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Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

Through a number of comprehensive reviews, the Institute of Medicine (IOM) has recommended that healthcare organizations develop safety cultures to align delivery system processes with the workforce requirements to improve patient outcomes. Until health systems can provide safer care environments, patients remain at risk for suboptimal care and adverse outcomes. Health science researchers have begun to explore how safety cultures might act as an essential system feature to improve organizational outcomes. Since safety cultures are established through modification in employee safety perspective and work behavior, human resource (HR) professionals need to contribute to this developing organizational domain. The IOM indicates individual employee behaviors cumulatively provide the primary antecedent for organizational safety and quality outcomes. Yet, many safety culture scholars indicate the concept is neither theoretically defined nor consistently applied and researched as the terms safety culture, safety climate, and safety attitude are interchangeably used to represent the same concept. As such, this paper examines the intersection of organizational culture and healthcare safety by analyzing the theoretical underpinnings of safety culture, exploring the constructs for measurement, and assessing the current state of safety culture research. Safety culture draws from the theoretical perspectives of sociology (represented by normal accident theory), organizational psychology (represented by high reliability theory), and human factors (represented by the aviation framework). By understanding not only the origins but also the empirical safety culture research and the associated intervention initiatives, healthcare professionals can design appropriate HR strategies to address the system characteristics that adversely affect patient outcomes. Increased emphasis on human resource management research is particularly important to the development of safety cultures. This paper contributes to the existing healthcare literature by providing the first comprehensive critical analysis of the theory, research, and practice that comprise contemporary safety culture science.

Social networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.

HMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.

The first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.

A hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.

The outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.