Search

The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

In the present digital world, Information and Communication Technology along with Internet of Things are considered as backbone for the smart city evolution. IoT is concentrated more nowadays, due to the convergence of images on Information Technology and human behaviour known as Operation Technology. It is in the progressing stage with notion on cyber-physical world, where things are facilitated to satisfy feasible needs. Similarly, Internet of Medical Things is gaining attention due to the current COVID-19 situation. It has gifted numerous sensors and devices with the increased accuracy, productivity and reliability to the healthcare industry. Increasing the sensor devices in smart city, results in the handling of large amount of data. Therefore, big data is focused due to their characteristics such as volume, variety, velocity and veracity. Extraction of hidden correlations and insights from data is performed through big data analytics or big data value chain. Such concepts are integrated with Artificial Intelligence and Machine Learning technologies which are more important in collecting the real-time data collection and helps in gaining knowledge on how the smart cities and their applications in health sector evolves to adapt the conditions. The main advantage on Big Data Analytics is that it helps in commonality along with heterogeneity principles. Even though cities are digitalized, urban health is still limited to IoT devices which fetch sensor data, eliminating the inclusion of human anatomy and biological data. Integration of these two data forms will help in attaining the target of Big Data and leads to the contextualized, sustainable and resilient smart cities which renders more living factors. This chapter will focus on role of Big Data in smart health applications which plays a vital role in safeguarding the human lives.

This article discusses a transformer-free, high-efficiency DC-DC converter besides renewable energy applications. The traditional buck-boost, classic Zeta, Sepic, and Cuk converter does have the benefits of a simple design, low cost, as well as the capacity to execute voltage step-up and step-down. Conversely, because of the detrimental consequences of the parasitic constraints of the device, the voltage conversion gain of the traditional DC-DC converter is much more restricted and the efficiency is also significantly smaller, whereas this proposed converter does have a higher voltage gain and efficiency because it is used in a single power switch, resulting in reduced switching losses and voltage stress. The said converter's design is very simple, which simplifies the operation control and reduces switching and conduction losses, leading to an efficiency of 97.4 percent. This converter seems to have the same capabilities as the Zeta converter, including continuous desired output current and desired buck-boost operation. Such an article offers the operation principle and steady evaluation, as well as a comparison with other existing high step-up configurations. The proposed converter employs a fuzzy logic controller, which improves the voltage level as well as reduces the time taken to set the voltage output of a conventional PI and ANN controller, especially in comparison to the FLC controller. For deployment, Experimental Result and MATLAB/Simulink has been used, and the modeling results indicate that the proposed controller performance has improved

Past few decades, nanostructures are useful in the active region and light-harvesting layers in thin film solar cells. In this work, the high reflectance alternative Si/SiO2 distributed Bragg reflectors (DBRs), and aluminum (Al) thin film layers were integrated as a backside reflectors in ultrathin film silicon solar cells to improve the light trapping mechanism using finite-difference time-domain (FDTD) method. DBR is known as one-dimensional photonic crystal (1DPC) used as backside reflectors with the addition of SiO2 buffer layer. Further, the transverse electric (TE) and magnetic (TM) field distribution shown the strong light scattering in the silicon active layer region by integration of top (ITO) and bottom (Al) nanogratings. Both polarization conditions yielded the highest current density (Jsc) of 22.03 (TE) and 30.42 mA/cm2 (TM) achieved within 50 nm active region thickness.

The concept of converting seawater into fresh drinking water should also be applied, which is feasible with the aid of the “Solar Still.” It is eco-friendly and cost-effective, but is not common due to its lower performance. Enormous research work is being conducted to increase the quality of the “Solar Still.” Various methods and their productivity are explored here, which are followed by researchers around the world. The most commonly employed methods used by researchers is focused on Phase Change Material and Energy Storage Material, which would generate distillate during the day. The specification of Phase Shift Materials and Energy Storage Materials is based on thermal conductivity. The cooling of the exterior surface of the glass cover provides a strong temperature differential between the glass cover and the water, which improves the rate of evaporation and condensation and decreases the environmental impact by creating a thin layer above the surface.

