Advancing Open Science

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments.

Background: Pediatric Major Depressive Disorder (pMDD) is one of the leading causes of disability in adolescents. There is currently no single explanation that fully accounts for the cause of the disorder, but various factors, including dysregulation of the immune and stress responses, have been linked to its onset. Oxylipins and endocannabinoids, derived from metabolization of n-3 and n-6 polyunsaturated fatty acids (PUFAs), regulate inflammation and have been suggested to attenuate inflammation associated with depression. This study aims to understand whether adolescents with pMDD have altered baseline levels of oxylipins and endocannabinoids compared to healthy adolescents. Methods: In this case–control study, we measured 60 oxylipins and endocannabinoids in plasma from 82 adolescents with pMDD and their matching healthy controls. Results: A Principal Component Analysis revealed substantial variability within each group and only a moderate degree of separation between them. In a paired analysis, the lipid mediators of controls exhibited higher concentrations of n-6 PUFA-derived prostaglandins and thromboxanes (PGE2, PGD2, PGF2a and TXB2), n-3 PUFA-derived TxB3, and the endocannabinoids AEA, EPEA, and DHEA. In contrast, cases had higher concentrations of the n-6 PUFA-derived 6-keto-PGF1a and the n-3 PUFA-derived PGD3. In addition, we observed a higher percentage of oxylipins and endocannabinoids derived from DHA (5.65 ± 5.46% vs. 4.72 ± 4.94%) and AA (16.31 ± 11.10% vs. 12.76 ± 13.46%) in plasma from controls, in line with the higher DHA and AA levels observed in erythrocytes from controls compared to cases. Conclusions: Overall, our results show lower plasma levels of endocannabinoids and lower DHA- and AA-derived oxylipins in adolescents with pMDD, supporting their role in the pathophysiology of pMDD. To infer a causative role of the n-3 and n-6 PUFA-derived oxylipins and endocannabinoids in pMDD, an intervention study with n-3 PUFA supplementation and monitoring of oxylipins and endocannabinoids would be necessary.

Background/Objectives: Cancer persists as a leading concern in the current medical field, and current therapies are limited by toxicity, cost, and resistance. Targeted inhibition of tubulin polymerization is considered as a promising therapeutic strategy for cancer treatment. Methods: Thirty-one new tubulin polymerization inhibitors were designed via molecular hybridization techniques, and BLI technology was employed to quantitatively investigate their interactions with tubulin. Antiproliferative activities against MCF-7, MDA-MB-231, A549, and HeLa cell lines was evaluated using the CCK8 assay. Apoptosis induction and cell cycle arrest were analyzed by flow cytometry. The anti-tumor activity of compound B6 was validated in a mouse melanoma tumor model. Results: Compounds exhibited varying degrees of antiproliferative activity against four tumor cell lines. Among them, compound B6 was the most promising candidate and displayed strong broad-spectrum anticancer activity with an average IC₅₀ value of 2 μM. The mechanism studies revealed that compound B6 inhibited tubulin polymerization in vitro, disrupted cell microtubule networks, and arrested the cell cycle at G2/M phase. Furthermore, B6 displayed significant in vivo antitumor efficacy in a melanoma tumor model with tumor growth inhibition rates of 70.21% (50 mg/kg). Conclusions: This work shows that B6 is a promising lead compound deserving further investigation as a potential anticancer agent.

Cryotherapy and radiofrequency (RF) treatments modulate tissue temperature to induce therapeutic effects; however, improper application can result in thermal injury. Traditional temperature-based monitoring methods rely on multiple thermal sensors whose accuracy strongly depends on their number and spatial positioning, often failing to detect early tissue crystallization. This study introduces a fractional order bioimpedance modelling framework for the early detection of tissue freezing during cryogenic and thermal medical treatments, with the feasibility and effectiveness of this approach having been reported in our prior publications. While bioimpedance spectroscopy itself is a well-est. The corresponablished technique in biomedical engineering, its novel application to predict and identify premature freezing events provides a new pathway for safe and efficient energy-based therapies. Fractional-order models derived from the Cole family accurately reproduce the complex electrical behavior of biological tissues using fewer parameters than classical integer-order models, thus reducing both hardware requirements and computational cost. Experimental impedance data from human abdominal, gluteal, and femoral regions were modelled to extract fractional parameters that serve as sensitive indicators of phase-transition onset. The results demonstrate that the proposed approach enables real-time identification of freezing-induced electrical transitions, offering a physiologically grounded alternative to conventional temperature-based monitoring. Furthermore, the fractional order bioimpedance method exhibits high reproducibility and selectivity, and its analytical figures of merit, including the limits of detection and quantification, support its use for reliable real-time tissue monitoring and early injury detection. Overall, the proposed fractional order bioimpedance framework enhances both safety and control precision in cryogenic and thermal medical applications.

