Assessment of variability in Asystasia gangetica (L.) T Anderson from the Western Ghats of Kerala, India

The variability shown by Asystasia gangetica (L.) T. Anderson has been thoroughly analyzed by considering gross and micromorphology. The species shows variability in flower color and leaf shape among the accessions collected from different geographical locations. However, the microspore sculpturing was found to be uniform and the seed surface architecture showed variation in one of the accessions as well as A. gangetica var. krishnae

Antioxidant activity in some selected Indian medicinal plants

The study was carried out to determine the antioxidant activity of selected medicinal plants namely Albizia amara, Achyranthes aspera, Cassia fistula, Cassia auriculata and Datura stramonium by inhibition of lipid peroxidation technique. The highest inhibition of lipid peroxidation activity wasobserved in A. amara (96%) followed by C. fistula (89%) and C.auriculata (89%). The potency of  protective effect of A. amara was about 4 times greater than the synthetic antioxidant butylated hydroxy toluene (BHT). The total alkaloid content varied from 24.6 ± 0.18 to 72.6 ± 2 mg g-1 in the extracts. Flavanoid contents were between 23.15 ± 0.2 and 63.3 ± 0.6 mg g-1 in the methanolic extracts of these plants. Our study indicates that the antioxidant activity of A. amara could be harnessed as a drugformulation

Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (APST), which is a two step approach to predict the direction of change of stock price indices. First, performs data approximation by using the technique called Multilevel Segment Mean (MSM). In second phase, prediction is performed for the approximated data using Euclidian distance and Nearest-Neighbour technique. The computational cost of data approximation is O(n ni) and computational cost of prediction task is O(m |NN|). Thus, the accuracy and the time required for prediction in the proposed method is comparatively efficient than the existing Label Based Forecasting (LBF) method [1].Comment: 11 page

Efficient organic-inorganic hybrid perovskite solar cells processed in air

Organic-inorganic hybrid perovskite solar cells with fluorine doped tin oxide/titanium dioxide/CH3NH3PbI3-xClx/poly(3-hexylthiophene)/silver were made in air with more than 50% humidity. The best devices showed an open circuit voltage of 640 mV, a short circuit current density of 18.85 mA cm-2, a fill factor of 0.407 and a power conversion efficiency of 5.67%. The devices showed external quantum efficiency varying from 60 to 80% over a wavelength region of 350 nm to 750 nm of the solar spectrum. The morphology of the perovskite was investigated using scanning electron microscopy and it was found to be porous in nature. This study provides insights into air-stability of perovskite solar cells

Breakdown voltage prediction for sphere and semispheroid geometries with Gaussian process regression-based model under the application of lightning impulses of both polarities

The design of high-voltage (HV) systems is principally dependent on the discharge voltage of their insulation. Sphere geometry and semispheroid geometry are extremely important in HV systems, such as ground rods and gas-insulated substations (GISs). Hence, in this work, a machine learning algorithm is proposed to develop a model to predict the discharge characteristics of air for sphere and semispheroid geometries. Finite element method (FEM) simulations have been performed to extract different electric fields and energy features of air gaps in the range of 5–40 mm under lightning impulses of both polarities. While developing the model, these features along with gap lengths are considered. The features have been used for training a machine learning algorithm based on the Gaussian process regression (GPR) to develop the model. The outcomes received from the model are ratified with measured experimental data. A good comparison between the two establishes the fidelity of the novel model. The proposed methodology is also compared with the other state-of-the-art techniques and found good. Remarkable performance has been acquired for other gap geometries as well

Knowledge attitude and behavior practices regarding clinical presentation, transmission, preventive measures and management of malaria and dengue among the health care personnel

Background: According to WHO, in 2020, there were an estimated 241 million cases of malaria worldwide. The estimated number of malaria deaths stood at 627000 in 2020. Similarly, the global incidence of dengue has grown dramatically with about half of the world's population now at risk. The present study is an attempt to assess the knowledge attitude and behaviour practices regarding clinical presentation, transmission, preventive measures and management of malaria and dengue among the health care personnel (HCPs).Methods: The present cross-sectional study was carried out in the department of community medicine, MGM medical college Indore. Among one tribal (Barwani) and one non-tribal district of Indore, participant selection was done by simple random sampling technique using chit method of all districts covered under Indore division. The ethical clearance was obtained from our institute ethical committee.  Results: The advice given by all the HCPs for the prevention of malaria infection is eradication of breeding site of mosquito by preventing water stagnation. The 75% ANMs, 90% lab technicians, 100% MOs, malaria inspectors and MPWs were aware of the time of the bite of female anopheles’ mosquito. Majority of the HCPs were aware of the time of the bite of female Aedes mosquito, the warning signs dengue infection and were of the opinion that they give advice of keeping drinking water containers (Cisterns, tanks) tight closed.Conclusions: All the HCPs were aware of the prominent symptoms of malaria and promoted actively the integrated vector control measures in their allocated areas of work

