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Internet of things is an emerging technology that efficiently and effectively improve our daily life activities. It reduces the cost of living by automating the manual processes. It integrates physical objects and devices via internet for synchronized communication. Currently the cost of electricity is reaching the sky so there is a need for some sources that can produce electricity naturally without any cost. Here, solar panels and solar plants take place for producing electricity in natural manner with sun light. Photo voltaic cells are used in these systems that converts the sun light into electricity. Mostly the solar systems are configured in rural or agricultural areas where electricity mediums are still not available. These systems are manually operated by humans. So there is need of efficient approach which automatically control and monitor the current, voltage and other parameters of solar systems and provides real time statistics to users. This research paper proposes an IOT based approach for solar power consumption and monitoring that allow the users to monitor or control the obtained power and EV battery. Majorly, solar plants are built in the locations where people cannot reach on daily basis so this approach allows the people to virtually control their systems from faraway places.

Context-aware recommender systems (CARS) are a key component in businesses, notably in the e-transactions domain, since they assume that reviews, ratings, demographics, and other factors may determine customer preferences. On contrary, while evaluating the sentiment underlying the reviews and the rating score, consumers’ opinion is typically conflicting. As a result, a framework that employs either a review or a rating for top-N recommendation directs to produce unsatisfied recommendations in addition to a meager rating problem and high computation time. To overcome all the problems, a novel sentiment enhanced stacked autoencoder (SSAE) with context-specific hesitant fuzzy item hierarchical clustering (CHFHC) approach is proposed which employs online and offline phases. In the offline-phase, the meager user-item rating matrix is smoothed by learning the users’ concrete preference to a complete matrix by the SSAE approach. They are clustered offline using the CHFHC approach into context-based similar item clusters. In the online-phase, the active user gets context-based recommendations from the most similar cluster that matches the active users’ current context situation. Hence the SSAE_CHFHC approach improves the quality of Top-N recommendation corresponding to the exact contextual situation of the active user with a minimal recommendation computation time. Experiments on the (5-core) Amazon and yelp datasets proved that the intended SSAE_CHFHC framework consistently outperforms state-of-the-art recommendation algorithms on a variety of evaluation measures.

One in three persons experience low back discomfort, which could be the highest global burden of disease. NSLBP is outlined as the rear pain results from posture that is poor due to which apt function of the muscles alters, it becomes dysfunctional after undergoing back pain. Among various acquirable options for management of low rear pain, William’s flexion exertion is one among the known methods for treating back pain. The set of exertion referred to as William’s flexion exercises is used within the study to enhance lumbar flexion and to manage low back pain by strengthening the muscles within the gluteus and abdominal regions. The samples were collected from the physiotherapy outpatient department at AVMCH. The study comprised of patients who had non – specific LBP was chosen in accordance with the selection criteria for the study, pain prior to intervention was assessed using VAS and at the end of the final intervention of one week, the intensity of the pain was again noted. It is advised that other outcomes be added in the future to reach a higher level of dependability as this study was confined to one outcome and psychological elements were not taken into consideration. Results concluded that significant improvement in the reduction of pain following William’s flexion exercise. This study demonstrated the value of William's flexion exercises for people with generalized low back pain was effective

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR). By integrating human and machine analysis, Visual Analytics provides the potential to address the above-mentioned significant challenges. The texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor. Using the Euclidean distance correlation metric, related medical pictures will be fetched by distinguishing the query image's attribute vector with the database images' respective attribute vectors. A vector of multiple features outperforms a vector of a single feature in terms of quality. The VAMIR implementation demonstrates that the search outcome for the user is acquired with 90% of recall and precision.

