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This article suggested some sampling strategies allied with Searls (1964) for mean estimation on current occasions under an optimal random non response framework using auxiliary information. The characteristics of each class or estimator under their respective strategy have been studied under the optimum replacement policy. We have gauged losses, which are carried out to show the dominance over complete response classes like Singh and Vishwakarma (2007, 2009). Appropriate recommendations have been made to the survey researchers for their real-life practical applications. [ABSTRACT FROM AUTHOR]

The study aimed to determine the effect of subacute heat stress on neurobehaviour and brain histology in wistar rats. A total 40 rats (20 male and 20 female) were divided into 2 groups (N=10 rats/sex/group). Group I, (Control group) maintained at comfortable: 22 ± 3°C temperature, Group II (Heat stressed group) rats were subjected to heat stress ﹫ 42 ±1°C for 2 hrs daily for the period of 30 days. The neurobehavioral observation was done on day 30 by open field test. At the end of the experiment, rats were humanely sacrificed and the brain was collected for histopathology. Group II male rats revealed higher anxiety level as significant increase (P<0.05) in no. of defecation and urination was noted. A significant decrease in no. of rearing, boxes crossed, central arena crossed and a significant increase in latency was observed compared to control rats pointing towards decreased activity. In females (Group II), no significant difference was noted in any of the parameters across the groups compared to control. Histopathological observation revealed congestion, vacuolation, neuronal degeneration. Heat stress has negatively impacted the locomotor function and caused increased anxiety in male rats. [ABSTRACT FROM AUTHOR]

AbstractThis article presents a class of estimators allied with Searls (1964) using a novel approach to estimate population variance on two occasion under successive sampling. Isaki (1983) encourages us to address the problem of estimating finite population variance in survey sampling, its application to the case of successive sampling is highly captivating, and the methodology proposed here will be useful to those involved in similar studies in the future. To the best of our knowledge, this is any authors’ first foray into this field to provide more efficient estimators than the regression type estimator. An empirical study based on real population and artificial population proves the utility of the proposed methods. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely adopted, owing to break-through performance and high accuracy. DNNs exhibit varying memory behavior involving specific and recognizable memory access patterns and access intensity, depending on the selected data reuse in different layers. Such applications have high memory bandwidth demands due to aggressive computations, performing several billion-floating-point-operations-per-second (BFLOPs). 3D DRAMs, providing very high memory access bandwidth, are extensively employed to break the memory wall, bridging the gap between compute and memory while running DNNs. However, the vertical integration in 3D DRAM introduces serious thermal issues, resulting from high power density and close proximity of memory cells, and requires dynamic thermal management (DTM). To unleash the true potential of 3D DRAM and exploit the enormous bandwidth under thermal constraints, there is a need to intelligently map the DNN application's data across memory channels, pseudo-channels, and banks, minimizing the effective memory latency and reducing the thermal-induced application slowdown. The specific memory access patterns exhibited by a DNN layer execution are crucial to determine a favorable data mapping method for 3D DRAM dies that potentially causes minimal thermal impact and also maximizes DRAM bandwidth utilization. In this work, we propose an application-aware and thermal-sensitive data mapping that intelligently assigns portions of the 3D DRAM to DNN layers, leveraging the knowledge about layer's memory access patterns and minimizing DTM-induced performance overheads. Additionally, we also deploy a DRAM low-power states based DTM mechanism to keep the 3D DRAM within safe thermal limits. Using our proposal, we observe a performance improvement of 1% to 61%, and memory energy savings of 1% to 55% for popular DNNs over state-of-the-art DTM strategies while running DNN inference. [ABSTRACT FROM AUTHOR]

Anthropogenic perturbations affect ecosystem functional attributes and, when coupled with species invasion, exacerbate the changes including soil properties and competitive interactions among species. Here, we investigated whether invasive plant species under metal stress condition eliminates seedbanks of soft carbon resources along a 518 km stretch of the Ganga River corridor. Soil samples with natural herbaceous cover (non-infested) showed significant difference (p<0.001) in terms of two carbon hydrolyzing enzymes - β-D-glucosidase and FDAase, as opposed with soils rich in C residues from Croton bonplandianus and Grangea maderaspatana. Such soil functional responses were associated with high C:N ratio and concentration of phenolics (180-240 μg g-1). Rising temperature and pulsed release of nutrients during summer favoured the growth of these species negating the potential effects of high concentrations of heavy metals present in soil. Understanding such indicators of ecosystem health in river corridors will allow prediction of shifts in carbon and nutrient biogeochemical cycles. [ABSTRACT FROM AUTHOR]

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation. [ABSTRACT FROM AUTHOR]

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others. [ABSTRACT FROM AUTHOR]

The deadliest kind of skin cancer is malignant melanoma. To help doctors make more precise diagnoses of skin malignancies, dermoscopy uses noninvasive high-resolution imaging. Melanoma is a malignant skin cancer that grows rapidly and aggressively. Malignant melanoma continues to rank among the world's most rapidly expanding malignancies because of this trait. After spreading to other organs or tissues, the likelihood of a positive response to therapy drops to 5%, and the likelihood of survival after 10 years drops to around 10%. There is currently no therapeutic option that involves surgical removal after it has metastasized. Therefore, it is of the utmost importance to diagnose malignant melanoma early. The false negative ratio for melanoma is the highest of all skin malignancies. One of the most popular and effective approaches to medical picture processing right now is Deep Neural Networks (DNN). While Deep Learning has shown promising results, there are still obstacles to overcome when applying it to these kinds of problems. These include issues like data volatility, noise sensitivity, and insufficiently large training datasets. With an emphasis on medical (clinical) image difficulties, this study offers strategies to help deep-learning models handle these concerns, specifically when it comes to skin cancer diagnosis. This research delves into the topic of melanoma classification using transfer learning and compares several state-of-the-art Convolutional Neural Network (CNN) designs. Many convolutional neural network (CNN) models have been pre-trained on the extensive ImageNet dataset; these models are among those that have been considered for this analysis. Dermoscopy pictures of the skin taken from the publicly available dataset are used to refine and test these models. To increase the dataset size, images are enhanced. The results show that various CNN architectures have varied strengths and weaknesses when it comes to this categorization job. The VGG16 model outperformed all others with a test accuracy of 85.76 percent and a train accuracy of 90.1 percent. This research sheds light on how to use dermoscopy pictures for effective deep-learning skin cancer screening and detection. There are more chances to adjust the model's hyperparameters and increase the variety and amount of the training data. [ABSTRACT FROM AUTHOR]

The present study is an attempt to equip a strategy with a cost-effective computational approach when non response is present under two occasion sampling. We have applied our computational cost strategy over Choudhary et al. (2004)'s non response setup for fixed precision and evaluated cost. In addition, we have also computed variance for some fixed cost. We have discussed the aforementioned procedure for three cases as when there is non response present on both occasions, first occasion and second occasion. A numerical illustration is demonstrated for validation of improved cost methodology where we also work out with optimum unmatched or matched fraction while Choudhary et al. (2004) do not provide the direct optimal result. [ABSTRACT FROM AUTHOR]