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Introduction: Extraction of distinguishing semantic level emotions posed in multi-languages over social media is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian or local languages combining with multi-languages over social media has become an essential challenge in the field of big data sentiment analysis. Methods: In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with help of lexicon-based features groups. Results: The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multilanguages text. Conclusion: Machine learning technnique SVM has outperformed for sentiment and emotion extraction.

The addition of natural gas (NG) with diesel fuel is an attempt to increase the combustion efficiency of the diesel engine and to reduce nitrogen-based emissions in this investigation. A three-dimensional computational study has been carried out on the combustion chamber of a NG–dieseldual fuel engine. Standard k- ɛ turbulence model was used to incorporate the turbulence inside the combustion chamber. The spray angle inside the combustion chamber was varied from 100° to 150°. The flow characteristics such as pressure, temperature, and velocity distributions, combustion characteristic of apparent heat release rate, and emission characteristic of NO emission of dual fuel engine were investigated at different spray and crank angles. The results show that the maximum pressure inside the cylinder increased from 129 to 131 bar when the spray angle was varied from 100° to 150° at a crank angle of 360°. The maximum temperature was found to be 1602 K at a spray angle of 150° and at crank angle of 360°. The range of velocity inside the cylinder was from 5 m/s to 15.6 m/s. At the spray angle of 150°, the mass fraction of NO emission was 1.15 × 10−09 and was the least among the different spray angles. The optimized spray angle was chosen such that the combustion can take place properly with the least emission and was found to be 150°. The results of the present study were compared with the existing literature and are found to be in good agreement.

The Internet of Drones (IoD) is an emerging domain due to its capability of being deployed in several domains. The drones in IoD are capable of exploring the platform and transfer commodities with ...

BACKGROUND Mineral metabolism is frequently disturbed in thyroid dysfunctions. Among thyroid dysfunctions, hypothyroidism is one of the most common form resulting from the deficiency of thyroid hormones. Studies done on serum calcium, phosphorus and magnesium in hypothyroid patients earlier have conflicting results. Hence the present study has been undertaken to study the levels of serum calcium, phosphorus, and magnesium among hypothyroid patients and analyse their correlation with thyroid stimulating hormone (TSH). METHODS The case control study was conducted in the Department of Biochemistry in Trichy SRM Medical College Hospital and Research Centre for a period of 6 months from 2017 January to July 2017. The study was undertaken involving 50 hypothyroid cases and 50 healthy volunteers as controls after proper ethical clearance and informed consent of all the study subjects. Serum calcium, phosphorus and magnesium were measured along with tri-iodothyronine (FT3), tetra-iodothyronine (FT4) and TSH among all study subjects. Statistical analysis of data was done using statistical package for social sciences (SPSS) software. RESULTS The mean value of serum total calcium and total magnesium was lower among hypothyroid cases and phosphorus value was increased as compared to controls. (P < 0.001) Statistically significant negative correlation was observed between serum calcium, magnesium and TSH among hypothyroid cases. Statistically significant positive correlation was observed between serum phosphorus and TSH among hypothyroid cases. CONCLUSIONS Among hypothyroid patients the values of serum calcium, magnesium and phosphorus is significantly altered. Thyroid disorders have an important role in bone and mineral metabolism. Thus, monitoring of these minerals among hypothyroid patients in regular follow up may effectively improve the clinical manifestations in them. Hence, monitoring of mineral status of the hypothyroid patients on follow-up will be of benefit to the patients. KEYWORDS Hypothyroidism, Calcium, Phosphorus, Magnesium, Minerals

A hybrid approach through combining genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) for modeling the correlation of laser beam cutting (LBC) parameters and enhancing the qu...

Background: Impulsivity, due to high comorbidities, is considered as one of the residual symptoms in patients with psychiatric illnesses. There is dearth of literature with respect to the effect of available treatment options on impulsivity and clinical outcome in major psychiatric disorders. Hence, our study aims to observe the change in impulsivity from baseline after 10 days of treatment and clinical outcome in patients with major psychiatric disorders [Schizophrenia, Bipolar Affective disorder (BPAD) and Alcohol Dependence Syndrome (ADS)]. Materials and Methods: This is a prospective analytical, non-randomized, quantitative open label study wherein total of 112 patients diagnosed with either of the three major psychiatric illness i.e., schizophrenia or BPAD or ADS were recruited into the study. BIS-11 was used to assess impulsivity at baseline and 10 days post admission. Clinical outcome was assessed using CGI scale. Data was analyzed using one-was ANOVA, paired t test and Post hoc analysis.Results: The difference between baseline mean scores and post treatment mean scores on all the subscales of BIS-11 was found to be statistically significant (p value

An extreme learning machine is a feed-forward neural network is a feature learning method used in image classification efficiently because of the faster learning rate, speed, good generalization ability, ease of implementation, and efficiency in classifications. This paper presents a new model for the classification of images by using an extreme learning machine classifier which works in two stages using deep learning techniques. A new framework is built by using the dropout technique of CNN for classifying images with less training time and also no need to over train neurons. The proposed model has a high accuracy of 98% using 1000 images. The proposed paper accomplishes the best results in image classification compare with other related methods on image classification.

Indian education system has faced many reforms and changes right from Gurukula education system in Vedic age to the present outcome-based education system. There lies a wide gap between industries and institutions in India due to the lagging of quality graduates. To bridge the gap, the outcome-based education system has been implemented widely in India. Still, an effective approach that reforms the education system is needed to fulfill the needs of industries. Worldwide, large numbers of universities have adopted Conception, Design, Implementation, and Operation (CDIO) approach in view to produce quality graduates. At this juncture, it is required to analyze and evolve the fruitful strategies in implementation of CDIO approach in Indian engineering education scenario. In the present work, the need of CDIO implementation has been critically analyzed and presented. Furthermore, the present Indian engineering education system has been analyzed on various levels. The implementation strategies have been proposed in two categories as macro and micro levels that are required by the government bodies. The requirements for implementing CDIO are briefed based on institution, faculty and students levels. In addition, the bottlenecks in implementation of CDIO are studied, and the tactics to overcome the same are also discussed elaborately.

Huge amount of unstructured data is posted on the cloud from various sources for the purpose of feedback and reviews. These review needs require classification for many a reasons and sentiment classification is one of them. Sentiment classification of these reviews quite difficult as they are arriving from many sources. A robust classifier is needed to deal with different data distributions. Traditional supervised machine learning approaches not works well as they require retraining when domain is changed. Deep learning techniques perform well to handle these situations, but they are more data hungry and computationally expensive. Transfer learning is a feature in the cross-domain sentiment classification where features are transferred from one domain to another without any training. Moreover, transfer learning allows the domains, tasks, and distributions used in training and testing to be different. Therefor transfer learning mechanism is required to transfer the sentiment features across the domains. This paper presents a transfer learning approach using pretrained language model, ELMO which helps in transferring sentiment features across domains. This model has been tested on text reviews posted on twitter data set and compared with deep learning methods with and without pretraining process, also our model delivers promising results. This model permits flexibility to plug and play parameters with target models with easier domain adaptivity and transfer sentiment features. Also, model enables sentiment classifiers by using the transferred features from an already trained domain and reuse the sentiment features by saving the time and training cost.

Summary Background The clinical presentation of reduced pupillary responses in orbital apex syndrome is currently not well understood clinically. The pupillary light reflex (PLR) is determined using dynamic pupillometry. Case details A newly diagnosed 35-year-old diabetic female patient had ocular symptoms including orbital pain and ptosis, but no visual loss. Cranial nerve autonomic impairment was identified with dynamic pupillometry and compared with cardiac autonomic activity using heart rate variability (HRV). Results PLR showed that pupil size and response were severely affected, with a small resting pupil size, and minimal response to a bright white light flash was seen, due to decreased sympathetic activity. However, HRV showed normal cardiac sympathetic activity. Conclusion These findings suggest that orbital apex syndrome may lead to changes in pupil size and response due to compression of sympathetic fibers at the apex of the orbit. Quantitative PLR measurements help to reveal relevant information on anatomical reflex pathways.

In this paper, a vibration control system with magnetorheological (MR) damper investigation is reviewed. At first a MR damper is investigated analytically using various finite element method software and the performance is investigated using experimental. The MR Dampers are designed and modelled for a scaled down setup. The application is in seismic resistance of buildings, automobile, physical and biological. Finally, the damper is investigated using various technique and methods used to study the performance is reviewed. This device reduces the vibration in both active and semi active control system effectively.

Data security in the cloud has become a dominant topic being discussed in recent times as the security of data in the cloud has been focused on by several researchers. However, the data security was enforced at the attribute level, the adversaries are capable of learning the method of data encryption even there are access restrictions are enforced at an attribute level. To challenge the adversaries with more sophisticated security measures, an efficient real-time service-centric feature sensitivity analysis (RSFSA) model is proposed in this paper. The RSFSA model analyses the sensitivity of different features being accessed by any service and at multiple levels. At each level, the method checks the set of features being accessed and the number of features the user has access grant to compute the FLAG value for the user according to the profile given. Based on the value of FLAG, the user has been granted or denied service access. On the other side, the method maintains different encryption schemes and keys for each level of features. As the features are organized in multiple levels, the method maintains a set of schemes and keys for each level dedicative. Based on the service level and data, the method selects an encryption scheme and key to perform data encryption. According to that, the service access data has been encrypted at the attribute level with a specific scheme and key. Data encrypted has been uploaded to the blockchain and the method modifies the reference part of the chain to connect only the blocks to which the user has access. The chain given to the user would do not contain any reference from a specific block to which the user has no access. The proposed method improves the performance of data security and access restriction greatly.

