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Using Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done. [ABSTRACT FROM AUTHOR]

Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques. [ABSTRACT FROM AUTHOR]

Introduction: Kodamaea ohmeri is a rare, recognized pathogen that has previously been isolated from environmental sources. The patients commonly affected by this yeast include immunocompromised as well as immunocompetent patients having several associated risk factors. Methodology: We report three cases in which K. ohmeri was isolated from blood using Bact T/ALERT. Identification was carried out by MALDI-TOF MS (Vitek-MS, BioMérieux, Marcy-l'Etoile, France) in addition to color characteristics on chromogenic media. The patients had diminished immune response on account of a multitude of comorbidities. Results: K. ohmeri can be misidentified as Candida tropicalis, Candida albicans, or Candida hemolounii by conventional methods; correct and timely identification can be achieved by MALDI-TOF MS. Antifungal susceptibility breakpoints for K. ohmeri are currently not defined. An Echinocandin was added to the treatment regimen of all three of the cases. Conclusions: Identification of K. ohmeri using conventional methods is difficult and unusual yeasts should be carefully observed, especially upon prolonged incubation. [ABSTRACT FROM AUTHOR]

The most recent and elegant solution to address the problem of scalability in network-on-chip (NoC) architectures is WiNoCs (Wireless NoCs).WiNoC, a wired-wireless hybrid architecture in NoC designs, is becoming increasingly popular for its fast unicast message delivery. However, modern applications often require multicasting and broadcasting messages instead. In the proposed work, the widely used simulation tool BookSim is employed, which offers reconfigurability and openness and it enables the analysis of both wired and wireless NoC architectures. This paper proposes a Tree-based Wireless NoC architecture approach incorporating MDND (message duplication in non-destination), which provides multicast support for WiNoC by exploring the inherent broadcast-type communication of wireless connection. Additionally, the proposed work supports the configuration of various aspects, including its multicast injection rate, enabling or disabling its multicast capabilities, and determining the number of multicast recipients for each packet. The proposed Tree-based approach showed a significant improvement in average multicast transaction latency compared to the traditional NoC. There is a noticeable reduction in the average network latency, the flit latency, and the total power. The analysis also shows a reduction in average packet latency for unicast messages and average packet latency for broadcast messages. [ABSTRACT FROM AUTHOR]

The majority of technological processes include dynamic systems, which are characterized by structural and parameter uncertainty. Conventional control procedures based on such models are unlikely to produce the desired performance since deterministic models cannot appropriately characterize these uncertainties. When dealing with complex nonlinear systems that are characterized by ill-defined and unknown elements, fuzzy-logic based system is an effective method. Fuzzy controllers—whose rule basis is built on the expertise of human experts—are frequently utilized in this context to achieve desired performance. This knowledge might not be adequate for some complex processes, thus different methods for creating IF-THEN rules have been suggested. In this paper, the design of higher-order fuzzy sets is discussed and a generalized novel algorithm is developed for implementing type-n fuzzy sets and it is applied for controlling the angle of steering for a vehicle in such a way that the vehicle tracks the desired path. In the proposed methodology, fuzzification and defuzzification are taking place n number of times, and defuzzification of higher order fuzzy values up to type-1 fuzzy set is performed using the center of gravity method. The obtained type-1 fuzzy values are used in the rule base and inference mechanism for calculating the final crisp output. The significance of the present research is that the computation cost is not as high as expected because both fuzzification and defuzzification processes are recursive and the reduced new type-1 fuzzy values have the additional ability to model the uncertainty and vagueness as compared to normal type-1 fuzzy values. A comparison of the controllers based on the type of fuzzy sets is done based on the square of error (SE). Both the disturbance signal and uncertainty scenarios were also considered. The use of higher-order fuzzy sets facilitates the controller to give the more accurate results for controlling the vehicle and the corresponding simulation results suggest the same. [ABSTRACT FROM AUTHOR]

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19. [ABSTRACT FROM AUTHOR]

Introduction Stenotrophomonas maltophilia is an emerging environmental, gram-negative, multidrug-resistant organism, associated with risk factors such as prolonged hospitalization, invasive procedures, admission to the intensive care unit, mechanical ventilation, use of indwelling catheters, administration of immunosuppressants or corticosteroids, human immunodeficiency virus infection, underlying malignancy, and organ transplantation. The organism, despite being of low invasiveness in immune-competent individuals, is difficult to treat because of intrinsic resistance to several antimicrobial agents. Materials and Methods This study focuses on commonly encountered resistance from among the isolates over a duration of 7 years from 2012 to 2018, analyzed retrospectively. Identification and susceptibility testing were performed using Vitek 2 (BioMérieux, Marcy-l'Etoile, France). Results Bloodstream infections were found to be most common (52.02%), followed by respiratory infections (35.83%). The median age of the patients was 36 years, and male to female ratio was 143:27. The median duration of hospital stay was 18 days, and mortality was seen in 18.82% of patients. Susceptibility to cotrimoxazole and levofloxacin was seen in 97.1% of isolates (168 out of 173) and 90.1% of isolates (156 out of 173), respectively. Conclusion Despite being effective in a majority of S. maltophilia isolates, both cotrimoxazole and levofloxacin have their shortcomings. Cotrimoxazole is bacteriostatic and can cause bone marrow suppression and resistance to levofloxacin sometimes develops during therapy. Thus, the therapy should be decided considering the characteristics of both of these drugs. [ABSTRACT FROM AUTHOR]

