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A blockchain-assisted secure federated learning architecture for intrusion detection in internet of things networks
Gender inequalities in high psychological distress vary across European regions and occupational subgroups
Abstract Gender inequalities in psychological distress have been found in various populations, including occupational groups and countries. There is a lack of studies that compare regions in a uniform operationalisation of distress and integrate occupational groups. 67,641 participants from 36 countries of the European Working Conditions Survey 2021 were analysed. High psychological distress was operationalized using the WHO-5 Well-Being Index. Using predicted probabilities and multilevel regression analysis, gender inequalities between countries, regions and occupational subgroups were calculated. Across most countries, women reported higher psychological distress. The magnitude of gender differences varied between countries, regions and occupations. However, overlapping confidence intervals limit the conclusiveness of the results. When stratifying for both region and occupation type, in the Southern European region and in blue-collar low-skilled occupations gender inequalities were highest. In the significant interaction model, white-collar occupations in Western Europe and blue-collar low-skilled occupations in Southern Europe had the highest gender inequalities. This study demonstrates heterogeneity in the magnitude of gender differences in high psychological distress across Europe and occupational groups. White-collar high-skilled occupations in Western Europe and blue-collar low-skilled occupations in Southern Europe were identified as vulnerable groups. The findings underscore the importance of considering regional patterns when addressing gender-based mental health inequalities in the European workforce.
Type D personality and quality of life among polish university students: a cross-sectional study
Divergent urban storm response to convective, frontal and tropical systems
Abstract Urbanization modifies precipitation 1,2 , yet previous studies have reported inconsistent results, with some cities experiencing rainfall enhancement and others showing suppression 3 . To reconcile these discrepancies, we examine how urban impacts vary across storm types using an event-based analysis. With three-dimensional radar reflectivity data (1995–2017), we identify more than 40,000 warm-season storms across four Texas cities (Dallas, Austin, San Antonio and Houston). Here we show that classifying storms into five types reveals distinct urban influences linked to storm scales and dynamics. Local-scale single-cell and isolated storms, driven by atmospheric instability, increase in frequency (7–31%), particularly at night. Synoptic-scale frontal storms show unchanged occurrence but contrasting intensity responses: cold fronts weaken over cities by 16–28%, probably because of thermal and roughness effects, whereas warm fronts exhibit enhanced reflectivity. Tropical systems show no consistent change in frequency or intensity but exhibit a shift of high-reflectivity grid cells towards lower altitudes over urban areas. Given the diverse climate and geography of Texas, this work provides a transferable framework for understanding urban–storm interactions in other regions. These findings move beyond the traditional ‘urban wet or dry islands’ model, advancing our understanding of how urbanization modulates extreme precipitation and informing climate modelling 4,5 and resilience planning for rapidly growing cities 6,7 .
Reproducible benchmark of wavelet-enhanced intrabody communication biometric identification
Abstract Intrabody communication (IBC) channels offer physiological diversity that may support future wearable biometric identification. Recent reports of over 99 per cent identification accuracy have frequently resulted from data leakage, where samples from the same subject are seen in both training and evaluation, yielding inflated and unreliable metrics. In this work, we establish a public, leakage-free benchmark for IBC biometrics built on a 30-subject open dataset, using strict subject-wise 80/20 splits repeated five times to ensure reproducibility. We systematically compare frequency-domain and time-frequency representations, including resampled spectra, discrete wavelet transform (DWT) statistics, and their fusion. Under the subject-wise embedded-friendly benchmark, the strongest classical configuration, Scattering + LightGBM, reaches 54.0 per cent accuracy, while db4-DWT and lifting-based wavelet statistics with Random Forest improve over the Simple-3 baseline (49.3 and 51.6 per cent versus 39.0 per cent). Separately, closed-set neural analyses provide exploratory upper bounds rather than leakage-free subject-wise results: a Raw MLP reaches 83.7 per cent accuracy, whereas adding DWT statistics does not improve this result (81.2 per cent for Combined MLP), and SpectralCNN reaches 74 per cent. Confusion matrix analysis reveals that residual errors are concentrated among subject pairs with statistically overlapping signatures, suggesting the presence of intrinsically hard users and a potential biometric ceiling for this modality. Embedded profiling on an STM32F446RE Cortex-M4 microcontroller indicates that lifting-based wavelet features enable low-latency, low-energy scoring, requiring approximately 0.55 ms and 18 micro-J per 256-point spectrum for Lift-bior feature extraction plus Random Forest inference (versus approx. 33 micro-J for the equivalent db4-DWT pipeline). All code, data split scripts, and Jupyter notebooks are released open source to facilitate reproducibility and enable rigorous future comparisons.
Unveiling population heterogeneity in health risks posed by environmental hazards using regression-guided neural network
Correction: Sensitivity-informed framework for enrichment distribution in MNR for thermal performance enhancement
Data-driven optimization of mechanical performance and durability of cementitious composites in acidic environments
Passive seismological approaches for localizing near-surface fiber-optic cables with DAS
Abstract Accurate knowledge of fiber-optic cable geometry is important for many applications of distributed acoustic sensing (DAS). However, the true positions of buried or installed fibers are often uncertain due to slack, bends, or deviations from documented routes. We present two passive, seismology-based approaches for cable localization that exploit information contained in DAS recordings. The approaches are evaluated based on synthetic tests under controlled conditions. Case A employs ambient noise cross-correlations with reference points to estimate relative travel times, whereas Case B uses the differential arrivals of plane waves from distant earthquakes with linearly independent slowness vectors. Both methods can be formulated using a least-squares approach that allows for the joint estimation of propagation velocity and geometry, thereby providing consistent solutions in the presence of noisy or uncertain travel-time measurements. Synthetic experiments show that cable positions can be recovered with an accuracy in the order of 100 m, even when apparent velocities are uncertain or the medium exhibits heterogeneity. The two methods provide independent geometric constraints that complement other sources of information on cable routing, although additional uncertainties are expected in field applications.
Quantum-inspired optimization of transformer–capsule networks for accurate brain tumor segmentation and classification
Spatial patterns and policy implications of invasive flora in the Horn of Africa
Abstract Invasive plants are fast-spreading species that pose serious and often irreversible threats to native biodiversity. This study presents a regional-scale analysis of the elevational distribution of invasive plant species, their relationship with road proximity, and their presence across different land uses. Data were synthesized following the Reporting Standards for Systematic Evidence Syntheses protocol using multiple databases, including Web of Science, Scopus, and Google Scholar. A total of 250 invasive species distributed in 63 families were documented. The dominant families were Fabaceae (16.8%), Asteraceae (9.6%) and Poaceae (6.4%). Herb was the predominant growth form, accounting for 47.6%, while liana/shrub was the least represented at just 1.2%. The native range of the species were predominantly Neotropical (30%) and African (23.1%). About 5.1% of the species were either helophytes or aquatic. Furthermore, nine (3.6%) of them were either hemiparasites or holoparasites. Kenya and Ethiopia had the highest records of invasive plant species, while Djibouti and Eritrea had the lowest. Invasive species declined significantly with increasing elevation ( P < 0.001) and were declining further from roads ( P < 0.008). Most occurrences were recorded in cultivated areas (33.51%), shrublands (23.1%), and urban or built-up regions (16.66%). The study highlights four main factors: (1) lack of reported protocols for assessing impact and classifying invasiveness, (2) potential ‘snowball effect’ in reporting, (3) paucity of data on invasive species and (4) lack of proper definition of operational terms. As a result, the number of invasive species reported in this study may not accurately represent the true extent in the region. To address this, it is recommended to develop or adopt region-specific, expert-led assessment tools and establish a collaborative framework for managing invasive plant species.
