Coping the arsenic toxicity in rice plant with magnesium addendum for alluvial soil of indo-gangetic Bengal, India

Arsenic (As3+) is a toxic metalloid found in the earth’s crust, its elevated concentration is a concern for human health because rice is the staple grain in eastern part of India and the waterlogged rice field environment provides opportunity for more As3+ uptake. Magnesium (Mg2+) is an important plant nutrient. Present work is a search for reducing As3+ toxicity in plants through Mg2+ application. The findings are quite impressive, the root to shoot biomass ratio showed more than 1.5 times increase compared to the control. Total protein content increased 2 folds. Carbohydrate and chlorophyll content increased two to three times compared to control. On the other hand, Malondialdehyde content showed a decline with the application of increased Mg2+ dose. The in-silico study shows a better interaction with As3+ in presence of Mg2+ but interestingly without stress symptoms. These findings from the research indicate that Mg2+ application can be effective in reducing As3+ induced stress in plants

Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis

Purpose: Health recommenders act as important decision support systems, aiding patients and medical professionals in taking actions that lead to patients' well-being. These systems extract the information which may be of particular relevance to the end-user, helping them in making appropriate decisions. The present study proposes a feature recommender, as a part of a disease management system, that identifies and recommends the most important risk factors for an illness. Methods: A novel mutual information and ensemble-based feature ranking approach for identifying critical risk factors in healthcare prognosis is proposed. Results: To establish the effectiveness of the proposed method, experiments have been conducted on four benchmark datasets of diverse diseases (clear cell renal cell carcinoma (ccRCC), chronic kidney disease, Indian liver patient, and cervical cancer risk factors). The performance of the proposed recommender is compared with four state-of-the-art methods using recommender systems' performance metrics like average precision@K, precision@K, recall@K, F1@K, reciprocal rank@K. The method is able to recommend all relevant critical risk factors for ccRCC. It also attains a higher accuracy (96.6% and 98.6% using support vector machine and neural network, respectively) for ccRCC staging with a reduced feature set as compared to existing methods. Moreover, the top two features recommended using the proposed method with ccRCC, viz. size of tumor and metastasis status, are medically validated from the existing TNM system. Results are also found to be superior for the other three datasets. Conclusion: The proposed recommender can identify and recommend risk factors that have the most discriminating power for detecting diseases

Effect of frying on physicochemical properties of sesame and soybean oil blend

Most common cooking oil, such as soybean oil, can not be used for high-temperature applications, as they are highly susceptible to oxidation. Sesame seed oil rich in natural antioxidants provides high oxidative stability. Therefore, blending sesame oil with soybean oil offer improved oxidative stability. This study aims to determine the effect of frying on the physicochemical properties of sesame and soyabean oil blend. Soybean oil (SO) was blended with sesame seed oil (SSO) in the ratio of A-40:60, B-60:40 and C-50:50 so as to enhance its market acceptability. The changes occurring in soybean and sesame seed oil blend during repeated frying cycles were monitored. The parameters assessed were: Refractive index, specific gravity, viscosity, saponification value, free fatty acid (FFA) , peroxide value, and acid value. Fresh and fried oil blends were also characterised by Fourier Transform Infrared Spectroscopy (FTIR). No significant changes were observed for refractive index and specific gravity values in oil blends. Viscosity of blend B blend was the least, making it desirable for cooking purposes. However, FFA, acid value and peroxide value increased after each frying cycle. The increment of FFA and AV was found low for blend A (10% and 10%,) than blend B (27%,13%) and blend C (13%,13%). The peroxide value of all samples was within the acceptable range. The results of the present study definitely indicated that blending sesame oil with soybean oil could produce an oil blend which is economically feasible and provide desirable physicochemical properties for cooking purposes

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference

Satisfaction of pregnant women regarding antenatal care at the selected Upazilla Health Complexes during COVID-19 pandemic

Background: The World Health Organization (WHO) envisions a world where every pregnant woman and newborn receives comprehensive care throughout the pregnancy, childbirth and the postnatal period. This study aims to assess the satisfaction of pregnant women with antenatal care services at selected Upazilla Health Complexes during the Covid-19 pandemic. The aim of this study was to evaluate the satisfaction levels of pregnant women with the antenatal care services provided at selected Upazilla Health Complexes during the COVID-19 pandemic. Methods: This descriptive cross-sectional study was conducted at three selected Upazilla Health Complexes in Kishorganj district, Bangladesh, namely Hussainpur Upazilla Health Complex, Karimganj Upazilla Health Complex, and Pakundia Upazilla Health Complex. Purposive sampling was employed to recruit 163 married pregnant women attending antenatal care services at these health complexes. Data were collected through face-to-face interviews ensuring privacy and analyzed using SPSS 26, employing descriptive statistics, chi-square tests, and odds ratios with 95% confidence intervals. Results: Most pregnant women (54.6%) were aged 18-23 years. Education levels: 40.5% had SSC education, 9.2% were illiterate. Respondents expressed high satisfaction with key aspects at the Upazilla Health Complex, including medicine supply and awareness about ANC services. Continuous monitoring is crucial for patient satisfaction. Medicine supply significantly impacted satisfaction, emphasizing its importance in healthcare quality. Conclusions: Despite challenges during the COVID-19 pandemic, Upazilla Health Complexes have effectively provided antenatal care, satisfying the majority of respondents, highlighting the importance of continuous monitoring.

A study to assess the knowledge regarding polycystic ovarian syndrome among the high school girls in a selected high school, Siliguri, West Bengal

Background: Polycystic ovarian syndrome is a common reproductive and endocrine disorder found in 6-10% of the female population. The global prevalence of PCOS is estimated between 4% and 20%. The world Health Organization data suggest that approximately 116million women (3.45%) are affected by PCOS globally. The age standardized incidence rate of PCOS in women of reproductive age was 82.44 per 100000 in 2017, 1.45% higher than in 2007. Methods: There are many research designs, but among them we have chosen non-experimental research design for this study. Sample of this research study was 105 high school students of Little Angels’ Senior Secondary School, Siliguri through consecutive sampling technique. The instrument used to assess level of knowledge among high school girls was self-structured questionnaire. Results: Findings revealed that among high school girls 7.62% had poor knowledge, 26.67% had average knowledge, 64.76% had good knowledge and 0.95% had excellent knowledge about polycystic ovarian syndrome. Conclusions: The study concluded that the selected variables namely age, religion, educational class, exercise has significant impact on the knowledge regarding polycystic ovarian syndrome among high school girls. The study can be replicated with a larger sample to the findings can be generalized to a larger population

Evaluation of GPU resource sharing solutions for virtualized environment based on evaluation metrics

The modern GPUs nowadays are immensely powerful and crucial since they have been managing and improving video, and graphics performance, as well as certain common non-graphics applications like AI. In a virtualized environment where nu-merous instances of operating systems, guests, or applications are running simulta-neously, many embedded systems with a single GPU are being used, and therefore the need to virtualize the GPU arose. The processing power of a physical GPU is abstracted and divided into several vir-tual instances by utilizing specialized software to virtualize the GPU. Multiple vir-tual workloads can run simultaneously on the same physical GPU while still run-ning independently of one another. The GPU can conduct numerous autonomous tasks while being safely isolated from one another thanks to virtualization. There has been a considerable amount of research in this area and many solutions have been provided. This thesis work gives an insightful study of these available virtualization techniques and an experimental overview of some GPU virtualization techniques based on evaluation metrics to understand it more deeply