• Energy Research

The urban flooding situations have arisen in the modern scenario of urbanization due to climatic changes. This work contributes to designing a planned and feasible urban rain flood ecosystem to promote the construction of a sponge city. It has various advantages of improving the water environment, controlling urban waterlogging, reducing runoff pollution, improving river and lake water quality, recycling rainwater resources, replenishing groundwater, and many more. This paper combines the design methods and advantages of the design results formed in decades using traditional regulation and utilizing it for the present study. It reconstructs and integrates the traditional regulation and sponge city construction requirements, thereby providing a feasible urban rain-flood ecosystem in the industrial and smart city scenario. Finally, the regulation of new paddy areas in Yanjin city of China is considered for experimentation, and the design of the regulation is applied using this setup. The design results obtained from the test of sponge city construction have operability and can improve the urban environment and enhance the vitality of the city. The control plan’s design results integrating the sponge city idea can provide effective technical support and guarantee the overall urban environment. The work presented in this article can assess and plan the flood mitigation measures to monitor this type of situation leading to flooding risk reduction in smart cities.

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.com .

Abstract This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.

Background: The two main stages are utilized for feature extraction, from which the first stage consists of a penalty weight to the neighbor graph’s edges. The edge penalty weights are minimized by the neighbor sub-graph extraction to produce the set of feature patterns. For noisy data, the second stage is helpful. Methodology: In order to realize the measurement of the geometric dimensions of the ship block, this paper uses the theory of computer vision and reverse engineering to obtain the data of the segmented- hull with the method of digitizing the physical parts based on the vision, and processes the data by using the relevant knowledge of reverse engineering. Result: The results show that the efficiency of the edge extraction algorithm based on mathematical morphology is 30% higher than that of the mesh generation method. An adaptive corner detection algorithm based on the edge can adaptively determine the size of the support area and accurately detect the corner position. Conclusion: According to the characteristics of the point cloud of ship hull segment data, an adaptive corner detection algorithm based on the edge is adopted to verify its feasibility.

This paper proposes a novel hybrid multi-objective algorithm named Multi-objective Spotted Hyena and Emperor Penguin Optimizer (MOSHEPO) for solving both convex and non-convex economic dispatch and micro grid power dispatch problems. The proposed algorithm combines two newly developed bio-inspired optimization algorithms namely Multi-objective Spotted Hyena Optimizer (MOSHO) and Emperor Penguin Optimizer (EPO). MOSHEPO contemplates many non-linear characteristics of power generators such as transmission losses, multiple fuels, valve-point loading, and prohibited operating zones along with their operational constraints, for practical operation. To evaluate the effectiveness of MOSHEPO, the proposed algorithm has been tested on various benchmark test systems and its performance is compared with other well-known approaches. The experimental results demonstrate that the proposed algorithm outperforms other algorithms with low computational efforts while solving economic and micro grid power dispatch problems.

Our aim was to assess sleep quality in different subgroups of preclinical medical students, and then to identify specific lifestyle factors, academic and social factors as well as Corona virus related factors that were associated with poor sleeping quality and poor psychological health. Study participants were all medical students at King Saud University of Medical Sciences in the first and second years (648 students), and the study was conducted from December 2021 to January 2022. We administered the survey on paper as well as online. We used three types of questionnaires in this study. The first was a self-administered questionnaire, the second was a validated Insomnia Severity Index (ISI) for finding sleeping problems, and the third was a validated DASS 10 for determining Depression, Anxiety, and Stress. A total of 361 pre-clinical medical students consisted of 146 (40.4%) males and 215 (59.5%) females. The majority of the students, 246 (68.1%), were in their second year. Furthermore, in the current study, students who had poor academic performance (15.8%), satisfactory academic performance (21.3%), or good academic performance (30.7%) had significant sleeping problems found (χ2 = 19.4; p = 0.001), among them poor academic performance students 21.6%, satisfactory academic performance students (29.3%), and good academic performance students (29.3%) had moderate to severe levelled sleeping problems. Similarly, poor, satisfactory, and good academic performers experienced the highest levels of anxiety (poor = 21.5%; satisfactory = 22.1%; and good = 22.8%); stress (poor = 22.4%; satisfactory = 25.2%; and good = 22.4%); and depression (poor = 40.5%; satisfactory = 40.5%; and good = 11.9%). The majority of students (64.8%) reported that during the pandemic crisis their anxiety levels were high. Additionally, students reported significantly high sleeping issues (χ2 = 10.6; p = 0.001) and also serious psychological issues (Anxiety = 34.9 (0.000); Stress = 32.5 (0.000); and Depression = 5.42 (0.01)). There was a high prevalence of sleep issues, anxiety, stress, and depression among the pre-clinical medical students, with significantly higher sleeping disorders, anxiety, stress, and depression levels among those medical students who struggle with their academic performances, poor lifestyle factor, and poor Social and COVID management.

This paper presents a new model using optimization approach for efficient prediction of load in real-life environment. Monte Carlo simulation and Schrödinger equations provide the effective number of solutions. This technique is useful in representation of relationships between different models. The proposed algorithm is verified and validated with various state-of-the-art approaches for solving economic load power dispatch problem to demonstrate its efficiency. Experimental results signify that the proposed algorithm is more precise than existing competing models.

Abstract This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.

Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy.