Search Equation Research Articles

Research on Microgrid Scheduling Based on Improved Crow Search Algorithm

Considering the characteristics of distributed power in microgrid, in order to maximize the advantages of distributed power generation technology in economy, environment, and energy, a multiobjective dispatching model of microgrid is proposed under the condition of satisfying system constraints and considering the operating costs and environmental costs of microgrid. The crow search algorithm (CSA) has the advantages of less parameter setting, simple implementation, and strong optimization ability and is often used in theoretical analysis and practical engineering applications. However, its disadvantage is that crows only search for candidate solutions according to their own experience, and their development ability is poor, especially for solving high-dimensional functions. In order to overcome these shortcomings, an improved crow search algorithm (CSA-PSO) is proposed based on the particle swarm optimization (PSO) algorithm. The two main improvements are as follows: (1) in order to avoid the blind selection of crow in search, the global optimal solution is adopted to modify the solution search equation to guide the search of new candidate solutions, so as to improve the development ability; (2) introducing a levy flight strategy to improve the single search mechanism of the CSA. In order to verify the performance of the CSA-PSO algorithm, 17 benchmark functions are simulated with other intelligent algorithms. The results show that the CSA-PSO algorithm has a good optimization effect in search accuracy, convergence speed, and robustness. Finally, this algorithm and other five algorithms are applied to the optimal scheduling problem of microgrid. By solving the objective function, the optimal scheduling output scheme of each distributed power supply is obtained. By using the CSA-PSO algorithm, the total operating cost of microgrid is reduced by at least 21.5%, which further verifies the effectiveness of the CSA-PSO algorithm.

Trends in Small Animal Reproduction: A Bibliometric Analysis of the Literature.

Simple SummaryReproduction in small animals is an expanding research area, with focus on breeding improvement and clinical management of domestic carnivores. The aim of the present study was to conduct a bibliometric analysis of the literature of the last decade on small animal reproduction, to point out main sources, most prolific countries, and emerging and neglected topics. Results show that research in biotechnologies for assisted reproduction has a central and increasing role in this field. Diversity in author keywords was also pointed out and a consensus to better categorize research in this field is proposed to reduce this problem in the future.Small animal reproduction (SAR) is a main research field in veterinary medicine and bibliometric analyses are useful to investigate trends in specific research areas. The objective of the present study was to conduct a bibliometric analysis of the literature of the last decade on SAR. A search equation was created, and documents were retrieved from the Web of Science database. Documents were manually revised, categorized and R software version 4.1.2 with Bibliometrix R package version 3.1 and MS Excel were used to perform the analyses. The included documents (n = 1470) were mainly research articles (78%). The top countries for the number of documents and citations were Brazil, United States, Italy, Poland, and Korea. These also account for the most prolific authors and institutions. Analyses by author keywords, categories, and recent reviews of the literature suggest that research on the canine species is more abundant than research on the feline one and that reproductive biotechnologies are a main research focus. Some clinical topics are still considered niche or neglected themes (e.g., semen collection in tomcats, neonatology). However, heterogeneity and ambiguity in keywords and categories are undeniable. This study offers interesting insights, providing definitions for main keywords in the field of SAR.

Electrohydrodynamic Drying in Agribusiness: Literature Review

In agribusiness, drying is a unitary operation that optimizes the production and preservation of products and raw materials. Drying is performed through different traditional methods, one of the most recently studied is the electrohydrodynamic drying EHD which uses an electric field that allows decreasing the processing time thus increasing the drying speed of raw materials and consuming less energy. In this article, a review was carried out through Scopus using a search equation with the keywords “Electrohydrodynamic drying,” “food” and “AGRI” which resulted in a total of 145 articles; which were analyzed through in-depth reading, analyzing aspects such as year, author, keywords, countries, quartile, journal, relationship with agroindustry, mathematical models used and applications in agro-industrial products, this analysis was complemented with the application of Vantage Point software through co-occurrence matrices and cluster analysis. Recent applications were found in Carrot, Chicken, Sea Cucumber, Goji Berry, Peppermint Leaf, Quince, Potato, Blueberry, Aquatic Products, Banana Slices, Grape Pomace, Blueberry, Apple, Mushroom, Wheat, and Mushroom Slices, mathematical models with application in EHD drying were also found, such as Henderson and Pabis, Page, Logarithmic, Quadratic, Newton/Lewis, Diffusion and exponential.

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi-coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real-world optimization problem, and four well-established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

ABSTRACT The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them.

Executive Dysfunction in Adolescent with Obesity: A Systematic Review

Obesity has been linked to low cognitive performance throughout the human life span. Alterations relate to attention, memory, language, and executive function. However, the obesity-executive function association remains unclear in adolescents. This study presents a systematic review of executive function alterations in adolescents with obesity. A search equation was proposed (Executive dysfunction AND Obesity AND Adolescents), with sixty studies meeting the inclusion criteria in the Clarivate Analytics Web of Science Core Collection database. A bibliometric analysis was conducted to identify the importance of the research topic and a citation network was built to establish the lines of research. Finally, the citation network was exported to Gephi to visualize the research groups studying the topic. Findings suggest 4 lines of research: (a) structural and functional brain abnormalities in adolescents with obesity, (b) inhibitory control alterations in adolescents with obesity, (c) effects of physical and cognitive activity on the executive function of adolescents with obesity, and (d) working memory alterations in adolescents with obesity. Obese adolescents were found to prefer immediate rewards to long-term ones. Nevertheless, effective interventions increase the intake of fruit and vegetables and reduce that of calorie-dense food. The authors recommend additional longitudinal studies to assess whether executive function alterations are either a cause or a consequence of adolescent obesity.

Working Memory Training in children with attention deficit hyperactivity disorder: A systematic review

Working memory training may help children with attention deficit hyperactivity disorder (ADHD), but robust evidence from systematic reviews is lacking. Children with poor Working memory ability struggle with academic and cognitive work compared to similar-aged peers without working memory deficits. Besides, working memory is correlated with inattention and disorganization in those with ADHD. The aim of this systematic review was to assess the effect of working memory training on symptoms and behaviors of children with ADHD. A search equation was proposed (ADHD OR attention deficit hyperactivity disorder AND working memory training), with twenty-four studies meeting the inclusion criteria in the Clarivate Analytics Web of Science Core Collection database. A bibliometric analysis was conducted to identify the importance of the research topic and a citation network was built to establish the lines of research. Finally, the citation network was exported to Gephi to visualize the research groups studying the topic. Findings suggest 3 lines of research: (a) Effects of working memory training on working memory, and academic performance in children with ADHD, (b) Effects of working memory training on executive functioning and child ADHD related symptoms, (c) Effects of working memory training on brain activity in child ADHD. Implications for clinical practice and school-based interventions are discussed

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Path planning is the key for unmanned aerial vehicle (UAV) to perform tasks efficiently, which needs to quickly obtain the optimal path in the complex environment. To solve the path planning problem in the complex urban environment, an improved artificial bee colony algorithm based on multi-strategy synthesis (IABC) is proposed to generate the appropriate path for the UAV. The IABC is based on the hybrid mechanism of chaotic mapping and Pareto principle to initialize the population, so as to fully search the solution space and provide an approximate optimal flight path for UAV. Meanwhile, in order to balance exploration and development, two new search equations are designed to generate candidate solutions, which provide more superior flight paths for UAVs. In addition, tangent random evolution mechanism is added to enhance the strategy of updating the algorithm offspring, maximize the quality of flight path generation, and effectively solve the path planning problem. Aiming at the complex urban environment and the multi-constraint optimization problem of UAV flight, the IABC is combined with cubic spline interpolation to generate a smooth path to meet the UAV flight maneuver characteristics. The improved algorithm proposed in this paper has been compared with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Search Artificial Bee Colony (SABC) and Gbest Artificial Bee Colony (GABC). The algorithm proposed in this paper has good feasibility and effectiveness in solving the UAV path planning problem.

On Security of Quantum Cryptography by Probabilistic Analysis

The existing quantum cryptography is a classical cryptography in nature and basically insecure because of its classical (conventional) bits, classical encryption algorithm and classical (public) channel. A novel topic about successful communication between the legitimate users, Alice and Bob, is discussed with probability of solution uniqueness of Bob’s decryption equation. We find, by probabilistic analysis, that success of communication between Alice and Bob is probabilistic with a probability bigger than 1/2. It is also novel to define insecurity of the quantum cryptography by probability of solution uniqueness of the search equation of Eve, the eavesdropper. The probability of Eve’s success to find the plain-text of Alice (and Bob) is greater than 1/2, and so the quantum cryptography is seriously insecure.

