Central University of Haryana, in Jant-Pali villages, Mahendragarh district of Haryana, India, has been established by an Act of Parliament: "The Central Universities Act, 2009" by the Government of India. The territorial jurisdiction of Central University of Haryana is for the whole of the Haryana. The first Convocation of the University was held on 1 March 2014. It is one of the 15 Central Universities established by MHRD, GoI.The university operates from campus in Jant-Pali villages, Mahendergarh 10 kilometres (6.2 mi) from the Mahendergarh on the Mahendergarh-Bhiwani road.[citation needed] Prof. (Dr.) Tankeshwar Kumar is the current vice-chancellor of the university.
This research study examines the impact of dynamic capabilities such as social media (SM), artificial intelligence (AI), entrepreneurial leadership (EL), and digital technology (DT) on E-commerce adoption (ECA) in the handicraft industry. This study will also investigate the knowledge management process (KMP), entrepreneurial orientation (EO) as a mediator, and knowledge entrepreneurship (KE) as a moderating variable. The present study implements a quantitative approach using the Smart PLS structural equation model. Questionnaires were sent to respondents (craft entrepreneurs and artisans) in various handicraft industries. The sample size of 410 was determined using purposive sampling (Judgmental sampling) through random sampling. Researcher analysed 11 hypotheses using a cross-sectional survey. It has been found that even the adoption of AI in e-commerce has not significantly affected the handicraft industry. Entrepreneurial orientation Entrepreneurial leadership traits are strongly linked to e-commerce adoption (ECA). Furthermore, KMP strongly mediates the association between EL and ECA. It has been observed that materials, either living or nonliving beings below waters (SDG 14), are also controlled by e-commerce by coastal countries. So this will also cover SDG8 along with SDG14 and SDG17. Here KE is much explored.Finally, a substantial link between DT, SM, AI, and ECA is uncovered. Managers and policymakers of small industries, especially the handicraft industry, who want to enhance their firms’ ability through knowledge management processes, entrepreneurial leadership, and orientation, are highly recommended to adopt e-commerce. Such factors affect craft industry e-commerce across a broad spectrum and correlate with and support theoretical frameworks, including dynamic capabilities theory (DCT) and the knowledge-based view (KBV).This study is unique in that it has proposed a Digital capability view (DCV) as an extension of DCT.
A new technique enabling to improve feature selection process based on weighted intuitionistic fuzzy (IF) similarity relation (WIFSR) is suggested in the present study. Firstly, we discuss a novel WIFSR by improving the idea of IF similarity relation. Secondly, IF granular structure (IFGS) is established on the basis of WIFSR. Thirdly, IF rough set model is outlined based on the idea of aforesaid IFGS. Next, positive region is computed based on the lower approximation of IF rough set. Then, dependency of decision dimension over set of conditional dimension is calculated based on positive region and cardinality of the decision system. With granular structures, features/dimensions can be represented with different levels of abstraction to provide a dynamic and flexible selection approach. Moreover, we present a WIFSR designed to measure the similarity between features by taking into account their relevancy and non-redundancy. Proposed approach effectively addresses the problem of feature selection by measuring degree of dependency between features in IFGS framework. Mathematical validation is illustrated for all the established notions. Proposed method is experimentally evaluated on various datasets, and we successfully demonstrate its efficiency in terms of determining the inherent features while safeguarding them from later uncertainty and noise. Our experimental results illustrate that the suggested approach, in the context of accuracy and standard deviation, outperforms the existing feature selection methods. At the end, a new scheme is demonstrated to enhance the overall prediction performances of machine learning methods for antiviral peptides.
Green synthesis of silver nanoparticles (AgNPs) is gaining significant attention due to their unique physicochemical characteristics and diverse applications. AgNPs can be synthesized using bacteria, yeast, plants, algae, fungi, microbial enzymes, etc. The present study focuses on the green synthesis of silver nanoparticles using Spirulina platensis (AgNP@SP). The formation of AgNPs was confirmed by a color change from green to red and by a surface plasmon resonance band observed at 420 nm in the UV–visible spectrum. The synthesized silver nanoparticles were further characterized using FE-SEM, XRD, and FTIR, and EDX analysis. Their antibacterial and antifungal activities were evaluated against four different microbial strains. The presence of functional groups such as carbonyl (C = O) and carboxyl (COOH) on the nanoparticle surface was attributed to their antimicrobial activity. The presence of silver in AgNP@SP was confirmed by EDX analysis, which revealed silver and oxygen contents of 64
The focus of the present study is to give short term forecast of monthly average wholesale prices of tomato using hybrid time series models. For this hybrid models of the linear seasonal autoregressive moving average (SARIMA) and the nonlinear Artificial Neural Network (ANN) have been considered for estimating and forecasting the monthly average wholesale prices of tomato. For this, the monthly average wholesale prices of tomato from January 2010 to December 2022 have been obtained from different markets of Haryana. The goodness of fitted SARIMA models have been measured using Akaike Information Criteria (AIC), log likelihood (LL), Root Mean Square Error (RMSE) Mean Absolute Percentage Error (MAPE). The performance of ANN models has been measured using performance measures RMSE MAPE. The post-sample forecast accuracy has also been measured using MAPE and standard error of prediction (SEP in
In the present study, the green light emitting Gd3-xGaO6:xEr3+ (x = 1-7 mol %) phosphors were synthesized via the solution combustion synthesis. Powder X-ray diffraction (PXRD) analysis confirms orthorhombic crystal structure in space group Cmc21. The crystallite size was evaluated using the Scherrer equation and Williamson-Hall (W-H) approach which show consistent results. The aggregated and uniformly distributed nature of the particles was noticed via Field Emission Scanning Electron Microscopy (FESEM) results. The element composition was determined by energy dispersive X-ray (EDX) spectroscopy. The PL spectra recorded under the 271 nm excitation displayed emission peaks at 409, 527, 548 and 661 nm attributed 2H9/2 -> 4I12/2, 2H11/2 -> 4I15/2, 4S3/ 2 -> 4I15/2 and 4F9/2 -> 4I15/2, respectively. Er3+ doping levels beyond 4 mol % lead to quenching which is governed by dipole-dipole interaction. The phosphors display remarkable color purity and chromaticity coordinates demonstrating their potential use as green emitters in WLEDs and solid-state lighting applications.
