Search

This present study deals with the facile synthesis of Zinc (Zn), Strontium (Sr) Co‐substituted hydroxyapatite (M‐HAP)/lignin composite for biomedical applications. For composite formation, HAP was isolated from egg shell through wet precipitation method, biopolymer of lignin was extracted from neem wood through organosolve technique then HAP composites (HAP, HAP/lignin, Zn‐HAP/lignin, Sr‐HAP/lignin, Zn, Sr‐HAP/lignin) were obtained using freeze drying performance. The synthesized HAP composites were characterized using X‐ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and Energy‐dispersive X‐ray analysis (EDAX). The antibacterial behavior of the HAP composites was investigated against E. Coli, B. Subtilies, P. Aeruginosa and S. Aureus pathogens. Then the mechanical property of the HAP composites was carried out using Vicker's microhardness test which reveals a better improvement in mechanical hardness. The antibacterial activity confirms that the HAP composite exhibits enhanced antibacterial activity. Further, the cell viability analysis shows that the co‐substitution of Zn and Sr in HAP lattice improved cell viability analysis, which indicates that the obtained Zn, Sr co‐substituted HAP/lignin biocomposite acts as a potential biomaterial for better biomedical application. [ABSTRACT FROM AUTHOR]

Intelligent technology and devices have affected nearly every aspect of human lives. The type of connection and means of communication among such a huge number of devices has led to development of Internet of Things (IoT), broad sector that has considerably increased awareness of the problem of energy management and lengthened the lifespan of networks. The design of networks has been made more difficult by complex communications. In this paper, a Hybrid Multipath Routing Cluster head prediction based on software defined networking enabled IoT and Heterogeneous context-aware graph convolution network Using Eagle Swarm Optimization (SDN-IOT-HCAGCN) is proposed. In this method, cluster is formed by coati optimization and cluster head selection by Heterogeneous context-aware graph convolution network, after that Eagle Swarm Optimization is used for multipath routing. The simulations are executed in MATLAB. The proposed technique is evaluated through different metrics, likes delay, number of dead nodes, packet delivery ratio, number of alive node, energy consumption and network life time. The proposed SDN-IOT-HCAGCN model provides 34.49%, 26.44% and 20.51% highest number of alive sensor nodes, 39.45%, 22.44% and 30.24% lowest number of delay when analyzed with the existing models, like Intelligent Energy-Aware Routing Protocol in Mobile IoT depending upon SDN (SDN IEAR-IOT-SDN), Edge-based Optimal Routing in SDN-enabled Industrial Internet of Things (EOR-SDN-IOT), Cooperative Self-scheduling routing protocol basis IoT communication for increasing lifetime duty cycled energy efficient protocol in SDN controlled embedded network (CSR-IOT-SDN) respectively. [ABSTRACT FROM AUTHOR]

In this research, we proposed a Terahertz (THz) refractive index-based Hollow-Core Photonic Crystal Fiber (HC-PCF) biosensor for examining various brain cancerous tissues. Six design variants with cladding segments ranging from 4 to 16 are analyzed using the finite element method (FEM). The biosensor demonstrates high sensitivity (94.9 to 97.46%), minimal Effective Mode Loss (EML) of 0.00246 cm−1with an effective mode area of 2.84 × 10−8 m2 and a power core ranges from 93% to 95.9% for the 16-segment cladding. The second contribution involves applying machine learning (ML), utilizing Autoencoder Augmentation Network (AEAN) for data augmentation and Bayesian Ridge Regression Multioutput Regressor (BRRMOR) for rapid prediction of biosensing parameters. The effectiveness of the ML model is demonstrated with a high r2 score of 0.992 for unknown HC-PCF structures, showcasing computational efficiency compared to Finite Element Method simulations. [ABSTRACT FROM AUTHOR]

