Search Results (24,105)

Background/Objectives: Chronic hepatitis B (HBV) and C (HCV) remain major public health challenges in Romania despite vaccination and antiviral therapy. Understanding infection patterns in different healthcare settings is essential for targeted elimination strategies. Methods: We conducted the prospective screening phase of the RE-LINK project (January–June 2025) through two nationwide private laboratory networks. Adults undergoing routine testing were screened for HBsAg and anti-HCV. HBsAg-positive samples were further analyzed for HBV DNA, HBeAg, anti-HBe, anti-HDV, and HDV RNA, while anti-HCV-positive cases were tested for HCV RNA. Risk factors were assessed using chi-square and logistic regression analyses. Results: Among 9149 individuals (66.6% women with a median age of 53 years), HBsAg prevalence was 2.9%, and anti-HCV was 1.3%, both increasing significantly with age (p < 0.001). Of all HBsAg-positive individuals, 12.5% had undetectable HBV DNA, 70.4% had low viremia (<2000 IU/mL), and 17.1% had high viral loads. Anti-HDV antibodies were detected in 2.3% of HBsAg-positive subjects, all with detectable HDV RNA (range 1250–680,000 IU/mL). Significant risk factors for HBsAg positivity were male sex, older age, urban residence, physician-indicated testing, neuropsychiatric comorbidity, family or parental hepatitis, and institutional/orphanage care, while HBV vaccination and moderate alcohol use were protective. Anti-HCV positivity correlated with older age, cardiovascular disease, elevated transaminases, transfusions, surgery, and HIV co-infection. Only 20.2% of anti-HCV-positive individuals were viremic. Conclusions: Private-laboratory screening reveals residual low-replicative HBV and declining viremic HCV, while community programs uncover HDV and advanced disease in vulnerable groups. A coordinated approach integrating private, community, and hospital-based pathways can accelerate elimination efforts and ensure that HDV is not overlooked. Full article

Hamatocaulis vernicosus and Calliergonella cuspidata commonly co-occur in base-rich fens, reflecting overlapping ecological niches. While C. cuspidata is a widespread and ecologically plastic fen species often associated with eutrophicated wetlands, H. vernicosus is a habitat-specialist species of conservation concern. This study investigated the competitive interactions between these two moss species and the role of microhabitat conditions in their coexistence. A reciprocal transplant experiment was conducted in a natural, rich fen in southeastern Lithuania using replicated experimental plots across different microtopographic and hydrological conditions. Species cover and spread were monitored to assess competitive performance following transplantation. The results showed that under wet conditions, H. vernicosus was able to expand into surrounding areas and successfully compete with C. cuspidata. In contrast, C. cuspidata showed limited spread within H. vernicosus patches under wet conditions and was gradually displaced. An advantage of C. cuspidata was observed only in hummocky microtopographic settings. These findings indicate that stable hydrological conditions maintaining microhabitat heterogeneity promote the coexistence of both species. Alterations in the water regime may reduce the competitive ability and long-term persistence of H. vernicosus, highlighting the importance of hydrology-focused management for its conservation. Full article

This study investigated how collaboration between academia and local authorities creates sustainable frameworks for addressing urban challenges through environmental, social and governance (ESG) principles, benefiting both education and the long-term resilience of cities. The paper discusses how establishing dialogue and setting common aims between educational institutions and local authorities, by adopting Participatory Action Research (PAR) approach, enables architecture schools to address civic responsibilities while advancing ESG goals in urban development. The collaboration addresses environmental sustainability through circular economy principles, promotes social inclusion through community engagement, and establishes transparent governance through institutional partnerships. This collaborative model was developed through three summer workshops in Aberdeen, delivered before the pandemic, which helped bridge the gap between theory (academic and educational hypotheses) and practice (tangible urban challenges facing public organisations). This unique experience, named Aber-net (reiterating the intention of creating a network of collaborations), demonstrated how merging research, professional expertise and educational frameworks can create ESG-driven partnerships that support responsible urban development, a model currently underrepresented in the UK. In conclusion, the paper discusses how these collaborative activities improved the perception of public spaces in Aberdeen while establishing a replicable ESG-aligned framework for sustainable partnerships. It examines the challenges and opportunities of creating academia-practice networks that embed ESG principles into urban development. Full article

