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Polymer-based matrix hybrid composites meet their demand in various engineering applications and food industries due to their excellent mechanical, thermal, corrosion, and biodegradable performance. The polymer-based hybrid composites have been a better choice for high thermal insulation at low cost. This experiment attempted to find the thermal adsorption characteristics, heat deflection temperature, linear thermal expansion, and thermal conductivity of epoxy hybrid composites, which contained four different layers of Kevlar and basalt fiber fabricated via a low-cost conventional hand mold layup technique. This experiment revealed that the effect of basalt/Kevlar fiber on epoxy increased thermal performance. The results noted that the hybrid composite consists of less Kevlar fiber with the maximum basalt fiber of sample 4, showed excellent thermal adsorption effect on weight loss limited at 70.98%, and a better heat deflection temperature and 11.78 × 10 − 6 per °C linear thermal expansion were obtained. Sample 3 exhibited a maximum thermal conductivity of 0.251 W/mK. However, the thermal adsorption of hybrid composite has been limited by more basalt fiber, leading to a 1 wt%/°C decomposition rate.

In this project design, fabrication, and analysis of sheet laminated Metal Matrix Composites will be carried out. It will be very useful for Aircraft Nose Cone, drone frames, and other surface materials of aircraft. The model of laminated metal matrix composite is designed using Catia V5 and also material to be applied to the body. Imported Geometry from Catia V5 to ANSYS will be analyzed by applying material properties and loads under different conditions. Based on ANSYS results the laminated metal matrix composite will experiment with. In that I take there are three types of Combinations of Laminated Metal Matrix Composites 1) Top and Bottom Aluminum Plate between Carbon fiber (aluminum top 1mm bottom 1mm +carbon 0.5 mm= 2.5mm thickness), 2) Top, Bottom, and Middle Aluminum in between Carbon fiber (aluminum (1+1+1)3mm + carbon 1mm= 4mm thickness), and 3) Top and Bottom Carbon and Middle Aluminum (Carbon (0.5+0.5) 1mm +aluminum 1mm =2mm thickness) which means 25%, 33%, and 50 % of carbon reinforced into aluminum metal.

The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a very vital role in IoT networks. As a result, the paper proposes a novel reconfigurable architecture as Field Programmable Gate Arrays (FPGA) that integrates deep learning models for effective SCA prediction as well as a countermeasure mechanism based on chaotic maps. Finally, the experimentation results prove the excellence of the proposed framework in terms of prediction accuracy (99%), sensitivity (98.9%), and specificity (98.9%) in comparison with LSTM, ELM SVM, RF, NB, and ANN. The results also demonstrated that the proposed framework requires only 50% of the total energy for network operation.

Photovoltaic systems have become more popular as people become more interested in developing energy from renewable resources. Even after the installations, however, there is still a lack of understanding about the importance of inspecting the condition of the PV modules. To keep the PV running, early hot-spot detection is required. For detecting hot-spots, thermal imaging is still a popular technique. This research proposes to develop a method for detecting hot-spots in thermal images of photovoltaic modules using artificial intelligence techniques. Pre-processing, segmentation with an Artificial Neural Network and identification are the three main processes. The proposed method appears to be a good choice for improving hot-spot detection in PV monitoring systems, according to the results of the experiments.

The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT-enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT-based deep learning-based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health-related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT-Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.

Purpose: A substantial proportion of Major depressive disorder patients do not react properly to antidepressants and are classified as having Treatment Resistant Depression (TRD). This study was undertaken to evaluate the demographic profile and management strategies for TRD in psychiatric department of a tertiary care super-specialty hospital. Methods: In this prospective observational study, a total of 27 subjects diagnosed with TRD were enrolled. Treatment resistant cases in the hospital setting were identified by using Montgomery Asberg Depression Rating Scale (MADRS). We evaluated the demographic profile, major adverse drug reactions and treatment provided for those patients and monitored for improvement of conditions. Results: The demographic profile result shows that females had predominance over males. Furthermore, family history also had a strong correlation with the occurrence of TRD. Sertraline, Venlafaxine and Sodium valproate combination was majorly used in the treatment of TRD patients. Among them, Venlafaxine was used commonly in TRD and the MADRS scores shown improvement in patient condition. Apart from drug therapy ECT when given in combination with antipsychotics were also effective in TRD. The major ADRs shown by TRD patients include weight changes, GI problems and sexual dysfunction.