A rising non-inflammatory condition that affects older people is osteoarthritis. The most typical location for osteoarthritis is the knee. When a muscle contracts and generates forces during an isometric workout, there is no noticeable change in the length of the muscle. Exercises known as multiple angle isometrics require the user to manually apply resistance to a range of joint positions. So, the goal of this study is to assess how well multiple angle isometric exercise reduces pain in people with osteoarthritis. Ten samples were collected from the Aarupadai Veedu Medical College and Hospitals outpatient physiotherapy department in Kirumampakkam, Pondicherry. Between the ages of 45 and 65, both males and females were collected. Participants underwent seven sessions of multiple-angle isometric exercise over the course of one week. The NPRS scale was used to assess pain before and after therapy. The use of a single outcome and a limited sample size are study limitations; it is recommended that more outcomes, such as strength and endurance, be added in the future to achieve a better level of dependability. The findings of this study revealed a considerable improvement in patients with osteoarthritis of the knee in terms of pain reduction. The research found that multiple angle isometric training reduces pain in those with osteoarthritis.

In this paper, a novel Monogenic Sobel Directional Pattern (MSDP) using fractional order masks is proposed for extracting features. The MSDP uses fractional-order Sobel masks to identify thin edges along with color and texture-based information thereby increasing performance. Other edge-detection methods can identify only thick edges. There are three modules namely feature extraction, dimension reduction via a novel discriminant analysis method, and classification using a Convolutional Neural Network (CNN). The proposed MSDP is insensitive to the rotation and scaling changes existing in the images. The Bat Algorithm-based Optimization (BAO) is used for the selection of the best parameters of MSDP. The best value is modified by the Pearson Mutation (PM) operator in an effort to aid the algorithm in avoiding local optima and achieving a balance between global and local searches. The proposed work uses CNN for classification and achieves higher classification accuracy for six datasets.

Introduction: Dry eye is a common condition that affects 85 percent of adult students worldwide, has no effective treatment, and results in significant loss of productivity at work. Aim: This study aims to assess the knowledge and awareness of dental students about dry eye disease. Materials and Methods: An online google form was used to distribute a questionnaire based study with 12 questions to 100 people. The study began with the participants being briefed on the trial's details. The study used a 100-person sample size. The information was gathered and statistically examined. Results: In this study, 96% of the students were aware that lack of sleep causes Dry Eyes Disease and 4% of the students were unaware of it. Around 83% of the students were aware that dry eye is reversible and 17% of the students were not aware of it. The overall result was positive, most of them were aware of the dry eye disease. This proves an outright positive result. Conclusion: The present study shows that the students under 75% were aware of Dry Eyes Disease but 25% were unaware of dry eye disease. There is no sufficient awareness among dental students. So, by this survey, we create more awareness among dental students.

Acid lime (Citrus aurantifolia Swingle) is one of the commercial fruit crops which occupies important place in the fruit industry, but yield levels of citrus orchards are still very low. Alternate nutrient management system could help in achieving high yield and quality of acid lime. Thus, an investigation was undertaken on the “Effect of nutrient management through bio-organic manures on quality of acid lime (Citrus aurantifolia Swingle)” in the Department of Horticulture, Faculty of Agriculture, Annamalai University, Annamalainagar, Tamil Nadu, India during 2016 – 2018. The experiment was laid out in randomized block design with twelve treatments, various organic manures, biofertilizers along with inorganic fertilizers including recommended dose of fertilization @ 100%, 75%, 50%, farm yard manure @ 100%, 50%, Vermicompost @ 100%, 50%, Biofertilizers ( 25g Azotobacter + 25g phosphate solubilising bacteria + 150g vesicular arbuscular mycorrhizal) were applied. The quality characters of acid lime were studied at different stages. The observations recorded viz., plant height (m), canopy spread east-west (m), canopy spread north-south (m), canopy height (m), no. of leaves per canopy, leaf area (cm2). The results revealed that plants treated with of T8 -75% RDF + 50% FYM + 50% Vermicompost + Biofertilizers (25g Azotobacter + 25g PSB + 150g VAM) was observed maximum plant height (m), canopy spread east-west (m), canopy spread north-south (m), canopy height (m), no. of leaves per canopy, leaf area (cm2).

Acid lime (Citrus aurantifolia Swingle) is one of the commercial fruit crops which occupies an important place in the fruit industry, but yield levels of citrus orchards are still very low. An alternate nutrient management system could help in achieving a high yield and quality of acid lime. Thus, an investigation was undertaken on the “Effect of nutrient management on productivity and quality of acid lime (Citrus aurantifolia Swingle)” during 2016 – 2018. The experiment was laid out in randomized block design with twelve treatments, various organic manures, biofertilizers along with inorganic fertilizers including recommended dose of fertilization @ 100%, 75%, 50%, farm yard manure @ 100%, 50%, Vermicompost @ 100%, 50%, Biofertilizers (25 g Azotobacter + 25 g phosphate solubilising bacteria + 150 g vesicular arbuscular mycorrhizal) were applied. The yield and quality characters of acid lime were studied at vegetative and reproductive stages. The observations recorded viz., fruit setting (%), fruit diameter (cm), number of fruits per plant, fruit weight (g), yield per tree (kg) and yield per hectare (tonnes). The results revealed that plants treated with T12 ( 50% RDF+ 75% Vermicompost + Biofertilizers (25 g Azotobacter +25 g PSB +150 g VAM) was observed maximum fruit setting (%), fruit diameter (cm), number of fruits per plant, fruit weight (g), yield per tree (kg), yield per hectare (tonnes). The maximum net income (Rs. 281511.5 / ha) and benefit:cost ratio (4.72: 1) was observed in the treatment T12-50% RDF + 75% FYM + 75% Vermicompost + Biofertilizers (25 g Azotobacter + 25 g PSB + 150 g VAM) which was superior to other treatments.

Introduction: Periodontal disease is an infection of the tissues that hold your teeth in place. It's typically caused by poor brushing and flossing habits that allow plaque, a sticky film of bacteria, to build up on the teeth and harden. Elimination of these pathogens from the site of infection remains a perplexing task, which demands the use of antibiotics. The emergence of drug resistant forms has spurred interest into identifying novel therapeutic targets against these pathogens. Aim: The present study employs virtual screening method to identify the protein network interaction of berberine with red complex pathogens. Materials and Methods: Computational tools were used to identify the targets, assess their functional role and virulence property. Further, the peptide epitopes present in the virulence factors were identified using the BepiPred tool. The subcellular location of the virulence proteins was also elucidated using PSORTb. Results: Berberine was found to target vital protein transporters such as TetR family transcriptional regulator and MerR family transcriptional regulator, which is known to play a crucial role in the survival of bacterial cells. Conclusion: Hence the present study provides preliminary data on the protein targets of berberine against red complex pathogens. However, in vitro studies using the compound is warranted to further confirm the efficacy of the compound.

An Investigation was conducted to study the effect of organic inputs and growth regulators on yield and quality of goldenrod (Solidago canadensis) was conducted at the floricultural unit, Department of Horticulture, Faculty of Agriculture, Annamalai University during 2018. The experiment was laid out in randomized block design with twelve treatments, various organic inputs and growth regulators including panchagavya @ 3%, vermiwash 1:5 dilutions, humic acid @ 0.2%, GA3 @ 250 ppm, NAA @ 250 ppm were applied. The yield and quality characters were studied at different stages. The observations recorded viz., yield of flower stalks/ha, days to first flowering, length of flower stalks and weight of individual flower stalks. The results revealed that plants treated with NAA @ 250 ppm + panchagavya @ 3% (T9) was observed highest flower stalk yield (1140.02 flower stalks/ha), No. of flower stalks/plant (24.48), weight of individual flower stalks, days to first flowering. Considering the overall performance, it was found that the plants treated with NAA @ 250ppm + Panchagavya @ 3% recorded better performance with regard to growth, yield and quality characters.

Context-aware recommender systems (CARS) are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a Trust-based Attentive Contextual Denoising Autoencoder(TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions and Caio dataset make obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.

