Two-phase Selection Research Articles

Cost-sensitive stacking ensemble learning for company financial distress prediction

Financial distress prediction (FDP) is a topic that has received wide attention in the finance sector and data mining field. Applications of combining cost-sensitive learning with classification models to address the FDP problem have been intensely attracted. However, few combined cost-sensitive learning and Stacking to predict financial distress. In this article, a cost-sensitive learning method for FDP, namely cost-sensitive stacking (CSStacking), is put forward. In this work, a two-phase feature selection method is used to select the optimal feature subset. A CSStacking ensemble model is developed with selected features to make a final prediction. The paired T test and non-parametric Wilcoxon test are employed to check the significant differences between CSStacking and benchmark models. An experiment over Chinese listed company dataset is designed to investigate the effectiveness of CSStacking. The experimental results prove that CSStacking can forecast listed companies’ financial distress five years ahead and improves the identification rate of financially distressed companies, highlighting its potential to reduce economic losses caused by misclassifying financially distressed companies. The results of comparing CSStacking with four types of benchmark models show that CSStacking performs significantly better than benchmark models. Furthermore, the findings illustrate that “asset-liability ratio”, “current ratio”, “quick ratio”, and “industry prosperity index” are critical variables in predicting financial distress for Chinese listed companies.

Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods’ efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively.

Ranking-based multi/many-objective evolutionary optimization with hierarchical evaluation rules and its application in water distribution network

To solve multi/many-objective optimization problems characterized by complex Pareto fronts, we propose a two-phase ranking-based multi-objective evolutionary algorithm with hybrid convergence rules. Firstly, we introduce a novel hybrid convergence criterion by integrating Pareto dominance, localized pruning power (LPP), and distance-based measure. Secondly, we design a two-phase ranking-based selection strategy. Specifically, we employ the acute angle between objective vector and reference vector to divide the current population into multiple sub-populations. In the first-phase ranking, the solutions within each subpopulation are sorted according to the hybrid convergence rule. The best solutions are chosen based on the angle-based diversity criterion into separate fronts. In the second-phase selection, a specific number of solutions with best diversity are further selected to the next generation based upon their front-level assignments. To obtain better diversity of selected solutions, we employ the maximum angle between solutions and the sum of normalized objectives of solutions to assist the selecting process. The experimental results, which encompass benchmark problems involving up to 10 objectives as well as two real-world application instances, validating our proposed algorithm’s competitiveness and effectiveness.

User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy

Gait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.

Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework

BackgroundArtificial intelligence (AI)-based medical devices and digital health technologies, including medical sensors, wearable health trackers, telemedicine, mobile health (mHealth), large language models (LLMs), and digital care twins (DCTs), significantly influence the process of clinical decision support systems (CDSS) in healthcare and medical applications. However, given the complexity of medical decisions, it is crucial that results generated by AI tools not only be correct but also carefully evaluated, understandable, and explainable to end-users, especially clinicians. The lack of interpretability in communicating AI clinical decisions can lead to mistrust among decision-makers and a reluctance to use these technologies. ObjectiveThis paper systematically reviews the processes and challenges associated with interpretable machine learning (IML) and explainable artificial intelligence (XAI) within the healthcare and medical domains. Its main goals are to examine the processes of IML and XAI, their related methods, applications, and the implementation challenges they pose in digital health interventions (DHIs), particularly from a quality control perspective, to help understand and improve communication between AI systems and clinicians. The IML process is categorized into pre-processing interpretability, interpretable modeling, and post-processing interpretability. This paper aims to foster a comprehensive understanding of the significance of a robust interpretability approach in clinical decision support systems (CDSS) by reviewing related experimental results. The goal is to provide future researchers with insights for creating clinician-AI tools that are more communicable in healthcare decision support systems and offer a deeper understanding of their challenges. MethodsOur research questions, eligibility criteria, and primary goals were proved using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and the PICO (population, intervention, control, and outcomes) method. We systematically searched PubMed, Scopus, and Web of Science databases using sensitive and specific search strings. Subsequently, duplicate papers were removed using EndNote and Covidence. A two-phase selection process was then carried out on Covidence, starting with screening by title and abstract, followed by a full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) was used to assess the quality and risk of bias. Finally, a standardized data extraction tool was employed for reliable data mining. ResultsThe searches yielded 2,241 records, from which 555 duplicate papers were removed. During the title and abstract screening step, 958 papers were excluded, and the full-text review step excluded 482 studies. Subsequently, in quality and risk of bias assessment, 172 papers were removed. 74 publications were selected for data extraction, which formed 10 insightful reviews and 64 related experimental studies. ConclusionThe paper provides general definitions of explainable artificial intelligence (XAI) in the medical domain and introduces a framework for interpretability in clinical decision support systems structured across three levels. It explores XAI-related health applications within each tier of this framework, underpinned by a review of related experimental findings. Furthermore, the paper engages in a detailed discussion of quality assessment tools for evaluating XAI in intelligent health systems. It also presents a step-by-step roadmap for implementing XAI in clinical settings. To direct future research toward bridging current gaps, the paper examines the importance of XAI models from various angles and acknowledges their limitations.