Purpose: RebiSmart® is an electromechanical multidose autoinjector developed for administering subcutaneous interferon beta-1a in patients with multiple sclerosis (pwMS). This online survey aimed to understand MS nurses' and pwMS preferences and perceptions regarding the features of an upgraded version of the RebiSmart device (RebiSmart 3.0) compared to other assistive devices used for multiple sclerosis (MS) therapy. Patients and Methods: Eligible MS nurses and pwMS from Germany, Italy, and the United Kingdom completed a double-blind, 30-minute online self-administered questionnaire, including a 10-minute video describing the features of RebiSmart 3.0 and its use in administering interferon beta-1a. Results: In total, 102 participants (MS nurses, n=52; patients, n=50) completed the survey. Overall, 70% respondents found the RebiSmart 3.0 device "very"/"extremely" appealing, 53% were "very"/"extremely" interested in learning more, and 71% stated they would be "very"/"extremely" comfortable using (pwMS) or educating (MS nurses) on it. Among current or recent RebiSmart 2.0 users (vs RebiSmart 2.0 nonusers), 67% (vs 52%) rated RebiSmart 3.0 "very" or "extremely" appealing, 52% (vs 43%) were "very" or "extremely" interested in learning more about the device, and 67% (vs 48%) stated they would be "very" or "extremely" comfortable using the RebiSmart 3.0 device. Respondents ranked customizable injection process (including injection speed, hold time, depth and rotation guide), self-injection process, and hidden needle as the most important self-assistive device features. RebiSmart 3.0 was rated higher than other self-injecting devices on all tested features. Overall, with respect to the top three features, 89% of the MS nurses and 73% of PwMS rated RebiSmart 3.0 "very good" or "excellent". After reviewing the video, 52% respondents had no questions, 67% nurses recommended providing more information on the customizable injection process feature of RebiSmart 3.0 to patients, and 88% nurses considered patient demonstration materials to be the most helpful type of information for them when initiating and educating pwMS on self-assistive devices. Conclusion: The overall reactions of MS nurses and pwMS to the RebiSmart 3.0 device features were positive. The incremental advances over previous versions of the device as well as in comparison with other currently available assistive devices were welcomed. The MS nurses identified key needs for patient education on the use of the device and the suitable approaches (training videos and educational leaflets) to support MS nurses and pwMS. [ABSTRACT FROM AUTHOR]

The use of scavenged tissue for reconstruction of an injured limb, called as "spare-part surgery. It allows primary reconstruction of injured part without harvesting tissue from other regions of the body and without further donor morbidity. The aim of this study is to evaluate the clinical experience and outcomes of spare parts surgery in mutilating hand injuries. Total 12 patients were included in this study from January 2022 to January 2023. In 7 patients bone from amputated part used as bone graft, 4 patients avulsed skin from amputated distal part was used as skin graft. In one patient digital nerve used as spare part for nerve. Outcomes were measured. In 75% of patients the spare part used was survived without any complications. 5 out of 7 patients with bone graft, bone graft was viable with maintenance of digit length. 3 out of 4 patients in whom skin graft was done, skin graft settled well. In 1 patient in whom digital nerve was used as nerve graft, Tinel's sign progressed and regain of touch sensation was noted in 6 months follow up. Complications were noted in 3 patients (25%). Re-intervention was done in 2 patients (16%). [ABSTRACT FROM AUTHOR]

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In this research, the tri-stage wrapper method is used because it reduces the computation time. The proposed study for the Automatic diagnosis of PCOS contains preprocessing, data normalization, feature selection, and classification. A dataset with 39 characteristics, including metabolism, neuroimaging, hormones, and biochemical information for 541 subjects, was employed in this scenario. To start, this research pre-processed the information. Next for feature selection, a tri-stage wrapper method such as Mutual Information, ReliefF, Chi-Square, and Xvariance is used. Then, various classification methods are tested and trained. Deep learning techniques including convolutional neural network (CNN), multi-layer perceptron (MLP), Recurrent neural network (RNN), and Bi long short-term memory (Bi-LSTM) are utilized for categorization. The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method + CNN delivers the highest precision (97%), high accuracy (98.67%), and recall (89%) when compared with other machine learning algorithms. [ABSTRACT FROM AUTHOR]