Bemisia tabaci (Hemiptera: Aleyrodidae) is a polyphagous invasive insect pest in the world that infests over 1000 plant species, spreads 300 plant viruses and associated with different endosymbiotic bacteria. In the current study, we looked at the genetic variation of B. tabaci populations and their association with endosymbiotic bacteria in Bihar (29 populations: 16 interhost and 13 interlocations). For this purpose, we conducted an inter-host survey and discovered that Brinjal was highly infested by B. tabaci, so, an interlocation survey was planned. The genetic variability among 29 populations were explored using nuclear markers i.e., Random Amplified Polymorphic DNA (RAPD), Simple Sequence Repeats (SSR) along with extra-nuclear marker (Cytochrome c oxidase subunit I -COI) for their molecular characterization. Interestingly, Bayesian phylogenetic analysis of 657 bp mtCOI sequences showed the existence of four cryptic species belonging to two genetic groups (Asia and China) viz., Asia I, Asia II 1, Asia II 7 and China 3. In this study we have reported different bacterial endosymbionts associated with 29 B. tabaci populations in Bihar. By amplifying of 16S rRNA of specific bacteria we were able to identify Portiera sp, the primary endosymbiont of B. tabaci, and other secondary endosymbionts like Arsenophonus sp, Wolbachia sp, Rickettsia sp, Hamiltonella sp and Cardinium sp. Overall, this study found that Asia II 7 and China 3 cryptic species were reported for the first time in Bihar region and Asia I was the dominant cryptic species and that secondary endosymbionts were not uniformly distributed in all the populations. Furthermore, our findings in mapping the distribution of B. tabaci populations and their associated endosymbionts in various ecosystems paved the way for future research and the development of area-wide management tactics for B. tabaci cryptic species in Bihar.

The attraction of the female melon fly, Zeugodacus cucurbitae (Coq.) (Diptera: Tephritidae) to three food baits were evaluated in snake gourd (Trichosanthes anguina L.) and ridge gourd (Luffa acutangula (L.)) ecosystems at both Coimbatore and Tiruppur districts during January to march 2020. Food bait admixtures were prepared by combining base baits (guava-B1, muskmelon-B2 and guava+ muskmelon-B3) and bait additives (yeast, food grade alcohol, cane sugar and ProtineX®). The result indicated that both ridge gourd and snake gourd at Coimbatore (23 fruit flies/trap/day) attracted more adult flies than at Tiruppur. Greater number of Z. cucurbitae were attracted to food bait traps in snake gourd field (24 fruit flies/trap/day) at both Coimabtore and Tiruppur. In common, the performance of guava bait admixture (21 fruit flies/trap/day) was relatively greater than other two baits by trapping more adult flies. At individual sites, the three food baits attracted equivalently higher number of females than males (about 70% females). The number of adult flies trapped at first week (42 fruit flies/trap/day) was high and gradually decreased in successive weeks and found low at 11th (2 fruit fly/trap/day) and 12th (1 fruit fly/trap/day) weeks. This management practice for Z. cucurbitae effectively reduced the pest population by attracting most females.

Cadmium Sulfide quantum dots were prepared in three solvents such as distilled water, ethanol, and isopropyl alcohol respectively. Solvent dependence of cadmium sulfide quantum dots was investigated under XRD, TEM, UV, PL, and I-V characteristics. The results showed CdS quantum dots with sizes ranging from 7.5 nm, 8.2 nm, and 9 nm which enabled the control of the optical properties and consequently the solar cell performance. The optical absorption spectrum was observed near the ultra-violet region. Absorbance spectra reveal that there is a blue shift in the absorbance as the quantum dot size decreases due to the quantum confinement effect. For photoluminescence spectra, the emission peaks were observed at 430 nm, 468 nm, and 470 nm respectively. Among the solvents, cadmium sulfide quantum dots prepared with ethanol has the highest power conversion efficiency of 0.68%.

