This study focuses on the Umm Lajj region, situated along the eastern margin of the Red Sea, Northwest Saudi Arabia. The study integrates aeromagnetic and land gravity data to characterize subsurface geological structures influencing groundwater aquifers and their potential susceptibility to seawater intrusion. Data processing and interpretation were conducted employing a suite of filtering techniques to enhance data resolution and interpretability. A Butterworth filter was applied to distinguish between deep-seated and near-surface anomalies, while source edge detection and depth estimation methods were utilized to delineate structural features. The results reveal prominent NNW-, NW-, and NE-oriented fault systems, as well as associated grabens and horsts, which are structurally linked to Red Sea rifting processes. A significant structural basin was identified in the central coastal area and is interpreted as a potential groundwater aquifer. Gravity anomaly patterns also highlight low-density, fault-controlled zones that are likely conduits for seawater intrusion into the aquifer system. Conversely, NW-trending magmatic dikes and uplifted blocks along the western boundary may serve as partial barriers, restricting the lateral movement of seawater. The combined interpretation of magnetic and gravity data offers a detailed structural framework that enhances the understanding of regional hydrogeology and contributes to future groundwater resource management. These insights are particularly valuable for evaluating the risks of seawater intrusion and the vulnerability of groundwater systems in arid coastal settings.

Ballastless track continuous welded rails (CWR) are used on a multi-span 100m simply supported steel truss bridge of a mixed passenger and freight railway, to study the mechanical characteristics and influence factors of CWR on the 100m simply supported steel truss bridge, a track-bridge-piers spatial finite element model was established based on the track-bridge interaction (TBI) principle. The influence of design parameters such as the number of bridge spans, the longitudinal stiffness of piers of the simply supported steel truss bridge, the arrangement of bridge bearings, the type of rail, and the track longitudinal resistance type on the mechanical characteristics of CWR on the simply supported steel truss bridge were systematically examined. The research results indicate that for multi-span simply supported steel truss bridges, the model can be simplified by considering 8 spans when the total number of spans exceeds 8. Likewise, when the number of adjacent concrete box girder spans exceeds 5, 5 spans can be adopted. The longitudinal stiffness of piers of the simply supported steel truss bridge has a significant influence on the rail braking force and the rail broken gap. Increasing the longitudinal stiffness of the piers from 399kN/cm to 5000kN/cm has a much greater effect on the forces and deformations of CWR on the bridge than increasing it further from 5000kN/cm to 10000kN/cm. The bridge expansion length is primarily governed by the arrangement of fixed bearings on the adjacent concrete box girder. Therefore, to reduce the forces in the CWR on the bridge, the simply supported steel truss bridges and the adjacent concrete box girders should adopt a consistent bearing arrangement. In addition, rails with a larger cross-sectional area can reduce the additional rail stress. The used of small resistance fastener systems for ballastless track decreases the rail expansion force and rail braking force by 56.72% and 18.05%, respectively, but it increases the rail broken gap by 37.49%. The research results can provide references for the design of CWR on the simply supported steel truss bridge.

Infrared (IR) and visible (VI) image fusion aims to integrate complementary information from heterogeneous modalities to enhance visual perception in a wide range of applications such as remote sensing, military surveillance, and autonomous driving. While transformer-based and diffusion-driven methods have advanced fusion quality, their computational complexity and resource demands limit practical deployment. Furthermore, combining features in a single step may result in the loss of critical information. To address this, we propose PGCFuse, a novel progressive deep learning framework to fuse VI and IR images. The proposed method employs a multilevel progressive feature extraction strategy to preserve multi-scale spatial features, capturing fine-grained local details and global contextual semantics simultaneously. Unlike single-stream hierarchical approaches, our encoder leverages four parallel streams with progressive downsampling, coupled with a multi-scale spatial attention fusion module to adaptively emphasize salient regions across modalities. In particular, we introduce a Gated Convmixer Block that combines depthwise separable convolutions with gating mechanisms to enhance modality-specific feature representation. An Adaptive Global Feature Aggregation block is introduced that models long-range dependencies to refine feature representations. A nested skip-connected decoder reconstructs the fused image while retaining critical structural and thermal information. Comparative evaluations on the benchmark datasets demonstrate that PGCFuse outperforms recent and SOTA DL-based approaches in fusion quality and computational efficiency.