To evaluate changes in health-related and vision-related quality of life (VRQoL) among patients with noninfectious uveitis who were treated with antimetabolites.Secondary analysis of a randomized controlled trial.Patients with noninfectious uveitis from India, the United States, Australia, Saudi Arabia, and Mexico.From 2013 through 2017, 216 participants were randomized to receive 25 mg weekly oral methotrexate or 1.5 g twice daily oral mycophenolate mofetil. Median changes in quality of life (QoL) were measured using Wilcoxon signed-rank tests, and differences between treatment groups were measured using linear mixed models, adjusting for baseline QoL score, age, gender, and site. Among Indian patients, VRQoL scores from a general scale (the National Eye Institute Visual Function Questionnaire [NEI-VFQ]) and a culturally specific scale (the Indian Visual Function Questionnaire [IND-VFQ]) were compared using Pearson correlation tests.Vision-related QoL (NEI-VFQ and IND-VFQ) and health-related QoL (HRQoL; physical component score [PCS] and mental component score [MCS] of the Medical Outcomes Study 36-Item Short Form Survey [SF-36v2]) were measured at baseline, the primary end point (6 months or treatment failure before 6 months), and the secondary end point (12 months or treatment failure between 6 and 12 months).Among 193 participants who reached the primary end point, VRQoL increased from baseline by a median of 12.0 points (interquartile range [IQR], 1.0-26.1, NEI-VFQ scale), physical HRQoL increased by a median of 3.6 points (IQR, -1.4 to 14.9, PCS SF-36v2), and mental HRQoL increased by a median of 3.0 points (IQR, -3.7 to 11.9, MCS SF-36v2). These improvements in NEI-VFQ, SF-36v2 PCS, and SF-36v2 MCS scores all were significant (P0.01). The linear mixed models showed that QoL did not differ between treatment groups for each QoL assessment (NEI-VFQ, IND-VFQ, PCS SF-36v2, and MCS SF-36v2; P0.05 for all). The NEI-VFQ and IND-VFQ scores for Indian participants were correlated highly at baseline and the primary and secondary end points (correlation coefficients, 0.87, 0.80, and 0.90, respectively).Among patients treated with methotrexate or mycophenolate mofetil for uveitis, VRQoL and HRQoL improved significantly over the course of 1 year and did not differ by treatment allocation. These findings suggest that antimetabolites could improve overall patient well-being and daily functioning.

An experiment was conducted with 55 rice varieties to assess the genetic diversity by using Mahalanobis D2 Statistical and characterization of genotypes using principal component analysis. All genotypes exhibited a wide and significant variation for 19 traits, by cluster analysis grouped into ten clusters. The maximum genotypes were included in Cluster 6 (16) followed by cluster 4 (10), cluster 3 (8), cluster 2 (7), cluster 5 (5), cluster 8 (4), cluster 1 (2), with 29.09, 18.18, 14.54, 12.72, 9.09, 7.27 and 3.63 proportion respectively, the rest of three clusters had one genotype each. Maximum cluster distance obtained between cluster×constituted by single entry (Pusa Basmati) showed highest inter cluster distance from cluster V (20727.37), VII (18414.79), I (17228.89) and cluster III (17010.24) are having very high inter cluster distance and also by cluster IX from cluster VIII (8852.36), VI (7559.67), I (7444.68) and cluster VII (6666.83) followed by cluster VI from cluster V (6225.95). The lowest inter cluster distance was observed between cluster II and cluster IV III and VI followed by between cluster I and cluster VIII, XI, II, VI and cluster IV. The intra cluster D2 values ranged from Zero (Cluster VII, IX, X) to 2233.91 (Cluster VIII). Contribution of amylose content was highest towards genetic divergence (23.43%) by taking 348 times ranked first followed by days to 50% flowering (23.37%) by 347 times, single plant yield (23.3%) by 346 times. The PCA analysis showed that first eight principal components accounted for about 85.4%.