Expression and characterization of recombinant antibodies against H7 subtype avian influenza virus and their diagnostic potential.

Monoclonal antibodies (mAbs) play a pivotal role in disease diagnosis as well as immunotherapy interventions. Traditional monoclonal antibody generation relies on animal immunization procedures predominantly involving mice; however, recent advances in in-vitro expression methodologies have enabled large-scale production suitable for both industrial applications as well as scientific investigations. In this study, two mAbs against H7 subtype avian influenza viruses (AIV) were sequenced and analyzed, and the DNA sequences encoding heavy chain (HC) and light chain (LC) were obtained and cloned into pCHO-1.0 expression vector. Then, the HC and LC expression plasmids were transfected into CHO-S cells to establish stable cell lines expressing these mAbs using a two-phase selection scheme with different concentrations of methotrexate and puromycin. Recombinant antibodies were purified from the cell culture medium, and their potential applications were evaluated using hemagglutination inhibition (HI), western blotting (WB), confocal microscopy, and enzyme-linked immunosorbent assay (ELISA). The results indicated that the obtained recombinant antibodies exhibited biological activity similar to that of the parent antibodies derived from ascites and could be used as a replacement for animal-derived mAbs. A kinetic analysis of the two antibodies to the AIV HA protein, conducted using surface plasmon resonance (SPR), showed concordance between the recombinant and parental antibodies. The data presented in this study suggest that the described antibody production protocol could avoid the use of experimental animals and better conform to animal welfare regulations, and provides a basis for further research and development of mAbs-based diagnostic products.

A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.

Two-phase selection of representative contracts for valuation of large variable annuity portfolios

A computationally appealing methodology for the valuation of large variable annuities portfolios is a metamodelling framework that evaluates a small set of representative contracts, fits a predictive model based on these computed values, and then extrapolates the model to estimate the values of the remaining contracts. This paper proposes a new two-phase procedure for selecting representative contracts. The representatives from the first phase are determined using contract attributes as in existing metamodelling approaches, but those in the second phase are chosen by utilizing the information contained in the values of the representatives from the first phase. Two numerical studies confirm that our two-phase selection procedure improves upon conventional approaches from the existing literature.

Childhood and Adolescents Sleep Bruxism Treatment: A Systematic Review.

Introduction Sleep Bruxism (SB) is a common condition in childhood that can cause multiple consequences such as abnormal tooth wear, tensional headaches, masticatory muscle pain, or fatigue. The literature reports some interventions, however the treatment for SB in children is not well-established. Objectives A systematic review was performed to investigate the effectiveness of the treatments described for SB in children and adolescents: pharmacological and psychological treatments; behavioral guidelines; and dental approaches. Materials and methods Randomized clinical trials comparing different SB treatments with a control group were searched in the electronic databases PubMed, Scopus, Web of Science, Cochrane Library, and VHL until August 04, 2021. Two independent reviewers selected the studies, extracted the data, and assessed the risk of bias. After a two-phase selection process, 07 articles were selected. The methodology of the selected studies was analyzed using the Cochrane Risk of Bias Tool. The criteria used to qualify the studies were based on randomization, allocation, blinding of participants and evaluators, and analysis of results. Results The signs and symptoms of SB were reduced with pharmacotherapy (hydroxyzine/diazepam) and medicinal extracts ( M. Officinalis ), but with occlusal splints and physiotherapy, this improvement was not statistically significant when compared to control groups. Conclusion Some evidence of the efficacy of pharmacotherapy (hydroxyzine/diazepam) and medicinal extracts ( M. Officinalis ) was found. However, this systematic review is not enough to establish a protocol for the treatment of SB. Besides, the individualized management of SB in this population should be considered, emphasizing the management of risk factors.