Background: Drug utilization plays a key role in helping the healthcare system to understand, interpret and improve the prescribing, administration and use of medications. The objective of the study was to assess the drug utilization pattern of drugs prescribed in conservative management of pancreatitis patients in a tertiary care teaching hospital.Methods: This prospective observational study was conducted in department of surgery, Rajah Muthiah Medical College and Hospital, Annamalai University. The study period was six months from November 2019 to April 2020. A total of 115 patients were selected based on inclusion and exclusion criteria.Results: The study population containing of 90% (n=104) males and 10% (n=11) females were included in the study. Most of the patients between age group of 25-44 years (48%). The most common etiology was alcoholic 68% (n=78). 7% of population with comorbidities (5% hypertension, 2% diabetes mellitus). In analgesics, tramadol (28%) and in antibiotics cefotaxime 21% was most prescribed. Lactated ringer solution (36%) was prescribed most as intravenous hydration. 7% (n=8) and 3% (n=4) of patients receiving enteral nutrition and parenteral nutrition. The average number of drugs per prescription was 8.5. Out of 1205 drugs, 61% of drugs prescribed in generic form, 39% were prescribed in brand name. The study analyzed that 69% of drugs were prescribed from essential drugs list (EDL) 2019 and 84% drugs prescribed from national list of essential medicines (NLEM) 2015. Number of prescriptions with injection were 79.07%. The overall antibiotic encounter rate 15%. Prescribed daily dose/defined daily dose (PDD/DDD) ratio of folic acid was 5.Conclusions: Drug utilization study can help in evaluating the quality of care given to the pancreatitis patients and promote rational use of medicines.

Worldwide, they are more than one in ten children are born prematurely. Eleven countries, have higher preterm birth rates Premature maturation (born before 37 weeks of pregnancy) is strongly associated with difficulty breathing diseases, that is, the leading cause of death for children under five. Because their lungs well developed, enemies in particular exposure to respiratory problems. One such the leading cause of child mortality worldwide Respiratory Distress Syndrome (RDS). Currently \ technology (recommended by WHO) by CPAP (Continuous Positive Airway Pressure), i.e. helps keep the alveoli open by giving continuous distribution of air / oxygen. This ensures that the children stay healthy residual air in the lungs, so it does not they strive to breathe and are continually saved problems such as hypoxia and even death CPAP standalone devices cost more than INR 150,000 ($ 2300) more expensive many developing hospitals. For the most part hospitals in restricted services, Oxygen treatment in the nose is often the solution available, on the basis of respiratory depression.

Fish classification is an essential requirement for biomass estimation, disease identification, and quality analysis. In aquaculture industries, fish classification is carried out in the processing unit. The fishes are out of water, subjecting them to structural deformation and orientation misalignments, makes classification challenging. A multisegmented fish classification technique using deep learning networks with naive Bayesian type fusion is proposed in this work to address these challenges. Fish images are acquired using an overhead camera. The fish head is identified by observing a minimal convexity deficiency region to facilitate segmentation. A multi-stage exhaustive enumerative optimization method is used to adjust the orientation, which can minimize unwanted background region in the image segment. Fish head, scales, and body are segmented from the fish image. For each fish segment, AlexNet is trained by using the transfer learning approach. A naive Bayesian fusion layer is introduced to fuse these trained deep learning networks and enhance classification accuracy. Experimental results illustrate a classification accuracy of 98.64% for ‘Fish-Pak’ image dataset with six different fish species and 98.94% for BYU fish dataset with four species. Comparative analysis with standard networks and ablation study demonstrates the accuracy and robustness of the proposed fusion architecture, respectively. Various fusion layers have also been analyzed, and observations illustrate the accuracy of the proposed NBC layer. Significant improvements in other classification performance metrics were also observed.

