Search Results (68,453)

W-type hexaferrites with compositions Ba_0.5Sr_0.5Zn_2-xMe_xFe₁₆O₂₇ (Me = Fe, Ni, Co, Cu, Mn; x = 1) and Ba_0.5Sr_0.5Zn_2−xMn_xFe₁₆O₂₇ (x = 0–2.0) were synthesized via solid-state reaction and optimized using a two-step calcination process to obtain single-phase or nearly single-phase structures. Their electromagnetic (EM) wave absorption properties were investigated by fabricating composites with 10 wt% epoxy and measuring the complex permittivity and permeability across two frequency bands: 0.1–18 GHz and 26.5–40 GHz. Reflection loss (RL) was calculated and visualized as two-dimensional (2D) maps with respect to frequency and sample thickness. In the 0.1–18 GHz range, only the Co-substituted sample exhibited strong ferromagnetic resonance (FMR) and broadband absorption, achieving a minimum RL of –41.5 dB at 4.84 GHz and a –10 dB bandwidth of 11.8 GHz. In contrast, the other Ba_0.5Sr_0.5Zn_2-xMe_xFe₁₆O₂₇ samples (Me = Fe, Mn, Ni, Cu) showed no significant absorption in this range due to the absence of FMR. However, all these samples clearly exhibited FMR characteristics and distinct absorption peaks in the 26.5–40 GHz range, particularly the Mn-substituted series, which demonstrated RL values below –10 dB over the 32.0–40 GHz range with absorber thicknesses below 1 mm. The FMR frequency varied depending on the substitution type and amount. In the Mn-substituted series, the FMR frequency was lowest at x = 1.0 and increased as x deviated from this composition. This study confirms the potential of Co-free W-type hexaferrites as efficient, cost-effective, and broadband EM wave absorbers in the 26.5–40 GHz range. Full article

Background/Objectives: Colorectal cancer (CRC) remains a leading cause of cancer-related deaths, with notable sex-specific differences in its incidence, diagnosis, and outcomes. Our previous work identified casein kinase 2 alpha (CK2α) as being capable of impairing DNA mismatch repair (MMR) via phosphorylation of MLH1, thereby increasing the tumor mutational burden. This study aimed to investigate sex-specific differences in CK2α protein expression in CRC. Methods: Immunohistochemical (IHC) analysis was performed on 161 CRC tumors and adjacent normal tissues to quantify the CK2α protein levels. A multi-cohort meta-analysis of proteomic and clinical data was conducted to validate our findings and assess the correlations with age, sex, and relevant signaling pathways. Results: Female CRC patients exhibited significantly higher CK2α expression than male patients, which was confirmed in two independent cohorts. Additionally, CK2α expression was positively correlated with age in female but not male patients. Cross-cohort correlation analyses linked CK2α levels with key proteins involved in estrogen receptor signaling and aging, including DEAD-box helicase 5 (DDX5), histone deacetylase 1 (HDAC1), proliferating cell nuclear antigen (PCNA), prohibitin-2 (PHB2), H/ACA ribonucleoprotein complex subunit 2 (NHP2), and dual-specificity mitogen-activated protein kinase kinase 3 (MAP2K3). Conclusions: CK2α is significantly overexpressed in the tumor tissue of female CRC patients and shows a strong age-related correlation. These findings suggest a sex- and age-specific regulatory mechanism potentially influenced by estrogen signaling or menopause. Such dimorphisms underscore the need for sex-specific strategies in CRC biomarker development and therapy. Full article

Small-target detection in Unmanned Aerial Vehicle (UAV) aerial images remains a significant and unresolved challenge in aerial image analysis, hampered by low target resolution, dense object clustering, and complex, cluttered backgrounds. In order to cope with these problems, we present AeroLight, a novel and efficient detection architecture that achieves high-fidelity performance in resource-constrained environments. AeroLight is built upon three key innovations. First, we have optimized the feature pyramid at the architectural level by integrating a high-resolution head specifically designed for minute object detection. This design enhances sensitivity to fine-grained spatial details while streamlining redundant and computationally expensive network layers. Second, a Dynamic Feature Fusion (DFF) module is proposed to adaptively recalibrate and merge multi-scale feature maps, mitigating information loss during integration and strengthening object representation across diverse scales. Finally, we enhance the localization precision of irregular-shaped objects by refining bounding box regression using a Shape-IoU loss function. AeroLight is shown to improve mAP50 and mAP50-95 by 7.5% and 3.3%, respectively, on the VisDrone2019 dataset, while reducing the parameter count by 28.8% when compared with the baseline model. Further validation on the RSOD dataset and Huaxing Farm Drone dataset confirms its superior performance and generalization capabilities. AeroLight provides a powerful and efficient solution for real-world UAV applications, setting a new standard for lightweight, high-precision object recognition in aerial imaging scenarios. Full article