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

Background/Objectives: DNA profiling in forensic investigation typically compares genetic profiles, usually derived from the analysis of STR markers. However, this method has limitations when there is no biological reference sample or match in the DNA database. The aim of the current study is thus to replicate, in an Italian cohort, epigenetic markers previously identified in the literature for distinguishing tobacco smokers from non-smokers or estimating chronological age, so as to help narrow down the pool of suspects. Methods: DNA methylation at four CpG dinucleotides located around the cg05575921 site of the AHRR gene, widely associated with tobacco consumption, was measured. Additionally, five CpG dinucleotides in the ELOVL2, FHL2, KLF14, TRIM59, and C1orf132 genes were examined for chronological age estimation in buccal swab samples of 102 volunteers through pyrosequencing. Results: A multiple linear regression model for estimating chronological age shows that ELOVL2-C7, C1orf132-C1, and TRIM59-C7 have a significant effect on age. In this model, the prediction error increases with age. Two logistic regression models were used for determining smoker/non-smoker status, proving that two CpG sites significantly influence the odds of being classified as a smoker. When ex-smokers are included in the non-smoking group, the model correctly classifies the two conditions in about 80% of cases. Conclusions: The results demonstrate that the models generated from pyrosequencing data are useful for identifying tobacco smokers and estimating an individual’s chronological age, particularly for younger subjects. Further studies are needed to develop models with higher predictive accuracy and to integrate these tools into regular forensic practice. Full article

Emerging mosquito-borne flaviviruses, such as Dengue virus (DENV) and Zika virus (ZIKV), pose major global public health threats due to their geographic expansion, climate change, and the absence of effective antiviral therapies. Antiviral development against these pathogens has primarily focused on two complementary strategies. On the one hand, the blocking of viral replication by directly inhibiting essential viral enzymes, and on the other, enhancing the host’s innate immune defenses via targeted activation of intracellular antiviral pathways. Among the viral proteins required for replication, the NS2B–NS3 protease complex is one of the most conserved and druggable targets, prompting extensive efforts to design both covalent and non-covalent inhibitors. Covalent inhibitors, such as boronic acids, aldehydes, trifluoromethyl ketones, phenoxymethylphenyl derivatives, and α-ketoamides, form irreversible or slowly reversible bonds with the catalytic serine residue (Ser 135), producing long-lasting and high-affinity suppression of protease activity. In parallel, several classes of non-covalent, particularly allosteric, inhibitors have emerged as promising alternatives with improved specificity and reduced off-target reactivity. A complementary antiviral strategy involves the use of agonists of key innate immune sensors such as TLRs, RIG-I, and the cGAS–STING axis, which mediate the release of interferons (IFNs). This review brings together current knowledge on these two mechanistically distinct yet convergent approaches, highlighting how both can ultimately restrict flavivirus replication. Future opportunities involving modified peptide scaffolds, advanced delivery systems, and drug-repurposing strategies are finally discussed for the development of next-generation therapeutics against DENV and ZIKV. Full article

The successful transfer of autonomous driving stacks (ADS) from simulation to the real world faces two main challenges: the Reality Gap (RG)—mismatches between simulated and real behaviors—and the Performance Gap (PG)—differences between expected and achieved performance across domains. We propose a Methodology for Closing Reality and Performance Gaps (MCRPG), a structured and iterative approach that jointly reduces RG and PG through parameter tuning, cross-domain metrics, and staged validation. MCRPG comprises three stages—Digital Twin, Parallel Execution, and Real-World—to progressively align ADS behavior and performance. To ground and validate the method, we present an open-source, cost-effective Development and Validation Platform (DVP) that integrates an ROS-based modular ADS with the CARLA simulator and a custom autonomous electric vehicle. We also introduce a two-level metric suite: (i) Reality Alignment via Maximum Normalized Cross-Correlation (MNCC) over multi-modal signals (e.g., ego kinematics, detections), and (ii) Ego-Vehicle Performance covering safety, comfort, and driving efficiency. Experiments in an urban scenario show convergence between simulated and real behavior and increasingly consistent performance across stages. Overall, MCRPG and DVP provide a replicable framework for robust, scalable, and accessible Sim2Real research in autonomous navigation techniques. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

