Fungal biosorption of the heavy metals chromium(VI) and nickel from industrial effluent-contaminated soil

Heavy metals are ubiquitous contaminants that have accompanied man since the earliest ancient times, and unlike other environmental pollutants, they are chemical elements that man does not create or destroy. In the present study, the aim was to determine the biosorption potential of heavy metal-tolerant fungi that were isolated from compost soil samples contaminated by industrial effluents. The isolation was performed on potato dextrose agar (PDA) media supplemented with heavy metals. Chromium-Cr(VI) and nickel-Ni. The most dominant fungal species were found to be Penicillium spp. This fungus was screened for its ability to tolerate heavy metals by the plate diffusion and broth method and was highly tolerant to fungal species. The fungi were assessed for their ability to remove heavy metals from the culture media, and the culture conditions for the fungus were experimentally optimized. The isolated Penicillium species was found to show maximum growth at 35°C with media pH 6 for an incubation period of 168 hours. The isolate was able to tolerate 60-70 ppm concentrations of heavy metals under normal conditions. The ability of the isolate to take up metal was very effective, as after 96 hrs of incubation, it was capable of removing approximately 93.8% of Cr(VI) and 95.6% of Ni from the culture media, and complete uptake was observed after a 144 hr incubation period. The molecular characterization revealed the only isolate to be Penicillium rubens (Accession no. LC536286). The morphological characteristics of this fungus make it capable of biosorption of heavy metals, imparting its bioremediation potential and economic importance.

Effects of addendum modification on root stress in involute spur gears

A study into the effects of addendum modification on root stress in involute spur gears with various pressure angles are presented in this project work. The range of addendum modification co-efficient is taken from negative value to positive value through zero by considering both the upper limit(peaking limit) and the lower limit (undercutting limit). The root stress factor is found out for various loading positions. The variation of root stress factor with addendum modification co-efficient is shown when only the driving gear is modified. A study into the effects of addendum modification on root stress using mathematical formulation as well as finite element analysis when both the driver and follower are modified at the same time also for different gear ratios. The value of root stress factor decreases with an increasing addendum modification coefficient when only the driver is modified. The root stress factor also decreases when pressure angle is increased. The root stress factor is further decreased when both the driver and follower are modified at the same time. The root stress factor decreases further as the gear ratio increases

WebGIS based Decision Support System for Disseminating NOWCAST based Alerts: OpenGIS Approach

WebGIS is a kind of distributed information system which holds the potential to make geographic information available worldwide It is cost effective and provides an easy way of disseminating geospatial data This paper outlines the design and development of a WebGIS based Decision Support System DSS for disseminating Nowcasting of Extreme Orographic Rain events generated at regular intervals from NETRA model Dissemination of events include heavy rainfall alerts all over India and cloudburst alerts over Western Himalayan Region every half an hour In India natural calamities like flood and cloudburst results in lot of causalities If any early Heavy rain alerts dissemination system is developed then it will protect several lives and mitigate damage of property or infrastructure in affected areas The development of such WebGIS based decision support system originates from this concept Objective of this paper is to describe the near real time WebGIS based Decision support System developed for disseminating rainfall alerts to the general public and administrators about heavy rain all over India and cloud burst over Western Himalayan region using interactive maps Users can also get non spatial information like number of affected cities and their names district level population census 2011 forecast date and time Radius of influence etc This WebGIS based decision support system can help government agencies NGO s and general public in planning to save lives properties and can be used for decision making to reduce economic and material loss from the resulting floods This paper also illustrates use of open source technologies for developing such WebGIS -DSS at low cost The principal development component includes GeoServer Java PostgreSQL OpenLayers and GeoExt The framework of the system can be divided into two categories 1 Dissemination system which includes visualization of centroid and precise locations of Heavy Rain all over India and cl

Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter

Many techniques related to image analysis have been proposed by researchers which are being used to detect a large number of diseases.  These images are carefully analyzed by radiologists and doctors, and after careful interpretation, the results are obtained which finally help in making an appropriate diagnosis. This is a complicated and time consuming task, which requires high levels of concentration.  Therefore, the experts who analyze the images mustn't suffer from fatigue or other common problems that can impair their performance. The present study attempts to reveal how a deep learning model using CNN with VGG16 is effective for the diagnosis and detection of skin cancer at its early stages. Therefore under the scheme, the 4000 images of raw skin cancer tissues are evaluated. The diagnostic model starts with pre-processing of images using the CLAHE along with the inculcation of the Gaussian filter. Thereafter, using hyper-parameter optimizer stochastic gradient descent, along with the effective learning rate 0.001, incorporating the training epochs of 50 nos. and pertaining the batch size 32 is formed. Consequently, as a result, the accuracy achieved is 99.70%, with a loss value of 0.0055%, a precision of 99.75%, a recall of 99.75%, and an f1-score of 99.50% respectively

An Automatic Detection of Brain Tumor using CNN & VGG19

According to the 2019 cancer statistics by WHO, brain tumors are considered the main cause of mortality related to cancer throughout the world and are known as one of the most common forms of cancer both in children as well as adults. Among the most common brain tumors, we have those that begin and tend to remain in the brain, which as meningioma with 34% of presence, another type of tumor is called glioma, arising from the surrounding tissue in the brain, it is part of 30% of all tumors in the brain, however, this glioma represents 80% of malignant tumors, making it the most common tumor common that causes death. However, this scheme depicts how convolutional neural networks using VGG19 model can provide an effective mechanism to detect brain tumors at an early stage using MRI images and can save the lives of mankind. Consequently, this research classified glioma brain tumor images using VGG-19 with HE preprocessing data. The model was tested to get a comparison of accuracy, precision, recall, and f1-score of the two test data, namely the original data and HE data. Based on the results of model testing, we can see in table2  that the original data produced the highest values of accuracy, precision, recall, and F1-score, with values of 97% accuracy, 100% precision, 97% recall, and 98% f1 score. While data using HE preprocessing has an accuracy value of 92%, precision of 100%, recall of 92%, and f1 score of 96%t.