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.

This study is concerned about the stabilization for delayed fuzzy neutral-type system (DFNTS) with uncertain parameters under intermittent control. Firstly, by constructing the augmented Lyapunov-Krasovskii functional (LKF) about different time delays along with single and double auxillary function-based integral inequalities (SAFBII, and DAFBII, respectively), a new class of delay-dependent adequate conditions are proposed, so that the robust fuzzy neutral-type system under consideration is guaranteed to be globally asymptotically stable (GAS). Secondly, the intermittent control (IC) is introduced to stabilize the system with mixed time-varying delays. In the view of inferred adequate conditions, the IC parameters are determined as for the arrangement of linear matrix inequalities (LMIs). It is noted that the strategies exploited in this work are apart from the other methods engaged in the literature, and the proposed conditions are less conservative. Finally, numerical examples are given to demonstrate the effectiveness of the developed techniques in this work. One of the practical applications is single-link robot arm (SLRA) model to show the viability and benefits of the structured intermittent control.

Introduction: RTA occurs commonly in young adults,so polytrauma including vascular injury is commonly managed in our institution,and this study is to analyse the out comes brachial artery injury repair Methods: This is a prospective study from august 2015 to may 2018,. All patients admitted with upper limb vascular trauma with brachial artery injury (n=27) included in our study. Results: Though graft thrombosis or anastomotic thrombosis seen in two patients,on 3rd or 4th post operative day, the limb salvage rate is 100% in our study. Conclusion: Since brachial artery injury commonly occurs in young individuals timely intervention saves the upper limb in most cases so that they can do their works which is essential for their family because they are the breadwinners of the family.

The security of the data becomes the main concern due to the quick rise in the exchange of data over the open networks and the Internet. The cryptographic techniques based on chaos theory reveal some novel and effectual orders to develop secure image encryption approaches. Images are the most attractive kinds of data in the encryption domain. In current years, the chaos-based cryptographic techniques have provided an efficient way to expand secure image encryption frameworks. This proposal temps to develop an optimized HCM for promoting novel image encryption. The proposed image encryption model involves 4 steps, such as image pre-processing, key generation, image encryption using optimized HCM, and image decryption. In the pre-processing, the RGB image is converted into a grayscale image, and the key is generated by the SHA-256 cryptographic hash algorithm. Further, the encryption of the image is performed by HCM with the integration of 2DLCM, and PWLCM. While hybridizing the two chaotic maps for image encryption, parameter tuning or optimization is performed for improving its performance. Moreover, Information entropy is considered as the objective model that has to be maximized while tuning the parameters, and the maximum value of information entropy will mean the best performance. An improvement in the well-known optimization algorithms termed as CI-WOA is adopted for performing the parameter optimization of HCM. Hence, the optimized HCM-based image encryption can be confirmed as an efficient and secure way for all types of image transmissions.

In the present times, a lot of research are being conducted in automobile sector for improving the performance of Internal Combustion Engines. In this paper, an attempt has been made to enhance the performance aspects and reduce emissions of High Velocity Oxy Fuel thermal spray coated piston incorporated Internal Combustion engine, by using Exhaust Gas Recirculation. By using Zirconium Dioxide and Aluminium Oxide, the piston was coated and used in the combustion evaluation experiments. The important internal combustion engine process parameters were optimized by using a central composite design model, which was evaluated using Analysis of variance. The combustion process parameters were optimized so as to enhance the Brake Thermal Efficiency to maximum extent and reduce emission of Oxides of Nitrogen (NOx) to minimum. At optimized combustion process parameters maximum Brake Thermal Efficiency of 29.1% and minimum NOx at 688 ppm, 21.8 ppm of unburnt hydro carbons and 11.08% smoke opacity was observed, which was validated to a very high level of predictability.

