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The present study compared the safety, efficacy, and tolerability of the new fixed-dose combination (FDC) of telmisartan 40 mg + bisoprolol 5 mg (TBP) tablets with the existing comparator FDC telmisartan 40 mg + metoprolol succinate ER 50 mg (TMS) tablets in patients with stage 1 and stage 2 hypertension.

Lightweight and high-strength materials are the significant demand for energy storage applications in recent years. Composite materials have the potential to attain physical, chemical, mechanical, and tribological qualities in the present environment. In this study, graphene (Gr) and biosilica (Bs) nanoparticle extracts from waste coconut shell and rye grass are utilized as reinforcement materials to add to the aluminum silicon (Al-Si) alloy matrix materials. Selective laser melting (SLM) is a rapidly manufacturing technology used to create Al/Gr/Bs hybrid composites. The specimens are made up of four distinct combinations of base matrix alloy (100 wt%), Gr (3, 6, and 9 wt %), and Bs (2, 4, and 6 wt %) with an aluminium alloy matrix. The influence on mechanical characteristics, specifically hardness, tensile strength, and wear, was investigated and compared to the basic matrix alloy. The fracture processes of wear specimens are studied using field emission scanning electron microscopy. Globally, graphene (Gr)-based materials for energy storage devices have been successfully manufactured and deployed in a variety of applications, including super capacitors, lithium-ion batteries, water splitting, fuel cell electrocatalysts, and solar cells. Environmentally friendly graphene (Gr) and biosilica (Bs) nanoparticles have the potential to be used as reinforcement in composite development. These findings showed how promising graphene (Gr) and biosilica (Bs), which are inexpensive reinforcement materials made from leftover coconut shell and rye grass, are for use in energy storage applications.

One of the most important problems for many firms is the decision-making process in supplier selection. The most inclusive process in the decision-making process is the analytical hierarchy process. This study gives instructions for setting supplier selection criteria for university procurement department purchasing activities. The decision-making method of the AHP is based on a multi-criteria examination of cost, flexibility, quality, delivery, and polish or machining property. This work provides clarity in the classification of supplier attributes that have been emphasised in the model business. It gives an idea about the AHP and also how to select the supplier using with necessary criteria. The data collection is done in the gear manufacturing industry. A total of 20 suppliers were considered for our evaluation. Out of 20 suppliers, three suppliers got the highest priority rank of 96.9% among all suppliers by considering various factors such as price, quality, on-time delivery, etc.

The triclustering of microarray genes contains a subset of genes that carry information about the behaviour of these genes under specific conditions over specific periods. In this paper, we present a meta-heuristic technique that is a hybrid cuckoo search algorithm used to solve the computationally intensive problem. In general, cuckoo search approach relies upon the fact that the cuckoo lays its eggs in the nest of host birds; however, if the egg is not identified and knocked down, the cuckoo's eggs are hatched. In this paper, we propose cuckoo search with harmony search for triclustering genes. In the experimental results on homo sapiens dataset, the proposed method yielded 0.12206 statistical difference from background. Moreover, this algorithm was evaluated over certain parameters and the experimental results outperformed other existing algorithms.

Query expansion is an approach, which plays a critical role on the web-based search and retrieval methods. It’s primary objective is to expedite the performance of the retrieval process in retrieving information by merging alternate word forms in the original query. The related word forms are retrievable from the document available in databases, which stores documents with a high density. A typical method for query expansion involves clustering of related documents and extracting closely related expressions using these clusters. However, traditional clustering poses a few problems to query expansion. Biclustering appears to be more effective in query expansion than traditional clustering. Recently a nature-inspired algorithm, namely the stellar mass black hole optimization has been introduced, to carry out the process of biclustering. Modification of the SBO has been performed to propose MSBO. Preliminary results have shown that the proposed modified stellar mass black hole (MSBO) method effectively clusters identical expressions and consequently the detected biclustering algorithms were effective in generating suitable words for expanding a given query. The MSBO has been tested on particular sets of documents with the objective of identifying groups of relevant categories of text that form the biclusters. Thus the proposed MSBO proves to be an effective query expansion tool. Based on the analysis of the obtained results, and on the comparisons performed with the existing methods, it has been found that MSBO is effective in generating results that are encouraging.

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models.

In this proposed work, the knowledge management process adopted for analysing healthcare data used to making the particular decision in care services. In this paper, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the knowledgeable information from the dataset and builds the decision table based on the collected features from datasets. The attributes that are unrelated are deleted by using PCA algorithm. The performance of our classification technique is measured with various metrics such as accuracy, F-measure, G-mean, precision, and recall. The experimental results while compared with some of the existing machine learning methods like probabilistic neural network, support vector machine and neural network, shows the higher performance of FCNN. This model presented in this paper act as a decision support pattern and knowledge management in healthcare for therapeutic specialists.

Proteins play a vital role by booming out a number of activities within an organism to withstand its life. The field of Natural Language Processing has successfully adapted deep learning to get a better insight into the semantic nature of languages. In this paper, we propose semantic approaches based on deep learning to work with protein amino acid sequences and compare the performances of these approaches with traditional classifiers to predict their respective families. The Bidirectional Encoder Representations from Transformers (BERT) approach was tested over 103 protein families from UniProt consortium database. The results show the average prediction accuracy to 99.02%, testing accuracy to 97.70%, validation accuracy to 97.69%, Normalized Mutual Information (NMI) score on overall data to 98.45, on test data to 96.99, on validation data 96.93 with high weighted average F1 scores of 99.02 on overall data, 97.72 on test data and 97.70 on validation data, and high macro average F1 scores of 99.00 on overall data, 98.00 on test data and 98.00 on validation data. From the results, it is justified that our proposed approach is outperforming well when compared to the existing approaches.