The present study was done to study the extent of diversity among the released and popular varieties of India and advanced cultures in groundnut. D² analysis for 15 characters resulted in the formation of nine clusters among the 30 genotypes studied. Highest intra-cluster distance was recorded for cluster VI followed by III, V, II and VII. Maximum inter-cluster distance was recorded between clusters VIII and IX followed by between clusters III and IX and between clusters VII and VIII. Based on mean performance, intra and inter-cluster distance, the genotypes of cluster VI (ALR 1 and COG0539) could be used for increasing the shelling per cent, oil content, pod yield / plant and no. of primary branches / plant. The genotypes in cluster III (CO 1, COG0549 and BSR 2) can be used for improving number of mature pods/ plant, hundred kernel weight, leaf area index and cluster VIII (JL 24) for total biomass and kernel yield / plant for trait based improvement programme. [ABSTRACT FROM AUTHOR]

A new sampling plan by integrating the resampling concept in a skip-lot sampling plan of type SkSP-3 with a single sampling plan as the reference plan is proposed and designated as SkSP-3-R plan. The performance metrics are derived by using the Markov chain formulation. A designing methodology using two points on the operating characteristic curve approach is proposed to determine the plan parameters of the SkSP-3-R plan involving minimum the average sample number. The execution of the proposed SkSP-3-R plan is discussed with an illustrative example. The comparative study shows that the proposed plan has better performance where the inspection is involved in terms of nonconforming items rather than nonconformities. In addition, it demonstrates that the proposed SkSP-3-R plan is more efficient than SkSP-2 plan, SkSP-3 plan, SkSP-2-R plan, and SSP. Based on average run length, the SkSP-3-R plan's characteristic of response-to-change in quality is also investigated. [ABSTRACT FROM AUTHOR]

Sampling plans developed based on the process capability indices have great impact in assuring the quality of procured goods since such indices are widely applied in industries to monitor the process quality. In this manuscript, a modified double sampling plan is proposed for variables inspection based on the process capability index Cpk. The proposed sampling plan, an alternative to the existing double sampling plan, can be used with less complexity for the inspection of normally distributed quality characteristics when both mean and variance are unknown. To determine the optimal parameters of the proposed plan for symmetric and asymmetric cases of fraction nonconforming with double specification limits, we use the methodology of two points on the operating characteristic curve. It is shown that the proposed plan is more advantageous than other existing sampling plans under variables inspection. Finally, an economic aspect of design of the proposed sampling plan is also presented. [ABSTRACT FROM AUTHOR]

Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm (IWDA) combined with an efficient machine learning approach-Support Vector Regression (SVR) for task failure prognostication which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications. The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows. The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques. [ABSTRACT FROM AUTHOR]

Collaborative learning using Active Learning Strategies (ALS) and Information Communication Technology (ICT) tools in programming subjects facilitate the studentsinstructors into effective ways to make everyone engaged in Teaching Learning Process. In such learning, learners required to respond one another’s ideas. Teachers are the key players in collaborative learning process by launching the learning environment, accommodating student requirements and ensuring the classroom activities. In this study, benefits of ALS and ICT tool usages have been analyzed for the Object-Oriented Programming course offered to engineering undergraduates. The impacts are analyzed in terms of teaching methodology, practicing lab, pass percentage, course outcome attainment, internal and terminal test performances and course exit survey. The outcomes indicate that, ALS and ICT approach yields positive result in performance. [ABSTRACT FROM AUTHOR]

The aim of this article is to introduce and investigate a new subclass of analytic functions involving Gegenbauer polynomials. We obtain for the introduced class various geometric properties giving the coefficient inequalities, distortion theorem, radius of close-toconvexity, starlikeness, convex linear combination, partial sums and convolution properties. Further, we obtain a neighborhood result for the class defined in the present paper. [ABSTRACT FROM AUTHOR]

The aim of this article is to introduce and investigate a new subclass of analytic functions involving Gegenbauer polynomials. We obtain for the introduced class various geometric properties giving the coefficient inequalities, distortion theorem, radius of close-toconvexity, starlikeness, convex linear combination, partial sums and convolution properties. Further, we obtain a neighborhood result for the class defined in the present paper. [ABSTRACT FROM AUTHOR]