A comparative study is made between two different Compact Linear Fresnel reflector concentrating (CLFRC) systems with tubular absorber. The CLFRC systems with constant width reflectors and varying width reflectors are taken for study. The systems are analyzed with design parameters and Material study. By varying various design parameters like width of the reflector plates, shift between the plates, the height of the receiver tube from the reflector plane and diameter of the receiver tube, by using the geometrical equations calculations are done using MATLAB and the optimum values are taken for the design. The solar flux is considered to be a constant of 0.6 kW/m2. Concentrated ratio and concentrated flux are determined for each case to select the best system. Results show that the CLFRC system with constant width plates has better performance with concentration power, C.P = 3.15 kW and concentration ratio, C.R = 103.3 are better than those with varying width plates, while considering optimum design parameters of height from reflector plane, width of the plates and diameter of the receiver tube as 1.5 m, 0.03 m and 0.015 m respectively. The best system is selected through theoretical study and software analysis the system is modified with different reflectors (Glass mirror, Acrylic sheet, Chromium coated sheet, Polished Aluminum sheet) glass mirror is found to be the best reflector based on it reflectivity (ρ = 0.98) and the absorber is coated with different coating materials (Black Copper-Cu, Black Chrome- Ni-Cu/Steel, Black Nickel- Steel/Ni coated steel, Ni-Nox- Al) the best coating material is found to be Black Chrome- Ni-Cu/Steel absorber through theoretical and CFD analysis.

Objective: The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. The primary objective of this paper is to provide high level of tumor region segmentation using optimization and machine learning techniques. Methods: The boundary edge pixels are detected using Kirsch's edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier. Results: The proposed method with PCA and CANFES classification approach obtains 97.6% of sensitivity (Se), 98.56% of Specificity (sp), 98.73% of Accuracy (Acc), 98.85% of Precision (Pr), 98.11% of False Positive Rate (FPR) and 98.185 of False Negative Rate (FNR), then the proposed Glioma brain tumor detection method using CANFES classification approach only.

A new hybrid technique for grid integration of solar photovoltaic system using modified incremental conductance maximum power point tracking algorithm with multi-output converter and multilevel inverter is proposed in the paper. The proposed hybrid technique is the joint execution of improved dolphin echolocation algorithm and gradient boosting decision trees and this way the proposed technique is named as IDE-GBDT technique. The novelty of the proposed work is the dolphin echolocation algorithm, which is integrated by the crossover and the mutation function so it is named as IDE. In the proposed technique, the multi-output converter is the combination of boost converter and switched capacitor function to generate different self-balanced output voltages. The utilization of multilevel inverter in the proposed system provides better quality of output voltage and current waveform thereby reducing the size of passive filters. Also, eliminates the requirement of bulky transformers for grid integration. Multicarrier unipolar phase disposition pulse width modulation technique is employed for triggering the switches of the multilevel inverter. The maximum power point tracking algorithm uses the estimated active power output of the generator as its input and generates command speed so that maximum power is transferred to the dc link. This control system also incorporates a loss minimization approach to minimize the losses in the generator and hence to improve the efficiency of the photovoltaic system. Finally, the performance of the proposed maximum power point tracking control of wind and photovoltaic power generation schemes is executed in Matrix Laboratory/Simulink working platform and the execution is assessed with the existing techniques. The proposed technique is compared with the existing techniques and the observed total harmonic distortion of the proposed technique in all the cases is 0.67%, 0.51%, 0.58%, 0.63%, 0.92%, and 1.03% and the total harmonic distortion is found to be very less compared with existing techniques.

In recent days, cancer is a deadly disease because of its spreading nature to other cells, and this disease is not identified at an early detection stage. Generally, the cancer is detected with the help of a biopsy method, which is a painful approach. Due to the development of technology, nowadays, it is identified with the help of image processing methods. Here, the image processing approach is used for identifying and classifying the skin cancer types, namely melanoma, common and atypical nevi. The methods used earlier for the detection and classification are artificial skin leison merging, Raman spectroscopy and back-propagation networks. Cancer is classified into many types like blood cancer, bone, colon, and stomach and skin cancer. Among these cancer types, skin cancer can be a dreadful disease, which is detected and then treated at the starting stage of the disease. Hence, this paper proposed an optimized neural and fuzzy approach for skin cancer classification. The fuzzy c-means segmentation is used for the detection of the cancer region. Firefly optimization determines the dominant feature for the training of the neural network. The dominant feature is determined by reducing the error rate of the classifier. The overall process is evaluated with the help of evaluation metrics like accuracy, specificity and sensitivity. In this proposed method, the best result is achieved for the pattern net by improving its accuracy by 4.9% from its previous Moth-Flame Optimization based classification in its evaluation.

