This study presents a landslide susceptibility assessment using multiple parameters by integrating remote sensing, GIS, and field observations in the Jammu and Kashmir region. Eight contributing variables were selected for landslide susceptibility analysis: Land Use and Land Cover (LULC), proximity to roads, streams, slope gradient, slope orientation (aspect), geology, geomorphology, and elevation. In addition, an extensive landslide inventory consisting of 669 landslide events was developed using the Field Landslide Inventory Mapping (FLIM) application, LISS-IV satellite data, and field observations, covering an area of 42,950.43 km². Landslide susceptibility mapping (LSM) was carried out using the Analytical Hierarchy Process (AHP) approach and validated with MaxEnt software and field-generated landslide data. The resulting landslide susceptibility map was classified into five categories: very high, high, medium, low, and very low susceptibility zones. Based on the AHP approach, these zones cover 3.65% (1,569.0483 km²), 24.43% (10,492.8912 km²), 51.56% (22,147.2369 km²), 18.81% (8,079.2199 km²), and 1.54% (662.0301 km²) of the study area, respectively. The weighted overlay and MaxEnt models proved effective for landslide vulnerability mapping, with MaxEnt achieving AUC values of 0.82 for training data and 0.807 for testing data, indicating good predictive performance. Field validation further showed that 87% of landslides occurred within high and very high susceptibility zones. The Jackknife test identified road proximity, slope, and stream proximity as the most significant independent variables influencing landslide occurrence. The generated landslide susceptibility map provides important insights for reducing landslide risk and serves as a valuable tool for infrastructure planning, community development, and disaster management in the region.
A. S. Jasrotia, Amit Sharma, I. C. Das et al.· Scientific Reports· 0 citations
Computer-assisted planning with patient-specific rods accurately reproduced the intended PSO and segmental lumbar correction but did not reliably predict global sagittal parameters, suggesting current planning tools might require refinement to improve the accuracy of predicted postoperative alignment using pre-bent rods.
Renzo A Laynes, Rafael Garcia de Oliveira, Kenneth T. Nguyen et al.· Journal of Neurosurgery : Sp...· 0 citations
A benchmark radiomics model to preoperatively identify the histological grade of spinal meningiomas is constructed, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs.
Adhith Palla, Nicolas K. Goff, Blake Perdikis et al.· Neurosurgical Focus· 0 citations
DSA is presented, an evidence-aware orchestration framework for multi-market stock research with large language model (LLM) agents that establishes implementation conformance for the tested software contracts, not superior report quality, forecasting accuracy, or investment returns.
Lingfeng Zhu, Yirui Shi· 0 citations
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It is shown that, in this sample, existing validation workflows often did not detect the particular controlled reproducibility-relevant changes introduced by the study, motivating reproducibility-oriented mutation testing as a complementary way to assess whether research-software safeguards constrain experimentally important choices.
Surf_2_Volume is presented, a workflow that combines Connectome Workbench, FreeSurfer, AFNI, AFNI, neuromaps, and Python image processing to convert cortical and subcortical CIFTI parcellations into Neuroimaging Informatics Technology Initiative (NIfTI) volumes.
Shu-Guang Yang, Zi-Yi Wang, Yue-Ru Shen et al.· 0 citations
This work proposes a model-based approach that optimizes model parameters, and evaluates first- and best-improvement algorithms, simulated annealing (SA), threshold accepting (TA), and two novel algorithms utilizing directional derivatives (dd) to guide exchanges.
This paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings).
The work identifies five distinct stages of the documentation review process: self review, technical review, editorial review, play testing, and post-publication feedback, and draws on practitioners with distinct expertise to address quality across content, presentation, and user experience.
Avinash Bhat, Ian Arawjo, Disha Shrivastava et al.· 0 citations
It is found that functional correctness alone is insufficient to assess the operational reliability of LLM-generated software before deployment in continuously running environments, and aging trends can also emerge in manually developed implementations.
César Santos, Michele Vitagliano, Roberto Natella et al.· 0 citations
The positive results of ISIT counseling on psychological distress symptoms and reducing perinatal grief during follow-ups conducted immediately post-intervention and 3 months after the intervention suggest that it will be useful for managing the complications of spontaneous abortion.
Nazanin Karimihamzekolaee, Hajar Adib-rad, Hajar Pasha et al.· Health Science Reports· 0 citations
ABSTRACT Physical activity plays an important role in the well‐being of patients with Chronic Obstructive Pulmonary Disease (COPD). The telEPOC program, developed at the Galdakao‐Usansolo University Hospital (Vizcaya, Spain), provided a telemonitoring data set resulting in a variable domain functional data set. In this sense, the aim is to study the impact of physical activity (measured as daily steps collected in a time interval delimited by the patient's participation in the experiment) on the rate of hospitalizations. To address this challenge, a novel basis expansion approach is proposed to fit a variable‐domain functional regression model, which assumes the functional nature of the covariate (daily steps) and incorporates the sample data into the model without alignment. This will enrich the interpretability of the model by incorporating the information provided by the domain through a two‐dimensional regression coefficient. In addition, a penalized estimation of the model is proposed in terms of a 2D P‐spline penalty, which allows considering different degree of smoothness for each dimension of the functional regression coefficient (anisotropic penalty). The performance of our methodology is also tested and compared with state‐of‐the‐art methods by means of a simulation study. Software for our method can be found in the VDPO R package available in CRAN.
Pavel Hernández-Amaro, María Durbán, M. C. Aguilera-Morillo et al.· Biometrical journal. Biometr...· 0 citations
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
