CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design

Dynamic control of proinflammatory cytokines Il-1β and Tnf-α by macrophages in zebrafish spinal cord regeneration

Spinal cord injury leads to a massive response of innate immune cells in non-regenerating mammals, but also in successfully regenerating zebrafish. However, the role of the immune response in successful regeneration is poorly defined. Here we show that inhibiting inflammation reduces and promoting it accelerates axonal regeneration in spinal-lesioned zebrafish larvae. Mutant analyses show that peripheral macrophages, but not neutrophils or microglia, are necessary for repair. Macrophage-less irf8 mutants show prolonged inflammation with elevated levels of Tnf-α and Il-1β. Inhibiting Tnf-α does not rescue axonal growth in irf8 mutants, but impairs it in wildtype animals, indicating a pro-regenerative role of Tnf-α. In contrast, decreasing Il-1β levels or number of Il-1β+ neutrophils rescue functional regeneration in irf8 mutants. However, during early regeneration, interference with Il-1β function impairs regeneration in irf8 and wildtype animals. Hence, inflammation is dynamically controlled by macrophages to promote functional spinal cord regeneration in zebrafish

Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

Background The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods For this analysis, we constructed burden-weighted dose–response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15–39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0–0) and 0·603 (0·400–1·00) standard drinks per day, and the NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0–0·403) to 1·87 (0·500–3·30) standard drinks per day and an NDE that ranged between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3–65·4) were aged 15–39 years and 76·9% (73·0–81·3) were male. Interpretation There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. Funding Bill & Melinda Gates Foundation

Evaluation of Correlation between Some Intra and Extra Cranial Horizontal Cephalometric Parameters

Introduction: Intra-cranial references plans used in cephalometry analysis are not considered as stable points and therefore their validity is low; however they are widely used. The purpose of conducting this study is to investigate correlation rate of a number of horizontal parameters of extra-cranial with common Horizontal analyses. Methods: In this descriptive and periodic study, lateral cephalometric radiography in NHP position for 46 patients (24 women and 22 men) in range of 22- 28 years old was prepared; all graphs were prepared by a person using the same device; then after tracing, correlation coefficient was achieved between TH/AB and TH-wist, wylie, wits, TH-wits McNamara, Schwartz, APP-BPP, and Downs. Results: The highest rate of correlation was observed between TH-wits linear parameters and angular parameters of AB/TH (r=0.975) which was statistically significant. ANB parameter had low correlation with Wylie analysis (r=+0.365) that was definitely significant. ANB angular parameters with other horizontal dimension parameters showed the highest correlation with Downs analysis (r=-0.870) and linear parameter APP-BPP (r=+0.730) which were statistically significant too. Conclusions: In this study the highest correlation coefficient was shown between TH/AB and TH-wits, ANB and Downs, Downs and wits, ANB and APP-BPP respectively

A hybrid imputation method for multi-pattern missing data: A case study on type ii diabetes diagnosis

Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods

Multi-trial Vector-based Whale Optimization Algorithm

The Whale Optimization Algorithm (WOA) is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales. In spite of its popularity due to simplicity, ease of implementation, and a limited number of parameters, WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems. To address this limitation, we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm (MTV-WOA) that incorporates a Balancing Strategy-based Trial-vector Producer (BS_TVP), a Local Strategy-based Trial-vector Producer (LS_TVP), and a Global Strategy-based Trial-vector Producer (GS_TVP) to address real-world optimization problems of varied degrees of difficulty. MTV-WOA has the potential to enhance exploitation and exploration, reduce the probability of being stranded in local optima, and preserve the equilibrium between exploration and exploitation. For the purpose of evaluating the proposed algorithm's performance, it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions. Moreover, MTV-WOA is compared with well-stablished, recent, and WOA variant algorithms. The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate. Additionally, we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms. Finally, we solved five engineering design problems to demonstrate the practicality of MTV-WOA. The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms

A systematic review of applying grey wolf optimizer, its variants, and its developments in different Internet of Things applications

The Internet of Things (IoT) shapes an organization of objects that can interface and share information with different devices using sensors, computer programs, and other innovations without human intervention. IoT problems deal with massive amounts of data with critical challenges such as complex and dynamic search spaces, multiple objectives and constraints, uncertainty, and noise that require an efficient optimizer to extract valuable insights. Grey wolf optimizer (GWO) is an efficient optimizer stimulated by the hunting mechanism of wolves. The increasing trend of applying GWO shows that although it is a simple algorithm with few control parameters, it effectively solves optimization problems, particularly in various IoT applications. Therefore, this study reviews applying GWO, its variants, and its developments in IoT applications. This systematic review uses the PRISMA methodology, including three fundamental phases: identification, evaluation, and reporting. In the identification phase, the target search problems are defined based on suitable keywords and alternative synonyms, and then 693 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 50 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 50 eligible articles to identify key themes related to GWOs in IoT applications. The eligible GWOs are reviewed in the development, commercial, consumer, and industrial categories. The paper visualized the spreading of eligible GWOs according to their publisher, application, journal, and country and then suggested future directions for research