Introduction: The programmed death receptor ligand 1 (PD-L1) is a cell-surface glycoprotein expressed in tumour cells (TCs) and is also upregulated in tumour infiltrating lymphocytes. The effect of PD-L1 expression on TCs and tumour-infiltrating lymphocytes (TILs) on acute radiation toxicity and response in oropharyngeal squamous cell carcinoma treated with concurrent chemoradiotherapy is less known. Material and methods: Squamous cell carcinoma of oropharynx with stage II-IVA (AJCC 8th) were recruited in this prospective observational study. Definitive radiation therapy (RT) of 70 Gray in 35 fractions at 2 Gray per fraction, 5 fractions a week in 2 phases was delivered with concurrent chemotherapy (cisplatin 40 mg/m2 weekly). Patients were assessed weekly for acute toxicities with Radiation Therapy Oncology Group criteria. Response assessment was done at 3 months post RT according to World Health Organization response assessment criteria. The programmed death receptor ligand 1 expression in TCs and TILs was correlated with acute toxicity and survival. Results: Of 51 patients, 20 (39.2%) had PD-L1 expression in TCs and 18 (35.3%) in TILs. Patients with PDL1 expression in TCs had fewer grade ≥ 3 oral mucositis (25% vs. 58%; p = 0.02) and grade ≥ 3 dysphagia (25% vs. 55%; p = 0.046). The programmed death receptor ligand 1-tumour infiltrating lymphocytes positives had lower ≥ 3 grade oral mucositis (22% vs. 58%; p = 0.02) and ≥ 3 grade dysphagia (17% vs. 58%; p = 0.007). Two-year overall and progression-free survival rate for the PD-L1-tumour-positive vs. PD-L1-tumour-negative group was not different (p > 0.5). Conclusions: Positive PD-L1 expression is associated with fewer acute radiation toxicities, and this could be used as a potential biomarker. [ABSTRACT FROM AUTHOR]

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). [ABSTRACT FROM AUTHOR]

Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. [ABSTRACT FROM AUTHOR]

In this paper, nature inspired search algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA) are used to design fractional order proportional and integral (FOPI) and artificial neural network (ANN) controller based distribution static compensator (DSTATCOM) and electronic load controller (ELC) for power quality improvement. Improvement in power quality is achieved using DSTATCOM and an ELC. DSTATCOM is designed using FOPI and ANN based controllers, as opposed to conventional PI controllers which are comparatively less efficient. PSO and GA techniques are employed to determine the optimal parameters for the controllers. The improvement in the performance of the ANN and FOPI as compared to PI controller for the DSTATCOM and ELC is validated using MATLAB based modeling and simulations. Linear consumer loads were used to perform a comparative study in terms of maximum percentage error. Further, we analyzed the system for a nonlinear load and demonstrated decrease in the harmonic distortion. [ABSTRACT FROM AUTHOR]

In this paper, a novel dynamic recurrent functional link neural network (DRFLNN) is proposed for the identification of unknown dynamics of the nonlinear systems. The proposed structure contains a self-feedback loop(s) as well as the adjustable weighted feed-through of the input signals to the output neuron(s). A learning algorithm is developed using the combination of Lyapunov stability and dynamic back-propagation method and is applied to derive the stable parameter adjustment equations. The performance evaluation of the proposed DRFLNN model is done by comparing it with the multi-layer perceptron (consisting of a single hidden layer), radial basis function network, Elman recurrent neural network (ERNN), nonlinear auto-regressive moving average, and the conventional functional link neural network. Three benchmark systems have been used on which all these models are applied. From the results, it is found that ERNN provided better prediction accuracy as compared to the remaining models and the second-best accuracy is obtained from the proposed model. Further, the ERNN model is more complex and offers more parameters to be tuned as compared to the DRFLNN model. Thus, the training of the ERNN model is quite difficult as compared to the DRFLNN. [ABSTRACT FROM AUTHOR]

In a power distribution system, faults occurring can cause voltage sag that can affect critical loads connected in the power network which can cause serious effects in the oil and gas industry. The objective of this paper is to design and implement an efficient and economical dynamic voltage restorer (DVR) to compensate for voltage sag conditions in the oil and gas industry. Due to the complexity and sensitivity of loads, a short voltage sag duration can still cause severe power quality problems to the entire system. Dynamic Voltage Restorer (DVR) is a static series compensating type custom power device. The overall efficiency of the DVR largely relies on the effectiveness of the control strategy governing the switching of the inverters. It can be said that the heart of the DVR control strategy is the derivation of reference currents. This paper deals with the extraction of reference current values using a controller based on a combination of probabilistic and fuzzy set theory. The basis of the proposed controller is that Gaussian Mixture Model (GMM) which is a probabilistic approach can be translated to an additive fuzzy interface system i.e. Generalized Fuzzy Model (GFM). The proposed controller (GMM-GFM) initially optimizes the membership functions using GMM and the final output is calculated using GFM in a single iteration i.e. with no recursions. In the control scheme two control loops are used: a feed-forward loop that uses the Proportional and Integral (PI) controller and the feedback loop uses GMM-GFM based controller. The controller is implemented and respective simulations are performed in the MATLAB SIMULINK environment for three-phase, three-wire distribution system with various issues. A comparative analysis is then done amongst all the three controllers which are based on the T-S, ML, and GMM-GFM modes respectively. The simulation results of this comparison rank the DVR with the GMM-GFM controller first, followed by the fuzzy logic Mamdani model and then with the fuzzy logic T-S model. [ABSTRACT FROM AUTHOR]