This paper deals with applying stacking dilated convolutional autoencoder beamforming for Terahertz wave Vehicular ad-hoc networks. A novel solution is proposed to determine the vehicle's position by big data techniques and deep learning algorithms. Novelty in the solution is obtained via stacking dilated convolutional autoencoder and it is used to train the vehicle traffic datasets.The vehicles direction information is stored in the database for beam alignment. Beam alignment process is used to find the location of vehicles from the database. Multi resolution codebook based beamforming is introduced for finding the vehicles direction information using beam alignment. The proposed terahertz band vehicle propagation model is employed in Vehicular ad-hoc network and results are obtained to determine accuracy and loss curve in multiple operating environments. This model is used to find the vehicles location information using stacking dilated convolutional autoencoder and codebook based beam alignment. Using the traffic dataset, the vehicles' location information is trained using deep learning algorithm. The dataset is useful for finding out the vehicle's location information using codebook-based beam alignment. In this article, the vehicles misinformation detection algorithm is also proposed and it is analyzed, where the proposed method gives improved performance in comparison to the existing method with random selection thereby contributing to achieve secure transmission. The research results obtained through this novel approach can be used as benchmark for analyzing and developing TeraHertz Vehicular-Adhoc Networks. [ABSTRACT FROM AUTHOR]

In this paper, we fabricate and demonstrate Molybdenum Tungsten Disulfide (MoWS2) composite based all-optical gas sensor for environmental monitoring applications. This proposed sensor is fabricated by coating the synthesized MoWS2 nanosheets on a side polished fiber (SPF) and analyzed the sensing performance of three important hazardous gases such as ammonia (NH3), Nitrogen dioxide (NO2) and Carbon monoxide (CO). The MoWS2 nanosheets are synthesized using the hydrothermal exfoliation technique. This novel nanocomposite offers broadband absorption and desorption of gas molecules due to more edge sites, which in turn enhances the sensing properties for various gases. Thus, the MoWS2 coated SPF sensor utilized for sensing three hazardous gases such as NH3, NO2 and CO. Besides, the gas sensor is operated at normal humidity (60–75%) and room temperature for varying gas concentration from 0 to 500 ppm. The sensor shows high sensitivity of 86, 72, and 48% for various gases such as NH3, NO2 and CO respectively. Furthermore, the results exhibit rapid response and fast recovery time, which are measured as 21 s and 60 s for NH3 gas, whereas 42 s and 71 s for NO2 gas and 93 s and 56 s for CO gas. The selectivity test for the sensor is conducted with methane and formaldehyde along with NH3, CO, and NO2 gases in the same experimental condition. Further, the results confirm that the sensor possesses a high degree of selectivity for NH3 gas among the other gases. Hence, the proposed sensor is eminently suitable to detect hazardous gases in high traffic and industrial fields. [ABSTRACT FROM AUTHOR]

The present study was carried out in the Department of Oilseeds, during rabi, 2021 at Tamil Nadu Agricultural University, Coimbatore to study the genetic variability, correlation and path analysis using thirty promising genotypes of groundnut. Kernel yield and its component characters were recorded. The magnitude of PCV and GCV values were higher for the characters viz., number of branches, total biomass, 100 kernel weight, leaf area ratio and leaf area index. Total number of branches, total biomass and 100 kernel weight exhibited high heritability and high genetic advance as per cent of mean. Kernel yield showed significant positive association with pod yield, mature pods, number of pods and shelling percentage. The importance of these characters was also confirmed through path analysis, as they had direct implications on kernel yield. As a result, selection based on these traits will result in increased kernel yield in groundnut. [ABSTRACT FROM AUTHOR]

