Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.

The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association.

This paper introduces a simplified presentation of a new computing procedure for solving the fuzzy Pythagorean transportation problem. To design the algorithm, we have described the Pythagorean fuzzy arithmetic and numerical conditions in three different models in Pythagorean fuzzy environment. To achieve our aim, we have first extended the initial basic feasible solution. Then an existing optimality method is used to obtain the cost of transportation. To justify the proposed method, few numerical experiments are given to show the effectiveness of the new model. Finally, some conclusion and future work are discussed.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.

Climatic variations along with a rise in temperature during the winter season impose severe heat stress during the anthesis stage of spring wheat, resulting in severe yield losses. The present study was conducted to evaluate the influence of heat stress on redox homeostasis in developing anthers and flag leaves of wheat. Five Indian bread wheat genotypes were studied under field conditions during the dry season, with two extreme sowing dates (timely and very late sown) to explore the effect of heat stress on anthesis stage. Results showed that elevated temperature during anthesis caused significant increase in reactive oxygen species (ROS) content and malondialdehyde (MDA) accumulation in developing anthers, triggering pollen mortality. Moreover, defective source (leaf) to the sink (anthers) mobilisation of starch also contributes in reducing pollen viability. However, ROS-induced oxidative damage of developing anthers under heat stress varied among the wheat genotypes depending upon differential antioxidant enzyme activities. Wheat genotype with enhanced antioxidant activities and reduced ROS built up in developing anthers sustained their grain yield, suggesting thermo-tolerance in wheat to be associated with antioxidant enzyme-mediated improved ROS-scavenging mechanism not only in leaves even in developing anther also. In the present study, heat stressed wheat genotype WH 730 exhibited effective source to sink mobilisation and sustainable grain yield with improved ROS scavenging, conferring greater potential for heat tolerance. We conclude that redox homeostasis and balanced source sink activity played a significant role for sustainable yield and heat tolerance in wheat.