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In this paper, a modified cheetah optimizer (MCO) algorithm is presented, which has been designed to address the optimal power flow (OPF) problem in power grids that utilize renewable energy sources (RES). The issue of uncertainty in cost models for wind turbines (WTs) and photovoltaics (PVs), which can result in overestimation or underestimation of RES, is addressed by including the uncertain cost value in the direct cost of these renewable units to calculate their cost value accurately. The MCO methodology was applied to various objective functions such as overall operating cost, voltage deviation, pollutant emissions, and power loss, which were evaluated under different cases. Regarding the valve point effect observed in case 1, the optimal response provided by MCO amounts to $781.9862. Upon assessing the emission costs in case 2, a resultant value of $810.6655 is determined. Considering the POZs in case 3, the aggregate cost is $781.7165. The minimum network loss is recorded in case 4, which is 2.0738 MW. By mitigating the voltage deviations in case 5 to 2.0738 p.u., the loss incurred exceeds twice that of the preceding case. Furthermore, due to its applicability to large-scale problems, the reserve constraint dynamic economic dispatch problem was chosen as an additional test case for the MCO. A backward-forward correction method was used to correct errors in three types of reserves, improving the solution quality. The effectiveness of the MCO in solving practical large-scale optimization problems was demonstrated by the results of the 10-unit and 30-unit dynamic economic dispatch, achieving lower cost values than previously published papers. In the 10-unit economic dispatch, the response surpasses the 15 top publications at $1,016,361. In the 30-unit dispatch, the MCO algorithm produced a unique solution of $3,048,405.

Purpose The purpose of this study shows how overall board diversity influences corporate risk-taking. Board diversity is quantified into task-oriented diversity (tenure and education) and relation-oriented diversity (age and gender). Further, this study tests whether the association of board diversity and corporate risk varies across state-owned firms (SOEs) and non-state-owned firms (NSOEs). Design/methodology/approach The authors used a sample of Chinese listed firms over the period 1999-2017. The results are estimated using the fixed-effects model. To deal with the endogeneity problem and single model bias, the authors use a dynamic model, i.e. two-step generalized method of moment’s model. Findings The results show that both task-oriented and relation-oriented diversity reduces corporate risk. Further, the authors document that overall board diversity reduces risk-taking across different types of firms, that is, SOEs and NSOEs. These results are consistent after controlling for endogeneity problems. Practical implications The results provide implications for enhancing corporate governance practices by considering overall board diversity as an important factor influencing corporate decisions. The findings suggest that policymakers and shareholders should consider different diversity attributes important for the composition of a board, which can enhance board outcomes. Originality/value Most of the prior studies considered only one dimension of diversity, and therefore, have overlooked the overall board diversity. Unlike prior studies, this study considers four board diversity attributes – age, gender, tenure and education, and further tests their association with corporate risk. Further, this study also examines the effect of overall diversity on corporate risk in SOEs and NSOEs.

This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model. Design/methodology/approach This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy. Findings The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market. Practical implications The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner. Originality/value Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.

This paper proposes a new intelligent algorithm named improved transient search optimization algorithm (ITSOA) integrated with multiobjective optimization for determining the optimal configuration of an unbalanced distribution network. The conventional transient search optimization algorithm (TSOA) is improved with opposition learning and nonlinearly decreasing strategies for enhancing the convergence to find the global solution and obtain a desirable balance between local and global search. The multiobjective function includes different objectives such as power loss reduction, enhancement of voltage sag and unbalance, and network energy not supplied minimization. The decision variables of the reconfiguration problem including opened switches or identification of optimal network configuration are determined using ITSOA and satisfying operational and radiality constraints. The proposed methodology is implemented on unbalanced 13-bus and 118-bus networks. The results showed that the proposed ITSOA is capable to find the optimal network configuration for enhancing the different objectives in loading conditions. The results cleared the proposed methodology's good effectiveness, especially in power quality and reliability enhancement, without compromising the different objectives. Comparing ITSOA to conventional TSOA, particle swarm optimization (PSO), gray wolf optimization (GWO), bat algorithm (BA), manta ray foraging optimization (MRFO), and ant lion Optimizer (ALO), and previous approaches, it is concluded that ITSOA in improving the different objectives.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.