In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonometry FLNN, Legendre FLNN (LeFLNN), Chebyshev FLNN, power series FLNN (PSFLNN) and Hermite FLNN. The recursive weights adjustment equations are derived using the combination of Lyapunov stability criterion and dynamic back propagation algorithm. In the simulation study, a total of three nonlinear systems (both static and dynamic systems) are considered for testing and comparing the approximation ability and computational complexity of the above-mentioned FLNNs. From the simulation results, it is observed that the LeFLNN has given better approximation accuracy and PSFLNN offered least computational load as compared to the rest models. [ABSTRACT FROM AUTHOR]

In this paper, a noise-robust palmprint recognition system is discussed with a novel feature extraction technique: two-dimensional Cochlear transform (2D-CT) based on the textural analysis of image sample. Orthogonality of 2D-CT is proved which shows the high robustness of the proposed 2D-CT to noise. To validate the proposed feature extraction technique, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, CASIA palmprint database of 312 persons, polyU palmprint database of 386 persons and achieved high accuracy. The proposed 2D-CT method is compared with discriminative and robust competitive code, double orientation code, competitive coding, ordinal coding, Gabor transform, Gaussian membership-based features, absolute average deviation and mean features. Further, K-nearest neighbor is used to validate the matching stage. The results show superiority of the proposed method over other feature extraction methods. [ABSTRACT FROM AUTHOR]

In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models. [ABSTRACT FROM AUTHOR]

Abstract A series of eight N 1-(2′- O ,4′- C -methylene-β- D - ribo furanonucleoside-3′-yl)- C 4-(coumarin-7-oxymethyl)-1,2,3-triazoles have been synthesized by Cu(I)-catalyzed azide-alkyne cycloaddition reaction of 3′-azido-3′-deoxy-2′- O ,4′- C -methyleneuridine and 3′-azido-3′-deoxy-2′- O ,4′- C -methylene-5-methyluridine with 7-propargyloxy coumarins in 82–88% yields. The synthesized coumarintriazolyl-bicyclonucleoside conjugates possess an extra bridge between 2′- O and 4′- C in the nucleoside moiety, which facilitates its pre-organization into N -type sugar puckering. This was confirmed by X-ray crystal structure studies on one of the conjugates, i.e. on N 1-(3′-deoxy-2′- O ,4′- C -methylene-5-methyluridin-3′-yl)- C 4-(4-phenylcoumarin-7-oxymethyl)-1,2,3-triazole. Photophysical studies carried out on the synthesized compounds demonstrate that they possess useful level of fluorescence with Stokes shift of approximately 70 nm. Graphical abstract Image 1 Highlights • A series of novel coumarintriazolyl-bicyclonucleosides have been synthesized. • Crystal structure of one coumarintriazolyl-bicyclonucleoside has been reported. • Synthesis of nucleoside monomers with N -configuration. • Photo-physical studies of eight coumarintriazolyl-bicyclonucleoside conjugates. [ABSTRACT FROM AUTHOR]

In biometrics, feature extraction is an important step to extract the unique information from physiological or behavioural characteristics of individual such as palmprint, faceprint, speech and gait. In this paper, a novel feature extraction technique is proposed for person authentication using palmprint based on gradients of gaborized image called Oriented Gabor Gradients (OGG). To validate the proposed feature extraction method, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, PolyU palmprint database of 386 persons. The proposed OGG method is compared with histograms of oriented gradients (HOG), gabor transform, gaussian membership based features (GMF), absolute average deviation (AAD) and mean features. Experimental results show primacy of the proposed technique over the existing ones in the literature and achieved higher accuracy. Lastly, K-nearest neighbor is used to validate the matching stage. [ABSTRACT FROM AUTHOR]

Quality management is for providing better resources to the customers. It is applicable for hospitals for giving best services. QM has an initiative i.e. FMEA, which is applicable in the purchasing process of the hospital (PPH) for calculating the RPN, which determines the risks linked with the problems occurring in purchasing the particular equipment for the hospital. RPN is conquered from past experience and engineering decisions which leads to errors and discrepancies. In this study, Neuro-Fuzzy approach based technique is applied to improve the purchasing process in Indian private hospitals. Neuro-Fuzzy approach eliminates insufficiencies in the assessment of the RPN, resulting in saving of time. PPH in India has never been improved before by applying neuro-fuzzy scheme based FMEA technique. Analyzed results show that the applied neuro-fuzzy method is able to solve the problems which arise from conventional FMEA approach and will effectively find out RPN. Proposed method provides quality assurance in the process. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the problem of simultaneous identification and predictive control of nonlinear dynamical systems using self‐recurrent wavelet neural network (SRWNN) is addressed. The structure of the SRWNN is a modification of the wavelet neural network (WNN). Unlike WNN, the neurons present in the hidden layer of SRWNN contain the weighted self‐feedback loops. Dynamic back‐propagation algorithm is employed to derive the necessary parameter update equations. To further improve the convergence speed of the parameters, a time‐varying (adaptive) learning rate is used. Four simulation examples are considered for testing the effectiveness of the proposed method. Furthermore, some disturbance rejection tests are also performed on the proposed method. The results obtained through the simulation study confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators. [ABSTRACT FROM AUTHOR]