Generally, synthesis of most of the doped carbon nanomaterials except carbon dots required multiple steps and stringent conditions. Herein we have attempted a single step synthesis of nitrogen and oxygen doped carbon nano-sponges (NS)by simple heating of precursors in dimethylsulfoxide rather than conventional methodologies. The structure, morphology and optical properties of NSwas well investigated by various characterization techniques. Deleterious effect of 4-nitroaniline (4-NA) has seized our attention as it could be a potential threat to living beings and in addition optical sensors for 4-NA is sparse. Therefore, we have examined the fluorescent sensing capability of NSin determination of 4-NA with limit of detection 0.88 ppm. The practical applicability of NSwas accomplished in soil samples. High surface area, porous nature and functionalities over NShave encouraged us to enquire its ability as supercapacitor. NSdemonstrated pseudocapacitive behavior with appreciable specific capacity of 262 C/g at 1 A/g and evinced perceptible cyclic stability and reversibility.

Gene expression data analysis is used in several areas including drug discovery and clinical applications. Biclustering in gene expression data is a subset of the genes representing consistent patterns over a subset of the conditions. In this case the conditions can be related to the disease types, the biclustering method is much hopeful in this application field. The proposed work finds the significant biclusters in large expression data using modified the Nelder-Mead method. The Nelder-Mead minimises a function of n parameters by comparing the n + 1 vertices of a simplex, and updating the worst vertex by moving it around a centroid. This work considers the median instead of centroid and differential evolution that takes place in the simplex to get the true global minimum. It is tested on benchmark datasets and the results are compared with standard benchmark algorithms. The results indicate that there is a substantial betterment in the purported study.

Microarray experiments generate vast amounts of data, leading to new requirements and challenges for bioinformatics. The crucial step in considering gene expression data is to discover a group of genes that have similar patterns. Biclustering is a two-dimensional clustering problem where the genes and samples are grouped simultaneously. Latest research showed that biclustering has a great potential in finding marker genes that are associated with certain tissues or diseases. Here, we present an efficient biclustering algorithm called Modified Harmony Search (MHS) method. The MHS algorithm includes a step for generating the new solution by using levy flight. Levy flight increases the diversity of the solutions of harmony search (HS) algorithm and the Pitch Adjusting Rate (PAR) is changed dynamically to escape from local optima. To know the performance of the MHS, it is applied on various benchmark optimisation functions and the benchmark gene expression data sets. The experimental results show that the MHS outperforms HS in optimisation functions and biclustering problems. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

Heating of water for domestic purpose by making use of solar energy is essential and it is one of the effective ways of utilizing solar energy. Initial cost of solar water heating (SWH) system is high, but the operation cost is zero. This paper discusses on improving the performance of a flat plate solar energy collector by changing the design parameters of the number of riser tubes and the arrangement of riser tubes in zig-zag pattern from the existing flat plate collector system (FPC) and using the different working nanoparticles with base fluids like aluminium oxide mixed with water. Experiments were conducted using copper tube in headers and risers with two different configurations and maintain the same dimensions. The performance comparison was made for all the two types of collectors in closed loop. The performance shows that the efficiency of FPC with zig-zag arrangement of tubes (Z-Configuration) is higher than that of conventional FPC. Thus the newly presented FPCs system produces higher efficiency than the existing conventional flat plate collector system.

The pristine silver metal–organic framework (Ag-MOF) is successfully synthesized by the one-pot synthesis method. It serves as the effective electrode material for supercapacitor application because of its excellent conductive frame network and electrochemical activities. The crystal structure, surface morphology, porosity, and chemical configuration of the Ag-MOF are studied and analyzed using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), Brunauer–Emmett–Teller (BET) theory, and X-ray photoelectron spectroscopy (XPS) analysis. The electrochemical behavior of the electrode material is determined using galvanostatic charge/discharge (GCD), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The aggregated platelike morphology of Ag-MOF provides more electrochemical active sites and shortens the pathway for ion intercalation/deintercalation which results in outstanding electrochemical performance. The maximum specific capacity of 606.2 C g–1is achieved at a current density of 1 A g–1in the three-electrode system. The asymmetric supercapacitor device delivers the maximum energy density of 48.69 W h kg–1at a power density of 608.73 W kg–1. Furthermore, the fabricated device retains an attractive cyclic stability of 131.14% for 5000 cycles at a current density of 5 A g–1claimed as the novel electrode material in the area of energy storage.

•Several applications of Distributed Generation (DG) networks uses Multilevel inverters, specifically photo voltaic (PV) systems.•Few major benefits of the Multilevel inverters are its power quality and the output voltages generated from the small PV voltage sources are high.•The 3-phase inverter proposed uses lower number of components.•The generated output voltage and three half-bridge cells for every phase are combined.•The future work concentrates on reducing the component without any compensation in achieving better THD by utilizing optimization method.

Vicinally substituted trimethylsilylmethyl cyclopropyl ketones undergo facile desilylative ring opening with Lewis acids under mild conditions. The enolates, thus generated, undergo aldol addition with aldehydes and ketones. The diastereoselectivity of the reaction with aldehydes depends on the nature of the Lewis acid used. The resulting aldol product is easily converted into a substituted tetrahydrofuran derivative.

(tert-Butyldiphenylsilyl)methylcyclopropanes undergo ring opening to furnish substituted dihydrofurans in good to excellent yields on treatment with TiCl4 in dichoromethane. The silicon that assists the regioselective ring opening is retained in the product to allow further functional group manipulations.