In recent years, the approximate computing becomes popular in the era of VLSI (very large scale integration) domain to arrive better power, area, and delay outcomes at the cost of lower precision loss. Also, the human beings are not so intelligent to see/observe/listen the processed digital data; means even if some of the data loss occurs human beings are unable to notice them. This behavior set the engineers to research on approximate computing which are very useful in the multimedia data processing, data communications, high-volume data storage, etc. In this study, the experiments such as hum-noise removal, filters on QRS detection are implemented on an Altera FPGA EP4CEF29C7 device using Quartus II 13.1 synthesis software tool and the simulation results on device utilization reports, the speed and the power are obtained. Simulation results reveal that the approximate computational filters offer better power, area, and speed results than the conventional ones. Also, Matlab 9.4 (R2018a) simulation was used to carry out the functional verification of the actual and approximate filters.

It is essential to find more renewable, sustainable ways of generating energy as the global energy demand is expected to grow by 30% by 2040. This work is concerned with the numerical prediction of thermal performance of flat plate solar collector, professor by varying the diameter of riser tube and changing the working fluid. A solar collector for heating a volume of 10 liters of working fluid is designed and investigated. 3D CAD model of the collector consisting of a fluid zone and two solid zones is generated for solving this conjugate heat transfer problem. Numerical analysis is carried out on the solar collector systems and their performance is predicted. The thermal and flow properties obtained for the solar collector systems are compared with each other to identify the best performing model of the solar collector. It is found from the numerical results that 14 mm riser tube with nano fluids gives comparatively best performance in terms of absorbing solar energy.

The study focuses on the analysis of the surface mounted components of the printed circuit boards (PCB) under random vibration loading. The surface mount technology used for the study involves ball grid arrays. In order to predict the fatigue life of the component under certain defense and automotive applications, modal analysis is done by using the finite element method. The results are verified experimentally using JEDEC standards to validate the finite element design used for the study, and random vibration experiments were performed with finite element analysis. The fatigue life of PCB under different random vibration environments is predicted and compared. The fatigue life of PCB is least with jet cargo environment and is maximum with rail cargo environment. The analysis reveals that under the jet cargo environment, more numbers of natural frequency excitations are present. It was found that upon loading, maximum stress was found to be induced at the corner of solder balls for packages mounted in the center of the PCB. Hence, it is suggested to incorporate design modification such that the central area of the PCB must not contain any packaging designs.

In digital image processing, image segmentation is the key methodology which is to be used frequently. In digital image processing, noise reduction and enhancement techniques are plays as a vital role. Brain is major and major organ of the human body which is to be controlled by the nervous system. In this paper, we proposed a brain tumor image enhancement technique with the help of the ICA-LDA (independent component analysis-linear discriminate analysis algorithm with ARHE (adaptive region based histogram enhancement) model. Image fusion technique is to apply for combination of the two or more input image. In this paper, the weighted average technique is to be used for image fusion techniques. The noise reduction and enhancement techniques are to be applied in preprocessing stage. The adaptive median filter is to be used for preprocessing stage. The ARHE (adaptive region based histogram enhancement) model is to be used for enhancement present in the preprocessing stage. The feature extraction and the feature optimization have to be utilized with the ICA (independent component analysis). The LDA (linear discriminate analysis) is to be used for the classification techniques. Using this classifier which is to separate the abnormal and normal stages. When the brain tumor is denoted as abnormal case then the morphological based segmentation is to be done. The simulation and result shows the analysis of various parameters such as specificity, sensitivity, positive predictive value, negative predictive value, accuracy, precision, and recall.