Las TIC en la enseñanza de la química: Una revisión sistemática

The objective is to analyze the teaching -in basic education- of chemistry through the use of ICT, for which a systematic review of the scientific literature published between 2015 and 2021 in the Scielo, Redalyc, Dialnet databases was developed. and Scopus, finding 100 articles based on 04 search equations related to the keywords ICT, CHEMICAL TEACHING, BASIC EDUCATION; After applying the inclusion and exclusion criteria, 35 articles were selected. As a result, we have that the application of ICT in the teaching of chemistry favors student learning while developing digital skills in the student. It is concluded that the use of ICT has changed the different spheres of education, technifying the traditional teaching methodology for an interactive methodology.

Accelerating artificial bee colony algorithm using elite information

In nature, the foraging behaviour of bee colony is always guided by some elite honeybees with the aim of maximising the overall nectar amount. Being inspired by this phenomenon, we propose an improved artificial bee colony (ABC) variant by using elite information. In our approach, as the main way of generating new offspring, two novel solution search equations are developed based on utilising elite information, which has the advantages of accelerating convergence rate. Moreover, to preserve the search experience of the scout bee phase, a new reinitialisation method is proposed based on using elite information. Extensive experiments are conducted on the CEC 2013 and CEC 2015 test suites, and other four relevant ABC variants are included in the comparison. The results show that our approach has better performance in terms of convergence speed and result accuracy.

Sostenibilidad y moda en la industria del cuero, una revisión sistemática

The purpose of this research is to analyze the literature around the leather tanning industry, based on a systematic review of literature in the SCOPUS database, whose objective was to conceptually analyze sustainability aspects from the content structure, environmental, social aspects and governance. The search equation yielded 785 articles in SCOPUS, which once filtered resulted in a total of 20 articles for in-depth analysis. To visualize the information, the VosViewer tool was used, which made it possible to represent the existing co-citation and co-authorship relationships through clusters. The results show what the background is, the topics of interest for academics, the countries with the longest history of research in the area and the collaborative work networks, as well as the most relevant authors and studies in this field and the impact trends. from the fashion and leather industry about sustainability.

Artificial Bee Colony Algorithm with Distant Savants for constrained optimization

Most of the scientific and engineering problems are defined as constrained optimization functions. It can be very difficult due to their complex structures. Artificial Bee Colony Algorithm (ABC) is a remarkable metaheuristic developed for global optimization problems. However, due to the inadequacy of ABC’s search capability, it cannot handle constraint optimization problems very well. In this study, an ABC variant adapted for solving constrained optimization problems called Artificial Bee Colony Algorithm with Distant Savants (ABCDS) is proposed to overcome this deficiency. ABCDS is based on a new and adaptable search equation that enables learning with savants that are at a certain distance from each other. Also, the algorithm is hybridized with competitive local search mechanism. To test the performance of ABCDS, benchmark set for Constrained Real-Parameter Optimization defined in CEC 2017 conference (CEC2017COP) and some of the problems in the benchmark set on real-world problems defined in CEC 2020 conference (CEC2020) are used. The results obtained by the algorithm are compared with recent ABC algorithms and some state-of-the-art algorithms. According to the experimental results, ABCDS is better and competitive than the compared algorithms.

Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm

The environmental impact and scarcity of fossil fuels have led to a tremendous increase in the demand and use of renewable energy resources. The use of photovoltaic (PV) energy is due to several benefits, such as low maintenance and operating costs. The modeling of the PV systems process requires the identification of PV cells parameters, which can be formulated in an optimization problem. This problem is a very challenging task as it is nonlinear and multimodal. Therefore, metaheuristic algorithms suggested recently may present unsatisfactory results at the level of solution quality and convergence speed. To ensure the effectiveness of any algorithm, the equilibrium between exploration and exploitation must be guaranteed. To achieve an accurate model of the photovoltaic cells, a comprehensive learning Rao-1 (CLRao-1) optimization algorithm is proposed in this work. Rao-1 is chosen due to its simplicity, easy comprehension, and implementation. The suggested modifications based on three mutually exclusive search equations do not affect its easiness. The effectiveness of our algorithms is assessed through PV cells and three modules. The results of the experimental and simulated data were in high agreement. Moreover, our algorithm achieved the best results in terms of reliability and accuracy.

Urban-Rural Partnership Framework to Enhance Food-Energy-Water Security in the Post-COVID-19 Era.

