Salt stress is one of the most important abiotic stresses as it persists throughout the plant life cycle. The productivity of crops is prominently affected by soil salinization due to faulty agricultural practices, increasing human activities, and natural processes. Approximately 10% of the total land area (950 Mha) and 50% of the total irrigated area (230 Mha) in the world are under salt stress. As a consequence, an annual loss of 12 billion US$ is estimated because of reduction in agriculture production inflicted by salt stress. The severity of salt stress will increase in the upcoming years with the increasing world population, and hence the forced use of poor-quality soil and irrigation water. Unfortunately, majority of the vegetable crops, such as bean, carrot, celery, eggplant, lettuce, muskmelon, okra, pea, pepper, potato, spinach, and tomato, have very low salinity threshold (ECt, which ranged from 1 to 2.5 dS m–1 in saturated soil). These crops used almost every part of the world and lakes’ novel salt tolerance gene within their gene pool. Salt stress severely affects the yield and quality of these crops. To resolve this issue, novel genes governing salt tolerance under extreme salt stress were identified and transferred to the vegetable crops. The vegetable improvement for salt tolerance will require not only the yield influencing trait but also target those characters or traits that directly influence the salt stress to the crop developmental stage. Genetic engineering and grafting is the potential tool which can improve salt tolerance in vegetable crop regardless of species barriers. In the present review, an updated detail of the various physio-biochemical and molecular aspects involved in salt stress have been explored.

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A Recommender System (RS) is a composition of software tools and machine learning techniques that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Numerous approaches have been proposed for providing recommendations. Certainly, recommendation systems have an assortment of properties that may entail experiences of user such as user preference, prediction accuracy, confidence, trust, etc. In this paper we present a categorical reassess of the field of recommender systems and Approaches for Evaluation of Recommendation System to propose the recommendation method that would further help to enhance opinion mining through recommendations.

Firefly Algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches used for solving optimization problems. The conventional FA use randomization factor during generation of solution search space and fireflies position changing, which results in imbalanced relationship between exploration and exploitation. This imbalanced relationship causes in incapability of FA to find the most optimum values at termination stage. In the proposed model, this issue has been resolved by incorporating PS at the termination stage of standard FA. The optimized values obtained from the FA are set as the initial starting points for the PS algorithm and the values are further optimized by PS to get the most optimal values or at least better values than the values obtained by conventional FA during its maximum number of iterations. The performance of the newly developed FA-PS model has been tested on eight minimization functions and six maximization functions by considering various performance evaluation parameters. The results obtained have been compared with other optimization algorithms namely genetic algorithm (GA), standard FA, artificial bee colony (ABC), ant colony optimization (ACO), differential equations (DE), bat algorithm (BA), grey wolf optimization (GWO), Self-Adaptive Step Firefly Algorithm (SASFA), and FA-Cross algorithm in terms of convergence rate and various numerical performance evaluation parameters. A significant improvement has been observed in the solution quality by embedding PS in the standard FA at the termination stage. The result behind this improvement is the better exploration and exploitation of the solution search space at this stage.

Purpose Annually, hundreds of drilling crew suffer from major injuries during performing oil and gas drilling operation because of the deficiency of an adequate hazard safety management system for real-time decision-making in hazardous conditions. According to previous studies, there is a sheer industrial need for an effective industrial safety management decision support system for accident prevention at oil and gas drilling sites at both drilling domains. Therefore, this paper aims to focus on the design and development of knowledge base decision support system (KBDSS) for the prevention of hazardous activities at Middle Eastern and South Asian origins’ onshore and offshore oil and gas industries during drilling operations. Design/methodology/approach In this study, data were gathered from safety and health professionals from targeted oil and gas industries in Malaysia, Saudi Arabia and Pakistan through quantitative and qualitative approaches. Based on identified data, KBDSSs (HAZFO Expert 1.0) were systematically developed and designed by adopting Database Development Life Cycle and Waterfall Software Development Life Cycle models. MySQL and Visual Studio 2015 software were used for developing and designing knowledge base and graphical user interface of the system. Findings KBDSS (HAZFO Expert 1.0) for accident prevention at onshore and offshore oil and gas drilling industries based on identified potential hazards and their suitable controlling measures aligned with international safety standards and regulations. HAZFO Expert 1.0 is a novel KBDSS that covers all onshore and offshore drilling operations with three and nine outputs, respectively, to achieve the current trend of Industry Revolution 4.0 and Industrial IoTs for workforce safety. Practical implications This industrial safety management system (HAZFO Expert 1.0) will be efficiently used for the identification and elimination of potential hazards associated with drilling activities at onshore and offshore drilling sites with an appropriate hazard controlling strategy. Originality/value Moreover, the developed KBDS system is unique in terms of its architecture and is dynamic in nature because it provides HAZFO Expert 1.0 data management and insertion application for authorized users. This is the first KBDSS which covers both drilling domains in Malaysian, Saudi Arabian and Pakistani industries.