Cloud computing is a new paradigm which provides subscription-oriented services. Scheduling the tasks in cloud computing environments is a multi-goal optimization problem, which is NP hard. To exaggerate task scheduling performance and reduce the overall Makespan of the task allocation in clouds, this paper proposes two scheduling algorithms named as TBTS (Threshold based Task scheduling algorithm) and SLA-LB (Service level agreement-based Load Balancing) algorithm. TBTS is two-phase scheduling algorithm which schedules the tasks in a batch. It supports task scheduling in virtual machines with distinct configuration. Furthermore, in TBTS algorithm, threshold data generated based on the ETC (Expected Time to Complete) matrix. Virtual machines which execute tasks with the estimated execution time lesser than threshold value are allocated to the particular task. SLA-LB algorithm is a online model which schedules the task dynamically, based on the requirement of clients, like deadline and budget as the two criteria. Prediction based scheduling is implemented in TBTS to increase the system utilization and to improve the load balancing among the machines by allocation of the minimum configuration machine to the task, based on predicted robust threshold value. SLA-LB uses the level of agreement and finds the required system to reduce the Makespan and increase the cloud-utilization. Simulation of proposed algorithms is performed with benchmark datasets (Braun, 2015) and synthetic datasets are generated with random functions. The proposed TBTS and SLA-LB final values of the proposed algorithms are analyzed with assorted scheduling models, namely SLA-MCT, FCFS, EXIST, LBMM, Lbmaxmin, MINMIN and MAXMIN algorithms. Performance metrics such as Makespan, penalty, gain cost and also the VM utilization factor of proposed algorithm compared with existing algorithms. The comparison analysis among various existing algorithms with TBTS and SLA-LB algorithms show that the proposed methods outperform existing algorithms, even in the scalability situation of the dataset and virtual machines.

The addition of binary numbers is the fundamental and most often used arithmetic operation on microprocessors, digital signal processors (DSP), and data-processing application-specific integrated circuits (ASIC). Binary adders are the crucial building blocks in very large-scale integrated (VLSI) circuits. Modular adders are fundamental arithmetic components typically employed in residue number system (RNS)-based digital signal processing (DSP) systems. In this paper, modified End-Around Inverted Carry (EAIC) area-efficient modulo adder is designed using various parallel prefix adders. In the proposed design, parallel prefix operation and carry correction techniques are adopted to eliminate the re-computation of carries. Compared with the same type of modulo adder structure, the proposed designs offer reduced area and delay reduced compared to the existing design.

Background Laparoscopic hysterectomy is a minimally invasive procedure with shorter operative time, decreased trauma, and less technical difficulty. However, the success of the procedure depends on various intrinsic and extrinsic factors. This study was done to evaluate the factors which influence the success of laparoscopic hysterectomy. Materials and methods This retrospective record-based cross-sectional study was carried out among 100 participants who underwent laparoscopic hysterectomy for various indications in our tertiary care hospital. The demographic and clinical profiles of these participants were recorded. Particulars related to the success of the surgery including duration of surgery and hospital stay, infections, and other complications were recorded. Results Our study showed that the duration of surgery and duration of hospital stay were significantly influenced by the parity. Multiparous women were more prone to longer duration of surgery and prolonged hospitalization. The observed difference was statistically significant (p < 0.05). Conclusion It is essential to develop a scoring mechanism by which each case can be selected or rejected for laparoscopic hysterectomy considering various parameters. This may be carried out by further exploratory research on identifying the key factors which influence the success of the procedure. How to cite this article Subburaj L, Saravanan S, Kumar R. Clinical Profile of Patients Undergoing Laparoscopic Hysterectomy. J South Asian Feder Obst Gynae 2020;12(5):277–280.