Design of Some Benzimidazoles as Target for α-Glucosidase Inhibitors

Diabetes mellitus is rising globally touching more than 180 million people worldwide. This is prevailing mostly in type 2 diabetes and according to WHO report the incidence is likely to be more than doubled by 2030. α-Glucosidase inhibitors work by reducing the amount of glucose that the intestines absorb from food. In this work, forty-five benzimidazole analogues were studied using 3D QSAR, HQSAR, pharmacophore mapping and based on their results 60 compounds were designed. The results show that the best Comparative Molecular Field Analysis (CoMFA) model has q2 = 0.742 and r2 = 0.973, and the best Comparative Molecular Similarity Indices Analysis (CoMSIA) model has q2 = 0.679 and r2 = 0.918. For HQSAR the best model has q2 = 0.773 and r2 = 0.964. The r2 value for boostrap for CoMFA and CoMSIA are 0.98 and 0.97 respectively. Pharmacophore mapping revealed varied bioactive regions of ligand. Thus, these compounds could be used as lead for designing the synthesis of potent alpha-glucosidase inhibitors. Keywords: Acarbose, Alpha-glucosidase inhibition, Benzimidazoles, Molecular modelling, Post-prandial hyperglycemi

Stress, Coping Strategies and Social Support as Predictors of Mental Health of Police Personnel of North India

Introduction: The profession of a police personnel is extremely stressful.  Coping strategies and social support are known to be robust buffers of stress Objective: To study Stress, Coping Strategies and Social Support as Predictors of Mental Health of Police Personnel of Uttar Pradesh, North India. Method: This was a cross-sectional study comprising of 300 male police personnel. Assessment was done using Occupational Stress Questionnaire, Brief COPE Scale and Mental Health Inventory. Multiple Regression Analysis was used to analyze the data. Results: Ambiguity stress, the belonging and appraisal support are found to be the strongest predictor of mental health of constables. Stress in the area of organizational structure, the appraisal support and maladaptive coping strategies are essential predictors of mental health of inspectors. Beside this, social support, belonging support, appraisal support and active coping are significant predictor of mental health of Officers

Synthesis and Biological Evaluation of Benzimidazoles as Target for α-Glucosidase Inhibitors

Diabetes mellitus is rising globally touching more than 180 million people worldwide. This is prevailing mostly in type 2 diabetes and according to WHO report the incidence is likely to be more than doubled by 2030. α-glucosidase inhibitors work by reducing the amount of glucose that the intestines absorb from food. In our previous work, forty-five benzimidazoles analogues were studied using 3D QSAR, HQSAR, and Pharmacophore mapping and based on their results 60 compounds were designed. Docking studies of those designed compounds showed that most of the compounds are bonding with important amino acids LEU 520, ARG 335 and ASP 69 through hydrogen bonds and steric interaction. In this work, synthesis of eleven compounds was done on the basis of molecular docking studies. Compounds containing hydroxyl and alkyl groups (compound no. 3, 9 and 10) were found to be five to eight folds more active with IC90 values in the range of 6.02 ± 1.10 to 33.25 ± 1.20 µg/ml, in comparison with the standard drug, Acarbose (IC90= 290.55 ± 0.081 µg/ml). Thus, these compounds after the toxicity studies could be of therapeutic use in treating diabetes. Keywords: Acarbose, Alpha-glucosidase inhibition, Benzimidazoles, Docking, Molecular modelling, Post-prandial hyperglycemi

A comparative approach between different optimize result in hybrid energy system using HOMER

To compare the different result of optimization of a hybrid energy system. A hybrid renewable energy system (HRES) is the combination of renewable and non-renewable sources which is playing a very important role for rural area electrification when grid extension is not possible or excessively expensive. Non renewable sources like diesel power generator (optional) are used in a HRES for backup when renewable energy supply is not sufficient. While the HRES is very important due to the smallest natural and physical contact compared to non renewable sources, this work proposed a comparison outcome with the help of different component by using HOMER software and get best optimize result for the model. This paper presents a wide-ranging review of various aspects of HRES. This paper discusses study, best sizing, and model, organize aspect and reliability issue

Analysis of labor induction in a tertiary care hospital

Background: Induction of labour (IOL) is a very common labour room procedure. Although labor is a natural physiological process, deliberate intervention in the form of induction may be required in many instances. It is needed in almost 20% of pregnant women for a variety of indications. The objective is to evaluate indications, different methods, and feto-maternal outcome of induced labour in tertiary care hospital.Methods: This was a retrospective study of IOL conducted in the department of obstetrics and gynecology, Shri Guru Ram Rai institute of medical and health sciences, Dehradun, Uttarakhand. Women who underwent IOL beyond 28 weeks gestation with single cephalic presentation with no contraindication for vaginal birth were included in the study. Statistical analysis was done with Microsoft excel.Results: A total of 1532 women delivered in the hospital during the study period. Among them, 498 women were induced (32.5%). Most common method of induction was misoprostol (40.36%) followed by prostaglandin E2 gel (26.90%).  Out of 498 inductions, 377 women delivered vaginally making success of induction around 75.70%. Among them, 335 women had normal delivery (67.26%) and 42 women required instrumental delivery (8.4%) and 121 women underwent lower segment caesarean section (LSCS) (24.29%).Conclusions: Elective inductions of labor in properly selected indications at optimized timings aid in achieving a favorable maternal and fetal outcome. Methods of inductions, timing and intrapartum monitoring plays an important role in influencing obstetric outcome