A photonic crystal fiber (PCF) structure which offers exceptional research prospects to design sensors is eccentrically found applicable in wide variety of fields and thus have prompted a lot of interest among researchers. However, intending to determine PCF design configuration producing desired optical response for worthwhile application forcefully necessitates investigating vast search space with suitable topological structure variants. Moreover, the existing Finite Element Method (FEM) based numerical simulation software demands intensively long computation time for each set of design parameters with quite several repetitions and is a challenging task with reduced computational complexity. In this regard, use of Deep Neural Networks (DNN) paves way to predict the outcome in less time. One of the most significant challenges encountered while training a neural network is to generate an extensive data set. With the motive of compensating for the same issue, we propose the use of Autoencoder (AE) network to achieve data augmentation. In this research, a pioneering approach to predict optical parameters of PCF based temperature sensor using AE and DNN is presented. The proposed model is designed to make appropriate predictions of optical properties even for unknown design space parameters. The comparative metric analysis explores the efficient performance of the model with high values of R-squared ($r^{2}$) score and less computation time in contrast to simulation run-time of FEM. Moreover, the proposed DNN model along with AE is proved to show very low collective mean squared error (MSE) in contrast to DNN without AE. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to introduce a new subclass of meromorphic functions with positive coefficients defined by a Hurwitz-Lerch zeta function and obtain coefficient inequality. convex linear combinations. extreme point. radii of close-to-convexity, starlikeness. convexity. Hadamard product and integral transforms for the functions f in this class. [ABSTRACT FROM AUTHOR]

Background: The aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) among them using WHO/ISH charts. Methods: All the people aged ≥ 30 years attending the health centers were screened for DM and HT. Weight, height, waist circumference, and hip circumferences were measured, and BMI and Waist-Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score, and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden's index were calculated for different cut-offs of IDRS and CBAC scores. Results: A total of 942 participants were included for the screening, out of them, 9.2% (95% CI: 7.45–11.31) were diagnosed with DM for the first time. Hypertension was detected among 25.7% (95% CI: 22.9–28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score > 4. As much as 26.1% were at moderate to higher risk (≥ 10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58–0.70), with 67.1% SN and 55.2% SP (Youden's Index 0.22). While the AUC for CBAC was 0.59 (0.53–0.65). For hypertension both the AUCs were 0.66 (0.62–0.71) and 0.63 (0.59–0.67), respectively. Conclusions: IDRS was found to have the maximum AUC and sensitivity thereby demonstrating its usefulness as compared to other tools for screening of both diabetes and hypertension. It thus has the potential to expose the hidden NCD iceberg. Hence, we propose IDRS as a useful tool in screening of Diabetes and Hypertension in rural India. [ABSTRACT FROM AUTHOR]

Background: The aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) among them using WHO/ISH charts.Methods: All the people aged ≥ 30 years attending the health centers were screened for DM and HT. Weight, height, waist circumference, and hip circumferences were measured, and BMI and Waist-Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score, and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden's index were calculated for different cut-offs of IDRS and CBAC scores.Results: A total of 942 participants were included for the screening, out of them, 9.2% (95% CI: 7.45-11.31) were diagnosed with DM for the first time. Hypertension was detected among 25.7% (95% CI: 22.9-28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score > 4. As much as 26.1% were at moderate to higher risk (≥ 10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58-0.70), with 67.1% SN and 55.2% SP (Youden's Index 0.22). While the AUC for CBAC was 0.59 (0.53-0.65). For hypertension both the AUCs were 0.66 (0.62-0.71) and 0.63 (0.59-0.67), respectively.Conclusions: IDRS was found to have the maximum AUC and sensitivity thereby demonstrating its usefulness as compared to other tools for screening of both diabetes and hypertension. It thus has the potential to expose the hidden NCD iceberg. Hence, we propose IDRS as a useful tool in screening of Diabetes and Hypertension in rural India. [ABSTRACT FROM AUTHOR]

In this paper we introduce and study a new subclass of meromorphically uniformly convex functions with positive coefficients defined by a differential operator and obtain coefficient estimates, growth and distortion theorem, radius of convexity, integral transforms, convex linear combinations, convolution properties and δ−neighborhoods for the class σ(α, s, b). [ABSTRACT FROM AUTHOR]