Evaluation and telemetry-based detection of GPS spoofing effects on UAV navigation using software-defined radio
Whole genome sequencing-based characterization of Escherichia coli isolated from raw beef in selected butcher shops in Addis Ababa and Burayu, Ethiopia
Sustainable waste assessment using waste biomasses in the removal of toxic textile dyes
Abstract Wasted tea of Salvia officinalis (WTSO) and the acorn cupule of Quercus coccifera (ACQC) were used as highly efficient biosorbents in the experiments. The aim is to convert waste materials into novel treatment materials which will be economically cheaper sources compared with conventional activated carbon. The powdered form of these materials, without any thermal or chemical pretreatment, was applied to wastewater containing Blue X GRL (BXGRL) and Red Violet 3R (RV3R) textile dyes. Various parameters affecting the separation such as pH, amount of biosorbent, contact time, dye concentration, and temperature were investigated. High removal efficiencies (up to 99%) and biosorption capacities could be readily obtained by these novel biosorbents for the studied toxic dyes. The most convenient pH and adsorbent dosage were found to be 8 and 0.1 g/100 mL, respectively. The values obtained from WTSO material with RV3R dye were fitted to Freundlich isotherm model, while the results from other material-dye sets were fitted to Langmuir model at 25 °C. The data from both ACQC and WTSO biosorbents were very well conformed to pseudo-second order reaction kinetic model with a R 2 value of 99% when compared with pseudo first order and Elovich models. The rate limiting step was found to be chemisorption according to the results of pseudo second order and intraparticle diffusion models. In adsorption studies using high concentrations, it was observed that the dye removal efficiency increased with temperature. The highest biosorption capacities for BXGRL were obtained as 344.83 mg/g with WTSO and 222.22 mg/g for ACQC at 25 °C according to the Langmuir isotherm model. The biosorption capacities of WTSO and ACQC were 147.06 and 106.38 mg/g, respectively, for RV3R dye at 25 °C as a result of Langmuir model.
Tuning the structural and physical properties of Hf5Si3 intermetallic compound under pressure: insights for next-generation high-temperature technology
Viscoelastic profiling of rare pediatric extracranial tumors using multifrequency MR elastography: a pilot study
Abstract Magnetic resonance elastography (MRE) is a noninvasive technique for assessing viscoelastic properties of soft biological tissues in vivo, with potential relevance for tumor evaluation. This exploratory study aimed to assess the feasibility of multifrequency MRE in pediatric extracranial solid tumors and to investigate potential associations between viscoelastic parameters and different rare pediatric tumor entities. Ten pediatric patients (mean age, 5.7 ± 4.8 years; four female) with extracranial solid tumors underwent multifrequency MRE in this prospective study. Shear waves at 30–70 Hz were subsequently generated and measured with a phase-sensitive single-shot spin-echo planar imaging sequence. The obtained shear wave fields were processed by wavenumber (k-)based multi-frequency inversion to reconstruct tumor stiffness and fluidity. Viscoelastic properties within the tumors were quantified and correlated with the apparent diffusion coefficient (ADC). Differences in stiffness and fluidity were assessed across histopathologically confirmed tumor entities, stratified into malignancy-based groups. MRE was successfully performed in all patients within less than five minutes. Viscoelastic properties varied among tumor entities, with a tendency toward higher stiffness, fluidity, and spatial heterogeneity in tumors assigned to higher malignancy groups (all p < 0.05). Stiffness ( p > 0.05) and fluidity ( p < 0.05) showed inverse associations with tumor ADC values. Multifrequency MRE can be integrated into pediatric MRI examinations and provides quantitative information on tumor viscoelastic properties. Differences in stiffness and fluidity were observed across pediatric extracranial solid tumors with higher values in tumors assigned to higher risk groups. These preliminary findings suggest that MRE-derived parameters may provide complementary information for tumor characterization.