Analysing the efficacy of training strategies in enhancing productivity and advancement in profession: theoretical analysis in Indian context

An assessment of those needs, also known as a needs analysis, must be carried out in order to ascertain if the organization\u27s requirements, objectives, and concerns can be achieved or addressed via training. In conducting our research, we reviewed training and development-related writing from 1971 to 2023. We believed that the use of more sophisticated training evaluation techniques and statistical approaches, together with an increase in the technological complexity of training design and methodology, set the post-1971 era apart. To be effective, a qualitative review must place more of a focus on qualitative methods of evaluating training effectiveness. Similar to earlier training and development reviews, the present study considered practitioner-oriented literature if it met the criteria listed below for inclusion. A thorough search of the academic literature was conducted to find empirical studies that assessed training programmes or examined the effectiveness of specific training components. After reviewing their abstracts, it was decided to keep 58 articles and papers since they had the proper information. Our research showed that organisations with a strong reputation for employee development are a completely different tale. The majority of businesses monitor the effects of their training efforts in the area of organisational effectiveness. For the second category, increases in productivity, revenue, and profitability are typical signs of organisational success. Overall, there is far more research on team and individual benefits than there is on organisational ones

The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures

Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI\u27s growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem

Some properties of a new subclass of analytic univalent functions defined by multiplier transformation

The purpose of the present paper is to study the integral operator of the form ∫z0{Inμf(t)t}δdt where $f$ belongs to the subclass $C(n,\alpha,\beta, \mu)$ and $\delta$ is a real number. We obtain integral characterization for the subclass $C(n,\alpha,\beta, \mu)$ and also prove distortion, rotation and radii theorem for this class. Relevant connections of the results presented here with various known results are briefly indicated. Mathematics Subject Classification (2010): 30C45, 30C50, 30C55

Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment

Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management

In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies. The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors. The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices. In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news

The role of antioxidants and free radicals in the healing effects of Bacopa monniera on acetic acid-induced colitis in rats

Background: The aim to study and elucidate the healing effects of ethanolic extract of dried whole plant of Bacopa monniera against experimental colitis in rats.Methods: Bacopa monniera whole plant extract was administered orally, once daily for 14 days, to rats after induction of colitis with acetic acid. We studied its effects on: faecal output, food and water intake, and body weight changes and also examined colonic mucosal damage, inflammation and status of antioxidants: superoxide dismutase, reduced glutathione; free radicals: nitric oxide, lipid peroxidation on 15th day of the experiment. Antibacterial activity of the extract was also studied using in vitro procedures. Statistical comparison was performed using either unpaired ‘t’ test or one -way analysis of variance (ANOVA) and for multiple comparisons versus control group was done by Dunnett’s test.Results: Bacopa monniera whole plant extract decreased colonic mucosal damage, inflammation, faecal output and increased body weight in acetic acid induced colitis. It also showed antibacterial activity and enhanced the antioxidant but decreased free radicals. Acute toxicity study indicated no mortality or other ANS or CNS related adverse effects even with ten time effective dose indicating its safety.Conclusions: Bacopa monniera whole plant extract is safe, effective and could be beneficial as a complementary agent in treatment of ulcerative colitis

Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency

Biochemical and biophysical characterization of Leishmania donovani gamma-glutamylcysteine synthetase

Abstractγ-glutamylcysteine synthetase (Gcs) is a vital enzyme catalyzing the first and rate limiting step in the trypanothione biosynthesis pathway, the ATP-dependent ligation of L-Glutamate and L-Cysteine to form gamma-glutamylcysteine. The Trypanothione biosynthesis pathway is unique metabolic pathway essential for trypanosomatid survival rendering Gcs as a potential drug target. Here we report the cloning, expression, purification and characterization of L. donovani Gcs. Three other constructs of Gcs (GcsN, GcsC and GcsT) were designed on the basis of S. cerevisiae and E. coli Gcs crystal structures. The study shows Gcs possesses ATPase activity even in the absence of substrates L-glutamate and L-Cysteine. Divalent ions however plays an indispensable role in LdGcs ATPase activity. Isothermal titration calorimetry and fluorescence studies illustrates that L. donovani Gcs binds substrate in order ATP >L-glutamate>L-cysteine with Glu 92 and Arg 498 involved in ATP hydrolysis and Glu 92, Glu 55 and Arg 498 involved in glutamate binding. Homology modeling and molecular dynamic simulation studies provided the structural rationale of LdGcs catalytic activity and emphasized on the possibility of involvement of three Mg2+ ions along with Glutamates 52, 55, 92, 99, Met 322, Gln 328, Tyr 397, Lys 483, Arg 494 and Arg 498 in the catalytic function of L. donovani Gcs

1H NMR-Based Metabolic Signatures in the Liver and Brain in a Rat Model of Hepatic Encephalopathy

Hepatic encephalopathy (HE) is a debilitating neuropsychiatric complication associated with acute and chronic liver failure. It is characterized by diverse symptoms with variable severity that includes cognitive and motor deficits. The aim of the study is to assess metabolic alterations in the brain and liver using nuclear magnetic resonance (NMR) spectroscopy and subsequent multivariate analyses to characterize metabolic signatures associated with HE. HE was developed by bile duct ligation (BDL) that resulted in hepatic dysfunctions and cirrhosis as shown by liver function tests. Metabolic profiles from control and BDL rats indicated increased levels of lactate, branched-chain amino acids (BCAAs), glutamate, and choline in the liver, whereas levels of glucose, phenylalanine, and pyridoxine were decreased. In brain, the levels of lactate, acetate, succinate, citrate, and malate were increased, while glucose, creatine, isoleucine, leucine, and proline levels were decreased. Furthermore, neurotransmitters such as glutamate and GABA were increased, whereas choline and myo-inositol were decreased. The alterations in neurotransmitter levels resulted in cognitive and motor defects in BDL rats. A significant correlation was found among alterations in NAA/choline, choline/creatine, and NAA/creatine with behavioral deficits. Thus, the data suggests impairment in metabolic pathways such as the tricarboxylic acid (TCA) cycle, glycolysis, and ketogenesis in the liver and brain of animals with HE. The study highlights that metabolic signatures could be potential markers to monitor HE progression and to assess therapeutic interventions