Cardiac troponin (cTn) is mainly used to diagnose acute coronary syndrome (ACS). However, cTn can also be elevated in critically ill patients secondary to demand ischemia or myocardial injury. The impact of cardiology consultation on the clinical outcomes of patients admitted to medical intensive care unit (ICU) with elevated cTn is unclear.A retrospective analysis of medical ICU patients with elevated cTn without evidence of ACS between January 2013 through December 2018. Patients were stratified based on documentation of cardiology consultation. The primary outcome was 1-year mortality. Secondary outcomes were in-hospital and 30-day mortality, the length of stay (LOS), further cardiac testing, 30-day readmission rate, new prescription of cardiac medications, and the predictors of a cardiology consultation.Of 846 patients screened, 766 patients were included, of whom 63.2% had cardiology consultation. Cardiology consultation group had longer median LOS (7 vs. 5 days, P = 0.007), additional cardiac testing (90.3% vs. 67.7%, P 0.001), and more new cardiac medications (52.1% vs. 16.3%, P 0.001). No difference was noted in-hospital mortality (adjusted odds ratio [aOR], 0.6, 95% CI, 0.4-1.1, P = .117), 30-day mortality (aOR = 0.8, 95% CI, 0.5-1.4, P = .425), 1- year mortality (aOR, 1.4, 95% CI, 0.9-2.2, P = .193), or cardiac-specific 30-day readmission rate (aOR, 7.0, 95% CI, 0.7-14.9, P = .137). History of coronary artery disease (CAD) was the most independent predictor for a cardiology consult (aOR, 2.2, 95% CI, 1.3-3.8, P .001).Cardiology consultation for elevated cTn in medical ICU patients was associated with increased cardiac testing and LOS, without significant impact on mortality.

In this work, a flexible electromagnetic microwave absorber working in tri-band and dual band (i.e.) WLAN, Wi-Max and ISM applications are designed, fabricated, and characterised experimentally. Th...

Introduction: Split renal function (SRF) in prospective renal donors is traditionally measured by means of scintigraphy. Recent studies have reported the utility of three-dimensional computerized tomographic renal volumetry as an alternative to scintigraphy in estimating SRF. As computed tomography (CT) angio is done routinely for donors use of the same CT for estimating renal functions can eliminate the radiation from scintigraphy. Methods: In our study, renal volume was estimated on computerized tomographic renal angiography images using Siemens volumetry software by drawing contours manually on images, wherein we found the mean total renal volume to be 212.7 ± 38 CC and mean split renal volume to be 49.76 CC ± 2.86 and 50.23CC ± 2.86 on the right and left side, respectively. Results: This split renal volume was corrected to body mass index and surface area wherein we found significant correlation between renal volume and function when both were corrected to body surface area (P < 0.0001). Conclusion: From our results, it appears that computerized tomographic renal angiography not only depicts anatomy but also can give information about renal function which needs further confirmation.

In this work a flexible wide bandpass filter with silver conducting layer on polyimide substrate have been used. The placed silver layer has good mechanical and electrical flexibility. Measured results are showing the fractional bandwidth value of 44% with return loss value of below −15 dB and insertion loss of −1dB. At various bending conditions, the proposed bandpass filter showing constant performance value. The formed silver layer also presents good adhesion with polyimide layer. Surface roughness and skin depth of the silver layer also analysed in order to know the variation in the performance value of the proposed bandpass filter. The conformality of proposed bandpass filter across 150, 300, 450, 600 and 900 bending are analysed and found negligible variation in the performance characteristics. Prototyped microstrip bandpass filter results are measured using ANRITSU-MS2037C combinational analyser and found suitable validation with simulation results.

Corporate Social Responsibility (CSR) recompenses several direct and indirect benefits to the business. The most important advantage of CSR is to provide a business organization with an improved social image. Shifting the accountability of serving to one another does not serve the purpose at all. Simply putting the mask of corporate social responsibility does not make an organization socially accountable rather the organization is literally warranted to interweave the philosophy of CSR into its mission to be socially answerable. Social responsibility includes the areas such as health, education, employment, poverty alleviation and quality of life. CSR must be made obligatory on the part of the corporate sector operating in the country. It is a matter of great contentment that the Government of India is in the process of taking certain statutory steps to pursue the corporate sector to have a proactive approach towards corporate social responsibility/community development. This paper contributes towards the societal development of CSR through four dimensions namely corporate social responsibility and its implementation, corporate social responsibility and sustainability, corporate social responsibility towards Goodwill, corporate social responsibility and Social Justice.

Introduction: The viral infection COVID-19 is highly infectious and has claimed many lives till date and is still continuing to consume lives. In the COVID-19, along with pulmonary symptoms, cardiovascular (CV) events were also recorded that have known to significantly contribute to the mortality. In our study, we designed and validated a new risk score that can predict CV events, and also evaluated the effect of these complications on the prognosis in COVID-19 patients. Materials and Methods: A retrospective, multicenter, observational study was done among 1000 laboratory-confirmed COVID-19 patients between June 2020 and December 2020. All the data of the clinical and laboratory parameters were collected. Patients were randomly divided into two groups for testing and validating the hypothesis. The identification of the independent risk factors was done by the logistic regression analysis method. Results: Of all the types of the clinical and laboratory parameters, ten “independent risk factors” were identified associated with CV events in Group A: male gender, older age, chronic heart disease, cough, lymphocyte count 7 mmol/L at admission, estimated glomerular filtration rate 37 S, D-dimer, and procalcitonin >0.5 mg/L. In our study, we found that CV events were significantly related with inferior prognosis (P < 0.001). Conclusions: A new risk scoring system was designed in our study, which may be used as a predictive tool for CV complications among the patients with COVID-19 infection.

Applications such as audio processing, signal processing, SDR and such will work with elite efficiency in recent decades of technology development, in the context of sign preparation and image handling, and are extremely convenient as far as the area is concerned. There may be more signal noises and inconsistencies in digital signal processing units which use all categories of gadgets. In contrast, these applications consume greater areas in practical deployment in VLSI due to the arithmetic operation of the Adders and Multiplier design. Here, the proposed advanced method intends to design a novel outlook of FIR filter by replacing adders and multipliers to the proposed signed and unsigned Truncation Multiplier and HSCG-SCG adders. In this approximate partial reduction of products, the structure of truncation multiplier will have a number of the adders. This proposed work also attempts to replace these adders to HSCG-SCG method. This proposed design will reduce the number of logic gates in arithmetic operation of multiplication, addition and also proves efficient in signed and unsigned methods of signal processing applications.

The performance characteristics of solar tunnel dryer used to dry mango product (mango slice) has been analysed under forced convection. A DC blower is used and it is run by a photovoltaic panel (200w). Solar drier and experimental setup has been designed and fabricated. Performance analysis of solar drier has been performed during summer season and the system is operated at 28 °C−70 °C. The experimental set up fabricated by locally available materials with simple construction which reduces the cost. The variation of thermal efficiency for different solar intensity, moisture content and moisture removal rate have been explained through experiments. Response surface methodology tool has been used for optimization and acceptability using desirability for mango slices.

Right now, study the material and geometric vulnerability consequences for the static, free vibration and dynamic conduct of sandwich shaft structures. A higher-request sandwich board hypothesis is considered for the examination. The flexible properties of the sandwich pillar are considered as 1-D non -Gaussian irregular field which causes neighborhood variety in mass and firmness frameworks of the shaft. The discretization of the non-Gaussian irregular fields is performed utilizing the development ideal direct estimation. To play out the numerical investigation, Monte-Carlo reproduction alongside the computationally proficient time-space ghastly component technique is proposed. Numerical outcomes are acquired for various limit conditions just as for various materials in the sandwich face sheet and center. Results got right now the impacts of material and geometric vulnerability in the reaction conduct of a sandwich shaft. The individual impact of center thickness and Poisson's proportion of the center on the static, free vibration and dynamic reaction is measured. It is seen that the vulnerabilities in material and geometric properties alongside stacking and limit conditions impact the static, free vibration and dynamic reaction.

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.

Effusions are extremely common and constitute a significant proportion of all cytologic specimens being examined in any laboratory of a general hospital.1 Fluid analysis plays an important role in clinical medicine. The major purpose of examination is for the presence of malignant cells and also to identify the effusion is of inflammatory, non specific and or neoplastic origin. Objectives: 1. To study the age and sex wise incidence of pleural effusions. 2. To study the incidence of non- neoplastic and neoplastic effusions. 3. To study the gross and microscopic features of effusions. Materials and Methods: Two years prospective study was done on 360 specimens of pleural effusions and were analysed. Centrifuged smears were stained and studied with clinical correlation. Results: Out of 360, 342(95%) were non neoplastic and 18(5%) were neoplastic effusions. Commonest malignancy was Adenocarcinoma. Conclusion: Non-neoplastic lesions were common in the study. Exudates were common in infections Tuberculosis), Pneumonia and Malignancy. Keywords: Pleural effusion, Exudate, Malignancy.

Drug-induced resistance, or tolerance, is an emerging yet poorly understood failure of anticancer therapy. The interplay between drug-tolerant cancer cells and innate immunity within the tumor, the consequence on tumor growth, and therapeutic strategies to address these challenges remain undescribed. Here, we elucidate the role of taxane-induced resistance on natural killer (NK) cell tumor immunity in triple-negative breast cancer (TNBC) and the design of spatiotemporally controlled nanomedicines, which boost therapeutic efficacy and invigorate “disabled” NK cells. Drug tolerance limited NK cell immune surveillance via drug-induced depletion of the NK-activating ligand receptor axis, NK group 2 member D, and MHC class I polypeptide-related sequence A, B. Systems biology supported by empirical evidence revealed the heat shock protein 90 (Hsp90) simultaneously controls immune surveillance and persistence of drug-treated tumor cells. On the basis of this evidence, we engineered a “chimeric” nanotherapeutic tool comprising taxanes and a cholesterol-tethered Hsp90 inhibitor, radicicol, which targets the tumor, reduces tolerance, and optimally reprimes NK cells via prolonged induction of NK-activating ligand receptors via temporal control of drug release in vitro and in vivo. A human ex vivo TNBC model confirmed the importance of NK cells in drug-induced death under pressure of clinically approved agents. These findings highlight a convergence between drug-induced resistance, the tumor immune contexture, and engineered approaches that consider the tumor and microenvironment to improve the success of combinatorial therapy. Significance: This study uncovers a molecular mechanism linking drug-induced resistance and tumor immunity and provides novel engineered solutions that target these mechanisms in the tumor and improve immunity, thus mitigating off-target effects.

Purpose Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection has grown into a pandemic and without a specific cure, disease management is the need of the hour through symptomatic interventions. Studies with severe acute respiratory syndrome-coronavirus (SARS-CoV) have highlighted the role of herbal medicines either in combination with antiviral drugs or by themselves in curtailing the severity of infection and associated inflammation. Divya-Swasari-Vati is an Indian ayurvedic formulation used in the treatment of chronic cough and lung inflammation, which is one of the first symptoms of SARS-CoV-2 infections. Methods In this study, we used a A549 cell xenotransplant in the swim bladder of zebrafish and modeled the SARS-CoV-2 infection by injecting the fish with a recombinant spike protein. The different groups were given normal feed or feed mixed with either dexamethasone (as the control drug) or Divya-Swasari-Vati. The changes in behavioral fever, infiltration of pro-inflammatory cells in the swim bladder, degeneration or presence of necrotic cells in the kidney, and gene expression of pro-inflammatory cytokines were studied to determine the rescue of the diseased phenotype. Results Challenge with the spike protein caused changes in the swim bladder cytology with infiltrating pro-inflammatory cells, skin hemorrhage, and increase in behavioral fever. This was also accompanied by increased mortality of the disease control fish. Treatment with Divya-Swasari-Vati reversed most of the disease symptoms including damage to the kidney glomerulocytes, and complete reversal of behavioral fever. Dexamethasone, used as a comparator, was only able to partly rescue the behavioral fever phenotype. Divya-Swasari-Vati also suppressed the pro-inflammatory cytokines, IL-6 and TNF-α, levels in a dose-dependent manner, under in vivo and in vitro conditions. Conclusion The study showed that the A549 xenotransplanted zebrafish injected with the recombinant spike protein of SARS-CoV-2 is an efficient model for the disease; and treatment with Divya-Swasari-Vati medicine rescued most of the inflammatory damage caused by the viral spike protein while increasing survival of the experimental fish., Video abstract Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/dylNo-Ayjlg

Root resorption is the loss of apical root tissue which is implicated by decrease in root-crown ratio. It usually effects from external surface to pulp chamber if not identified in the early stage. Several natural conditions place roots at risk of resorption without any pathology. Knowing the pressure or etiology of the resorption will help the orthodontist in the treatment aspect. To date literature regarding root resorption due to various malocclusions is deplete in Indian population. To measure and assess the root crown ratios of maxillary incisors with vertical malocclusions, deleterious habits, and validate by two different radiographic techniques.90 Patients enrolled for the orthodontic treatment were allocated to three groups - open bite, deep bite & control groups and were subdivided based on nail biting habit. Panoramic radiographs, Periapical radiographs (by paralleling technique) for maxillary incisors (360 teeth) were taken and traced. Length of the root & crown were assessed and root crown ratios were calculated and the results substituted to statistics using SPSS. Comparison of Root-crown ratios in vertical malocclusions, revealed statistically significant (P < 0.001) differences in Open bite group for all maxillary incisors in both panoramic and periapical radiographs. No statistical significant differences were found in the deep bite group. Significant association was found between nail biting habit and Root-crown ratio. Gender predilection was found to be absent.Decreased Root- crown ratios were observed in open bite patients. Nail biting patients show significant root resorption. Both panoramic radiographs and periapical radiographs with paralleling technique can be used for assessing root resorption.

Introduction Pupillometry is a simple, non-invasive method capable of measuring static and dynamic pupil response in several physiological and clinical conditions. The latest digital camera can be slightly modified to obtain real time pupil response under infrared videography and analyzed by image analysis. So the current study has undertaken to confirm characterization of the system by comparing with manual measurements in healthy volunteers. Methods This study was conducted in 40 healthy volunteers aged between 18 and 25 years. The system was calibrated by measuring the known length of 3 different lines for reliability. Later real time static pupil pictures were measured and compared with the manual examination of pupil determined by a ruled scale with 0.5 mm accuracy. Results The Image J software was precalibrated with known length and estimated 5 mm, 10 mm and 15 mm lines respectively. The results are 5.01 ± 0.14 mm, 10.01 ± 0.36 mm and 15.02 ± 0.23 mm. There were no significant differences observed between the values analyzed by two examiners. Later the results were compared between manual examination and real time image analysis. Conclusion The present study suggested that the static and dynamic pupil measurements with real time pupillometer are accurate and probably as reliable as those obtained by manual examination of the pupil using the flashlight test. This system is cost-effective, portable in nature which can produce authentic results of the pupil.

In general, Solar Photovoltaic (SPV) is integrated to grid through a DC-DC converter and Voltage Source Converter (VSC) for real power injection (called two-stage conversion). In view of efficiency point, the single-stage conversion becomes popular and in which Maximum Power Point Tracking (MPPT) of SPV and real power injection are achieved with VSC alone. But, if Battery Energy Storage (BES) supported by bidirectional DC-DC converter is presented in the single-stage conversion system, then co-ordination between DC-DC converter and VSC is required to achieve simultaneous operation of MPPT and real power injection. In this paper, a co-ordinated control of single-stage grid connected SPV and BES system is proposed along with energy management. In which, the algorithm coordinates VSC and bidirectional DC-DC converter based on the State of Charge (SoC) of the battery such that MPPT and power injection is achieved simultaneously. The proposed method not only injecting real power, but also works to compensate load reactive power and unbalanced neutral current minimization. Further, an active rectification operation during non-SPV hours is discussed for better utilization of VSC capacity. The multi-functional features of the proposed method are explained using simulation studies and are also validated through experimental studies.