It’s obviously true that cordiality is an area that can add to the monetary development of any nation and the travel industry is a significant part of same which incorporates number of areas like housing, catering, transportation, guest attractions, diversion and so forth Food and drink administration area is viewed as one of the significant fields which bring a serious extraordinary arrangement of benefit and fulfilling the vacationer. Food and drink administration tasks are proceeding to improve and grow, together with progresses in quality. The travel industry relies upon great nature of food and refreshments administration on the grounds that the greater part of the vacationer spends over 25% (Outlook June, 2012 / Emerging Trends in Food and Beverage Services Retailing in India) of their costs on same. In current situation individuals don’t care for ordinary assistance style for food and drink however they need some development which can be engaging, appealing and respectable even actual climate which incorporate help staff, atmosphere, table setting and lighting is additionally vital to draw in the visitors. Administration quality plays a vital situation to make the clients happy and demands them to return to the eatery. The food and drink industry is dependent upon various patterns and these patterns affect eatery business achievement or disappointment. This review centers around recent fads and advancements conceded by food and refreshment administration style and why actual climate is significant for any café.

The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (Pmax), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98%, which is much better than that of other optimization techniques.

Introduction: Hydatid disease of the bone is a parasitic infestation in the form of cystic echinococcosis by a tapeworm echinococcus granulosus. Its manifestation in the bone is relatively low and has the ability to mimic other skeletal pathologies. Case Report: We came across a 30-year-old male farmer with complaints of sero-purulent discharge from a sinus tract wound in the axilla for the past 3 years. He had been previously evaluated elsewhere with blood investigations, pus culture/sensitivity tests, MRI, and CT scans. Blood reports and culture/sensitivity tests were inconclusive and imaging tests were suggestive of a sinus tract originating from the right scapula. He was incorrectly diagnosed as a case of Tuberculosis of the Scapula and started on anti-tubercular medications for 9 months which was ineffective. To confirm the diagnosis, a biopsy was taken from the scapular lesion through a posterior approach. Histological tests revealed a cystic lesion composed of trilaminar membrane consisting of dead and degenerating scolices consistent with “Hydatid Cyst of Bone.” Conclusion: Hydatid disease of the bone has an inconsistent clinical picture which makes diagnosing this rare disease even more difficult. A high suspicion for hydatid infestation in bone pathologies could help in diagnosing the disease at the earliest.

In photovoltaic systems, maximum power point tracking (MPPT) methods are used to get the maximum power out of them. The presence of several peaks in a PV array’s power-voltage characteristics is due to partial shadowing conditions that increase the complexity of the tracking operation. In this paper, the Global Maximum Power Point (GMPP) is calculated using latest meta-heuristic optimization algorithm known as Jellyfish Search (JS). This unique MPPT approach is used to reduce PV module tracking time and improve the tracking efficiency. Using MATLAB/SIMULINK, the effectiveness of the suggested JS algorithm is assessed by contrasting it with the traditional P&O approach in terms of tracking speed and precision. The simulation findings indicate that the JS algorithm’s tracking ability is better than that of the conventional P&O method. In the experimental results, the JS algorithm reduces convergence time by 56.6% when compared to the PSO algorithm. Also, the proposed JS algorithm generates output power higher than the PSO MPPT algorithm using the duty cycle ratio value at the expected peaks.

BackgroundXanthine oxidase (XO) enzyme is directly associated with pathogenesis of gout and indirectly with cancer, diabetes and metabolic syndromes. Allopurinol is a xanthine oxidase inhibitor which is useful in the treatment of gout but it causes side effects in humans and birds. Therefore, this study sought to identify the alternative compound from the natural resources with fewer side effects than the conventional ones.ObjectivesThe present study was designed to findout the xanthine oxidase inhibitory activity ofPiper betlephytocompounds in comparison with Allopurinol.MethodsThe detection of phytocompounds present in theP. betleL. extract was identified through Gas Chromatography Mass Spectrometry (GC-MS) analysis. Theinsilicoanalysis and ADMET properties were evaluated for the GC-MS derivedP.betlephytocompounds. Among the 32 phyto compounds ofP.betle, 18 were showed favourable affinity with xanthine oxidase and their different conformational structures were docked in schrodinger module with xanthine oxidase enzyme structure. The interpretation of results was carried out through GLIDE XP Scoring, GLIDE Energy, MM-GBSA energy and hydrogen bond and Pi-Pi interactions. Further the absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis were also carriedout using QIKPRO programme.ResultsThe results revealed that, the twelve phytocompounds ofP.betleshowed GLIDE XP docking score (−6.881 to - 4.766 Kcal/mol) higher than allopurinol (−4.535 Kcal/mol) and 11 phytocompounds showed higher GLIDE energy (−42.822 Kcal/mol to - 36.706 Kcal/mol) than allopurinol (−32.676 Kcal/mol). Chromonol and eugenol were the most potential compounds ofP.betlewhich showed both hydrogen bond and Pi-Pi interactions with the target xanthine oxidase as that of standard drug allopurinol. The selected phytocompounds satisfied the ADME descriptors and have no violation of Lipinski’s rule of five.ConclusionsTheinsilicoand ADMET profile study on the phytocompounds ofP.betlepredicted their promising potential xanthine oxidase inhibition activity and they could be developed as alternate molecules against synthetic agents byinvivoexperiments for the treatment of xanthine oxidase associated diseases like hyperuricimia and cardiovascular disorders.

File searching turns out to be difficult since nodes in MANETs move around freely and can exchange information only when they are within the communication range. Broadcasting can quickly discover files, but it leads to the broadcast storm problem with high energy consumption. File sharing applications in mobile ad hoc networks (MANETs) have attracted more and more attention in recent years. The efficiency of file querying suffers from the distinctive properties of such networks including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica creation with minimum average querying delay. Specifically, current file replication protocols in mobile ad hoc networks have two shortcomings.

The movement and the travel industry have been arising as one of the quickest and biggest developing monetary areas worldwide. It contributes around the world work and Domestic Product expanded essentially. In India now daily Tourism is a sunrise industry. It is likewise contributing to work age. Furthermore, it is an instrument of unfamiliar trade for the nation and a monetary action that helps neighborhood and host networks. The Tourism area is one way to foster the country and its chronicled places are one of the vital components for Indian Tourism. In any case, this area is confronting difficulties like the absence of good quality foundation of the travel industry, worldwide worries for wellbeing and security of vacationers, the street and thruway administrations charge structures across different states, incompetent labor in this area. The paper is offering imperative to factors that influence Tourism Sector. The information is taken from different public and global reports, diaries, books, magazines and other writing of this discipline. The Indian travel industry is assuming a significant part in the monetary improvement of numerous areas of the state by creating business. Gujarat the travel industry, helps the world popular Indian customary Art and specialty.

An experiment using twenty genotypes of chickpea (Cicer arietinum L.) was conducted during Rabi season of 2019-2020, to find the genetic diversity using D2 statistics. The experiment was laid out in randomized block design with three replications at the experimental field of Himgiri Zee University, Dehradun. The observations was recorded on nine quantitative characters where five randomly selected plants were taken the average was computed while the traits days to 50 % flowering and days to 100 % maturity was taken from plot basis. Results revealed that the genotypes were grouped into 4 clusters where Cluster-I had fifteen genotypes and cluster II had three genotypes while one genotype each was present in cluster III and IV. The seed yield per plant contributed maximum towards genetic diversity (32.00 %) followed by plant height at maturity (14.00 %) and number of secondary branches (9.00 %). The maximum intra cluster distance was found in cluster II (164.691) indicating that the 15 genotypes including in the cluster II were most divergent. However, maximum inter cluster distance was noticed between cluster I and cluster II (313.247) which could be used in hybridizing program.

Background Sclerotinia sclerotiorum is the causal agent of white mould, stem and head rot diseases on a wide variety, including cabbage in the field and storage. Control of this pathogen by using commercial disease management methods is extremely difficult. Therefore, this study was performed to identify the potential biological control agent. Results The antagonist fungal isolates, a mycoparasite of S. sclerotiorum, were isolated and described as KET Cm1, PAL Cm 2 and TDK Cm 3. They were subjected to morphological and microscopic observation. The growth of antagonist fungal isolates on different media revealed that PDA and half-strength Czapek dox agar medium showed the maximum mycelial growth (28.67 and 28.33 mm) and pycnidial production in the PDA medium. The studies on pH and temperature found that growth and sporulation of the antagonist fungal isolates were optimum at pH 5.5 (34 mm) and 20 °C. The molecular confirmation of antagonist fungal isolates was carried out using ITS 1 and ITS 4 primers, which target 18S-28S rRNA gene fragment. The isolates were partially sequenced and showed that more than 99% identity with Coniothyrium minitans already exist in the NCBI database. The accession numbers (MW093134, MW111282 and MW136938) were obtained for all the three isolates and also confirmed using specific primers Cm sp 1F and Cm sp 1R. Among the tested isolates, the antagonistic activity of C. minitans TDK Cm 3 showed the maximum mycelial growth inhibition of 48.8% S. sclerotiorum and was used for further studies. Penetration and formation of appressoria on the hyphae of S. sclerotiorum by C. minitans isolate TDK Cm 3 were observed through the scanning electron microscope. The ethyl acetate fractions of TDK CM 3 isolate were evaluated against S. sclerotiorum by agar well diffusion assay, paper disc assay and poison food technique in vitro. Through GC–MS analysis, the effective antifungal compounds were identified. Conclusion The present study focused on the identification and characterization of effective Coniothyrium isolates as eco-friendly, integrated disease management strategies against cabbage head rot disease.

Background: The cultivation of cabbage is impacted heavily due to cabbage head rot causing plant pathogen, Sclerotinia sclerotiorum. The present study was conducted to know the prevalence of cabbage head rot pathogen in Tamil Nadu and to assess its virulence. Methods: In this study, the survey was conducted on major cabbage-growing regions of Tamil Nadu. Pathogen associated with cabbage head rot was isolated and Koch’s postulates were conducted. Further the isolated pathogen was characterized morphologically and molecularly through ITS and species-specific primers. Results: Among the surveyed areas, Nanjanadu village in Nilgiris district showed the highest percent disease incidence of 45.23% compared to other regions. A total of ten S. sclerotiorum isolates (TNAU-SS-1, TNAU-SS-2, TNAU-SS-3, TNAU-SS-4, TNAU-SS-5, TNAU-SS-6, TNAU-SS-7, TNAU-SS-8, TNAU-SS-9, TNAU-SS-10) were obtained during isolation. Based on the virulence assay, TNAU-SS-5 (MZ379266) isolate showed a quicker lesion size of 3.77 cm2 in comparison to the control. The molecular conformation of pathogen was carried out using ITS 1 and ITS 4 primers targeting 18S-28S rRNA gene fragments. Furthermore, the isolates were confirmed with specific primers and identified as S. sclerotiorum.