This paper performs the comparative study of two feed-forward neural networks: radial basis function network (RBFN), multilayer feed-forward neural network (MLFFNN) and a recurrent neural network: nonlinear auto-regressive with exogenous inputs (NARX) neural network for their ability to provide an adaptive control of nonlinear systems. Dynamic back-propagation algorithm is used to derive parameter update equations. To ensure stability and faster convergence, an adaptive learning rate is developed in the sense of discrete Lyapunov stability method. Both parameter variation and disturbance signal cases are considered for checking and comparing the robustness of controller. Three simulation examples are considered for carrying out this study. The results so obtained reveal that RBFN-based controller is performing better than that of NARX- and MLFFNN-based controllers. [ABSTRACT FROM AUTHOR]

This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a simpler structure. Thereafter, we have developed a dynamic back-propagation learning algorithm for tuning the parameters of DRNN. Further, to guarantee the faster convergence and stability of the overall system, dynamic (adaptive) learning rates are developed in the sense of Lyapunov stability method. The proposed scheme is also compared with multi-layer feed forward neural network (MLFFNN) and radial basis function network (RBFN) based identification models. Numerical experiments reveal that DRNN has performed much better in approximating the dynamics of the plant and have also shown more robustness toward system uncertainties. [ABSTRACT FROM AUTHOR]

A series of β- d -ribofuranosyl coumarinyl-1,2,3-triazoles have been synthesized by Cu-catalyzed cycloaddition reaction between azidosugar and 7- O -/7-alkynylated coumarins in 62–70% overall yields. The in vitro antimycobacterial activity evaluation of the synthesized triazolo-conjugates against Mycobacterium tuberculosis revealed that compounds were bactericidal in nature and some of them were found to be more active than one of the first line antimycobacterial drug ethambutol against sensitive reference strain H37Rv, and 7 to 420 times more active than all four first line antimycobacterial drugs (isoniazid, rifampicin, ethambutol and streptomycin) against multidrug resistant clinical isolate 591. Study of in silico pharmacokinetic profile indicated the drug like characters for the test molecules. Further, transmission electron microscopic experiments revealed that these compounds interfere with the constitution of bacterial cell wall possibly by targeting mycobacterial InhA and DNA gyrase enzymes. Study conducted on the activities of the test compounds on bacterial InhA and DNA gyrase revealed that the most bactericidal test compound, N 1 -(β- d -ribofuranosyl)- C 4 -(4-methylcoumarin-7-oxymethyl)-1,2,3-triazole ( 6b ) and its corresponding directly linked conjugate N 1 -(β- d -ribofuranosyl)- C 4 -(4-methylcoumarin-7-yl)-1,2,3-triazole ( 11b ) significantly inhibited the activity of both the enzymes. The results were further supported by molecular docking studies of the compound 6b and 11b with bacterial InhA and DNA gyrase B enzymes. Further, the cytotoxicity study of some of the better active compounds on THP-1 macrophage cell line using MTT assay showed that the synthesized compounds were non-cytotoxic. [ABSTRACT FROM AUTHOR]

The present work describes different research techniques for collecting and organizing speech database in different scenario at the institute and successfully structuring the text independent speaker identification database in Indian context. In order to get the Multi-Scenario dataset, each speaker performed multiple sessions recording in reading style with English and Hindi language with same passages but under different conditions. This work analyzed different scenario affecting the performance of speaker recognition system when tested under dissimilar training conditions. Here four different scenarios are considered; sensor and environment, language, aging and health. To study the effect of sensor, language and environment on the performance of ASR system a database of 200 speaker was created. Under different environmental conditions, four different types of sensors in parallel configuration were used to study the sensor mismatch conditions over testing and training phase. The database contains speech samples of the individual in English and Hindi in read speech styles under two environment i.e. a controlled recording chamber and library. To study the aging effect, an aging NSIT speaker database (AG-NSIT-SD) of 53 famous personalities was collected from online source varying over a period of 10–20 years. Further to study the effect of health, a cough and cold NSIT speaker database (CC-NSIT-SD) of 38 speakers was also collected to study the performance of system. Apart from this, the effect of different noise types on the speaker identification was also studied on different sensors. [ABSTRACT FROM AUTHOR]