A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi-Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős-Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts-Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics.

Development and Characterization of Near-Isogenic Lines Derived from Synthetic Wheat Revealing the 2 kb Insertion in the PPD-D1 Gene Responsible for Heading Delay and Grain Number Improvement.

Spikelet number and grain number per spike are two crucial and correlated traits for grain yield in wheat. Photoperiod-1 (Ppd-1) is a key regulator of inflorescence architecture and spikelet formation in wheat. In this study, near-isogenic lines derived from the cross of a synthetic hexaploid wheat and commercial cultivars generated by double top-cross and two-phase selection were evaluated for the number of days to heading and other agronomic traits. The results showed that heading time segregation was conferred by a single incomplete dominant gene PPD-D1, and the 2 kb insertion in the promoter region was responsible for the delay in heading. Meanwhile, slightly delayed heading plants and later heading plants obviously have advantages in grain number and spikelet number of the main spike compared with early heading plants. Utilization of PPD-D1 photoperiod sensitivity phenotype as a potential means to increase wheat yield potential.

How to Measure Foot Self-Care? A Methodological Review of Instruments

Foot self-care is an important element of caring for and promoting foot health. However, little is known about the validity and reliability of existing foot self-care instruments. The purpose of this review is to describe and analyze the focus, content, and psychometric evidence of existing instruments for measuring foot self-care. A methodological review of three international scientific databases-Medline (PubMed), CINAHL (Ebsco), and Embase-was conducted in May 2022. The search produced 3520 hits, of which 53 studies were included in the final analysis based on a two-phase selection process. A total of 31 instruments were identified, of which six were observed to have been used more than once. Subsequently, the methodological quality of these six instruments was evaluated. It is noted that although a considerable variety of instruments are used in measuring foot self-care, only a small proportion are used consistently. In general, the psychometric testing instruments seem to primarily focus on analyzing content validity and homogeneity. In the future, comprehensive testing of instrument psychometrics could enhance the cumulative evidence of the methodological quality of these instruments. Furthermore, researchers and clinicians can use the information in this review to make informed choices when selecting an instrument for their purposes.

A two-phase gene selection method using anomaly detection and genetic algorithm for microarray data

Cancer diagnosis based on gene analysis is one of the main research areas in bioinformatics and machine learning. Microarray is a technology that can simultaneously study the expression level of thousands of genes in a sample. However, mutation or change in gene expression of only a small number of genes can lead to cancer, and basically, the expression level of most genes is the same between cancerous and healthy samples. On the other hand, the main challenge in microarray data is the high number of genes compared to the very small number of samples. This issue makes gene selection an essential step in microarray analysis. In this paper, we have proposed a new two-phase gene selection method for microarray data. In the first stage of this method, with a different approach, the genes that are the main features of the microarray are considered as training samples instead of cancerous and healthy samples; afterward, we reduce the number of genes to a great extent via anomaly detection. In the second stage, we apply a guided genetic algorithm to the genes obtained from the previous step to reach the final effective genes. Based on the experimental results, our method can reduce the number of genes up to at least 99% on all datasets. Besides, in addition to the very high reduction rate of genes, we managed to significantly increase the classification accuracy using the selected genes.