Background: Arterial stenosis produces nonlinear changes in vascular impedance that are challenging to investigate in real time using either benchtop flow phantoms or high-fidelity computational fluid dynamics (CFD) models. Objective: This study aimed to develop and evaluate a low-cost printed circuit board (PCB) analog capable of reproducing the hemodynamic effects of progressive arterial stenosis through an R–L–C mapping of vascular mechanics. Methods: A lumped-parameter (0D) electrical network was constructed in which voltage represented pressure, current represented flow, resistance modeled viscous losses, capacitance corresponded to vessel compliance, and inductance represented fluid inertance. A variable resistor simulated focal stenosis and was adjusted incrementally to represent progressive narrowing. Input U_in, output U_out, peak-to-peak V_pp, and mean V_avg voltages were recorded at a driving frequency of 50 Hz. Physiological correspondence was established using the canonical relationships. $R={\displaystyle \frac{8\mu l}{\pi r^4}}$, $L={\displaystyle \frac{pl}{\pi r^2}}$, $C={\displaystyle \frac{3\pi r^3}{2Eh}}$, where μ is blood viscosity, ρ is density, E is Young’s modulus, and h is wall thickness. A calibration constant was applied to convert measured voltage differences into pressure differences. Results: As simulated stenosis increased, the circuit exhibited a monotonic rise in U_out and V_pp, with a precise inflection beyond mid-range narrowing—consistent with the nonlinear growth in pressure loss predicted by fluid dynamic theory. Replicate measurements yielded stable, repeatable traces with no outliers under nominal test conditions. Qualitative trends matched those of surrogate 0D and CFD analyses, showing minimal changes for mild narrowing (≤25%) and a sharp increase in pressure loss for moderate to severe stenoses (≥50%). The PCB analog uses a simplified, lumped-parameter representation driven by a fixed-frequency sinusoidal excitation and therefore does not reproduce fully characterized physiological systolic–diastolic waveforms or heart–arterial coupling. In addition, the present configuration is intended for relatively straight peripheral arterial segments and is not designed to capture the complex geometry and branching of specialized vascular beds (e.g., intracranial circulation) or strongly curved elastic vessels (e.g., the thoracic aorta). Conclusions: The PCB analog successfully reproduces the characteristic hemodynamic signatures of arterial stenosis in real time and at low cost. The model provides a valuable tool for educational and research applications, offering rapid and intuitive visualization of vascular behavior. Current accuracy reflects assumptions of Newtonian, laminar, and lumped flow; future work will refine calibration, quantify uncertainty, and benchmark results against physiological measurements and full CFD simulations. Full article

Historic towns serve as vital carriers of both tangible and intangible cultural heritage, preserving unique historical memories. Quantitative analysis of their architectural facades is crucial for scientific conservation and cultural continuity. While existing studies predominantly employ qualitative descriptions or small-sample analyses, a systematic and replicable quantitative methodology remains elusive. To address this gap, this study innovatively proposes an integrated framework combining UAV oblique photogrammetric modeling, multivariate statistics, and spatial time series analysis. This framework aims to establish a methodological system for analyzing the morphological characteristics of building facades in historic districts. The study selected main streets from four ancient towns in the Chengdu Plain—Pingle, Anren, Xinchang, and Yuantong—and performed 3D reconstruction and morphological indicator extraction on 365 contiguous facade samples. Factor analysis was employed to reduce dimensionality, identifying three dimensions influencing facade morphology. Combined with cluster analysis for classification, the study systematically categorized four statistically significant and architecturally meaningful facade types. Furthermore, it quantified the sequential patterns and combination modes of street-facing distributions, providing crucial theoretical support and reference for the preservation, renewal, and sustainable development of ancient towns. Full article

