Open randomised controlled study to evaluate the efficacy of Karsha Vati and Telmisartan in the management of Essential Hypertension: A Pilot Study

Hypertension appears to be the important risk factor of the modern society; the cause behind this is busy and stressful life, absence or less physical activities. A clinical study was conducted on patients, to evaluate the efficacy of Karsha Vati and Telmisartan in the management of essential Hypertension. The present study was designed on newly diagnosed cases of Hypertension. The Aim and Objective of this study was to evaluate the efficacy of Karsha Vati and Telmisartan in the management of Hypertension. This study was planned on 60 diagnosed cases of Hypertension in each Trial and Controlled group who were orally administered with Karsha Vati and Telmisartan respectively. Result obtained from study revealed that Karsha Vati (Trial group) shows good effect in relieving the subjective criteria’s viz, Shiroruka, Bhrama, Klama, Nidra-vikruti, Hrud-dravata than controlled group. There was no significant difference seen between the two groups

Photophysics of fluorescent silver nanoclusters

Fluorescence imaging has been increasingly relied upon as the method of choice for many biological and medical applications. As demands for more sensitive and higher resolution imaging are ever-increasing, it is critical that photostable, and robust fluorophores capable of delivering high emission rates are available. Fluorescent silver nanoclusters offer an attractive compromise between the photostability and brightness of quantum dots and the compact versatility of organic chromophores. They have been shown to be superior in many roles, including as single molecule fluorophores and bulk multiphoton biological staining agents. The two-photon absorption cross sections are several orders of magnitude larger than commercially-available dyes, and they have demonstrated superior photostability under high intensity irradiation. In addition to the endogenous effects of the cluster, its small size of only a few atoms renders it highly susceptible to surface and environmental effects, which manifests, for example, in the observed photoinduced charge transfer between the silver cluster and oligonucleotide. This state has been shown to be highly advantageous in imaging applications, as control of this state enables better control over the time-averaged emission rate of the molecule. The mechanism of charge transfer, and the possible means by which this state can be controlled will be also be investigated in this work.Ph.D.Committee Chair: Dickson, Robert; Committee Member: Brown, Ken; Committee Member: Curtis, Jennifer; Committee Member: Payne, Christine; Committee Member: Perry, Josep

The Impact of Artificial Intelligence Integration on Minimizing Patient Wait Time in Hospitals

Reduced patient wait-times benefit not just patients' health but also the overall efficiency of the healthcare system, which is particularly crucial given the aging population and rising demand for medical services in recent decades. Reducing the time that outpatients have to wait is one of the most crucial actions that must be taken to improve the patient experience. Artificial intelligence and machine learning may be applied in health care and medicine to enhance insights, reduce waste and wait time, and increase speed, service efficiency, accuracy, and efficiency. The purpose of this research is to determine whether or not the deployment of AI in hospital management system help reduce the amount of time that patients have to wait for their appointments. The Random Forest Regression, Pairwise multiple regression, and the pairwise Pearson correlation have been performed. This research also included additional features such as the number of the office personnel, the number of doctors, the quantity of equipment, and the health expenses in order to eliminate any potential omitted variable biases. According to the findings of the Random Forest Regression, the integration of AI and ML seems to be required to cut down on the amount of time that patients have to wait. The size of the office personnel, the number of doctors, and the number of pieces of equipment are found to be significant factors in lowering the amount of time spent waiting. It was determined that the aspect of the cost was the least significant in terms of reducing the amount of time spent waiting. According to the findings of our study, the healthcare care center needs to expand the integration of AI in order to cut down on the waiting time for patients and to improve the overall experience they provide for them. The findings also suggest that wait times depend on many factors. Thus, focusing on a few factors does not significantly reduce wait time

Incidence of coronary artery disease before valvular replacement in isolated severe aortic stenosis patients in Western Rajasthan

Background: The aim of the study was to evaluate the incidence of Coronary artery disease (CAD) and predictors of CAD in patients with severe AS in western Rajasthan population.Methods: Data from all consecutive patients with severe AS undergoing AVR at a major tertiary cardiac and vascular center in Udaipur were entered in a prospective registry beginning in 2015. Significant CAD was defined as one or more major coronary arteries having an estimated narrowing of ≥70% and left main coronary arteries having an estimated narrowing of ≥50% on coronary angiography. We excluded patients with multiple valve disease, significant aortic regurgitation, or prior CAD or valve surgery.Results: Mean age of 55 enrolled patients was 52.64±15.5 years. Diabetes mellitus and hypertension were present in 3.64% and 5.45% of patients, respectively. Moderate and severe Left ventricular ejection fraction (LVEF) was found in 16.36% and 10.91% patients, respectively. Only 5.45% patient had severe CAD and thus underwent AVR and coronary artery bypass grafting, and rest 94.55% patients underwent AVR. Mean age of patients who underwent AVR was 51.75±15.36 years and who underwent AVR and CABG was 68±11.14 years with no significant association (p=0.078). Proportion of patients requiring AVR and CABG was significantly higher in moderate (22.22%) and severe LVEF (16.67%) as compared to normal or mild (p=0.034).Conclusions: Coronary angiography before AVR will be considered in patients with multiple risk factors for cardiovascular disease or in patients above 68 years of age without risk factors for cardiovascular disease. However, larger studies on heterogeneous population are required to prove our findings.

Mining Public Opinion about Hybrid Working With RoBERTa

As the businesses recover from the COVID-19 epidemic, a new working paradigm is emerging: the hybrid work arrangement. A hybrid work method is a working approach that enables workers to work from several places, such as at home, on the move, or in the workplace. People are expressing their opinions on different social media outlets about the new work model. Organizations and businesses value public views. Because public perspectives will allow decision-makers to adapt promptly to rapidly transforming cultural, commercial, and social environments. Opinion mining is traditionally used to summarize the quantity of positive and negative responses in a given text using sentiment analysis techniques. Opinionated material from social media sites is used to identify people's enthusiasm or displeasure with a certain issue under debate. This study analyzes the public sentiments (positive, negative, and neutral) on a hybrid work model using Twitter API and the Robustly Optimized BERT Pre-training Approach (RoBERTa).   Out of 1 thousand tweets containing the term “hybrid work”, 37 (4.2%), 305 (33.3%), and 658 (62.5%) tweets were classified as negative, neutral, and positive, respectively.  We also compared the public sentiments about hybrid work with those of remote work. The RoBERTa classified 8(1.6%), 436 (85.9 %), and 62 (12.5%) tweets as negative, neutral, and positive, respectively.  The results showed that The majority of individuals showed favorable sentiment toward the hybrid work arrangement. The findings also demonstrate that “hybrid work” has an affinity with “remote work”, “ai”, “digital transformation” and “future of work”

Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty

AI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today's organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world.  The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively.  The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what's useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them

Distributed Apportioning in a Power Network for providing Demand Response Services

Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand. In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid. The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article . A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner. The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES). The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching realtime power reference commands to simulated power electronics interface emulating LIS units for demand response.Comment: 7 pages, 11 Figures, IEEE International Conference on Smart Grid Communication