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The sustainable power source systems (RESs) are an appealing alternative to energize the group as they are condition amicable, free of cost, and all-overrunning. The effectiveness of these vitality systems is low and can be enhanced by coordinating them in parallel. In this paper, hydro (7.5?kW) and PV (10?kW) are taken as RESs and associated with the utility grid. Because of the discontinuous idea of both the hydro and photovoltaic energy sources, utility grid is associated with the system for guaranteeing the ceaseless power stream. The hydro power generation system uses the 3-phase synchronous machine and converters. The AC/DC/AC converter is used as interface to connect the hydro turbine to the utility grid to adjust the generated voltage to the utility grid voltage. The solar generation system is the combination of PV array, boost converter, and solar inverter. The control of both the hydro and solar power plants is provided through the constant current controller. The analysis has been done to verify the existence of the proposed system. Results demonstrate that the proposed system is able to be put into service and can feed the community.

Transmission Network Expansion Planning (TNEP) is a basic part of the power system planning that verifies where, when and how many new transmission lines should be added to the network. In this paper, expansion planning has been implemented by merging lines loading parameter in the TNEP problem using an improved Discrete Honey Bee Mating Optimization (DHBMO) algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. DHBMO algorithm combines the power of genetic algorithms and simulated annealing with a fast problem specific local search heuristic to find the best possible solution within a reasonable computation time. For this reason, the potential of the proposed approach for optimal solution of TNEP problem has been investigated and tested on the Garvers network and a real transmission network in Iran in comparison with the Discrete Particle Swarm Optimization (DPSO) method. The results evaluation show that the using the proposed DHBMO based method, the expansion costs is significantly reduced and the network adequacy is increased considerably than the DPSO algorithm. Also, regarding the convergence curves of both approaches, it can be seen that precision of the proposed algorithm to solve the TNEP problem is more than DPSO one.

Tuberculosis and HIV co-infection is a major public health problem worldwide. Tuberculosis in HIV-infected patients presents clinical and paraclinical atypicalities making the diagnosis of this pathology difficult.We report in this manuscript the observation of a patient with multifocal tuberculosis revealing HIV infection.

This paper presents various types of interpolation techniques to obtain a high quality image The difference between the proposed algorithm and conventional algorithms (in estimation of missing pixel value) is that if standard deviation of image is used to calculate pixel value rather than the value of near most neighbor, the image gives the better result .. The proposed method demonstrated higher performances in terms of PSNR and SSIM when compared to the conventional interpolation algorithms mentioned.

Smile is an expression that exhibits a feeling of joy, success, sensuality, affection, and reveals self-confidence and kindness. The harmony of the smile is determined not only by the shape, the position, and the color of the teeth, but also by the gingival tissues. In case the gingival tissues do not exhibit harmony with that of smile the individual loses self-confidence. The maintenance of esthetics in the anterior zone is important and especially if the patient is female the importance increases. The present case report, highlights the importance of perio-esthetics in maintaining the harmony of the gingival tissues.

Periodontal regenerative therapy aims to predictably restore the tooth’s supporting periodontal tissues and should result in formation of a new connective tissue attachment (i.e. new cementum with inserting periodontal ligament fibres) and new alveolar bone. Numerous clinical trials have shown positive outcomes for various reconstructive surgical protocols. Reduced probing depths, clinical attachment gain, and radiographic bone fill have been reported extensively for intrabony and furcation defects following scaling and root planing, open flap debridement, autogenous bone grafting, implantation of biomaterials including bone derivatives and bone substitutes, guided-tissue regeneration (GTR) procedures, and implantation of biologic factors, including enamel matrix proteins. The present article aims at providing an overview on basic principles of periodontal regeneration, various materials being used and recent techniques in periodontal regeneration and factors influencing the outcome.

The oxidative decolourization of AMCOT Red 3BLF in aqueous medium has been studied using photo Fenton process. Fenton’s reagent (H2O/Fe3+) was used to generated hydroxyl radical (?OH). A visible light source was used to provide radiation for photo Fenton process. The experimental parameter such as pH, air, light, time and catalyst strongly influenced the dye removal rate in photo Fenton process. The result shows that the AMCOT Red 3BLF bleaches upto 70% within 4 hrs and degrades into CO2 and H2O. The proposed mechanism for the reaction of ?OH with AMCOT Red 3BLF dye molecules is summarized.

Intestinal phytobezoars are rare causes of small bowel obstruction. Treatment consists of surgical extraction by enterotomy. We report a case of small bowel obstruction on a phytobezoar in a 66-year-old woman treated by enterotomy followed by manual extraction.

Thevetia peruviana, cultivated as an ornamental plant and planted as large flowering shrub, belongs to the family Apocynacea. The aim of this study is to screen the phytochemicals present in the leaves of Thevetia peruviana and further analysis of the components present in it by GC- MS analysis.The leaves were sequetially extracted based on the polarity viz., hexane , acetone and methanol and subjected to phytochemical screening which revealed the presence of bioactive compounds such as alkaloids, flavanoids, cardiac glycosides, steroids, terpenoids, tannins, phenols, quinones and saponins.The GC- MS analysis of the acetone extract revealed the presence of 33 compounds. This study forms a basis for the biological characterization and importance of the compounds identified.

Plant diseases are widespread by a variety of pests, weeds and pathogens and can have devastating effects on agriculture if not treated in time. Farmers face a myriad of challenges due to adequate water supply, premature rain, storage facilities, and diseases of multiple crops. Plant diseases are a major threat to farmers with enormous production and economic loss. Identifying disease on a few hectares of farmland can be a daunting task, even in the presence of modern technology. Accurate and rapid disease prediction for early crop disease treatment has proven to be productive for healthy crops as well, minimizing personal financial loss. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. Crop diseases are a constant challenge for smallholders, threatening income and food security. The proliferation of smartphones and recent revolutions in computer vision models has created a method of image classification in agriculture. Convolutional neural networks (CNNs) are considered the cutting edge of image recognition and offer the possibility of making timely and clear diagnoses. This article examines the performance of a pre-trained ResNet 34 model in the detection of plant diseases. The developed model can be used as a web application to detect seven plant diseases from healthy leaf tissue. A dataset containing images of 8,685 leaves. Captured in a controlled environment and established for model training and validation. Verification results show that the proposed method can achieve an accuracy of 97.2% and an F1 score of over 96.5%. It demonstrates the technical feasibility of CNN in plant disease classification and points the way to AI solutions for smallholders.