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation.

Time delays in the images of gravitationally lensed quasars play crucial role in understanding the geometry and physical context of the gravitational lens systems (GLS). In case of the short time delays on hourly/daily timescales, correlating the X-ray/gamma-ray data is the best way to determine them as the variability of quasars at these energies is usually faster than at lower ones. Here, we demonstrate the usage of our web tool for correlation function analysis, applying the cross-correlation (asymmetrical) function to the Chandra and auto-correlation (symmetrical) one to the XMM-Newton light curves of the images of the quasar in the famous GLS Q2237+0305 (“Huchra lens”/“Einstein Cross”). We also describe the way to distinguish between the GLS time delay and periodicity in light curves based on translational symmetry of cross-correlation function in case of periodicity of the signal. We have estimated the delays between the gravitationally lensed images and the timescales of (quasi)periodical flux variations of the quasar in the Einstein Cross.

Background/Objectives: Within the last decade, there has been a 19% increase in stroke-related mortality among individuals aged 45–64. Understanding stroke characteristics is crucial, particularly in the younger age groups. This study describes the key demographics and clinical and anthropometric characteristics based on age categories in young adults admitted to the stroke unit in Abu Dhabi. Methods: This retrospective observational study had data between October 2024 and March 2025. Data were analyzed descriptively using SPSS, with a more detailed analysis conducted across two age-based groups. Results: A total of 51 patients were included, with the median age of 40 (IQR: 37–48) and 44 (86.3%) being males. The median hospital length of stay was 4 days (2–9 days). Most of the patients, 47 (92.2%), had ischemic stroke, with 24 (45.1%) presenting with right-side weakness, and bilateral weakness in 4 (7.8%). The median NIHSS score on admission was 4 (IQR 2–9). Prior to admission, 18 (35.3%) of the patients were known hypertensive, and 12 (23.5%) were diabetic. In terms of anthropometric measurements, the median waist-to-height ratio was 0.58 (0.5–0.69) and BMI was 25.7 (24.2–29.4), with 31 (60.8%) of the patients categorized as either obese or overweight. The statistical significance difference across the age groups was found in the gender distribution only (p = 0.034). Conclusions: In the UAE, more young men are experiencing Stroke due to lifestyle-related factors, many of which can be prevented. This growing trend calls for early screening, better prevention efforts, and tailored rehabilitation programs.

The ubiquity of smartphones has transformed them into primary repositories of sensitive data; however, traditional one-time authentication mechanisms create a critical trust gap by failing to verify identity post-unlock. Our aim is to mitigate these vulnerabilities and align with the Zero Trust Architecture (ZTA) framework and philosophy of “never trust, always verify,” as formally defined by the National Institute of Standards and Technology (NIST) in Special Publication 800-207. This study introduces a robust continuous authentication (CA) framework leveraging multimodal behavioral biometrics. A dedicated application was developed to synchronously capture touch, sliding, and inertial sensor telemetry. For feature modeling, a heterogeneous deep learning pipeline was employed to capture modality-specific characteristics, utilizing Convolutional Neural Networks (CNNs) for sensor data, Long Short-Term Memory (LSTM) networks for curvilinear sliding, and Gated Recurrent Units (GRUs) for discrete touch. To resolve performance degradation caused by class imbalance in Zero Trust environments, a Grid Search Optimization (GSO) strategy was applied to optimize a weighted voting ensemble, identifying the global optimum for decision thresholds and modality weights. Empirical validation on a dataset of 35,519 samples from 15 subjects demonstrates that the optimized ensemble achieves a peak accuracy of 99.23%. Sensor kinematics emerged as the primary biometric signature, followed by touch and sliding features. This framework enables high-precision, non-intrusive continuous verification, bridging the critical security gap in contemporary mobile architectures.