Globally in recent days, the potential risk for patients with diabetes mellitus is the prevalence of diabetic retinopathy, which is a silent disease with no early symptoms and is the imperative cause of vision loss. An early diagnosis can be used to prevent for visual loss and blindness. In the regular screening process, assistance of computerized diagnosis can considerably minimize an ophthalmologists work and improve inter and intra viewer variability. A generalized method of semi automatic exudates characterization to diagnose diabetic retinopathy with exudates screening system of retinal image is presented in this paper. This system uses morphological processing based retinal blood vessel suppression, Semi automatic masking of optic disc structure and morphological component analysis based texture enhancement followed by segmentation and Adaptive Neuro-Fuzzy Inference System (ANFIS) based classification method to discriminate between normal and pathological retinal structures. The novelty of this system relies on the appropriate sequential application of exclusive image processing techniques in synergy with ANFIS classifier to improve the accuracy of exudate lesions characterization. The performance of the system has been evaluated by comparing it with various state of the art existing methods in terms of several performance metrics such as Accuracy, Average error rate, F-Score and Kappa value. The obtained numerical results prove that the proposed system with ANFIS classifier demonstrated superior performance in identification of exudate lesions.

Purpose The purpose of this paper is to discuss how the rush of technological change will consolidate the worldwide reach of the internet with more capacity, specifically to control the physical world, including the machines, industrial facilities and frameworks that characterize cutting-edge technology. Design/methodology/approach The data were collected from 203 respondents predominantly from emerging economies, specifically India and SEA. Most of the participants are working professionals. Structural equation modelling was used to analyze data, as it is a popular statistical technique because of its ability to model selected independent variables and take into account all possible forms of measurement error to test an entire theory. Findings The Industrial Internet of Things (IIOT) platform comprises four fundamental capabilities: connectivity, big data, advanced analytics and application development. The IIOT has the potential to provide a high level of synergies between the 4 Ms of manufacturing, namely, man, machine, material and method. Research limitations/implications The collected data are predominately from India and SEA (close to 75 per cent), while contributions from other regions are comparatively less, so the findings cannot be generalized to the global context. Practical implications It is in the interest of service providers to collaborate and provide a universal solution to retain legacy systems to minimize the investment and reduce the security threat, which could boost IIOT adoption while ensuring that manufacturers are able to leverage this new technology efficiently. Originality/value The framework obtained has good quality of validity and reliability indicators. Thus, an alternative framework has been added to customer expectation which is currently a popular topic in the technological changes.

A hybrid on-chip approach based on the variants of soft computing techniques is proposed in this work to evaluate the performance metrics of a 6T static random access memory (SRAM) cell in 45-nm technology. The performance metric evaluated for the SRAM cell is the data retention voltage (DRV) with optimal memory requirements. Each of the SRAM memory cells intends the chip to possess low density but operates at high speed. This paper formulates a hybrid soft computing framework comprising a deep backpropagation neural network which trains the fuzzy inference system to determine the performance metrics of 6T SRAM cell. The weight and bias parameters of the deep learning neural framework are optimized through a cat swarm optimization algorithm so as to reduce the elapsed convergence time of the new hybrid soft computing model. Evaluation process is executed based on the driven outputs from deep learning model to the fuzzy inference system (FIS) module so as to achieve the best values of DRV. Data retention voltage plays a major role in reducing the substantial leakage current and static noise margin intends to retain the data without losing them. Deep backpropagation neural network gets trained with deep learning procedures and optimizes the rule parameters and membership parameters of the FIS design structure. The performance metric DRV under various constraints prove to be better and effective in comparison with the solutions from the existing literature works for the same configuration of 6T SRAM cell using 45-nm technology.