Methane emissions from abandoned coal mining operations represent a persistent environmental and safety challenge in post-mining regions undergoing urban redevelopment. As urban infrastructure expands over former underground workings, the uncontrolled migration of mine gas can compromise public safety, exacerbate greenhouse gas emissions, and undermine sustainable development goals. This study investigates the origin of methane emissions detected in an urban area of the municipality of Lupeni, Romania, following the commissioning of a new natural gas distribution pipeline installed within a historically mined perimeter. The emissions had not been previously reported and were unexpectedly discovered during technical inspections conducted after the gas network installation, highlighting the absence of historical data on gas presence in this area. This is the first documented case of an accidental discovery of methane emissions in an urban perimeter overlying historical coal mine workings in Romania, granting this study a pioneering status, both scientifically and in terms of urban risk management. The findings emphasize that administrative mine closure does not equate to risk closure, as latent methane emissions may reactivate during urban transformations (e.g., excavations, utility upgrades, drainage changes). To ensure a scientifically sound and sustainable risk assessment, an integrated diagnostic framework was applied, combining chronological field monitoring with chromatographic gas composition analysis. This methodology enabled precise attribution of the methane source to abandoned underground mine workings, excluding the public gas network as a contributor. Based on this diagnosis, a controlled drainage and methane recovery system was implemented, resulting in the elimination of detectable concentrations at all monitoring points by February 2025. The captured methane was redirected for local energy use, transforming an environmental liability into a usable resource. This intervention supports circular economy principles and aligns with EU climate and energy transition goals. The proposed methodological framework provides a replicable model for identifying and managing residual mine gas in post-industrial urban environments. Although emission fluxes were not quantified, concentration-based screening enabled risk diagnosis, prioritization, and targeted intervention. These findings are relevant to EU Regulation (2024/1785) on methane emission reduction, emphasizing the need to include post-mining methane (AMM) in urban planning and environmental monitoring strategies. Limitations of the study include the absence of baseline data and the inability to calculate total methane flux. However, the results support immediate and practical risk mitigation and highlight the need for future work focused on long-term monitoring and emission quantification. This case provides critical insights for other post-mining cities in Central and Eastern Europe facing similar challenges at the intersection of legacy coal infrastructure and modern urban development. This study is designed as a concentration-based diagnostic and risk-oriented case study and does not aim to quantify methane emission fluxes. Full article

Environmental degradation in Iraq is a critical issue that requires strong monitoring. One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference vegetation index (NDVI) and net primary production (NPP), and their response to climatic and hydrological factors. To address the gap in assessments that simultaneously quantify the influence of streamflow, rainfall, and temperature across distinct land cover classes in arid and semi-arid regions, we developed a replicable multi-source geospatial framework. We used MODIS data within the Google Earth Engine platform to perform spatiotemporal analysis. We applied models to detect NDVI trends on a pixel-by-pixel basis. This study provides the first integrated, data-driven assessment of vegetation sensitivity to streamflow versus climate in the Thi-Qar Governorate using a harmonized multi-source dataset. This combines the FAO WaPOR NPP dataset with hydrological (streamflow) and climatic (CHIRPS rainfall, MODIS LST) variables within an analytical workflow to extract anthropogenic water management from climatic drivers. The results showed variations in the NDVI and productivity in the southern and southwestern regions, indicating areas of both degradation and improvement. The analysis found that 12% of the study area showed improvement, while 56.5% of the area showed degradation. Additionally, we classified the study area as either vegetation (cropland) or non-vegetation (fallow arable land, bare areas, and sand dunes). A multiple regression model was then applied to these categories to examine the relationships between streamflow, precipitation, land surface temperature (LST), and the NDVI. The multiple regression for the entire region showed that these factors explained 45.1% of NDVI variation, with streamflow being the most significant positive driver (p < 0.001). The result showed that the NDVI in cropland and arable land was strongly positively correlated with both precipitation and streamflow (R = 0.78, R = 0.75). In contrast, bare land and dunes showed weaker relationships (R = 0.26 and 0.51, respectively). Of these factors, streamflow had the most significant influence in explaining vegetation change (partial correlation p = 0.53), indicating the importance of human management in addition to climate. Full article