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

China corporation bond default prediction is important and can be formulated as an imbalance classification problem solved by static ensemble classifiers. However, dynamic ensemble selection (DES) classifiers have not been applied to this typical problem in the context of business research. DES classifiers are capable of selecting an ensemble classifier for each test sample, leading to better classification performance than static ensemble classifiers. Most existing DES classifiers can not address imbalance classification optimally and only use single criteria of competent for classifier selection, resulting in sub-optimal classification performance. In this paper, we propose an enhanced DES classifier, named META-DES-Diversity, that inherits strengths of data sampling, meta-learning, criteria of diversity, and dynamic weighting fusion scheme to alleviate such limitations. Specifically, the synthetic minority over-sampling technique is initially used to balance the training set before generating a candidate classifier pool. To select an ensemble classifier with a highly competent, the meta-learning framework META-DES is utilized to consider multiple criteria of competence. In complement with the meta-leaning framework, a two-phase selection strategy is utilized to perform competence and diverse ensemble classifier selection. Note that a competence-driven decision fusion scheme is employed to effectively fuse classification results from selected ensemble classifiers. Experiments on 14 two-class imbalanced data sets from the KEEL repository and one self-collected China corporation bond data set show the effectiveness and superiority of the proposed enhanced DES classifier.

Cervical Vestibular-Evoked Myogenic Potentials in Individuals with Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Vestibular evoked myogenic potentials (VEMPs) in individuals with diabetes mellitus (DM) provide evidence as how diabetes can bring about changes in the peripheral nervous system. Cervical VEMP (cVEMP) evaluates the function and integrity of the sacullo- collic pathway and ocular VEMP (oVEMP) evaluates the utriculo-collic pathway. cVEMP is an ipsilateral inhibitory response of the sternocleidomastoid muscle. cVEMP is recorded at higher intensity above 80-85 dBnHL with biphasic waveforms having initial peak positivity P13 followed by a negativity N23. We performed a systematic review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to evaluate cVEMP in diabetes mellitus. The search was conducted in the databases: PubMed, Cumulative Index of Nursing and Allied Health Literature, Scopus, and Cochrane library using the keywords "diabetes mellitus" and "vestibular evoked myogenic potential" or "cVEMP." A two-phase selection process was used for the final inclusion of studies, and the methodological quality of these studies was assessed using the Newcastle Ottawa scale (NCOS). Meta-analysis was performed using a random-effects model. For comparisons between DM and healthy controls, a significant difference was observed for cVEMP amplitude (P = 0.01). Our meta-analysis's results suggest peripheral vestibular dysfunction can be observed in DM. It appears that cVEMPs may be useful in the early detection of neuropathy in DM.

Does use of animal therapy during dental care help to reduce anxiety in children and adolescents? A systematic review.

Animal-assisted therapy (AAT) is considered a useful technique to reduce anxiety in children and adolescents in medical settings. To investigate whether the use of AAT helps to reduce anxiety during dental care in children and adolescents. Systematic review that included randomized and nonrandomized clinical trials with children up to 18-years of age undergoing dental appointments. The databases Embase, Cochrane, Pubmed/Medline, LILACS, PsycInfo, Scopus, and Web of Science were searched including gray literature. Random-effects meta-analyses using mean difference (MD) and narrative synthesis (vote counting) were implemented. Risk of bias was assessed using RoB2 for randomized clinical trials. The certainty of the evidence was performed using GRADE. A total of 1103 references were identified, and after a two-phase selection, three studies were included. Anxiety, behavior, and pain were the outcomes. A meta-analysis with 146 participants was performed for anxiety at three time points: before treatment (MD -0.40, CI: -1.06 to 0.26; I2 = 0%; p=.24), during treatment (MD -3.64, CI: -11.18 to 3.91; I2 = 94%; p=.34), and after treatment (MD -5.97, CI: -17.08 to 5.14; I2 = 98% p=.29). There was no difference during dental treatment with or without ATT (dogs), as well as for narrative analysis for any outcome. The risk of bias was high mainly because of the randomization and outcome measurement. There is no evidence to support or refute that the presence of AAT during dental care can help reduce anxiety in children (5-11 years). Studies with larger samples are suggested. Protocol registration (CRD42021293593).

