Pacific University (formally known as "Pacific Academy Of Higher Education And Research University" (PAHER) (Hindi: पाहेर) is a private university in Udaipur, Rajasthan, India. It was established in 2010 by the PAHER Society. In April 2014 it was granted provisional membership by the Association of Indian Universities. Recently the university has been granted UGC approval for its operations u/s 22 and 2f of UGC act 1956..
IntroductionSocial media advertising is a crucial factor for predicting consumer behavior. Further credibility, authenticity, and sustainability of social postings enhance consumer purchase intentions in general and online shopping in particular. This empirical study investigated the impact of social media advertising on consumer behavior and the role of credibility, perceived authenticity and sustainability. The study also investigated the mediating effect of trust in the relationship between social media advertising and consumer behavior.MethodsTo measure the impact of the social media advertising effect on consumer behavior, eight reflective constructs, namely, credibility, sustainability, perceived authenticity, social media advertising effectiveness, satisfaction, purchase intentions and perceived value, were assessed. For the mediation analysis, consumer behavior was modeled as a higher-order construct with three sub-dimensions: satisfaction, perceived value and purchase intentions. Furthermore, the construct social media effectiveness is also modeled as a higher-order construct with four sub-dimensions: credibility, sustainability, authenticity and social media advertisement effectiveness. The data were collected from active social media users who engage in brand advertising and online shopping. A total of 500 valid responses were subjected to exploratory and confirmatory factor analysis, and the hypotheses were tested via structural equation modeling.Results and discussionThe SEM results reveal that constructs credibility and sustainability are strong predictors of consumer behavior in the context of social media advertising effectiveness. The mediating variable of trust partially mediated the relationship between social media effectiveness and consumer behavior. The outcome has several practical theoretical implications for industry. The brand managers should identify that consumer trust and engagement are not only by-products of risk but also actively cultivated through strategic advertising efforts. To maximize the effectiveness of their social media campaigns, brands should focus on building reliability through transparency, enhancing authenticity through reliable storytelling and promoting sustainability with real commitment.
Prednisone, a steroid therapeutically vital for its anti-inflammatory and immunosuppressive activity, often necessitates thorough impurity profiling. Impurity profiling involves monitoring process impurities during drug substance screening and examining degradation products (DPs) in drug formulations, thereby enabling future stability studies that are critical to ensuring product quality. Prednisone-as reported in the European Pharmacopoeia 11.4-is associated with 10 impurities, primarily process-related impurities; however, the method used to detect these impurities has some limitations. In this study, a single reproducible ultrahigh-performance liquid chromatography method has been developed to quantify 12 impurities. The limitations of the European Pharmacopoeia method are also discussed. Forced degradation studies were performed using the new method, and the results demonstrated an adequate mass balance. This method also enables the identification and quantification of two novel degradation products, detected at relative retention time (RRT) of 0.24 and 0.93. The impurity at RRT of 0.24 forms rapidly under mild alkaline conditions at room temperature, with significant formation occurring within a short period. The impurity at RRT of 0.93 is generated via an oxidative degradation pathway. The presence of these impurities was first noted during the initial stages of drug product development and pre-formulation studies. Using this insight, optimized approaches were implemented to effectively minimize the impurity levels in the final formulation. The new DPs were identified and characterized by nuclear magnetic resonance spectroscopy and liquid chromatography-high resolution mass spectrometry. The impurities were established as 17-formyloxy prednisone (DP1) and 1,2-epoxy prednisone (DP2), respectively, and a preliminary toxicity evaluation was undertaken using quantitative structure-activity relationship models from CASE Ultra software. A plausible degradation mechanism was proposed to guide its control strategies. The new analytical method was validated as per the principles of International Council for Harmonization guidances.
As the demand for sustainable and efficient transportation grows, small electric vehicles (SEVs) have emerged as a crucial segment in the automotive industry. This comprehensive review paper explores the latest advancements in the structural design of SEVs. This review delves into recent developments in materials, design methodologies, and engineering technologies including key topics, integration of lightweight materials for enhanced efficiency, advanced aerodynamic designs for improved performance, and the adoption of innovative manufacturing techniques for cost-effective production. The paper examines the challenges and prospects in the structural design of SEVs, particularly focusing on safety standards and adaptability to urban environments. This review aims to provide a holistic understanding of the structural intricacies of SEVs and their impact on performance, sustainability, and consumer appeal. This paper provides a valuable resource for researchers, engineers, and policymakers involved in the development and promotion of eco-friendly automotive technologies.
The heating, ventilation, and air conditioning (HVAC) system is a critical component of energy consumption in commercial buildings. To optimize energy usage and reduce costs, it is crucial to monitor and analyze the HVAC system’s performance and its impact on occupant behavior. In this paper, we present an IoT-based monitoring and data analysis approach for HVAC energy usage and occupant behavior in commercial buildings, using statistical methods and machine learning algorithms to identify patterns and correlations. The approach involves installing temperature sensors both inside and outside the building and occupancy sensors to collect data on the HVAC system’s performance and occupant behavior. The collected data is analyzed using a variety of statistical and machine learning techniques, including linear regression, clustering, and decision trees, to identify patterns and correlations between HVAC energy usage and occupant behavior. The findings show that indoor temperature is closely related (70
