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Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault. [ABSTRACT FROM AUTHOR]

Background: Hybrid Electric Vehicle (HEV) uses two sources of energy namely primary and auxiliary source. If only the battery bank is used as a power source of vehicle, then the performance of the vehicle is not satisfactory due to continuous charging and discharging mode, also the direction and amount of the battery current are changed continuously causing stress in the battery bank. Objectives: Objective of the proposed work to control the power flow between battery and supercapacitor (SC) so as the dc bus voltage remains constant. The error is speed is to be minimised for desired operating conditions. Methods: The objective is achieved by using the unidirectional buck-boost converter with battery bank and bidirectional buck-boost converter with SC Auxiliary source provides the energy during acceleration and store the energy at the time of braking. The controller with tuned PID parameters ensures the responses of quickly with minimal overshoot. This paper presents control technique for a Battery and Supercapacitor operated HEV and the tuning of PID controller using Teaching Learning Based Optimization (TLBO), particle swarm optimization (PSO), and Gray Wolf Optimization (GWO). The results are compared under various operating condition on the performance parameters integral absolute error (IAE), integral squared error (ISE), integral of the time-weighted absolute of the error (ITAE) and integral of the time-squired of the error (ITSE). Conclusions: The result suggests the PID parameters tuned with GWO technique give minimum error coefficients, The error is further reduced when SC is introduced with the battery as input source. [ABSTRACT FROM AUTHOR]

The 2020 COVID-19 pandemic introduced significant information challenges for older adults worldwide. Given the widespread disparities in information infrastructure and access between developing and developed countries, the challenges presented during this period may be even more grave in the developing world. This interview study examines how older adults (age 65+) in two countries—the USA and India—experienced information needs, sources, and barriers. The results indicate distinct experiences among the two populations, with individuals in the USA expressing more diverse needs, while Indian respondents focused on the impact of COVID-19. The American respondents also indicated much greater use of Internet resources compared to the Indian respondents, who used television and print resources more frequently. Far more Indian respondents reported significant challenges finding information to satisfy their information needs. The findings of this study have important ramifications for the design of intervention, education, and support for the information-related needs of older adults worldwide. [ABSTRACT FROM AUTHOR]

The purpose of study is to measure the coverage of LIS academia in Google Scholar and Microsoft Academic using scientometric indicators. The study is based on simple search queries in Google Scholar and Microsoft Academic for each LIS faculty whose information is available on their university website. Duplicate names for LIS faculties have been filtered by adding the extra search string with their names and non-LIS publications have been removed from the study. The study found more coverage for Google Scholar than Microsoft Academic. Google Scholar based "document types" are quite unclear and considered as its' limitations. The study is limited to 26 LIS faculties from five Central Universities of North-East India. The results are limited due to use of simple search in Google Scholar and Microsoft Academic rather than profile search. The study provides insight for the individual researcher and LIS departments to focus on indexing of their research in Google Scholar and Microsoft Academic. [ABSTRACT FROM AUTHOR]

Study analyses scientometric assessment of LIS research performance of OPEC member countries. The research publication data indexed in Scopus for the OPEC member countries were extracted and used for the analysis. The data are analyzed to identify quantitative research performance of OPEC member countries in terms of total documents, citable documents, and non-citable documents. The citation impact is measured by different parameters, like total citations, citations per document, Relative Citation Impact, self-citations, and self-citations per document. Finally, the quality of the document is assessed by means of h-index. Nigeria has been found to be most productive country in LIS research and Iran is receiving highest citations and also in h-index performance amongst OPEC member countries. Iraq, Libya, and Ecuador are far away in LIS research productivity and needs strong steps to improve LIS research productivity for future endeavor. [ABSTRACT FROM AUTHOR]

In real world, applications designing of a robust face recognition system have always been a big challenge. This paper presents an approach to face recognition using embedding of dual tree complex wavelet transform, Self-organizing map and ensemble of weak classifier. The DT-CWT is applied on the images to obtain dynamic and multi scale informational characterization of the face images. Thus, a multidimensional feature vector is formed by combining DT-CWT coefficients. Self Organizing Map (SOM) is further used in embedding the feature vector which also results in reduction of feature vector. Finally, ensembles of k-Nearest Neighbor (k-NN) weak classifiers are used for classification of recognition system. The proposed approach is tested on image of ORL database. The experiment shows an impressive recognition result that culminated during the testing. [ABSTRACT FROM AUTHOR]