The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security advancements and systems to battle the expanding complex nature of digital assaults. This paper talks about how machine learning is being utilized in digital security in resistance and offense exercises, remembering conversations for digital assaults focused at machine learning models. In this review, we are proposing a scientific categorization of IDS, which considers information protests to be essential measurements to group and condense IDS Literature based on machine learning and based on profound knowledge. The review explains initially the idea and scientific grade of IDSs. Machine learning calculations are presented at that point for the many time used in IDSs, measurements and presented benchmark datasets. Next, we take the proposed ordered framework as a benchmark in conjunction with the agent writing and explain how to understand key IDS issues with machine learning and profound systems. At long last, difficulties and future advancements are talked about by assessing ongoing agent examines. This paper proposes IDS dependent on highlight determination and bunching calculation utilizing channel and wrapper techniques. Channel and wrapper strategies are named include gathering dependent on direct connection coefficient (FGLCC) calculation and cuttlefish calculation (CFA), separately.

Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)-density changes in the lung following radiotherapy. Radiotherapy planning datasets and follow-up chest CTs were obtained in eight patients treated for targets within the lung or hilar region. Image pixel-wise radiation dose exposure versus change in observable CT Hounsfield units was recorded for early (2–5 months) and late (6–9 months) time-points. Four NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data. The quality of fits was assessed by five statistical criteria. All four models fit the data significantly (p 50 and γ50. The D50 estimates were more robust than γ50 to image registration error. For analyzing population-based sub-clinical CT pixel intensity-based dose response, all four models performed well.

The Portent of data security in cloud setting is well studied. There are number of approaches has been discussed for the data security in multi cloud environments. The previous methods suffer to achieve higher performance in data security and privacy preservation. To improve the performance, an attribute level trusted access measure is presented. The method classifies the user data into several classes and for each class of attributes different access restriction has been enforced. The access between different cloud has been restricted based on the attribute level trusted access measure (ALTAM). The ALTAM measure for each request has been estimated based on the depth of privacy has been accessed by the request and the number of data values the user has access in the cloud and outside the cloud. Based on all these the method estimates the trust measure. According to the trust measure, the method restrict the access of data in multi cloud environments. The method produces efficient result in access restriction and privacy preservation.

The cloud computing (CC) model is diverse as well as has broad phenomena because of which it is being preferred widely. Due to security challenges, the user cannot utilize CC to its full potentials. Storing their sensitive data in an insecure place is not even an option for these users. Many techniques were posited for securely amassing the data in the cloud; however, during massive transmission, none of them maintain end-to-end security. And, prevailing methods are also unable to solve the key intricacy and avoid key secrecy expose. Thus, for trouncing these disadvantages and also assuring the data transmission (DT) as of the source–destination, a secure DT scheme with the help of a distributed cloud server (CS) and data deduplication is proposed. The uploaded user data can well be amassed in disparate CS to ameliorate the user data’s security. It can be done by, first, extracting the features of the user data along with the CS. After that, the Hybrid Meerkat clan algorithm (HMCA) chose the CS, which is optimally centered on the features. Next, the SHA512 algorithm performs the data deduplication of the input user data. Subsequent to data deduplication, the input file is compressed and encrypted using a two-stage lempel–ziv algorithm and optimized CP-ABE-ECC algorithm. Lastly, the encrypted file is stored in the chosen CS. The experimentation is implemented to analyze the proposed method’s performance. The outcomes exhibited that the proposed work performed better contrasted to other prevailing algorithms.

Day-by-day to reduce the greenhouse effects and increase the power generating capacity the dependency of renewable energy systems are increasing. Due to its uncertainty and continuously changing characteristics introducing power quality issues into the power system. The challenging task for electrical engineers is to produce quality of electrical power with less carbon emissions, especially in a weak grid systems. In this paper, built a new featured hybrid technological concept of renewable energy generating resources to reduce the greenhouse carbon emissions. For this, developed mathematical models of two renewable energy sources, proton exchange membrane fuel cell and wind energy system with a squirrel cage induction generator. To enhance the quality of power built a most promising custom power devise, an Artificial Neural Network controller based Unified Power Quality Conditioner. Further, presented performance of an Articial Neural Network Controller based unied power quality conditioner: to handle the voltage variations due to wind fluctuations, to enhance quality of power due to sags and swells.