This study presents the first nationwide, system-level investigation of occupational stress and professional burnout among probation counselors in Romania, in the context of increasing caseloads, complex job demands and limited institutional support. Building on a comprehensive theoretical analysis, we employ a sociological research design involving a representative sample of 247 probation counselors from all 42 national probation services. Using the Maslach Burnout Inventory—Human Services Questionnaire, along with stress-related factors, we examine the prevalence, most important factors and typologies of burnout. Advanced quantitative techniques—including multiple linear regression, principal component analysis and K-means clustering—allow for a robust identification of key predictors of emotional exhaustion and three distinct psychosocial profiles: stress-resistant seniors, under involved younger staff and overworked, frustrated employees. We also conducted a confirmatory factor analysis (CFA) to check the validity of the MBI-HSS. This typology offers a novel conceptual framework for understanding professional burnout in probation, highlighting systemic vulnerabilities and distinct risk categories. Nevertheless, limitations exist: self-reported data may underestimate stress, and omitting variables like resilience or work meaning constrains explanatory depth. Despite these constraints, this study addresses a significant gap in Romanian probation research and lays the foundation for future longitudinal and qualitative studies. These should incorporate psychological and organizational factors to improve targeted interventions and human resources strategies. Full article

The increasing complexity and digitization of modern supply chains have created an urgent demand for transparent, traceable, and trustworthy systems of data management. Blockchain, with its core features of immutability, decentralization, and smart contracts, has emerged as a promising solution for strengthening data-driven supply chain operations. This paper presents a narrative review synthesizing insights from academic research, industry reports, and regulatory documents to examine blockchain’s role in enhancing transparency, traceability, and trust. References were identified through targeted searches of major databases and gray literature sources, with emphasis on diverse sectors and global perspectives, rather than exhaustive coverage. The review maps how blockchain’s technical capabilities—such as data integrity preservation, access control, automated validation, and provenance tracking—support these outcomes, and assesses the empirical indicators used to evaluate them. A sectoral applicability analysis distinguishes contexts in which blockchain adoption offers clear advantages from those where benefits are limited. The review also identifies critical research gaps, including inconsistent definitions of core concepts, insufficient interoperability standards, overreliance on subjective performance measures, and lack of longitudinal cost–benefit evidence. Finally, it proposes directions for future research, including the development of sector-specific adoption frameworks, integration with complementary technologies, and cross-border regulatory harmonization. Full article

The Chengdu–Chongqing Economic Mega Region (CCEMR), as a strategic economic hub
inWestern China, is increasingly facing challenges in balancing urban growth, agricultural
stability, and ecological conservation within its territorial spatial planning framework. This
study addresses the critical need to integrate multidimensional resilience assessment into
China’s territorial spatial planning system. A framework for functional resilience assessment
was developed through integrated GIS spatial analysis, with three resilience dimensions
explicitly aligned to China’s “Three Zones and Three Lines” (referring to urban, agricultural,
and ecological space and spatial control lines) territorial planning system: urban resilience
was evaluated using KL-TOPSIS ranking, where weights were derived from combined Delphi
expert consultation and AHP; agricultural resilience was quantified through the entropy
method for weight determination and GIS raster calculation; and ecological resilience was
assessed via a Risk–Recovery–Potential (RRP) model integrating Ecosystem Risk, Recovery
Capacity (ERC), and Service Value (ESV) metrics, implemented through GIS spatial analysis
and raster operations. Significant spatial disparities emerge, with only 1.29% of CCEMR
exhibiting high resilience (concentrated in integrated urban–ecological zones like Chengdu).
Rural and mountainous areas demonstrate moderate-to-low resilience due to resource constraints,
creating misalignments between resilience patterns and current territorial spatial
zoning schemes. These findings provide scientific evidence for optimizing the delineation
of the Three Major Spatial Patterns: urbanized areas, major agricultural production zones,
and ecological functional zones. In this research, a transformative methodology is established
for translating resilience diagnostics directly into territorial spatial planning protocols. By
bridging functional resilience assessment with statutory zoning systems, this methodology
enables the following: (1) data-driven resilience construction for the Three Major Spatial
Patterns (urbanized areas, major agricultural production zones, and ecological functional
zones); (2) strategic infrastructure prioritization; and (3) enhanced cross-jurisdictional coordination
mechanisms. The framework positions spatial planning as a proactive tool for adaptive
territorial governance without requiring plan revision. Full article