The grey water is the waste water that is generated in the households or office buildings from streams without fecal contamination. Sources of grey water include water from sinks, showers, baths, washing machine or dish washers. As grey water contains fewer pathogens than domestic waste, it is generally safer to handle and easier to treat and reuse onsite for toilet flushing, landscape or crop irrigation. The removal of toxic content in grey water in this era is one of the most needed process by now. A constructed wetland (CW) is an artificial wetland created for the purpose of treating anthropogenic discharge such as domestic, industrial wastewater and storm water runoff. The treatment of grey water for the removal of toxic content using constructed wetland along with plant species is the most simple and widely accepted eco-friendly method for the better treatment of grey water. This paper addresses the treatment of grey water using constructed wetland with Canna Indica plant and reduces the toxic content in grey water. Grey water and treated water is tested for pH, Total dissolved solids, Turbidity, Chemical oxygen demand, Total hardness, Biochemical oxygen demand, Electronic conductivity and the removal efficiency is discussed with the results.

Background: Obesity is one of the fastest growing threats of health and well-being for both developed and developing countries. The aim of the study was to evaluate the awareness about obesity among general public.Methods: After obtaining institutional ethics committee approval and written informed consent from the participants, anthropometric measures like height, weight, hip and waist circumference for computing body mass index and waist hip ratio were taken. Using portable bioelectric impedance analyzer body fat percentage was measured. A validated questionnaire including socio demographic details and questions evaluating the awareness about obesity were given to the participants.Results: Totally 100 participants were evaluated, of which majority being female. Mean height of the male and female participants was 164 and 152cm respectively. Similarly, mean weight was found to be 72 and 65 kg. Based on the body mass index only 19% participants were within normal range, remaining 81% were overweight and obese. Percent body fat and waist hip ratio was found to be increased in both sexes. Majority of the participants were aware that obesity is associated with health problems but most of them were unaware how it is measured and the dietary foods to be taken to reduce obesity. However 64% participants think that obesity can reduce the life expectancy of the individual.Conclusions: Majority of the participants were obese and overweight while awareness about preventive measures was still lacking. Hence health care personnel need to take greater effort to educate and inform people about the implications.

Background: It is now known that repeated pain in neonatal period influences neurodevelopmental outcome. It is necessary to assess and manage neonatal pain with appropriate interventions. The objective is to study whether expressed breast milk effectively reduces neonatal pain after venepuncture in comparison to placebo using sterile water.Methods: In this randomised controlled trial, neonates were randomised into two groups-expressed breast milk group and placebo group with 40 neonates in each group. Two minutes before venepuncture, 2 ml of test solution was administered. Mean Premature Infant Pain Profile (PIPP) score and mean cry time were recorded in neonates of both groups after venepuncture and compared.Results: At 0-30 second, 1-1 ½ minute, 3-3 ½ minute, 5-5 ½ minute after venepuncture, neonates in placebo group had mean PIPP score of 11.475, 10.125, 9.125, 7.575 respectively. At the same time intervals, mean PIPP score was 9.375, 7.825, 6.475, 5.05 in neonates of expressed breast milk group. Mean cry time after venepuncture was 105.65 seconds and 75.825 seconds in the placebo group and expressed breast milk group respectively.Conclusions: Compared to placebo, expressed breast milk significantly reduced mean PIPP score and cry time in neonates.

Wireless Sensor Network (WSN) assures to revolutionize sensing in a spatially dispersed and dedicated manner in various applications. It requires adopting a security mechanism for critical applications. Though many cryptographic algorithms are available it strength depends upon the key used for encrypting and decrypting the collected data. Key Management Scheme (KMS), a technique for establishing and maintaining the key relation, play a vital role in any security mechanism. The proposed work was focused on pairwise key establishment using QR based matrix Decomposition method. Though many metrics are available the prime factor for any WSN is key connectivity. The proposed KMS focuses on 100% key connectivity compared to probabilistic approach. For each communications different symmetric matrices are generated and are decomposed into Q and R matrix. This scheme is implemented in LPC2148 with Xbee to check its feasibility in real time applications. Performance analysis shows that the proposed method achieves 100% key connectivity, better resilience and scalability.