Digital communication developed into an imperative element of infrastructure now-a-days. Many applications that operate infrastructure systems are internet-centered. Because of the nontrivial risks of information/data leakage, communication in the systems ought to be secured. Many techniques like cryptography are being employed on a large scale to furtively transmit information. But in the data transmission procedure, attaining the high data security along with high system efficiency is a difficult task. To trounce these difficulties, an efficient data transmission scheme utilizing improved advanced encryption standard with Butterfly Effect (IAES-BE) is proposed. The IAES-BE does the encryption procedure to thwart the data as of the attackers. The adaptive flower pollination algorithm (A-FPA) is employed for the key generation that augments the key’s complexity. After that, the generated key is implemented to the IAES-BE for encrypting the input plain-text as ciphertext. Next, the ciphertext is decrypted through revising the IAES-BE. Thereby, the original data will be attained and safely transmitted to the receiver. Centered on the investigational outcomes, the proposed work offered superior performance to the remaining data transmission methods.

Background: The SanketLife is a low cost, portable, pocket sized 12 lead ECG mechanised by SanketLife app running on compatible iOS and Android phones that connect wirelessly via Bluetooth technology to the device. Objective: The current study was conducted to assess the diagnostic accuracy of SanketLife ECG in comparison to standard 12 lead ECG (GE-2000) in detection of cardiovascular diseases. Research design and methods: This was a prospective diagnostic test accuracy trial conducted in outpatient settings of a tertiary cardiac care centre in India. A total of 100 patients, attended cardiology OPD, were included in the study. Consecutive ECGs were taken by 12 lead standard ECG as well as by SanketLife ECG. Diagnostic accuracy variables such as sensitivity, specificity, negative and positive predictive value, negative and positive likelihood ratios were estimated. Ethical permission was taken from the Institutional ethical committee. Results & conclusion: The analysis showed a high degree of agreement and accuracy of SanketLife in detecting major cardiovascular conditions (Major Minnesota codes) such as Left and right bundle branch block, ST-segment elevation and ST-segment depression, AV conduction block. SanketLife showed high sensitivity (98.15%) and specificity (100%) in diagnosing major cardiovascular conditions. Keywords: SanketLife, 12-Lead, Accuracy, Sensitivity, Agreement, Minnesota

Supply chain (SC) disruption management is a complex issue that involves building SC resilience through enhancing the organizational ability to anticipate, adapt, respond, recover and learn from disruptions; it needs participation and coordinated efforts of all SC stakeholders and their processes. There is a need for a qualitative, interpretive, holistic and flexible framework in the literature. The main purpose of this work is to propose a framework. Therefore, the present study uses an interpretive method, namely “situation–actor–process–learning–action–performance” (SAP–LAP) analysis, to enhance the understanding and analyze the SC resilience building and improvement in the case company, ABC, a prominent automaker in India. Findings reveal several issues, including the establishment of risk management culture, building collaborative capabilities, framing flexible contracts, enhancing the awareness level of risks, security, and resilience among stakeholders, seamless information sharing, require attention. The efficacy and simplicity of the SAP–LAP framework may enable SC stakeholders to initiate resilience-building processes.

Medical Image processing has tremendous applications in medical diagnosis. This broadsheet offerings the idea of a fusion of MRI(Magnetic Resource Imaging)-CT (Computed tomography) using Coverlet wavelet transform(CWT), which is used to find the disease location in an image. In the Medical field, CT provides maximum information on denser tissue with less amount of distortion and higher resolution images. Whereas, on the other hand, MRI provides information on softer tissue with much distortion. However, both are similar; the main difference lies where CT uses X-rays to produce images while MRI uses radio-Waves to produce images. This paper presents a fusion by concatenating of images using a coverlet wavelet transform technique. The presentation is estimated on the source of locating the disease in the resultant image. In this research, various image type like MRI, CT, PET, ECT, SPECT models has been collected and apply the fusion process such that calculate the performance analysis parameters like SSIM, PSNR, entropy, CWT, etc. this research consist of processing and classification in the step -1 process the image with fusion model has been implemented, for classification estimate the samples with probabilistic functions. Finally calculated the parameters for disease finding and location estimation such that this research is helpful for disease location estimation and finding. At final achieves the better outcomes compared to existed methods.

The chapter focuses on utilizing a hybrid approach of response surface methodology and dragonfly algorithm for investigations and optimization of the selective inhibition sintering (SIS) process to improve the mechanical strengths such as tensile and flexural of fabricated high density polyethylene parts. The layer thickness (LT), heater energy (HE), heater and printer feedrate (HFR & PFR) are considered as the independent variables for the investigation. The SIS experiments are planned and conducted through a response surface methodology-based box-Behnken design approach to fabricate the test specimens. The optimal SIS parameters are obtained through a swarm intelligence metaheuristic technique namely dragonfly algorithm (DFA). The optimal parameter settings of LT of 0.102 mm, HE of 28.46 J/mm2, HFR of 3.22 mm/sec, and PFR of 110.49 mm/min are achieved through DFA for improved tensile and flexural strengths of 26.21 MPa and 65.71 MPa, respectively. Further, the prediction ability of DFA was compared with particle swarm optimization algorithm.