Climate change significantly affects the well-being of urban populations. Thus, there is an increasing need for public green spaces in cities, as biologically active surfaces play a critical role in modifying the urban climate—cooling temperatures and providing shelter. Some institutional gardens, like cemeteries and hospital gardens, are hidden treasures: they are open but excluded from citizens’ mental maps, while usually having a rich green mass. This article aims to explore these hidden green surface elements, presenting their advantages and disadvantages by measuring their local climate effects. Three institutional gardens located in different urban environments were selected for analysis in the sample area of Budapest to explore how the surrounding built-up areas of the city modify the urban climate. The climate analyses were prepared with the ENVI-met climate simulation program. In the case of both hospital gardens and cemeteries, our studies show that their green spaces have great potential to increase the sense of comfort for both users of the green spaces and inhabitants of the neighborhood. In densely built-up urban areas, it is particularly important to involve institutional green spaces in public use, because with appropriate development they can contribute to cities’ adaptation to climate change. Full article

Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article

Background/Objectives: Survivors of sepsis can develop left ventricular (LV) systolic function with focal myocardial fibrosis. The relationship between diffuse myocardial fibrosis or oedema and LV systolic function remains unknown in this patient cohort. This study sought to address this knowledge gap using cardiovascular magnetic resonance (CMR) parametric mapping methods. Methods: Sepsis survivors who underwent CMR at a UK cardiac centre were included. CMR images analysed include cines, native T1-mapping, native T2-mapping, and post-contrast T1-mapping. Synthetic extracellular volume (ECV) fraction was also estimated. Native myocardial T1 values, native myocardial T2 values, and ECV values were compared against LV ejection fraction (LVEF). Results: Of the 37 sepsis survivors (age 53 ± 16 years old; 57% males), the mean left ventricular ejection fraction (LVEF) was 55% (IQR 43–62), and 43% of the patients had LV systolic dysfunction (LVEF < 50%). Mean native myocardial T1 values were 1055 ± 65 ms (septal) and 1051 ± 60 ms (global). Mean synthetic ECV values were 0.30 ± 0.04. Mean native myocardial T2 values were 52 ± 7 ms (septal) and 53 ± 6 ms (global). Septal and global native myocardial T1 values were not significantly correlated with LVEF (rho = 0.080, p = 0.637; rho = 0.036, p = 0.831, respectively). Synthetic ECV was not significantly correlated to LVEF (rho = −0.082; p = 0.723). Septal and global native myocardial T2 values were weakly correlated with LVEF (rho = 0.261, p = 0.281; rho = 0.216, p = 0.375, respectively). On ROC analysis, the performance of native myocardial T1 values, ECV, and native myocardial T2 values for predicting LV dysfunction was modest (AUC: 0.53 ± 0.10, 0.54 ± 11, and 0.68 ± 0.14; all p > 0.05, respectively). Conclusions: CMR markers of diffuse myocardial fibrosis (native T1-mapping and ECV) and myocardial oedema (native T2-mapping) have weak relationships with left ventricular systolic function in this study cohort of sepsis survivors. Further work is needed to better assess the role of diffuse myocardial fibrosis and oedema in the pathophysiology of post-sepsis cardiomyopathy. Full article

This study presents a comprehensive bibliometric review of 136 academic publications on smart grids, microgrids, and semiconductor technologies in the context of sustainable energy management. Data were collected from the Web of Science Core Collection and analyzed using VOSviewer to identify intellectual structures, thematic clusters, and research trajectories. The results demonstrate the increasing prominence of semiconductor-enabled solutions in advancing renewable energy integration, grid optimization, and energy storage systems. Five major research themes are identified: renewable energy and smart grid integration; distributed microgrid systems; optimization models; control strategies; and system-level resilience and cybersecurity. The analysis reveals a temporal evolution from foundational engineering (2020–2021) to intelligent, digitally enhanced energy systems (2022–2025), with a growing emphasis on electric mobility, digital twins, and advanced energy management techniques, such as convex optimization. Beyond mapping trends, this study underscores critical research gaps in the non-English literature, multi-database integration, and practical deployment. The findings provide actionable insights for researchers, policymakers, and industry leaders by highlighting technological maturity, real-world applications, and strategic implications for energy transition. By aligning digital intelligence, semiconductor innovation, and sustainable energy goals, this review advances a forward-looking agenda for resilient and equitable energy systems. Full article

Background and Objectives: The extracellular volume fraction (fECV) of the liver, as measured by contrast-enhanced computed tomography (CT), has been shown to correlate closely with the histological stages of hepatic fibrosis. This study aimed to investigate the diagnostic performance of a liver extracellular volume fraction derived from dual-energy CT (DECT) for evaluating liver functional reserve based on the Child–Pugh class in cirrhotic patients, compared with other noninvasive markers. Materials and Methods: This retrospective study included 258 patients with liver cirrhosis who underwent contrast-enhanced DECT. The fECV was measured using iodine maps derived from equilibrium phase images obtained 3 min after contrast injection at 100/140 Sn kVp. Statistical analyses included Welch’s ANOVA with post hoc tests, Spearman’s correlation, and ROC analysis. The area under the curve (AUC) was compared among fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]) using DeLong’s test. Intra- and interobserver reliability of fECV was assessed with the intraclass correlation coefficient (ICC). The area under the receiver operating characteristic curve (AUC) for differentiating Child–Pugh classes was compared among the fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]). Results: The fECV increased significantly with advancing Child–Pugh classes (p < 0.001), showing a moderate correlation with Child–Pugh class (r = 0.53). The mean differences in fECV among the Child–Pugh classes were 8.88 between A and B (95% confidence interval [CI], 5.85–11.92; p < 0.001) and 7.42 between B and C (95% CI, 1.92–12.91: p < 0.001). The AUC for differentiating Child–Pugh classes A and B demonstrated no significant differences among the fECV (0.84), APRI (0.83, p > 0.99) and FIB-4 (0.83, p > 0.99), except for MELD, which had a significantly higher AUC (0.94, p = 0.047). For differentiating Child-Pugh classes B and C, the fECV demonstrated a significantly higher AUC (0.78), compared with FIB-4 (0.50, p = 0.038) and APRI (0.49, p = 0.037), whereas no significant difference was observed between fECV and MELD (0.92, p = 0.12). The intra- and interobserver reliabilities of the fECV measurements were excellent (ICC, 0.93; 95% CI, 0.91–0.95 and 0.91; 95% CI, 0.88–0.92, respectively). Conclusions: DECT derived fECV is a useful noninvasive marker for assessing liver functional reserve based on the Child–Pugh classification. Full article

Background/Objectives: Cancer is a major public health burden in Serbia and a factor influencing long-term disaster readiness by straining health system capacity. This study examined spatial and temporal trends in incidence and mortality for eight major cancers among women in Central Serbia (1999–2021) to inform targeted prevention and preparedness strategies. Methods: Standardised rates from national datasets were analysed using the Mann–Kendall trend test and Sen’s slope estimator. Geographic disparities were mapped in ArcGIS Pro 3.2. Mortality trends were assessed only for statistically reliable series. Results: Breast cancer incidence increased in six counties, while cervical cancer declined in several areas, likely reflecting screening success. Colorectal, bladder, pancreatic, and lung and bronchus cancers showed rising incidence; lung and bronchus cancer mortality increased in 16 counties, indicating growing demand for chronic respiratory care. These shifts may reduce surge capacity during disasters by increasing the baseline burden on healthcare infrastructure. Regional disparities highlight uneven system resilience. Conclusions: Aligning cancer control measures—especially for high-burden cancers like lung—with emergency preparedness frameworks is essential to strengthen health system resilience, particularly in resource-limited regions. Full article

