Browse Articles

Projecting the spatio-temporal habitat suitability of Striga hermonthica under climate change scenarios in Ethiopia using ensemble modeling

Synthesis, biological evaluation and in silico studies of novel propargyl-tethered isatin hydrazones as monoamine oxidase inhibitors for Parkinson’s disease

Using ensemble learning and explainable AI to predict bank marketing customer subscription

Short-term photovoltaic power forecasting using DTW-based K-Medoids clustering and a hybrid VMD-CEEMDAN-1DCNN-S-Mamba model

Abstract The short-term prediction of photovoltaic (PV) power is critical for grid stability and efficient renewable energy dispatch. However, the strong volatility of PV generation due to weather changes poses major challenges for accurate forecasting. This paper proposes a hybrid model combining DTW-based K-Medoids clustering, two-stage VMD-CEEMDAN decomposition, and a 1DCNN-S-Mamba network for multi-step PV power forecasting.Historical PV power and irradiance sequences are clustered into sunny, cloudy, and rainy patterns using K-Medoids with Dynamic Time Warping (DTW), which is more robust to outliers and temporal shifts than traditional K-Means. Variational Mode Decomposition (VMD) extracts the main trend components, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is applied to the residual to further mitigate high-frequency noise. The decomposed components, along with meteorological variables and weather-type codes, are input into the 1DCNN-S-Mamba model, where the 1DCNN captures local multiscale features and the bidirectional S-Mamba models long-range dependencies.Tested on a two-year dataset with a 5-min resolution from three Australian PV stations, the model achieves an $$R^2$$ of 0.9822, an MAE of 0.1188 kW, and an RMSE of 0.2451 kW. It outperforms eleven benchmark models, especially under cloudy and rainy conditions. Ablation studies confirm the effectiveness of the two-stage decomposition and the 1DCNN module. The proposed framework provides a practical solution for high-accuracy PV power forecasting.

siRNA-mediated silencing of placenta-specific protein 1 (PLAC1) alters CD4+, CD8+, and regulatory T cells in a murine colon cancer model

Evaluation of mechanical behavior and microstructural studies of paper sludge ash-treated fine grained soil: A step towards sustainable building materials

Facile Cobalt-supported Zeolite 4 A as ecofriendly catalyst to prepare 2-Furyl- hydrazine-1-carbothioamides and 1,2,4 Triazole-3-thiones with anticancer investigation

Abstract In recent years, crystalline porous zeolite materials have been extensively investigated owing to their distinct characteristics, such as high surface area, pore volume, size, and thermal stability. In this work, 500 nm to 1 μm sized thermally stable cobalt-supported zeolite 4 A was developed via impregnation method, employed as an efficient and reusable catalyst in synthesizing novel thiosemicarbazide and 1,2,4 triazole derivatives using Co-Zeolite 4 A in ethanol: water (1:1) as a green solvent, and found most active among Ni and Fe-supported Zeolite 4 A, with 89% yield. FT-IR, XRD, and BET data revealed characteristic metal-support interactions, crystalline domains, and reduction in surface area (10.1 to 9.4 m 2 /g), confirming successful cobalt incorporation and governing its catalytic activity. The catalyst has maintained its efficacy over five reaction cycles. The anticancer activity was meticulously evaluated with most potent 3,5-bis trifluoromethyl 1,2,4 triazole 4c, displayed IC 50 8.74 µM and 29.51 µM against HeLa and PC3 cancer cell lines. In silico docking indicated that compound 4c strongly interacted with the tumor suppressor protein p21, and DFT studies confirmed the chemical reactivity of compounds 3a-3c. This synthetic approach offers high yields, faster reaction times, robust and reusable catalyst, and environmentally sustainable platform for organic transformations with therapeutic potential.

Bayesian belief network model to predict human-wildlife conflict in protected areas

Abstract Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Medicine ball throw distance weighted with height-to-mass ratio aligns with other physical fitness tasks

Feasibility and associated factors of same-day discharge after hysteroscopic-laparoscopic surgery for infertility under ERAS: a prospective observational study

A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI

Abstract Echo-planar imaging (EPI), commonly employed in functional MRI (fMRI), is highly susceptible to magnetic field inhomogeneities, leading to pronounced geometric distortions in reconstructed images. These distortions can result in substantial structural discrepancies between EPI and anatomical images acquired using the magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) method, thereby making it challenging to achieve accurate co-registration and subsequent localization of functional mapping. This issue can be effectively addressed by employing an anatomical imaging sequence that exhibits distortion profiles identical to those in EPI, referred to here as MP2EPI (magnetization-prepared 2 EPI). While this approach enables effortless co-registration with functional scans, it also introduces geometric distortions into the anatomical reference imaging, which limits its utility for analyses that rely on morphometric measurements or atlas-based segmentation. Distortion in MP2EPI can be corrected using additional data acquired with the reversed phase-encoding (PE) direction, which, however, significantly increases total acquisition time. To overcome this limitation, this work presents a novel MP2EPI sequence that simultaneously acquires reversed PE data within a single MP2EPI acquisition, without increasing the overall scan time. The primary focus of the current work is the technical implementation and validation of this sequence in the context of submillimeter fMRI at 7T.