Here, a highly precise human identification system is developed using a newly proposed biometric trait-palm-phalanges print (PPP). For PPP, NSIT database has been used which includes palm-phalanges. This database consists of anterior hand images of fifty individuals with ten samples each. To crop the region of interest from hand samples to get palm-phalanges, database is preprocessed. First, it has been shown that each finger phalange can be used as a biometric modality and give moderate/sufficient performance for low-accuracy system. For feature extraction, histograms of oriented gradients, GMF feature, mean and AAD methods have been used. To further enhance the performance, score-level and feature-level fusion strategies are applied and compared. Score-level fusion is performed using different fusion rules. Next for feature-level fusion, five methods are used: (1) simple concatenate, (2) PCA feature fusion, (3) linear discriminant analysis feature fusion, (4) fusion codes and (5) supervised local-preserving canonical correlation analysis method. Receiver operating characteristics (ROC), equal error rate, area under the curve of ROC and decidability index ( d) are used to show the performance of the system qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

This paper presents main paradigms of research for feature extraction methods to further augment the state of art in speaker recognition (SR) which has been recognized extensively in person identification for security and protection applications. Speaker recognition system (SRS) has become a widely researched topic for the last many decades. The basic concept of feature extraction methods is derived from the biological model of human auditory/vocal tract system. This work provides a classification-oriented review of feature extraction methods for SR over the last 55 years that are proven to be successful and have become the new stone to further research. Broadly, the review work is dichotomized into feature extraction methods with and without noise compensation techniques. Feature extraction methods without noise compensation techniques are divided into following categories: On the basis of high/low level of feature extraction; type of transform; speech production/auditory system; type of feature extraction technique; time variability; speech processing techniques. Further, feature extraction methods with noise compensation techniques are classified into noise-screened features, feature normalization methods, feature compensation methods. This classification-oriented review would endow the clear vision of readers to choose among different techniques and will be helpful in future research in this field. [ABSTRACT FROM AUTHOR]

In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plant's dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plant's dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN. [ABSTRACT FROM AUTHOR]

This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynamics of plants are assumed to be unknown, and to tackle the problem of unknown plant's dynamics, parameter variations and disturbance signal effects, a separate neural network-based identification model is set up which will work in parallel to the plant and the control scheme. Weights update equations of all neural networks present in the proposed scheme are derived using both gradient descent (GD) and Lyapunov stability (LS) criterion methods. Stability proof of LS-based algorithm is also given. Weight update equations derived using LS criterion ensure the global stability of the system, whereas those obtained through GD principle do not. Further, adaptive learning rate is employed in weight update equation instead of constant one in order to have fast learning of weight vectors. Also, LS- and GD-based weight update equations are also tested against parameter variation and disturbance signal. Three nonlinear dynamical systems (of different complexity) including the forced rigid pendulum trajectory control are used in this paper on which the proposed scheme is applied. The results obtained with LS method are found more accurate than those obtained with the GD-based method. [ABSTRACT FROM AUTHOR]

We noted that TRI-DOT was nonreactive, whereas VIDAS was reactive with 2.48 s/CO ratio for anti-HIV ½, antigen (p24) was not detected by VIDAS (A signal/cutoff ratio of >=0.25 is considered reactive). Sir, Human immunodeficiency virus (HIV) testing as a part of screening is accomplished by TRI-DOT Rapid HIV flow-through test (DIAGNOSTIC ENTERPRISES, H.P, India) and VIDAS® HIV DUO ULTRA, 4th generation assay (BioMérieux, Marcy-l'Etoile, France) at the Serology Laboratory of our center. COVID antibodies using VIDAS® severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) IgG and IgM were found 0.87 (s/CO) for IgM and 15.50 for IgG. [Extracted from the article]

Background: Lead exists extensively in the environment and it is a heavy metal, which is neurotoxic in nature which is harmful to children. In contrast to trace elements such as iron (Fe) and zinc (Zn), lead has no recognized beneficial effects in human body. With the economic development in India in recent years, the concentration of lead has increased in the environment and henceforth, lead pollution has become a severe problem in the country. Lead poisoning can create an immense damage and irreversible harm to foetal growth, the growth of teenagers and psychological, behavioural and cognitive development. Aim and Objectives: To estimate the lead content in the sample of toys. Material and Methods: 97 different toys were collected and coded as TS, these toys were kept in plastic lock cover which were lead proof. At first the toys were arranged in order according to the serial number of the code (TS1 - TS97). The FP X-ray fiuorescence (Innov-X ray analyzer) instrument was placed with the window of the analyzer on the surface of the toy vertically in such a way that there was no gap between the toy surface and the sampling window. The parts per million (ppm) values of all toys were recorded. Results: Out of 97 samples tested, 22 samples were above permissible limits. The different materials demonstrate significant correlation with the lead content in ppm. Lead is used as a stabilizer in some toys as well as children's items made from PVC plastic. Baby toys made up of plastic have higher concentration of lead. Lead can leach out of products when children handle and swallow them involuntarily. [ABSTRACT FROM AUTHOR]