STUDY OBJECTIVES: Sleep plays a vital role in the cognitive and neurodevelopmental outcome of children; therefore, adequate sleep is needed to improve academic performance. The primary objective of this study was determine the prevalence of sleep behaviors and their effect on academic performance in schoolchildren age 6–12 years and to translate the findings into greater opportunity for healthy development and academic success. METHODS: Our study included 791 healthy children between ages 6 and 12 years from schools that are parts of the Central Board of Secondary Education in the South Indian urban population. Pro forma and The Children’s Sleep Habits Questionnaire (CSHQ in local language translation) was given to the parents, and any doubts regarding the questionnaire were clarified during parent-teacher meetings. The previous 2 cycles of academic grades for these children were collected and categorized into A, B, and C grades accordingly. Then a statistical analysis of the completed CSHQ questionnaires was performed. RESULTS: On analyzing the CSHQ questionnaire, 71.9% of the children studied had a score higher than 41, which is in the clinical range indicating a significant prevalence of altered sleep habits. Other risk factors included nocturnal enuresis (3.5%), snoring (10.6%), night terrors (8%), and teeth grinding (6.4%). CONCLUSIONS: Altered sleep habits have a major effect on the academic performance of the school-age children. Assessment of sleep habits should be included in routine pediatric office visits. CITATION: Sivakumar CT, Rajan M, Pasupathy U, Chidambaram S, Baskar N. Effect of sleep habits on academic performance in schoolchildren age 6 to 12 years: a cross-sectional observation study. J Clin Sleep Med. 2022;18(1):145–149.

BACKGROUND Appendicitis is the most common cause of acute abdomen which requires prompt emergency intervention. Aside from symptomatology and specific physical examination findings, ultrasonography and computed tomography (CT) scans have become the most useful tools, with accuracies up to 85 to 99%. Appendicitis has been shown to have a strong association with hyperbilirubinemia. In the present study, we are evaluating the role of serum bilirubin in acute appendicitis. METHODS A total of 100 patients with clinical diagnosis of acute appendicitis was studied in the Department of General Surgery, Sri Venkateswara Ramnarain Ruia Government General Hospital, S.V. Medical College, Tirupati, during the period November 2016 - October 2017. It is a prospective analytical study. All routine investigations including LFT and TLC were determined in all the cases. All suspected medical conditions which interfere with liver function tests were investigated wherever necessary. The histopathological examination report of the appendix specimen was collected. RESULTS Normal bilirubin values were seen in 30% patients while, 70% had raised bilirubin levels. Hyperbilirubinemia was found in most of the patients diagnosed with both uncomplicated appendicitis (68.5%) and complicated appendicitis (85.7%). CONCLUSIONS Therefore, in our study we conclude that serum bilirubin estimation, a simple, cheap and easily available test can be added as a promising new additional laboratory tool to the routine investigation list of clinically suspected cases of acute appendicitis for the confirmation of diagnosis and for early surgical intervention by predicting complicated appendicitis.

Background: Optimal management of gastroesophageal reflux disease (GERD) requires a concerted team of physicians rather than an individual approach. While an integrated approach to GERD has previously been proposed, the practical execution of such a “center of excellence” (COE) has not been described, particularly in a community setting. Ranging from initial consultation and diagnosis to surgical intervention for complex disease, such an approach is likely to provide optimal care and provide surveillance for patients with a complex disease process of GERD.Methods: We report our approach to implement an integrated heartburn center (HBC) and our experience with the first cohort of patients. Patients treated in the HBC were followed for 2 years from initial consultation to completion of their appropriate treatment plan, including anti-reflux surgery. The performance prior to the HBC set-up was compared to that post-HBC. Performance was measured in terms of volume of patients referred, referral patterns, length of stay (LOS), and patient health-related quality of life (HRQL) pre- and post-surgery.Results: Setting up the HBC resulted in referrals from multiple avenues, including primary care physicians (PCPs), emergency departments (EDs), and gastroenterologists (GIs). There was a 75% increase in referrals compared to pre-center patient volumes. Among the initial cohort of 832 patients presenting to the HBC, 20% had been on PPIs for ≥12 years. Thirty-eight patients were found to have Barrett's esophagus (4.6%) (up to 10 times the general population prevalence). Two patients had dysplasia. Seven patients (0.8%) received radiofrequency ablation (RFA) for Barrett's esophagus and two patients received endoscopic mucosal resection (EMR) for Barrett's esophagus-related dysplasia. The most common comorbidities were chronic pulmonary disease (16.8%) and diabetes without complications (10.6%). Patients received treatment for newly identified comorbid conditions, including early maladaptive schemas (EMS) and generalized anxiety disorder (GAD) (n = 7; 0.8%). Fifty cases required consultation with various specialists (6.0%) and 34 of those (4.1%) resulted in changes in care. Despite the significant increase in patient referrals, conversion rates from diagnosis to anti-reflux surgery remained consistent at ~25%. Overall HRQL improved year-over-year, and LOS was significantly reduced with potential cost savings for the larger institution.Conclusions: While centralization of GERD care is known to improve outcomes, in this case study we demonstrated the clinical success and commercial viability of centralizing GERD care in a community setting. The integrated GERD service line center offered a comprehensive, multi-specialty, and coordinated patient-centered approach. The approach is reproducible and may allow hospitals to set up their own heartburn COEs, strengthening patient-community relationships and establishing scientific and clinical GERD leadership.