Ecological and health-related issues have given rise to scholars and investigators' concerns in exploring the determinantal effects of organic food. But little attention has paid to the actual buying behaviour regarding organic foods; especially in developing economies such as Pakistan. Therefore, this study aims to analyze the actual consumer's purchasing behaviour about organic food. A theoretical framework based on green perceived value was developed to observe the study's objectives that determine the consumers' buying behaviour and intention. The moderating effects of food neophobia was also analyzed. Structural Equation Modeling (SEM) approach is used for analysis, and data collection was made from millennial, which is considered the most significant consumer part in Pakistan. Answers of 1324 university students indicate that all values, i.e., social (0.101), functional (0.314), conditional (0.228), and emotional (0.521) has a significant and positive impact on buying intent of users. Furthermore, consumers' intention to buy organic produce (0.282) is positively linked with buying behaviour, and food neophobia (0.091) also moderates positively between intention to purchase and organic food consumption. Moreover, the current study offers valuable insights to manufacturers, researchers, and advertisers of natural food. The study concluded the suggestions for future studies, and the limitations of the study are also stated.

Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

No abstract

No abstract

This study investigated the impact of three land tenure arrangements on organic farming (OF) in terms of increment of efficiency, yield, and investment in soil-improving activities by using farm-level data gathered from three districts located at Punjab, Pakistan. A multivariate tobit model that captured the probable substitute and investment choices, as well as the endogenous nature of land tenure arrangements, has been employed in this analysis. The empirical outcomes displayed that rights of land use affected the decisions made by farmers to invest in land and to improve efficiency. In detail, owner-farmers with secure rental arrangements invested more in improving their land and productivity compared to those with unsecured lease agreements. The yield per hectare was the highest for owner cultivation farm, while sharecropper output seemed the lowest, which are in agreement with the hypothesis of Marshallian inefficiency.