• More negative glacier-wide mass balance corresponds to higher catchment-wide runoff and vice versa. • Hydrology is dominated by precipitation in Monsoon catchment regime and by glacier mass balance in Alpine catchment regime. • Glacier buffer system is more critical in Alpine catchment regime than Monsoon catchment regime. Glacier mass balance (MB) and runoff relationship is poorly explored in the Himalaya due to limited in-situ data. A simplified glacio-hydrological model is used to reconstruct the long-term MB and runoff on Dokriani Bamak Glacier (DBG) in the Monsoon regime and Chhota Shigri Glacier (CSG) catchments in the Alpine regime of the Himalaya. Since 1950, DBG and CSG show limited mass wastage with mean annual glacier-wide MB of – 0.09 ± 0.35 m w.e. a−1 and – 0.12 ± 0.28 m w.e. a−1, respectively. The mean annual catchment-wide runoff are also almost similar 0.13 ± 0.01 106 m3 a−1 and 0.14 ± 0.01 106 m3 a−1 from DBG and CSG catchments, respectively over 1950–2020. Available in-situ short-term MB and runoff data from eight glacierized catchments do not show any significant MB-runoff relationship, except for CSG. The long-term modelled MB and runoff data on both DBG and CSG catchments suggest that the higher catchment-wide runoff corresponds to more negative MB, and vice versa. This is in contrast to the previously suggested MB-runoff relationship on DBG catchment. Present detailed analysis also indicates that the hydrology of the DBG catchment is mainly dominated by the summer precipitation, while in CSG catchment, it is mainly controlled by the glacier-wide annual MB. In dry years, the corresponding negative annual MB on both the glaciers provides additional glacier-degraded runoff to the catchment streams that can be as high as 13% and 38% of total runoff on DBG and CSG catchments, respectively. [ABSTRACT FROM AUTHOR]

The emergent crisis of the COVID-19 pandemic has posed enormous challenges for clinical laboratories to speed up diagnostics. The current reference standard for the diagnosis of COVID-19 is real time reverse transcriptase PCR on various platforms. However, even with automation, the turnaround time is huge enough to keep up with ever increasing numbers of patients. With increasing surge of COVID cases we need rapid diagnostic tests with good sensitivity and specificity. Comparison between Abbott ID NOW COVID-19 and real time reverse transcriptase PCR as a reference method. Specimens from seventy-two individuals were obtained over a period of two months which were processed for ID NOW and RTPCR at a dedicated COVID-19 centre of AIIMS. Dry nasal swabs were used for ID NOW while nasopharyngeal swabs along with throat swab were used for RTPCR. Among the participants, 15 were healthcare workers. Mild COVID was seen in 36 participants, moderate in 19 and severe in 9. Eight participants had non COVID illness. From the given samples, we observed that ID NOW has a sensitivity of 93.22% (55/59) specificity 100% (13/13), PPV 100% (55/55) and NPV 76.47% (13/17). ID NOW is a convenient, rapid molecular test which makes it suitable for both in laboratory use and as a point of care test. It can be a rapid rule-in test for COVID-19. Negative results, however, have to be interpreted as per the context. • Abbott ID NOW is a Point of care (POC) test. • Abbott ID NOW was found to have Sensitivity of 93.22% and Specificity of 100%. • It is important to follow manufacturer's instructions strictly for better sensitivity. • Evaluation of diverse patient profile is the strength of the study. • It has a short turn-around time, 5–13 min for Positive results and 13 min for Negative results makes this an attractive alternative for COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Three triazole-linked nonionic xylo-nucleoside dimersTL-t-TxL,TL-t-ABzxLandTL-t-CBzxLhave been synthesized for the first time by Cu(I) catalyzed azide-alkyne [3 + 2] cycloaddition reaction (CuAAC) of 1-(3′-azido-3′-deoxy-2′-O,4′-C-methylene-β-D-ribo-furanosyl)thymine with different alkynes,i.e.,1-(5′-deoxy-5′-C-ethynyl-2′-O,4′-C-methylene-β-D-xylofuranosyl)thymine, 9-(5′-deoxy-5′-C-ethynyl-2′-O,4′-C-methylene-β-D-xylo-furanosyl)-N6-benzoyladenine and 1-(5′-deoxy-5′-C-ethynyl-2′-O,4′-C-methylene-β-D-xylofuranosyl)-N4-benzoylcytosine in 90%–92% yields. Among the two Cu(I) reagents, CuSO4.5H2O-sodium ascorbate in THF:tBuOH:H2O (1:1:1) and CuBr.SMe2in THF used for cycloaddition (click) reaction, the former one was found to be better yielding than the latter one. [ABSTRACT FROM PUBLISHER]

This research proposes a pattern/shape-similarity-based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance-based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real-life scenario, it is tested on four different time series. The first is a benchmark Mackey-Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