Epicardial adipose tissue (EAT), the visceral fat layer next to the myocardium, has become an important focus in heart failure with preserved ejection fraction (HFpEF). When enlarged and inflamed, EAT increases pericardial restraint, releases fibroinflammatory mediators, and disrupts myocardial energetics, thereby reproducing the high-pressure, exercise-intolerant HFpEF phenotype regardless of body mass index. Modern echocardiography, cardiac CT, and MRI, enhanced by artificial intelligence texture analytics, now enable precise depot-specific quantification, making EAT a measurable therapeutic target. Early interventional studies suggest that caloric restriction, bariatric surgery, SGLT2 inhibitors, GLP-1 receptor agonists, statins, PCSK9 antibodies, and colchicine can reduce EAT volume or alter its inflammatory profile, with concurrent improvements in haemodynamics and biomarkers. However, definitive outcome trials are still pending. Priority directions include standardising imaging cut-offs, mapping EAT immune–metabolic niches, and testing combined metabolic–inflammatory regimens to translate EAT modulation into precision therapy for HFpEF. This review aims to synthesise current mechanistic, diagnostic, and therapeutic insights on EAT in HFpEF and outline future research priorities. Full article

Geopolymers represent an innovative and environmentally sustainable alternative to traditional construction materials, offering significant potential for reducing anthropogenic CO₂ emissions. Among these, phosphoric acid-activated metakaolin-based systems have attracted increasing attention for their chemical and thermal resilience. In this study, we present a comprehensive structural and mechanical evaluation of metakaolin-based geopolymers synthesized across a wide range of Al/P molar ratios (0.8–4.0). Six formulations were systematically prepared and analyzed using X-ray powder diffraction (XRPD), small-angle X-ray scattering (SAXS), Fourier-transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance (ssNMR), and complementary mechanical testing. The novelty of this work lies in the integrated mapping of composition–structure–property relationships across the broad Al/P spectrum under controlled synthesis, combined with the rare application of SAXS to reveal composition-dependent nanoscale domains (~18–50 nm). We identify a stoichiometric window at Al/P ≈ 1.5, where complete acid consumption leads to a structurally homogeneous AlVI–O–P network, yielding the highest compressive strength. In contrast, acid-rich systems exhibit divergent flexural and compressive behaviors, with enhanced flexural strength linked to hydrated silica domains arising from metakaolin dealumination, quantitatively tracked by ²⁹Si MAS NMR. XRPD further reveals the formation of uncommon Si–P crystalline phases (SiP₂O₇, Si₅P₆O₂₅) under low-temperature curing in acid-rich compositions. Together, these findings provide new insights into the nanoscale structuring, phase evolution, and stoichiometric control of silica–alumino–phosphate geopolymers, highlighting strategies for optimizing their performance in demanding thermal and chemical environments. Full article

Autonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities. The commercialization of autonomous driving requires guaranteed safety and accuracy, underscoring the need for robust localization and environmental perception algorithms. In cost-sensitive platforms such as delivery robots and electric vehicles, cameras are increasingly favored for their ability to provide rich visual information at low cost. Despite recent progress, existing visual–inertial odometry systems still suffer from degraded accuracy in challenging conditions, which limits their reliability in real-world autonomous navigation scenarios. Estimating 3D positional changes using only 2D image sequences remains a fundamental challenge primarily due to inherent scale ambiguity and the presence of dynamic scene elements. In this paper, we present a visual–inertial odometry framework incorporating a depth estimation model trained without ground-truth depth supervision. Our approach leverages a self-supervised learning pipeline enhanced with knowledge distillation via foundation models, including both self-distillation and geometry-aware distillation. The proposed method improves depth estimation performance and consequently enhances odometry estimation without modifying the network architecture or increasing the number of parameters. The effectiveness of the proposed method is demonstrated through comparative evaluations on both the public KITTI dataset and a custom campus driving dataset, showing performance improvements over existing approaches. Full article