Traditional mindfulness-based interventions have been shown to reduce depression symptoms in pregnant women, although in-person classes may pose significant accessibility barriers, particularly during the COVID-19 pandemic. Mobile technology offers greater convenience, but little is known regarding the efficacy of self-paced, mobile-delivered (mHealth) mindfulness interventions in this population. This study tested the feasibility and acceptability of offering such an intervention for pregnant women with moderate-to-moderately-severe depression symptoms. We conducted a single-arm trial within Kaiser Permanente Northern California (KPNC). Participants were identified through KPNC's universal perinatal depression screening program. Eligible participants included English-speaking pregnant women (<28 weeks of gestation) with moderate-to-moderately-severe depressive symptoms without a regular (<3 times/week) mindfulness/meditation practice. Participants were asked to follow a self-paced, 6-week mindfulneomen with moderate-to-moderately-severe antenatal depression symptoms. The preliminary data further suggest that an efficacy trial is warranted.There have been many changes in the medical field due to technological advances. The progression in technologies provides lot of opportunities to extract valuable insights from huge amount of unstructured data. selleck chemicals llc The literature documents published by the researchers in medical domain consists enormous amount of knowledge. Many organizations are involving in retrieving the hidden information from the literature documents. Extracting the drug names, diseases, symptoms, route of administration, species and dosage forms from the textual document is an easy task due to the innovation of technologies in the Natural Language Processing. In this article, a new hybrid based approach is proposed to identify named entity from the medical literature documents. New dictionary has been built for route of administration, dosage forms and symptoms to annotate the entities in the medical documents. The annotated entities are trained by the blank Spacy machine learning model. The trained model provide a decent accuracy when compared with the existing model. The hybrid model is validated with the dictionary and human (optional)to calculate the confusion matrix. It is able to identify more entities than the prevailing model. The average F1 score for five entities of the proposed hybrid based approach 73.79%.One of the key debates about applying virtue ethics to business is whether or not the aims and values of a business actually prevent the exercise of virtues. Some of the more interesting disagreement in this debate has arisen amongst proponents of virtue ethics. This article analyzes the central issues of this debate in order to advance an alternative way of thinking about how a business can be a form of virtuous practice. Instead of relying on the paired concepts of internal and external goods that define what counts as virtuous, I offer a version of speech act theory taken from Paul Ricoeur to show how a business can satisfy several aims without compromising the exercise of the virtues. I refer to this as a polyvalent approach where a single task within a business can have instrumental, conventional, and imaginative effects. These effects correspond to the locutionary, illocutionary, and perlocutionary dimensions of meaning. I argue that perlocution provides a way in which the moral imagination can discover the moral significance of others that might have not been noticed before, and furthermore, that for such effects to be practiced, they require appropriate virtues. I look at two cases taken from consultation work to thresh out the theoretical and practical detail. This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil winnovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.This paper presents a model based on mediative fuzzy logic in this COVID-19 pandemic. COVID-19 (novel coronavirus respiratory disease) has become a pandemic now and the whole world has been affected by this disease. Different methodologies and many prediction techniques based on various models have been developed so far. In the present article, we have developed a mediative fuzzy correlation technique based on the parameters for COVID-19 patients from different parts of India. The proposed mediative fuzzy correlation technique provides the relation between the increments of COVID-19 positive patients in terms of the passage of increment with respect to time. The peaks of infected cases in connection with the other condition are estimated from the available data. The mediative fuzzy logic mathematical model can be utilized to find a good fit or a contradictory model for any pandemic model. The proposed approach to the prediction in COVID-19 based on mediative fuzzy logic has produced promising results for the continuous contradictory prediction in India.