The experiences of Chinese pregnant women who have undergone fetal reduction: a qualitative study

Falling fertility on the left as key driver of US birth decline

Medical students’ perceptions of learning modalities: development and psychometric validation of the e-learning and face-to-face learning experience questionnaire

Abstract Amidst the growing demand for effective blended learning, particularly post-COVID-19, understanding the key factors influencing student learning experiences is paramount. This study addresses this need by designing and validating a comprehensive questionnaire (ELFEQ) to assess these factors across both face-to-face (F2F) and e-learning environments in medical education. A quantitative, cross-sectional study was conducted with 298 students from Shiraz University of Medical Sciences (SUMS), Iran. Data were collected using the researcher-developed 27-item ELFEQ questionnaire (rated on a 5-point Likert scale). Exploratory Factor Analysis (EFA) with Varimax rotation was used for initial factor extraction, and Confirmatory Factor Analysis (CFA) was subsequently performed. Reliability was assessed using Cronbach’s alpha. Data analysis was performed using SPSS Version 24. The analysis revealed six primary factors influencing student learning experiences, including Peer Interaction, Teacher-Student Interaction, Examination and Assessment methods, Emotional Comfort, Content Quality, and Assignments. The highest factor loading was observed for peer interaction and collaborative learning. The questionnaire accounted for approximately 70% of the ELFEQ construct, with a reliability coefficient of α = 0.933. The Content Validity Index (CVI) was 0.976, and the Content Validity Ratio (CVR) was 0.898. The Kaiser-Meyer-Olkin (KMO) measure yielded a value of 0.920, indicating excellent sampling adequacy, while Bartlett’s Test of Sphericity was significant ( P  < 0.001), confirming the suitability of the data for factor analysis. The findings suggest that effective educational practices must integrate diverse teaching methods and assessment strategies to accommodate various learning styles. Both F2F and online environments offer unique advantages that can be leveraged through a blended approach. The study underscores the importance of creating inclusive and supportive learning environments that prioritize student comfort and engagement, ultimately enhancing educational outcomes.

Projected heatwave hazards in an eastern Amazonian metropolis under contrasting CMIP6 scenarios

NeuroSymbolicPhishDefend for adaptive multimodal phishing detection against evolving artificial intelligence driven threats

Mechanism of matrine inhibiting retinoblastoma cell growth by downregulating NR1D2 to regulate phosphorylation of the PI3K/AKT signaling pathway

Seed and soil provenance in the production and survival of Melanoxylum brauna seedlings

Automated quantification of lens epithelial cell density using deep learning: validation and large-scale clinical application

VKORC1L1–mediated vitamin K recycling counters ferroptosis to promote endothelial repair

Abstract Vitamin K is classically known for hepatic γ-carboxylation via VKORC1, yet accumulating evidence suggests broader redox-regulatory roles. Here, we characterize the VKORC1 paralogue VKORC1L1 as an anti-oxidative enzyme that couples the vitamin K cycle to ferroptosis defense in the endothelium. In human coronary artery endothelial cells (HCAEC), menaquinone-7 (Vitamin K2, MK-7) increased viability and attenuated RSL3-induced lipid peroxidation, ferroptosis, and NF-κB-dependent inflammatory activation. In vivo, a MK-7 enriched diet accelerated reendothelialization after electric carotid injury in C57BL/6J mice without altering hemostasis. Silencing VKORC1L1, but not the coagulation-linked VKORC1, diminished HCAEC proliferation, raised reactive oxygen species, and triggered NF-κB activation. Importantly, MK-7 failed to rescue these effects in VKORC1L1-deficient cells, supporting a functional requirement for VKORC1L1 in vitamin K-mediated cytoprotection. Transcriptomics of VKORC1L1-deficient cells revealed a lipid peroxide/NF-κB/TNF pathway that amplified oxidative inflammation. This signaling axis was blocked by the lipid-radical scavenger ferrostatin-1 or by inhibiting TNF shedding. Collectively, these data identify VKORC1L1 as a redox-responsive component of the endothelial vitamin K cycle that limits lipid peroxides, suppresses ferroptosis, restrains TNF-driven inflammation, and promotes vascular repair.