The aim of present study was to assess the fluoride (F) content in the irrigation water, soil, and crop plants in the Amangal mandal of Rangareddy district, Telangana state, India. The F content in the irrigation water ranged from 0.27 to 4.40 ppm with a mean value of 1.75 ppm during the kharif (monsoon) season, while in the rabi (post monsoon) season, it was between 0.68 to 4.61 with an average of 2.22 ppm. The F concentration in the irrigation water was higher in the rabi season compared to the kharif season and found to be significantly positively correlated with pH, bicarbonate, sodium, sodium adsorption ratio, and residual sodium carbonate, and negatively with calcium. The available F content in the soils during the kharif season ranged from 0.04 to 0.29 ppm with an average of 0.13 ppm and during the post rainy season the range was 0.05 to 0.24 ppm with mean value of 0.14 ppm. The available F in the soil was significantly positively correlated with the soil pH and exchangeable sodium. Among the crop plants and their parts, the F concentration was lowest in the rabi green gram seed (0.008 mg/kg) and highest in the kharif spinach crop root (0.084 mg/kg). Although the F concentrations were higher than the maximum permissible limit (1.0 or 1.5 ppm) in most of the groundwater samples, the available F was low in all the soil samples collected from the farmers' fields irrigated with the groundwater. The F concentrations in the root, shoot, and edible portions of all the fifteen crops studied were well below the toxic limits. [ABSTRACT FROM AUTHOR]

The replacement of sensor node battery in wireless sensor network (WSN) is considered to be the highly crucial task in hostile environments. The process of partitioning the region of sensing in WSNs into clusters is determined to be the ultimate solution for attaining maximized network lifetime and gaining high energy efficiency. However, proper selection of CHs plays an anchor role in enhancing the network lifetime, since they need to utilize addition energy to handle the task of data gathering and data aggregation from the sensor member nodes of clusters and finally disseminate them to the base station.In this paper, a Hybrid Artificial Bee Colony (ABC) and Harmony Search Algorithm-based Metaheuristic Approach (HABC-HSA-MA) is proposed for attaining efficient CH selection with the view to sustain stable energy utilization with enhanced network lifetime. This HABC-HSA-MA includes the global optimization potential of Harmony Search Algorithm (HSA) and local exploitation potential of the classical ABC algorithm for achieving significant CH selection for stabilizing energy and extending network lifetime. It also incorporated the benefits of harmony adjusting factor for inducing the process of improving the dynamic search efficiency during the process of CH selection in order to prevent worst candidates from being selected as CHs. The simulation results of the proposed HABC-HSA-MA confirms an enhancement in mean network lifetime of 23.64% and balanced average energy consumption rate of 20.28% compared to the benchmarked hybrid meta-heuristic CH selection schemes. [ABSTRACT FROM AUTHOR]

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can be utilized for earlier evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the undersampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. [ABSTRACT FROM AUTHOR]

Concurring to information from the Centres for Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result shows that Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset. [ABSTRACT FROM AUTHOR]

Customized Quality of Service (QoS) and Quality of Experience (QoE) prediction play an important role in helping users to utilize high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address trustworthy of QoES (Quality of Service and Experience) values, which is insist on unreliable centralized prediction frameworks, that leading to an imprecise selection of web service. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, trusted environment, etc., we propose an extended Personalized and Generalized fuzzy TOPSIS approach for QoES prediction over ethereum blockchain to remove unreliable services. Then, the Blockchain-based extended fuzzy TOPSIS approach is employed to evaluate the proposed approach on real-world web services datasets. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than conventional techniques. [ABSTRACT FROM AUTHOR]

A series of Isatin derivative Schiff base ligands have been prepared by the nucleophilic addition of 5-Bromo isatin with various amine derivatives and characterized by CHNS analysis and spectral data. Similarly, two of salicylaldehyde ligand have been prepared by the nucleophilic addition of Salal with amine derivatives. In order to investigate the coordination behavior of these ligands and their metal complexes of the type M(acac)x, L [M = Cu(II), Ni(II); L = Schiff base ligands; x = 0 or 2] mixed ligand (chelate) have been prepared from the reaction of these ligands with their corresponding metal (Ni, Cu) acetylacetonates. The present paper was an approach to understand the chelating mixed ligand formation in complexes. All the isolated Shiff base ligands and mixed acac metal complexes were characterized by using IR, 1 H NMR, UV-Vis, molar conductance and TGA/DTA analysis. The biological activities of all the isolated ligands and their corresponding mixed acac metal complexes have been used to screening against the microorganisms both Gram-positive and Gram-negative bacteria such as E.coli and S.sureus respectively, fungi A. niger and C.albicans and the results have been compared with standard and control. The main idea of these types of biological screening is to understand the role of these isolated compounds in pharmaceutical industries for drug development. [ABSTRACT FROM AUTHOR]