Emerging avian avulaviruses (AVVs) have been identified in Antarctic ecosystems; however, their biological properties and current circulation remain poorly understood. This study utilized historical egg-isolated Antarctic penguin avulavirus strains (AVV17, AVV18, and AVV19) and combined molecular characterization with field surveillance during the 2024–2025 Antarctic season. The historical isolates were molecularly confirmed using RT-qPCR targeting the L gene, confirming their classification as AVV17, AVV18, or AVV19. These isolates were tested for replication indicators in REM 134, MDCK cells and embryonated chicken eggs. Only AVV18 showed evidence of productive replication in REM 134 cells, as indicated by decreasing Ct values and the presence of cytopathic effects.AVV17 and AVV19 were detectable by RT-qPCR but did not exhibit cytopathic changes or replication dynamics. None of the viruses replicated efficiently in MDCK cells. Further propagation of AVV18 in embryonated chicken eggs showed viral amplification in some eggs. Concurrent surveillance at five sites in the South Shetland Islands detected low-level circulation of AVV17–19, exclusively in cloacal swabs, with no viral RNA found in environmental samples. These findings link historical isolates to current circulation, highlighting selective replication among Antarctic AVVs and limited ecological spread in penguin hosts. Full article

The highly efficient endodyogeny of tachyzoites is a key process driving acute infection by Toxoplasma gondii. The centrocone is a specialized and critical structure for parasite cell division, but the regulatory mechanisms of centrocone proteins in T. gondii remain poorly understood. In this study, we characterized the centrocone protein SMC_N1, which exhibited periodic expression in tachyzoites, peaking during the synthesis phase. Conditional depletion of SMC_N1 was achieved in the type I RH strain and type II cyst-forming PRU strain using the mAID system combined with CRISPR-Cas9. Depletion of SMC_N1 disrupted IMC assembly, endodyogeny and nuclear division, as well as the stable inheritance of the apicoplast and centrosome, resulting in severe defects in intracellular replication and impaired tachyzoite growth. Collectively, these results indicate that SMC_N1 regulates cell division by coordinating organelle inheritance and cytoskeletal dynamics, ensuring proper replication of T. gondii tachyzoites and provide insights into mechanisms controlling parasite proliferation. Full article

Urban retail environments are social and economic manifestations of a city, enhancing economic growth and social cohesion. However, they increasingly face challenges from economic downturns, changing consumer preferences, and spatial dynamics, making their ability to adapt and remain viable a critical concern. In this context, retail resilience refers to the capacity of urban retail environments to absorb disturbances, adapt to change, and sustain their economic and social functions over time. Despite growing interest in urban resilience, the operationalization of retail resilience through spatially explicit and measurable indicators remains limited, as many assessments focus on city or regional scales and overlook variations at the neighborhood level. Thus, this paper aims to develop a geospatial multi-criteria model yielding a composite Urban Retail Resilience Index (URRI) to analyze and interpret retail resilience in Saida’s urban retail environment through an adaptive cycle lens. The URRI combines indicators related to diversity, spatial proximity, and socioeconomic conditions, and is applied using two weighting scenarios—baseline and stakeholder-based weights—to test the model’s robustness and reflect local priorities. The results reveal distinct spatial variations in retail resilience across the study area, enabling the identification of hotspots for interventions and highlighting the role of accessibility and diversity in shaping the adaptive capacity. These findings confirm that Saida’s retail resilience is closely linked to walkability and socio-cultural characteristics. The proposed geospatial multi-criteria model provides a robust and replicable framework for assessing retail resilience, offering practical insights for urban planners and policymakers. Full article