Exponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute

The main objective of the present study is to develop a new modified unbiased exponential type product estimator for the estimation of the population mean. The proposed estimator possesses the characteristic of a bi-serial negative correlation between the study variable and its auxiliary attribute. Efficiency comparison has been carried out between the proposed estimator and the existing estimators theoretically and numerically

Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions

Background: The emerging Artificial Intelligence (AI) based Conversational Agents (CA) capable of delivering evidence-based psychotherapy presents a unique opportunity to solve longstanding issues such as social stigma and demand-supply imbalance associated with traditional mental health care services. However, the emerging literature points to several socio-ethical challenges which may act as inhibitors to the adoption in the minds of the consumers. We also observe a paucity of research focusing on determinants of adoption and use of AI-based CAs in mental healthcare. In this setting, this study aims to understand the factors influencing the adoption and use of Intelligent CAs in mental healthcare by examining the perceptions of actual users. Method: The study followed a qualitative approach based on netnography and used a rigorous iterative thematic analysis of publicly available user reviews of popular mental health chatbots to develop a comprehensive framework of factors influencing the user’s decision to adopt mental healthcare CA. Results: We developed a comprehensive thematic map comprising of four main themes, namely, perceived risk, perceived benefits, trust, and perceived anthropomorphism, along with its 12 constituent subthemes that provides a visualization of the factors that govern the user’s adoption and use of mental healthcare CA. Conclusions: Insights from our research could guide future research on mental healthcare CA use behavior. Additionally, it could also aid designers in framing better design decisions that meet consumer expectations. Our research could also guide healthcare policymakers and regulators in integrating this technology into formal healthcare delivery systems. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/1

RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes

Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification

Breast cancer prognosis risk estimation using integrated gene expression and clinical data

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group

Computational methods for breast cancer diagnosis, prognosis, and treatment prediction

The research presented here develops a robust reliability algorithm for the identification of reliable protein interactions that can be incorporated with a gene expression dataset to improve the algorithm performance, and novel breast cancer based diagnostic, prognostic and treatment prediction algorithms, respectively, which take into account the existing issues in order to provide a fair estimation of their performance

Stochastic Modeling of a Concrete Mixture Plant with Preventive Maintenance

In this paper, a stochastic model for concrete mixture plant with Preventive Maintenance (PM) is analyzed in detail by using a supplementary variable technique. In a concrete mixture plant eight subsystems are arranged in a series. The system goes under PM after a maximum operation time and work as new after PM. The time to failure of each subsystem follows a negative exponential distribution while PM and repair time distributions are taken as arbitrary. A sufficient repair facility is provided to the system for conducting PM and repair of the system. Repair, maintenance and switch devices are perfect. All random variables are statistically independent. Various measures of system effectiveness such as reliability, mean time to system failure (MTSF), are derived using a supplementary variable technique. The numerical results for reliability and availability are obtained for particular values of various parameters and costs

Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger

Rice (Oryza sativa L.) supplies nourishment to about half of the population of the world’s inhabitants. Of them, more than 2 billion people suffer from ‘hidden hunger’ in which they are unable to meet the recommended nutrients or micronutrients from their daily dietary intake. Biofortification refers to developing micronutrient-rich diet foods using traditional breeding methods and modern biotechnology, a promising approach to nutrition enrichment as part of an integrated strategy for food systems. To improve the profile of rice grain for the biofortification-related traits, understanding the genetics of important biofortification traits is required. Moreover, these attributes are quantitative in nature and are influenced by several genes and environmental variables. In the course of past decades, several endeavours such as finding the important quantitative trait loci (QTLs) for improving the nutrient profile of rice seeds were successfully undertaken. In this review, we have presented the information regarding the QTLs identified for the biofortification traits in the rice

Diversity and status of migratory and resident wetland birds in Haridwar, Uttarakhand, India

Migration is the seasonal habitual movement, exhibited by many avian species along a flyway from breeding to wintering grounds and vice versa all over the world. Migratory birds are very sensitive to even small changes in water level which may be affected by flood or drought on their breeding and wintering grounds. High rains during monsoon season can cause flood conditions in the lower hills and Gangetic plains including Haridwar district. In our study, conducted during last ten years (2009-2018), we covered Bheemgoda Barrage and Missarpur Ganga Ghat of Haridwar, Uttarakhand, where 46 species of Migratory (M) and Resident Migratory (RM) wetland birds were observed. Bird survey indicated that there was a significant increase (p = 0.064, t-test) in the population of certain species such as Bhraminy Shelduck (67%), Black Headed Gull (31%), Gadwall (7%), Northern Pintail (59%), Red Crested Pochard (10%) and Tufted Pochard (47%) in Missarpur Ganga Ghat as compared to Bheemgoda Barrage (based on the average abundance of the species observed during study period). It may be pointed out that after flood and loss of vegetated island, there was significant decrease (p= 0.023, t-test) in the population of species such as Black necked stork (76%), Great crested grebe (56), Pallas gull (47%) at Bheemgoda barrage, while some species such as Bar headed goose, Common pochard did not arrive in Bheemgoda barrage after the flood. The study would help to understand the effect of climatic change on water birds species distribution in natural and man-made wetlands

Cross-Sectional Study to Find Out the Prevalence of Cardiovascular Diseases Through Detection of ECG Abnormalities in Undiagnosed Population Using a Handheld ECG Device, SanketLife Pro Plus

: In India, cardiovascular diseases (CVDs) are now the main cause of sudden death. Statistics on prevalence or nationally representative surveillance statistics, however, are lacking. Aim: The objective of this cross-sectional study was to assess the ECG findings in general OPD patients not yet diagnosed with any CVD using SanketLife Pro Plus handheld ECG device. Materials and methods: The study data was extracted from a free ECG test camp, which was organized in the common OPD waiting area at Indraprastha Apollo Hospitals in New Delhi. Of the 100 persons screened, 78% had sinus rhythm and 13% had tachycardia. Apart from these, no other major findings were detected in the study population. One percent ST depression and 4% T-wave inversions were the significant findings of concern, suggestive of myocardial ischemia or infarction, especially in the undiagnosed population. Conclusion: Considering the sample size, even at a 1% incidence of major ECG abnormalities, the outcome is indicative of a possible underlying danger, which is avoidable with early detection and thorough awareness. A mass ECG screening along with collection of relevant data and appropriate research design may help to identify the population at risk. Besides the ECG screening, a stroke risk assessment should be done and prophylaxis must be given to the individuals who have been diagnosed with CVDs