Cosmo-dynamics of dark energy models resulting from a parametrization of HH in f(Q,T)f(Q,T) gravity

Our objective in this paper is to study the late-time behavior of the universe in a model resulting from a parametrization of the Hubble parameter ($H$) in $f(Q,T)$ gravity. We have considered the flat Friedmann-Lemaitre-Robertson-Walker (FLRW) as the background metric and discussed the model in $f(Q,T)$ gravity, where $Q$ and $T$ are non-metricity and the trace of the energy-momentum tensor respectively. The complicated field equations are solved in a model-independent way by using a simple parametrization of $H$. Some geometrical parameters and physical parameters for the obtained model are calculated, and their cosmic evolution is described through some graphical representation. The physical dynamics of the model are discussed in some detail. Finally, we found the model's validity by checking the energy conditions, kinematic behavior, and the speed of the sound for the obtained models from the parametrization of $H$. The interesting results of the models are compelling to the present scenario of late-time cosmic acceleration.Comment: 16 Pages, 26 Figure

Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language

GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about the impact it will have on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60\% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development

Dental conditions in patients with psychiatric disorders – A pilot study

Background: Individuals with mental illness are vulnerable to developing many physical health issues, as their oral health deteriorates due to poor self-hygiene associated with mental illness. Therefore, this pilot study was undertaken to assess the dental status of individuals with mental illness. Method: This cross-sectional study involved 61 psychiatric patients recruited from psychiatry department. Psychiatric diagnosis was finalized as per ICD 10 criteria and dental conditions were assessed by consultant dentist. Results: The study included 61 patients, with 31 (51%) men and 30 (49%) women aged 18 to 60 years. Most patients (54%) were urban residents, and 46% were rural. Among the patients, 39% were diagnosed with common mental disorders, 38% with severe mental disorders, and 23% with substance use disorders. Most participants with psychiatric conditions had dental caries and gingival disease, with missing teeth being common. Few participants reported grossly decayed teeth, and only 9% maintained good oral hygiene. Conclusion: This study demonstrates a higher prevalence of dental issues among people with psychiatric disorders. It highlights the need for greater emphasis on oral health in this population and underscores the potential impact of psychiatric medications, dental hygiene habits, and socioeconomic factors on oral health outcomes

Influence of Soil Water Content and Soil Amendments on Trace Metal Release and Seedling Growth in Serpentine Soil

This study was conducted to evaluate the synergistic effects of organic amendments and soil water status on trace metal release from serpentine soil

Shape memory textiles for smart compression management for chronic venous disorders – A review

In conventional compression treatment using bandage or stocking, always there has been a problem of achieving and maintaining the recommended compression gradient and level. In addition, these devices are incapable of offering dynamic (massaging) compression, often preferred especially for senior and non-active patients to improve blood flow. To overcome these challenges, the application of shape memory materials is proven to provide a dynamic or selective pressure change directly on the limb. Memory material-based stockings or bandage have the potential to tackle the drawbacks of existing stockings by allowing users to modify pressure levels externally as needed during compression therapy, i.e. as a smart wound care device. This paper reports the consolidated information on traditional compression systems, their challenges, and modern methods involving active compression bandages based on smart materials technology (via shape memory polymer or shape memory alloy), which develop intermittent active pressure to alleviate the symptoms of lower limb problems

Shape memory textiles for smart compression management for chronic venous disorders – A review

131-145In conventional compression treatment using bandage or stocking, always there has been a problem of achieving and maintaining the recommended compression gradient and level. In addition, these devices are incapable of offering dynamic (massaging) compression, often preferred especially for senior and non-active patients to improve blood flow. To overcome these challenges, the application of shape memory materials is proven to provide a dynamic or selective pressure change directly on the limb. Memory material-based stockings or bandage have the potential to tackle the drawbacks of existing stockings by allowing users to modify pressure levels externally as needed during compression therapy, i.e. as a smart wound care device. This paper reports the consolidated information on traditional compression systems, their challenges, and modern methods involving active compression bandages based on smart materials technology (via shape memory polymer or shape memory alloy), which develop intermittent active pressure to alleviate the symptoms of lower limb problems

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD+r training were evaluated at the \emph{argmin} of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized root-mean square error, the concordance correlation coefficient (CCC), and the structural similarity index (SSIM). The CD+r reconstructions, chosen using the stopping condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs (p=0.95) across R and that were significantly higher than the L1 SSIMs (p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps, respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD+r. We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10 figures, 4 tables

The need for a National Archaeological database

India’s economic development is evident in its industrial growth, extensive transportation network, and rapidly expanding cities, towns and villages. While this growth has numerous positive aspects, it also has the potential to cause irrevocable damage (directly or indirectly) to rich archaeological heritage of the country. The present study makes three contributions. First, it examines several archaeological sites where economic developmental activities have caused significant damage. Second, it demonstrates how the risk of further damage can be minimized using geospatial solutions to protect and manage such sites. Third, it conceptualizes a framework for incorporating spatial and non-spatial knowledge of archaeological sites into a National Archaeological Database. We propose that this national archive should be made publicly accessible under the Digital India programme, where it can assist decision makers (development authorities, state departments, etc.) and help citizens plan for future economic growth while preserving the fragile remnants of our past

Sex‐Specific Associations of Oral Anticoagulant Use and Cardiovascular Outcomes in Patients With Atrial Fibrillation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139084/1/jah32481-sup-0001-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139084/2/jah32481.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139084/3/jah32481_am.pd