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In computer application scenario, data mining task is rarely utilized in power system, as an enhanced part, this work presented data mining task in power systems, to overcome frequency deviation issues. Load frequency control (LFC) is a primary challenging problem in an interconnected multi-area power system.

This paper adopts lion algorithm (LA) for the LFC of two area multi-source interconnected power systems. The LA calculates the optimal gains of the fractional order PI (FOPI) controller and hence the proposed LA-based FOPI controller (LFOPI) is developed.

For the performance analysis, the proposed algorithm compared with various algorithm is given as, 80.6% lesser than the FOPI algorithm, 2.5% lesser than the GWO algorithm, 2.5% lesser than the HSA algorithm, 4.7% lesser than the BBO algorithm, 1.6% lesser than PSO algorithm and 80.6% lesser than the GA algorithm.

The LFOPI controller is the proposed controlling method, which is nothing but the FOPI controller that gets the optimal gain using the LA. This method produces better performance in terms of converging behavior, optimization of controller gain, transient profile and steady-state response.

This paper aims to identify, analyze and evaluate the major enablers for the sustainable public distribution system (PDS) supply chain in India in lessening food insecurity by distributing essentials food grains at a subsidized rate.

The major enablers for the sustainable PDS supply chain were explored by conducting the literature survey and discussion with academic and warehouse experts. Then, the fuzzy-DEMATEL (decision-making trial and evaluation laboratory) technique was applied to develop a causal model that analyses the interaction among the identified enablers.

This study recognizes fifteen enablers through literature survey and experts' opinions. The present work concludes that “proper identification of the PDS beneficiaries” and “willingness and commitment of the top management and policymaker” are the two major enablers for the sustainable PDS supply chain.

This work would be helpful for profoundly understanding the major enablers, and how they are affecting the entire PDS supply chain. The study would be beneficial for the general people and the entire society straightforwardly by providing suggestions for food security.

Identifying and analyzing the major enablers for the sustainable PDS supply chain helps to visualize the problem more effectively and efficiently. Besides, the causal model explains a comprehensive perspective on the identified enablers.

The purpose of this paper is to explore the barriers to sustainable tourism management (STM) implementation in a protected area (PA) of a developing country, India, by taking a case study of National Chambal Sanctuary (NCS).

This study develops a framework to analyse the interaction among a set of barriers of STM using the interpretive structural modelling approach.

In this study, 16 relevant barriers responsible for the failure of STM in Indian scenario have been selected. The lack of coordination among various stakeholders and the lack of government incentives are found as the most significant barriers among the selected barriers of STM implementation in the sanctuary.

This study provides most influencing barriers and how these barriers hinder the sustainability efforts in NCS. The study’s main limitation is its generalisation. The problems in implementing sustainable practices may differ with the region.

This study provides strong practical inferences to both practitioners as well as academicians. The practitioners are suggested to focus on identified barriers and formulating strategies to achieve sustainability in the tourism sector. Academicians may propose the solutions and necessary interventions for identified barriers.

Identification and presentation of barriers to STM implementation in the context of a PA are rare to find in literature.

This study aims to assess the recreational value of the National Chambal Sanctuary highlighting the potential benefits of conservation and the need for sustainable practices.

This study uses the Individual Travel Cost Method to evaluate the expenses of individuals visiting the National Chambal Sanctuary.

The study reveals that the National Chambal Sanctuary has a significant recreational value, with an average visitor value of INR 35,335.69 or USD 434 and an annual value of INR 132,473,501.81 or approximately USD 1.62m, indicating substantial economic contribution.

The study's limitations may stem from data collection constraints, visitor reporting biases or other factors affecting estimates' accuracy. Future research could explore socio-economic factors or factors affecting low tourist inflow in India's protected areas (PAs).

This study suggests raising entrance fees for the National Chambal Sanctuary to ensure financial sustainability, based on its high recreational value and average consumer surplus. This has practical implications for policymakers, conservationists and the tourism industry.

The study underscores the significance of protecting PAs like the National Chambal Sanctuary, suggesting that public attitudes towards biodiversity conservation can be influenced by highlighting its economic and recreational value and promoting awareness of its significance.

The study evaluates the recreational value of a wildlife sanctuary in India, offering insights into conservation's economic benefits and sustainable practices and promoting further research.

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.

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.

The purpose of this study to find an alternate method to minimize waste i.e., eggshell and rice husk ash. In this paper, eggshell (ES) and rice husk ash (RHA) particles are used as reinforcements for examining their effect on the coefficient of thermal expansion (CTE), grain size (GS) and corrosion behavior for developed composite material.

In this investigation, 5 Wt.% each of ES and RHA reinforcement particles have been introduced. To investigate the microstructures of the developed composite material, scanning electron microscope was used. Physical and mechanical properties of composite material are tensile strength and hardness that have been examined.

The result of this paper shows that number of grains per square inch for composition Al/5% ES/5% RHA composite was found to be 1,243. Minimum value of the volume CTE was found to be 6.67 × 10–6/°C for Al/5% ES/5% RHA composite. The distribution of hard phases of ES particles in metal matrix is responsible for improvements in tensile strength and hardness. These findings demonstrated that using carbonized ES as reinforcement provides superior mechanical and physical properties than using uncarbonized ES particles.

There are several articles examining the impact of varying Wt.% of carbonized ES and rice husk reinforcement on the microstructures and mechanical characteristics of metal composites. CTE, GS and corrosion behavior are among of the features that are examined in this paper.

The paper aims to examine the effects of floods on economic growth in India from 1980 to 2019, taking into account the role of foreign direct investment (FDI) inflows and foreign aid.

The study uses augmented Dickey–Fuller (ADF) and Phillip–Perron (PP) tests to determine the stationarity of the variables. Several models, including autoregressive distributed lag (ARDL), fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegration regression (CCR), are used to examine the impact of floods on economic growth.

The bounds test determines the long-term relationship between floods, FDI inflows, economic growth and foreign aid. According to the ARDL and FMOLS models, floods have a negative long-term and short-term impact on India’s economic growth. Furthermore, FDI inflows and foreign aid are beneficial to economic growth. The findings of the ARDL and FMOLS models are confirmed by the DOLS and CCR models. Granger causality establishes a unidirectional causality that extends from floods to economic growth. Further diagnostic tests show that the estimates are free of heteroskedasticity, serial correlation and parameter instability.

Indian government needs to invest more in research and development on flood management techniques. Institutional strengthening is also required to implement pre- and post-flood prevention measures properly. Sound disaster financing strategy and proper water bodies management should be prioritised. Foreign investment opportunities should be encouraged by strengthening international relations.

This is the first time-series study that analysed the effects of floods on economic growth in India. Moreover, the paper contributes to floods literature by applying several econometric models for robustness check.