Protein encoder: An autoencoder-based ensemble feature selection scheme to predict protein secondary structure

Proteins play a vital role in the human body as they perform important metabolic tasks. Experimental identification of protein structure is expensive and time consuming. The prediction of protein secondary structure is significant to identify the protein tertiary structure and its folds. The feature subset selection from high dimensional protein primary sequence is a key to improve the accuracy of Protein Secondary Structure Prediction (PSSP). Therefore, it is essential to select the relevant features from high dimensional data to predict the protein secondary structure. This work presents a novel method for the PSSP problem based on a two-phase feature selection technique. The first stage utilizes an unsupervised autoencoder for feature extractions. Whereas, the second stage is an ensemble of three feature selection methods, namely, generic univariate select, recursive feature elimination, and Pearson's correlation. This phase combines multiple feature subsets using mutual information to select the optimum feature subset. For classification, different resultant subset features are used. These include random forest, decision tree, and multilayer perceptron. Two sets of experiments are performed on five datasets for the assessment of proposed work. The proposed solution is compared with three state-of-the-art methods based on Q3 accuracy, Q8 accuracy, and segment overlap score. Obtained results show that the proposed framework performs better in the majority of the cases than the past contributions. The proposed framework achieves Q8 accuracies of 82%, 80%, 79%, 73%, and 74% and Q3 accuracies of 90%, 90%, 92%, 79%, and 74% on CB6133, CB6133-filtered, CB513, CASP10, and CASP11 datasets, respectively.

A novel two-phase hybrid selection mechanism feeder to improve performance of many-objective optimization algorithms

A novel two-phase hybrid selection mechanism feeder to improve performance of many-objective optimization algorithms

A Many-Objective Evolutionary Algorithm Based on Two-Phase Selection

Aims: The main purpose of this paper is to achieve good convergence and distribution in different Pareto fronts. Background: Research in recent decades has shown that evolutionary multi-objective optimization can effectively solve multi-objective optimization problems with no more than 3 targets. However, when solving MaOPs, the traditional evolutionary multi-objective optimization algorithm is difficult to effectively balance convergence and diversity. In order to solve these problems, many algorithms have emerged, which can be roughly divided into the following three types: decomposition-based, indexbased, and dominance relationship-based. In addition, there are many algorithms that introduce the idea of clustering into the environment. However, there are some disadvantages to solving different types of MaOPs. In order to take advantage of the above algorithms, this paper proposes a manyobjective optimization algorithm based on two-phase evolutionary selection. Objective: In order to verify the comprehensive performance of the algorithm on the testing problem of different Pareto front, 18 examples of regular PF problems and irregular PF problems are used to test the performance of the algorithm proposed in this paper. Method: This paper proposes a two-phase evolutionary selection strategy. The evolution process is divided into two phases to select individuals with good quality. In the first phase, the convergence area is constructed by indicators to accelerate the convergence of the algorithm. In the second phase, the parallel distance is used to map the individuals to the hyperplane, and the individuals are clustered according to the distance on the hyperplane, and then the smallest fitness in each category is selected. Result: For regular Pareto front testing problems, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 19, 21, 30, 26 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 8, 5, 1, and 6 cases. For the irregular front testing problem, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 20, 17, 25, and 21 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 6, 8, 1, and 6 cases. Conclusion: The paper proposes a many-objective evolutionary algorithm based on two phase selection, termed MaOEA/TPS, for solving MaOPs with different shapes of Pareto fronts. The results show that MaOEA/TPS has quite a competitive performance compared with the several algorithms on most test problems. Other: Although the algorithm in this paper has achieved good results, the optimization problem in the real environment is more difficult, therefore, applying the algorithm proposed in this paper to real problems will be the next research direction.

Children's and parents' perceptions concerning surgical attire: a systematic review.

ABSTRACTObjective: To review the literature about children’s and parent’s perceptions on surgical attire.Data source: A systematic search was conducted in the databases EMBASE, Latin American and Caribbean Health Sciences (LILACS), PubMed, PsycINFO, Scopus and Web of Science. Grey literature was searched on Google Scholar, Open Grey and ProQuest Dissertations, and Theses Database.Data synthesis: A total of 2,567 papers were identified. After a two-phase selection, 15 studies were included in narrative synthesis. Children favored wearing white coats in five of the nine included studies (55.5% [95%CI 48.3-62.7]; p=1.00). With respect to parents’ preferences, results of vote counting showed that in 11 of 15 included studies, they favored physicians wearing white coats (73.3% [95%CI 67.9-78.6]; p=0.11).Conclusions: Children and parents have preferred physicians to wear a white coat with a very low certainty of evidence.