Heart disease, known interchangeably as "Cardio Vascular Disease," blocks the blood vessels in the heart and causes heart attack, chest pain, and stroke. Heart disease is one of the leading causes of morbidity and mortality worldwide and it is one of the major causes of morbidity and mortality globally and a trending topic in clinical data analysis. Assessing risk factors related to heart disease is considered as an important step in diagnosing the disease at an early stage. Clinical data present in the form of electronic health records (EHR) can be extracted with the aid of machine learning (ML) algorithms to provide valuable decisions and predictions. ML approaches also play a vital role in early diagnosis and therapeutic monitoring of heart disease. Several research works have been carried out recently to predict heart disease. To this end, we propose a novel hybrid recurrent neural network (RNN)‐logistic chaos‐based whale optimization (LCBWO) structured hybrid framework for predicting heart disease within 5 years using EHR data. Meanwhile, in the hybrid model established multilayer bidirectional LSTM is used for feature selection, LCBWO algorithm for structural improvement and fast convergence, and LSTM for disease prediction. This research used 10 cross‐validations to obtain generalized accuracy and error values. The findings and observations provided here are focused on the knowledge obtained from the EHR report. The results show that the proposed novel hybrid RNN‐LCBWO framework achieves a higher accuracy of 98%, a specificity of 99%, precision of 96%, Mathews correlation coefficient of 91%, F‐measure of 0.9892, an area under the curve value of 98%, and a prediction time of 9.23 seconds. The accurate predictions obtained from the comparative analysis shows the significant performance of our proposed framework. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during kharif, 2018 at College Farm, College of Agriculture, Rajendranagar, Hyderabad in strip plot design with four agronomic management options for mitigating the effects of saline irrigation water which were taken as main plots and evaluated with three popular rice varieties of Telangana and one saline tolerant check variety which were transplanted in sub plots and replicated thrice. The CSR 36 variety has recorded highest plant height (cm) and number of tillers per hill at harvest fb RNR 15048. In situ green manuring recorded higher LAI over application of FYM, leaching treatment and control in all the varieties. The highest dry matter production (kg ha-1) and panicles m-2 was recorded by CSR 36 under all the management practices and it was fb RNR 15048 with in situ green manuring. Significantly higher number of grains panicle-1 was registered by RNR 15048 under in situ green manuring over other interactions. Compared to control all the varieties, except JGL 11118 showed higher test weight with in situ green manuring and FYM application. The highest grain yield was recorded in CSR 36 fb RNR 15048 under in situ green manuring respectively. [ABSTRACT FROM AUTHOR]

Introduction: Physical inactivity has various harmful effects but in case of chronic diseases patients perceive various barriers for physical activity. This study aims to find such barriers in Chronic Kidney Disease patients. Methodology: This is an observational study conducted in dialysis unit of a tertiary care hospital with 104 both female & male patients of age group 40–60 years undergoing hemodialysis (HD) at least for past 6 months. Patients with recent MI, stroke & with physical impairment were excluded. Consented patients participated .Human Activity Profile Questionnaire (HAP) was used to assess the patient’s physical activity level and patient related perceived barriers for physical activity . Result: Among 104 patients, male-female ratio was 62:42, mean age was54.56 years, mean dialysis vintage was 27.85 months (range 6-120 months). Hypertension (91.3%), Diabetes (53.8%) were most frequent chronic co-morbidities. According to MAS and AAS of HAP, 55.8%, 43.3%, 1% patients were respectively impaired, moderately active & active. The most frequently reported barriers were fatigue or tiredness (54.8%), shortness of breath (51.9%) (p 0.03), fear of getting hurt (22.1%), body pain (21.2%). Other reported barriers are joint pain, family protection, lower limb swelling, foot ulcer, chest pain, less self confidence, anxiety, overstressed, depression. Shortness of breath is the most influential barrier and statistically significant (p0.04). Conclusion: There were a number of patient related perceived barriers for physical activity were identified and among that fatigue & SOB were found to be influential factors. [ABSTRACT FROM AUTHOR]