This study examined the knowledge, attitudes, and practices (KAP) among caregivers of children with epilepsy. A cross-sectional survey was conducted from September 2024 to February 2025 in two hospitals in Zibo, China. Structural equation modeling (SEM) was applied to explore the relationships among the KAP components. A total of 385 caregivers participated; most were female (59.5%) and urban residents (44.7%). Regarding education, 39.5% held an associate degree or higher, and 49.1% were employed. Most children were aged 7-12 years (45.7%). Mean scores for knowledge, attitudes, and practices were 19.76 ± 2.68, 23.43 ± 3.18, and 23.57 ± 2.52, respectively. Although overall knowledge levels met the threshold for adequacy, important gaps remained, such as uncertainty regarding vaccination safety. Attitudes were often negative, with 70.9% of caregivers believing epilepsy would adversely affect career and marriage prospects and 35.6% expecting social prejudice. In addition, some caregivers reported potentially unsafe practices, such as inserting objects into the mouth during seizures. SEM results indicated that knowledge was positively associated with attitude (β = 0.303, P < 0.001) and practice (β = 0.204, P = 0.001), while attitude showed a strong association with practice (β = 0.708, P < 0.001). Knowledge also demonstrated an indirect association with practice through attitude (β = 0.215, P < 0.001), although the magnitude of this effect was modest. In conclusion, caregivers of children with epilepsy demonstrated knowledge levels that met the adequacy threshold but exhibited persistent misconceptions, negative attitudes, and suboptimal caregiving practices. These findings suggest that improving caregiving behavior may require multifaceted interventions that address not only knowledge but also attitudinal and contextual factors.

Circulating biomarkers reflecting inflammation and endothelial dysfunction have been proposed as indicators of cerebral small vessel disease; however, their relevance during the hyperacute phase of ischemic stroke remains uncertain. We aimed to determine whether circulating biomarkers measured on admission differ between small ischemic stroke subtypes and whether they provide prognostic information beyond conventional predictors. We conducted a retrospective analysis of 168 patients with acute ischemic stroke treated with intravenous thrombolysis within 4.5h of onset, classified by MRI as having recent small subcortical infarcts (RSSI; n = 84) or small cortical or cortico-subcortical infarcts (non-RSSI; n = 84). We measured admission blood levels of interleukin-1β, interleukin-6, matrix metalloproteinase-9 (MMP-9), vascular cell adhesion molecule-1 (VCAM-1), neurofilament light chain (NfL), C-reactive protein (CRP), and leukocyte ratios, and evaluated their associations with 3‑month functional outcomes using the modified Rankin Scale. Our findings showed that RSSI patients were younger (66 vs. 74 years, p < 0.001) and had smaller infarcts (0.68 vs. 2.55 mL, p < 0.001). Higher NfL (OR 2.07, p < 0.001) and VCAM‑1 levels (OR 1.84, p = 0.034) were associated with poorer outcomes. Notably, the prognostic contribution of CRP, MMP‑9, and VCAM‑1 was more pronounced in patients with RSSI. These findings suggest that circulating biomarkers may offer additional prognostic value, with patterns varying across small ischemic stroke subtypes, though their clinical utility warrants further investigation.

An extreme winter icing event from 1 to 7 February 2024 in central-eastern China induced sequential hydrometeor phase transitions (freezing fog, freezing drizzle, intense freezing rain, and mixed-phase precipitation) at Hunan's Gutaishan wind farm. To investigate the underlying physical mechanisms, this study utilized multisource observational data, ERA5 reanalysis, and turbine operational records. The combined influence of the South Branch Trough and the Western Pacific Subtropical High drove a prolonged convergence of southwesterly warm-moist advection with easterly cold air, maintaining a stable temperature inversion. During the freezing-fog stage, this cold-warm-cold inversion structure trapped near-surface moisture, leading to the accumulation of supercooled droplets that ultimately caused a shutdown of 50% of the operational turbines. The freezing-drizzle stage was governed by the warm-rain mechanism. Due to the absence of ice-phase hydrometeors in shallow clouds (cloud-top temperatures > -10°C), purely liquid drizzle drops fell through the underlying cold layer to become supercooled, triggering a farm-wide shutdown. The intense freezing-rain stage followed the melting mechanism. Abundant ice-phase hydrometeors from deep clouds (cloud-top temperatures < -20°C) melted completely within a pronounced warm layer (vertical temperature inversion strength of 10.0°C km- 1) and subsequently became supercooled in the underlying cold layer, accelerating blade icing. Finally, the mixed-phase stage was driven by a melting-refreezing mechanism. Mid-level dry-cold air intrusion weakened the warm layer, causing descending solid hydrometeors to undergo only partial melting. These hydrometeors then formed mixed-phase precipitation (coexisting ice pellets and trace supercooled rain) within the deep cold layer, maintaining the farm-wide shutdown.

Cancer proteomics is crucial for understanding tumor biology, yet consistent quantification of clinically relevant proteins across analytical platforms remains challenging. Here, we compared Reverse Phase Protein Array (RPPA) and Liquid Chromatography-Mass Spectrometry (LC-MS) using identical lysate aliquots prepared from matched tumor and adjacent normal tissues from breast and kidney cancer patients. We focused on proteins encoded by genes represented in the Ion AmpliSeq Cancer Hotspot Panel v2 and included representative cellular markers. RPPA was applied to the larger sample set, whereas data-dependent, data-independent, and targeted parallel reaction monitoring LC-MS modes were performed on a smaller subset of lysates. RPPA identified tumor-normal differences for several proteins and cell markers, while LC-MS quantified a smaller overlapping subset in the matched samples analyzed by both platforms. Cross-platform comparison showed that agreement was partial and strongly protein dependent: some targets displayed concordant tumor/normal fold changes, whereas others exhibited weak or even opposite-direction changes. Targeted PRM modestly improved agreement for selected discordant proteins, but substantial discrepancies remained. These data indicate that RPPA and LC-MS are not interchangeable for quantifying all targets in this panel, calling for a rather confirmatory approach: concordant findings increase confidence in selected candidate biomarkers, whereas discordant findings identify targets that require cautious interpretation.