Food, energy, and water (collectively referred to as ‘FEW’) security forms the key to human survival as well as socioeconomic development. However, the security of these basic resources is increasingly threatened due to growing demand. Beyond the widespread implications on public health, Coronavirus disease (COVID-19) has further raised additional challenges for FEW security, particularly for urban populations, as they mainly outsource their FEW demands from rural areas outside their physical boundaries. In light of that, this study reviews existing literature on FEW security to highlight the growing relevance of urban–rural linkages for realizing FEW security, especially against the backdrop of the COVID-19 pandemic. To achieve this, relevant research documents have been identified through Elsevier’s Scopus database and other sources (by applying search equations). The authors have accordingly underlined the necessity of shifting the conventional urban-centric approach to city region-centric development planning for the post-COVID-19 era. To this end, a framework has been suggested for translating physical urban–rural linkages to a partnership enhancing a collective response. The major elements of this framework are the conceptualization of national-level policies to support urban–rural linkages. The framework can play the role of a science–policy–action interface to redesign the FEW system in city regions.

An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.

Validation of Digital Applications for Evaluation of Visual Parameters: A Narrative Review.

The current review aimed to collect and critically analyze the scientific peer-reviewed literature that is available about the use of digital applications for evaluation of visual parameters in electronic devices (tablets and smartphones), confirming if there are studies calibrating and validating each of these applications. Three bibliographic search engines (using the search equation described in the paper) and the Mendeley reference manager search engine were used to complete the analysis. Only articles written in English and that are evaluating the use of tests in healthy patients to measure or characterize any visual function aspects using tablets or smartphones were included. Articles using electronic visual tests to assess the results of surgical procedures or are conducted in pathological conditions were excluded. A total of 19 articles meeting these inclusion and exclusion criteria were finally analyzed. One critical point of all these studies is that there was no mention of the characterization (spatial and/or colorimetrical) of screens and the stimuli used in most of them. Only two studies described some level of calibration of the digital device before the beginning of the study. Most revised articles described non-controlled comparatives studies (73.7%), reporting some level of scientific evidence on the validation of tools, although more consistent studies are needed.

An Enhanced Naked Mole Rat Algorithm for Optimal Cross-Layer Solution for Wireless Underground Sensor Networks

Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.

Role of the PNPase enzyme in the transport of mtRNA in lymphatic cancer: Bibliographic review

Lymphoma is the most common type of blood cancer today and, as its name suggests, it begins in the lymphatic system. The origin of this disease is related to mitochondrial defects, generated by mutations in the PNPase enzyme or polynucleotide phosphorylase, whose main functions are to import and degradation mitochondrial RNA. For this reason, the main objective of the present work was to carry out a bibliographic review of scientific publications that made the role of this enzyme relevant, in relation to mitochondria as the cause of lymphatic cancer.
 The methodology used consisted of a bibliometric analysis based on the use of different databases, in which search equations formed from keywords were introduced. Then, the selection of articles related to the study topic and published in the last 20 years was carried out. Subsequently, the journals were analyzed, based on the H index, in order to observe which supported the hypothesis of the role of PNPase in lymphoma and which stated the opposite.
 The results showed that a total of 441,288 scientific publications were obtained, of which 133 were selected to carry out this work. As for the journals, those with the highest H index were Nature and Cell.
 It can be concluded that PNPase plays a very important role in the transport of mitochondrial RNA, and that the factor NF-Y is involved in the control of cell growth, therefore, both have a crucial role in the development of this disease. Therefore, research on both PNPase and NF-Y is essential to establish the specific genetic characteristics that define the early lesions of lymphatic cancer and the consequent determination of their treatment.

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

Although various clustering algorithms have been proposed, most of them cannot handle arbitrarily shaped clusters with varying density and depend on the user-defined parameters which are hard to set. In this paper, to address these issues, the authors propose an automatic neighborhood-based clustering approach using an extended multi-objective artificial bee colony (NBC-MOABC) algorithm. In this approach, the ABC algorithm is used as a parameter tuning tool for the NBC algorithm. NBC-MOABC is parameter-free and uses a density-based solution encoding scheme. Furthermore, solution search equations of the standard ABC are modified in NBC-MOABC, and a mutation operator is used to better explore the search space. For evaluation, two objectives, based on density concepts, have been defined to replace the conventional validity indices, which may fail in the case of arbitrarily shaped clusters. Experimental results demonstrate the superiority of the proposed approach over seven clustering methods.