Objective Redox imbalance either inside platelets or in their immediate surroundings prove detrimental to their physiologic functions during haemostasis. This study was therefore aimed to assess the effect of peroxide radicals on platelet functions and underlying signalling mechanisms using asparagine-conjugated diperoxovanadate (DPV-Asn). Methods Platelet aggregation, ATP secretion, TxB2 release, intra-platelet calcium mobilization, protein tyrosine phosphorylation, GPIIbIIIa activation by PAC1 labelling and sCD40L release (enzyme-linked immunosorbent assay) was monitored using various concentrations of DPV-Asn. Cell viability was assessed by Annexin V labelling, MTT assay, LDH leakage and mitochondrial membrane potential by JC-1. Results Platelet aggregation induced by DPV-Asn was chiefly regulated by dense granule secretion, thromboxane A2 (TxA2) generation, intra-platelet [Ca2+] influx, GPIIbIIIa activation and sCD40L release, which were significantly reduced in presence of U73122 (PLC inhibitor), aspirin (COX), SB203580 (p38 inhibitor), and PD98059 (ERK inhibitor). This was further corroborated by enhanced tyrosine phosphorylation of numerous platelet proteins including PLC-γ2, which apparently played a central role in transducing peroxide signals to regulate [Ca2+] influx and phosphorylation of p38 and ERK1/2 MAP kinase. Discussion Peroxide radicals critically regulate the thrombo-inflammatory functions of platelets via the PLCγ2-p38-ERK1/2-TxA2 pathway, which closely resembles the clinical scenario of various pathologies like hyperglycemia and atherosclerosis during which oxidative stress disrupts platelet functions. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid of Neural Network (NN) and Fast Traversal Filter (FTF) based controller in each area is used to determine the optimal parameters of Load Frequency Control (LFC) of a realistic two area power system. The two area power system is modeled considering the various non -linearities like governor dead band, generation rate constraint (GRC) and boiler dynamics. Input to the controller i.e. the error signal is divided into two parts- linear and non- linear. The linear part of the input signal is minimized by the FTF algorithm, whereas the non- linear part is minimized by the NN algorithm. The output of the controller is the sum of the outputs of NN and FTF networks. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. This is highly desirable in power quality problems. The various components of power system are reduced to transfer functions and the system performance is analyzed for 1% step load perturbation in area1 with different controllersproportional and integral (PI), neural network (NN) and NN+FTF based controllers. The simulations demonstrate the fast and smooth performance of the power system with the proposed controller. Simulated results evince the superiority of the proposed hybrid controller. [ABSTRACT FROM AUTHOR]

This paper presents a novel scheme called “Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System.” The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students' attention on authentic real-world problems. At the same time, it enhances their learning. Students gain hands-on experience and improve their skills in product development, self-directed learning, teamwork, and project management. There has been a tremendous rise in the popularity of intelligent control techniques like fuzzy logic for use in industrial control applications in recent times. These techniques, which were primarily conceived for nonlinear control applications, are best implemented using embedded controllers, which can use their capabilities to the maximum. While courses in electrical and computer engineering cover several areas like digital and analog integrated circuits, microprocessors and control Systems, and process control, few of these integrate all these areas with focus on the application of intelligent techniques in real-time systems. Also, there is a growing need in industry for engineers who can perform software design and system integration for various applications in embedded control. This paper aims at developing such a practical task as one of the major projects in the eighth semester of the program offered by the Instrumentation and Control Engineering (ICE) Department of Netaji Subhas Institute of Technology (NSIT), Delhi University, India, to design a real-time embedded controller implementing an intelligent control technique, fuzzy logic, for control applications. These applications might, for example, be level control, flow control, or pressure control. The paper discusses an example of a real-time pressure control system for which a microcontroller-based fuzzy proportional-integral-derivative (PID) controller has been simulated and implemented with satisfactory results. [ABSTRACT FROM AUTHOR]

In the present work we have attempted to study the electroluminescent (EL) properties of ZnS: Sm, Li luminophor.The phosphor is synthesized using solid state reaction method. A comparative study has been made for electroluminescent emission characteristics of five samples viz ZnS:Cu,Li, ZnS:Sm, ZnS:Sm,Cl; ZnS:Sm,Li; ZnS:Cu,Sm,Li. It is observed that the intensity of brightness varies with the applied voltage and excitation frequency according to the universal laws and color of emission is found to be greenish. Li is found to enhance the EL intensity of ZnS: Sm phosphor. Structural studies using XRD have also been performed. The average grain size of ZnS: Sm, Li is estimated to be 38.9μm [ABSTRACT FROM AUTHOR]

• We developed a glacier mass balance-runoff model for a monsoon-dominated catchment in the central Himalaya. • Decadal mass balances suggest that Dokriani Glacier was close to steady-state conditions preceding 2000. • Heterogeneous debris-cover distribution over lower ablation area protects the glacier from higher melting. • Snow- and ice-melt runoff contribute 56% of the total catchment-wide runoff. • Mass balance and runoff are most sensitive to a unit change in air temperature. Glacier-wide mass balance and catchment-wide runoff were reconstructed over 1979–2018 for Dokriani Glacier catchment in Garhwal Himalaya (India). A glacier mass balance-runoff model, including temperature-index, accumulation, and rain modules was used for the reconstruction using daily air-temperature and precipitation fields from ERA5 reanalysis products. The model was calibrated using 6 years of observed annual glacier-wide mass balances (1993–1995 and 1998–2000) and observed summer mean monthly runoff (1994, 1998–2000) data. Model validation was done using satellite-derived snow line altitudes and field-observed runoff (1997–1998). Modelled mass balance on Dokriani Glacier is moderate with annual loss of −0.25 ± 0.37 m w.e. a–1 over 1979–2018. The mean winter glacier-wide mass balance is 0.72 ± 0.05 m w.e. a–1 while mean summer glacier-wide mass balance is −0.97 ± 0.32 m w.e. a–1 over 1979–2018. The mean annual catchment-wide runoff is 1.56 ± 0.10 m3 s−1 over 1979–2018. Maximum runoff occurs during summer-monsoon months with a peak in August (6.04 ± 0.34 m3 s−1). Rainfall contributes the most to the total mean annual runoff with 44 ± 2% share, while snow melt and ice melt contribute 34 ± 1% and 22 ± 2%, respectively. The heterogeneous debris-cover distribution over lower ablation area (<5000 m a.s.l.) retards melting and protects the glacier. Modelled decadal mass balances suggest that Dokriani Glacier was close to steady-state conditions over 1989–1997 because of negative temperature anomalies and positive precipitation anomalies over this period. Mass balance and runoff are most sensitive to the threshold temperature for melt with sensitivities of 0.77 m w.e. a–1 °C−1 and −0.20 m3 s−1 oC–1, respectively. [ABSTRACT FROM AUTHOR]