The autonomic dysfunction of heart and persistent tachycardiais seen with reduced functional capacity following surgical revascularization. Evidence on the role of exercise training is limited in sympathovagal dysfunctions. This Randomized controlled trail included stable post coronary bypass surgery patients in control (13) and study group (12). The study group received thrice/week training for 12 weeks after discharge and the control group received a routine care. The R-R Interval Variability and the functional capacity (6-minute walk test) assessed before and after 12 weeks of the training. The study group had increase in functional capacity and a positive correlation between the RRIV analysis and functional capacity after training (p<0.05). Phase II exercise training after coronary bypass surgery had a significant improvement of the functional capacity. There was a significant positive correlation between R-R interval variability and the functional capacity in the training group. [ABSTRACT FROM AUTHOR]

Background: Diabetes prevalence and complications are increasing which emphasizes the need of management in various possible ways. Physical inactivity tends to decrease the functional capacity. Similarly, Physical activity and sleep are inter related. Due to dearth of details on the physical activity level, functional capacity, sleep debt levels and their relationship in diabetic population, this study was taken. Objective: To measure Physical Activity level, Functional capacity, Sleep debt and the relationship between them in type 1 and type 2 Diabetics. Method: In this observational study on diabetic patients of 30 -65 years age without any diabetic complications, the Global Physical Activity and Sleep Inventory Questionnaire were used to evaluate Physical Activity and Sleep debt. Functional capacity was assessed by 6-minute walk test. Body Mass Index, Waist Hip ratio, Blood sugar were measured. Data were analyzed by Mann-Whitney U test, Chi-square test, Odds ratio, Spearman correlation method using SPSS software 15.1version. Results: The physical inactivity was higher in type 1 diabetic patients (93.33%) than type 2 (69.67%). By odds ratio, type 1 diabetic individuals had more risk of Sleep debt. Patients with low Physical Activity level had more Sleep debt and there was significant (p< 0.05) negative correlation between Physical Activity level and Sleep Inventory score in type2 diabetics. Conclusion: Type 1 diabetic had low physical activity level in spite of having functional capacity similar to type 2. Sleep debt was more in type 1 diabetic and individuals with low physical activity level. Thus difference exist between type 1 and 2 diabetes in the physical activity and sleep debt, which needs to be considered during exercise training diabetic management. [ABSTRACT FROM AUTHOR]

Background: Physical activity level among young adults are grossly reduced due to urbanization and tremendous increase in technology and this has led to increase in changes in body fat composition and functional capacity. Physical inactivity poses various threats to health of an individual. The relationship between body composition, physical activity and functional capacity needs to be understood and studied hence this study was taken up. Objective: To find the association of body composition with physical activity and functional capacity. Methodology: An observational study with sample of 150 both men and women of age group between 18–30 years. Anthropometric measurements were taken like Body Mass Index, Waist-Hip ratio, Skin fold, % body fat. Physical activity level was assessed using IPAQ long form and functional capacity was assessed by 6 minutes walk test. Results were analyzed by Regression analysis and Pearson's correlation method by SPSS software 23.0 version. Results: In this study we observed about 92% participants were moderately active and 4% were highly active and 4% had low physical activity. The mean 6minutes distance achieved by male is 645.03 ± 57.498 and female is 566.22 ± 64. There is a significant effect of body composition on functional capacity, the Body Mass Index being the most influential factor. Conclusion: This study concludes that in college students Body Mass Index was the most influential factor for functional capacity. There is a need to create an awareness about physical activity among college students and need for regular fitness assessment for this group of population. [ABSTRACT FROM AUTHOR]