We report a materials-engineering strategy to enhance triboelectric nanogenerator (TENG) output by embedding a ternary g-C₃N₄/TiO₂@polyaniline (GTP) nanocomposite into PVDF tribopositive electrodes and pairing them with PTFE tribonegative layers of systematically varied composition from 30 to 60 wt%). XPS, TEM, FESEM/EDX, UV-Vis, and DSC confirm that TiO₂ nanoparticles are well distributed on g-C₃N₄ nanosheets and interconnected through a conducting PANI network, with evidence of interfacial Ti-O-C/Ti-N bonding, Ti3⁺ associated defect states, and band-tail absorption (Urbach energy, EU = 0.31 eV), indicating localized electronic states that support charge storage/transfer." In vertical contact-separation mode, the devices exhibit a composition-dependent trade-off: the 30 wt% PTFE configuration delivers the highest short-circuit current (Isc = 2.86 μA) and peak power (Pmax = 10.3 μW), whereas the 60 wt% PTFE device yields the maximum open-circuit voltage (Voc = 4.56 V). These results demonstrate that voltage-current characteristics can be tuned through interfacial state engineering and controlled adjustment of the tribonegative layer composition. To confirm repeatability and stability under manual tapping, the best-performing 30 wt% and 60 wt% devices has been validated using an automated cycle-resolved statistical analysis.

The outcomes of mechanical circulatory support (MCS) in patients with cardiogenic shock (CS) following acute myocardial infarction (AMI) are controversial. We included 1513 patients from six Gulf countries with AMI and CS between 2020 and 2022. Eight hundred twenty patients did not receive MCS, and 693 received MCS, predominantly via an intra-aortic balloon pump (IABP). Patients receiving MCS were more critically ill, with 94.95% in SCAI shock stages D or E versus 46.95% in the non-MCS group. While unadjusted in-hospital mortality was significantly higher in the MCS group (61.0% vs. 32.2%; p < 0.001), this difference was attributable to baseline risk. In a propensity score-matched analysis of 430 patient pairs, there was no significant difference in in-hospital mortality (46.7% vs. 43.7%; p = 0.429 for PCI patients) or long-term survival. Mortality was not related to the time of MCS initiation. Among patients with SCAI Stages D and E, the use of MCS was not associated with improved short- or long-term clinical outcomes. The findings, driven by a predominantly IABP-based MCS strategy, suggest that the higher observed mortality in patients receiving MCS reflects their greater illness severity, and that a survival benefit for this MCS approach was not demonstrated. This underscores the need for patient selection and timing of MCS to optimize outcomes.

The growing demand for sustainable materials has stimulated the development of bio-based composites, yet the combination of natural fibers and industrial waste fillers in polymer matrices has not been exploited synergistically. In line with Sustainable Development Goal 12 (SDG-12), the paper focuses on reusing the steel industry by-product Linz-Donawitz (LD) sludge to enhance the mechanical properties of epoxy composites when used with the jute fiber. Six composite specimens with constant jute fiber loading (20 wt%) and a range of LD sludge content (0-25 wt%) were prepared using hand lay-up technique. The best composition (60 wt% epoxy, 20 wt% jute, 20 wt% LD sludge) resulted in tensile strength of 61.84MPa (28.8% better than neat epoxy), flexural strength of 31.81MPa (41.8% better) and impact strength of 18.026 kJ/m2. Interfacial defects and agglomeration of particles led to a decrease in mechanical properties beyound 20 wt% sludge. This experimental data was used to train four machine learning models to forecast mechanical properties given compositional inputs. On training data, XGBoost achieved R2 = 1.0000 with near-zero errors (MAE = 0.0005MPa, RMSE = 0.0008MPa). However, when trained on a small dataset of six specimens, this perfect fit is mostly due to memorization of the training data, as opposed to predictive power. The findings suggest the risk of overfitting, mainly in the cases of Decision Tree and Gradient Boosting models. More realistic estimates of model performance are given by cross-validation (R2 = 0.94 ± 0.04 in the case of XGBoost). The ML models can thus be used to analyze exploratory composition-property trend analysis in this particular composition space, as opposed to extrapolative prediction. These results both validate the possibility of hybrid composites that use industrial waste to obtain mechanical performance equivalent to standard natural fiber composites and indicate that waste can be valorized, although any assertion of ML predictive capacity should be carefully hedged due to limitations in the datasets.