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty-two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation-based performance demonstration and finally conclusive summary of the developed tool/technique along with the future scope. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial conditions. [ABSTRACT FROM AUTHOR]

A series of eight N1-(β-D-ribofuranosyl)-C4-(coumarin-4′′-yl)-1,2,3-triazoles have been synthesized by Cu(I)-catalyzed click reaction of 1-azido-1-deoxy-2,3,5-tri-O-benzoyl-β-D-ribofuranose with differently substituted 4-ethynylcoumarins followed by debenzoylation of the resulted N1-(2′,3′,5′-tri-O-benzoyl-β-D-ribofuranosyl)-C4-(coumarin-4″-yl)-1,2,3-triazoles in 71 to 89% overall yields. The structures of all the synthesized compounds were established on the basis of their spectral data analysis that was further confirmed by X-ray data analysis of one of the model benzoylated compounds, i.e. N1-(2′,3′,5′-tri-O-benzoyl-β-D-ribofuranosyl)-C4-(7″-isopropoxycoumarin-4″-yl)-1,2,3-triazole. [ABSTRACT FROM AUTHOR]

Strength of high performance concrete (HPC) is not depends upon water to cement ration only but it is also persuaded by the several components of the concrete. The HPC is a vastly compound material, which create its behavior property very difficult in modeling to analyze. The main aim of this paper is to provide the utilization possibilities of Gene Expression Programming (GEP) to predict the HPC compressive strength (HPCCS) at highly complex behavior. A set of 1030 samples of HPC was collected from open access repository that was developed in the laboratory and represented suitable experimental results, which includes eight attributes (i.e., age, blast furnaces slag, cement, fly ash, water, superplasticizer, fine aggregate and coarse aggregate). The obtained results are compared with other computational intelligence technique (i.e., RBF neural Network) to validate the performance analysis of the proposed approach. This method is used to predict the HPCCS in high strength level. Moreover, as per available literature, this is the first attempt to implement the GEP in this domain to predict the HPCCS. [ABSTRACT FROM AUTHOR]

In cloud computing datacenter infrastructure, virtualization has enabled service providers to create abstraction of their physical resources and increased the infrastructure utilization. At the datacenter, cloud users services are implemented using Virtual Machines (VMs). With the popularity of cloud paradigm, numbers of enterprise applications deployed over cloud systems are increasing. This shift in cloud paradigm has brought new challenges of server consolidation, load balancing, resource management, and server fault situation. Cloud managers trigger VM migration to achieve all these challenges. VM migration allows migration of running VMs from one datacenter to another. In migration process to keep the VM alive, VM memory pages are transferred in multiple iterations. Thus, there is an increase in the requirement of network resources consumption during migration process. However, inaccurate bandwidth allocation for a VM migration request can lead to performance degradation. Therefore, there is a need to devise an efficient bandwidth allocation strategy. In this paper, we propose a multistage bandwidth allocation technique which aims to minimize the bandwidth allocation at each round of migration mechanism. The proposed technique is implemented by the simulation carried out in Matlab software. To ensure the applicability for real-time hosted applications, multistage scheme is evaluated using both low and high dirty rate conditions. We also considered distributed cloud deployment situation by using sequential and multiple VM migration test cases. The results demonstrated shows, as compared to existing VM migration bandwidth allocation schemes, our proposed technique outperform in terms of downtime, migration time, bandwidth provisioned and migration iterations count. Additionally, we also explored multistage technique with fuzzy logic to improve the VM selection process. The significance of this study is that, multistage technique allows bandwidth allocation in each iteration as variable to current dirty rate scenario, thereby increasing the revenue of cloud providers. [ABSTRACT FROM AUTHOR]

This paper proposes the solution to an optimal scheduling problem of a thermal-wind power system. Here, the power output from a wind energy generator (WEG) is assumed to be schedulable, therefore the wind power penetration limits can be determined by the system operator (SO). The intermittent nature of wind power and speed is modeled using the Weibull density function. Here, 3 objective functions i.e., total operating cost, voltage stability enhancement index and system losses are selected. The total generation cost minimization objective has the cost of power generated from thermal and WEGs, under and over estimation costs of wind power. In the present paper, a multi-objective optimal power flow (MO-OPF) problems are framed by considering different objective functions simultaneously, and they are solved using the multi-objective Glowworm Swarm Optimization (MO-GSO) technique. The proposed optimization problem is solved on a modified IEEE 30 bus test system with two wind farms situated at two different buses in the system. The obtained simulation results show the suitability of proposed MO-OPF method for large scale power systems. [ABSTRACT FROM AUTHOR]