitted between humans, and zoonotic visceral leishmaniasis (ZVL) cycle, caused by L. infantum, with domestic dogs as the main reservoirs. ZVL represents the most severe clinical form and poses a public health risk due to a high fatality rate. In Kenya, the epidemiology and transmission dynamics of Leishmania infection among the canine population in endemic areas such as Baringo are poorly characterized. A cross-sectional epidemiological study was conducted in Baringo County, Kenya, one of the endemic foci for human visceral leishmaniasis. Blood samples were collected from 140 dogs in Rabai, Sabor, Mbechot, Kapkuikui, and Chemolingot and analyzed using Rapid Diagnostic Test (RDT) and Enzyme-linked immunosorbent assay (ELISA). Data on potential epidemiological factors and clinical status for dogs were collected using a questionnaire. Statistical analysis was performed using Pearson’s chi-square (χ²) test or Fisher’s exact test to assess associations between Leishmania seropositivity and potential risk factors. A stepwise logistic regression procedure with an entry significance level of p < 0.20 was employed to identify factors associated with leishmaniasis seroprevalence. Cohen’s kappa (κ) agreement was used to assess the diagnostic performance and accuracy of the two serological tests in the absence of a gold standard. The overall seropositivity in dogs was 7.79% and 12.14% at 95% CI for the RDT and ELISA, respectively. Multivariate analysis identified breed and clinical signs as significant factors associated with Leishmania seropositivity in dogs. Local-breed dogs were more likely to be Leishmania seropositive than mixed-breed dogs (OR = 11.92; 95% CI: 1.79–79.37; p = 0.010). Dogs without clinical signs exhibited significantly lower odds of ELISA seropositivity (OR = 0.06; 95% CI: 0.01–0.29; p < 0.001). Dogs with proper care and housing conditions had reduced risk of Leishmania seropositivity compared with poorly managed dogs. Significant association with seropositivity was observed for sex, age, origin, and reason for keeping dogs. Diagnostic evaluation showed that the RDT had an excellent specificity and Positive Predictive Value (100%), with good overall agreement with ELISA, although sensitivity was 64.7%. Hence, RDT can be a reliable alternative screening tool compared with ELISA as the reference standard. The study demonstrated the presence of Leishmania infection in the dog population in Baringo County, Kenya, a significant public health risk. Early detection and diagnosis of infections in dogs through an integrated One Health approach during routine surveillance are essential. This will facilitate treatment and/or culling of infected dogs and vector control, subsequently reducing the risk of human transmission. Hence, it will go a long way to achieving WHO’s roadmap of eliminating leishmaniasis by 2030. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article