AIM : The aim is to evaluate the utility of liver and tumor stiffness elastography values in predicting the technical success of HCC patients treated by thermal ablation and also to assess the correlation between tumour stiffness elastography values and risk of local tumour recurrence. 50 patients with CHILD PUGH clas MATERIALS AND METHODS: s A liver function and having HCC (less than 3 in number) undergoing thermal ablation for the same were selected.Preprocedurally tumor and liver stiffness elastography values were recorded.All of the patients had atleast one cross sectional imaging followup(CECT/MRI). ROC curve was used to nd the ideal cutoff for nding tumor stiffness value, liver stiffness value with respect to recurrence. The technical success of thermal ablation for RESULTS: treating HCC in our study was 98%.The association between tumor stiffness elastography value and the risk of developing residual hepatoma was not statistically signicant(p value-0.480). The optimal cutoff tumor stiffness value for predicting the development of recurrence was determined as 12.4kPa which was found to be a statistically signicant factor for predicting the risk of developing local tumor recurrence(p value of 0.001,sensitivity of 84.21% and specicity of 80.65%,PPV-72.73%, NPV- 89.29% and Accuracy 82%). Tu CONCLUSION: mor stiffness values obtained by 2D-SWE were found to be a signicant predictive factor for the development of recurrence in patients with HCC treated by thermal ablation. Tumor stiffness values obtained by 2D-SWE were not found to be a signicant predictive factor for the residual hepatoma in patients with HCC treated by thermal ablation

This Letter presents an ultrasonic waveguide sensor for measuring fluctuations in a liquid level using leaky longitudinal wave L(0,2) in a coolant loop system. A 20% cold-worked D9 fuel pin clad tube of 1000 mm long, 6.6 mm outer diameter and 0.45 mm wall thickness was used as a waveguide on which the L(0,2) mode was transmitted/received along the axial direction at 500 kHz. When the waveguide got introduced into a coolant loop and partially immersed in a non-viscous liquid (water/liquid sodium), the amplitude of L(0,2) mode got reduced due to leakage. The rate of leakage depends on the characteristics of the waveguide and the embedding liquid medium. By utilising this physical property of L(0,2) mode, it is possible to predict the variations in liquid level during dynamic conditions. Experimental measurements were carried out on a mock-up test loop in which water was used as embedding liquid and two artificial notches were made on the waveguide at different depths were used as reference levels. The time-amplitude signals were monitored and the reflected energies from the bottom end of the waveguide and the notches were used as the threshold and reference amplitudes respectively for identification of water level.

Background: Neonatal jaundice is accountable for 70% neonatal morbidity and 10% mortality. It is the most common cause of re-admission after discharge from hospital. Here, we have studied the effect of Fenofibrate as an adjunct to phototherapy in treating neonatal hyperbilirubinemia. Materials and Methods: This study was conducted on 90 neonates with hyperbilirubinaemia requiring phototherapy, they were randomly allocated to two groups, both the groups received phototherapy whereas the intervention group received single dose of fenofibrate (10mg/kg) in addition. Total serum bilirubin after 48 hours and duration of phototherapy was measured in both groups. Results: The study proved that there is a decrease in duration of phototherapy and decline in serum bilirubin after 48 hours of phototherapy in intervention group, but the difference is not statistically significant. Conclusion: There is a non-significant role of Fenofibrate in decreasing neonatal hyperbilirubinemia.

Performance improvement of the solar thermal power generation systems can be improved by the choosing the suitable materials and the coatings of the reflectors and receiver. The conventional reflector such as glass and the advanced reflector such as polymers based reflectors are discussed. The coating used in this types of Reflectors for the coatings for the protection from the dirt and corrosion are mentioned. Receiver of the solar collector should have the high specular absorbtance. To improve the absorbtance solar selective coatings have been used by the researchers.

Challenge of an early prediction of customer churn is a major demand among research community. To understand an intention of a customer on reasons to make a churn as well time taken by a customer to churn is always an unknown mystery. Though good numbers of research works have suggested works on customer churn an exact measure of accurate churn and approaches to suggest on retention is the major discussion of this paper. Traditional approaches such as ACO, PSO are supports on appreciable churn prediction but consider more time to converge, whereas GWO algorithm supports with minimal time to converge as well improved accuracy of 89.26% along with actual churn match compared to PSO and ACO approaches. CUPGO also focuses on customer retention of 34.81% to retain valuable customers. CUPGO works on a large dataset collected over two consistent years.