Iodine status during pregnancy in India and related neonatal and infant outcomes

Objective: To document iodine status in Indian pregnancies, associations with maternal diet and demographics, and offspring developmental measures. Design: Longitudinal study following mothers through pregnancy and offspring up to 24 months. Setting: Rural health-care centre (Vadu) and urban antenatal clinic (Pune) in the Maharashtra region of India. Subjects: Pregnant mothers at 17 (n 132) and 34 weeks’ (n 151) gestation and their infants from birth to the age of 24 months. Results: Median urinary iodine concentration (UIC) was 203 and 211 μg/l at 17 and 34 weeks of pregnancy, respectively (range 26–800 μg/l). Using the UIC distribution adjusted for within-person variation, extreme UIC quartiles were compared for predictors and outcomes. There was no correlation between UIC at 17 and 34 weeks, but 24 % of those with UIC in the lowest quartile at 17 weeks had UIC in the same lowest quartile at 34 weeks. Maternal educational, socio-economic status and milk products consumption (frequency) were different between the lowest and highest quartile of UIC at 34 weeks. Selected offspring developmental outcomes differed between the lowest and highest UIC quartiles (abdominal circumference at 24 months, subscapular and triceps skinfolds at 12 and 24 months). However, UIC was only a weak predictor of subscapular skinfold at 12 months and of triceps skinfold at 24 months. Conclusions: Median UIC in this pregnant population suggested adequate dietary provision at both gestational stages studied. Occasional high results found in spot samples may indicate intermittent consumption of iodine-rich foods. Maternal UIC had limited influence on offspring developmental outcomes

Biosynthesis and characterization of silver nanoparticles generated from peels of Solanum tuberosum (potato) and their antibacterial and wastewater treatment potential

Global food production consumes a large fraction of energy budget, land area, and freshwater; however, a larger fraction of the produce is lost or unutilized, which has potential to produce useful products for human use. The biogenic synthesis of silver nanoparticles from such waste food appears to be a promising strategy. A conservative estimate of 70–140 thousand tons of potato peels is produced annually by food-chain companies globally; however, they are primarily utilized to produce substandard feed for livestock or manure. For the formation of highly profitable compounds, enhancement of value, and the process of extraction, such as nanocomposite, organic antioxidants, and organic meal inclusions, potato peels can be used as a cheap, productive, and readily available source of raw material. In the present research, silver nanoparticles (AgNPs) were extracted from the peels of potato (Solanum tuberosum). The fabrication of potato peel-derived AgNPs was established using UV-visible spectroscopy analysis. Approaches like X-ray diffraction (XRD), attenuated total reflection-infrared (ATR-IR) spectroscopy analysis, and field emission scanning electron microscopy (FESEM) were used to determine the characteristics of the AgNPs. Additionally, strains of Gram-positive bacteria such as Staphylococcus aureus (S. aureus) (ATCC 25923) and Gram-negative bacteria such as Escherichia coli (E. coli) (ATCC 25922) were used to determine the antibacterial activity of AgNPs via the disc diffusion technique. The antibacterial properties of AgNPs could help protect food from microbial contamination. Furthermore, AgNPs were tested for their potential application in purification of industrial wastewater. The results revealed that AgNPs derived from the potato peels could be used in industrial and biomedical applications and possess excellent antibacterial activity. Our research suggests that AgNPs can be extracted from a safe and ecofriendly fabrication technique from largely unused potato peels that have a great potential for inhibiting the bacterial growth and for the in situ purification of wastewater in the upcoming years. Therefore, besides value addition to the farm produce, such recycling of potato peels is likely to reduce the burden of the solid waste volumes in agro-centers, kitchen wastes, and food industries across the globe