In this paper, the simultaneous optimization of capacitors and DSTATCOM in the radial distribution system is performed to minimize the cost of network active losses along with the cost of installation and investment in reactive power, considering the reliability of compensators and incorporating the network load uncertainty. The decision variables include the installation location and the capacity of compensators, which are defined by a novel meta-heuristic algorithm termed the improved exponential distribution optimizer (IEDO). The conventional exponential distribution optimizer (EDO) is inspired by exponential distribution theory, which uses the spiral motion strategy within the EDO to improve optimization performance and prevent it from getting trapped in local optima. Simulation scenarios are implemented in three cases: (I) capacitor optimization, (II) DSTATCOM optimization, and (III) simultaneous optimization of capacitor and DSTATCOM in the network without (scenario I) or considering the compensator’s reliability and the load uncertainty using the unscented transformation (scenario II). The simulation results of IEDO showed that Case III has the best performance by achieving the lowest cost, the highest percentage of net savings, and the most favorable voltage profile in comparison with other scenarios. The superiority of the IEDO has also been confirmed in contrast to widely recognized optimization methodologies. In addition, the results of Scenario II are clear: the system cost has increased by 8.76%, 8.79%, and 8.72%, and the net savings have decreased by 6.48%, 6.62%, and 6.42%, compared to Scenario I for cases I–III, respectively.

The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept. The Mobile Video Network (MoviNet) is a member of the memory-efficient architectures discovered through Neural Architecture Search (NAS), which balances accuracy and efficiency by integrating spatial, temporal, and spatio-temporal operations. Our research implements the MoviNet model on the UCF101 and HMDB51 datasets, pre-trained on the kinetics dataset. Upon implementation on the UCF101 dataset, a generalization gap was observed, with the model performing better on the training set than on the testing set. To address this issue, we replaced batch normalization with EvoNorm, which unifies normalization and activation functions. Another area that required improvement was key-frame selection. We also developed a novel technique called Optimal Frame Selection (OFS) to identify key-frames within videos more effectively than random or densely frame selection methods. Combining OFS with Mish nonlinearity resulted in a 0.8–1% improvement in accuracy in our UCF101 20-classes experiment. The EMO-MoviNet-A2* model consumes 86% fewer FLOPs and approximately 90% fewer parameters on the UCF101 dataset, with a trade-off of 1–2% accuracy. Additionally, it achieves 5–7% higher accuracy on the HMDB51 dataset while requiring seven times fewer FLOPs and ten times fewer parameters compared to the reference model, Motion-Augmented RGB Stream (MARS).

Before product development, Requirement Engineering (RE) is the fundamental need to know customer preferences for any product. Traditionally, RE is carried out in several ways, particularly by conducting interviews, questionnaires, surveys etc. but these methods provide limited amount of data. As user’s preferences vary from country to country for any type of application, it is very hectic and time consuming to collect user requirements from different countries manually. As the internet is widely used now a days, a large number of customer’s reviews are available online that can be used to obtain the requirements for any product without manual work. Online customer reviews can be divided into three types: user experience, bugs and feature requests. Among these 3 categories, feature requests can be very useful for stakeholders (analysts/ requirements engineers) to acquire the requirements of each application. So, the approach is proposed for feature requests extraction from mobile application reviews of travel industry. In this paper, 4 categories of mobile apps of travel industry belonging to 5 countries have been extracted from Google Play Store and Apple Store. For each category, data from 5 different mobile applications have been considered. Since, the review of users from different countries is in their respective language, those reviews are translated into a standard language i.e. English, and then feature requests from these reviews have been extracted. After that, features are retrieved from user reviews and topic modeling is performed on extracted features since one or more features can be modelled under one topic. To know the opinions of users for any feature request, sentiment analysis is also performed on feature request sentences. These feature requests are also classified as Functional and Non-functional Requirements since it is very useful for application developers to improve or maintain the product to better facilitate the application users

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework’s effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting.

Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated.