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Since the introduction of differential evolution (DE) algorithms, they have achieved remarkable success in the field of evolutionary algorithms and engineering applications. In single-objective DE algorithms, most researchers tend to focus on improving mutation operators and parameter control, while overlooking the study of selection operators. However, the study of selection operators still holds great potential in enhancing the performance of DE algorithms. This study proposes a fitness-distance-based selection (FDS) strategy and a new scaling factor control method. FDS is divided into two stages. The first stage is to determine whether an individual needs to accept discarded trial vectors. The second stage involves selectively accepting these discarded trial vectors, which is based on the information related to the discarded trial vectors and the corresponding target vector. A new setting for the scaling factor parameter is proposed, designed to more effectively assist FDS in enhancing algorithm performance. Based on these strategies, an improved variant of the DE algorithm, called fitness-distance-based DE (FDDE) algorithm, is further proposed by this study. To verify the performance of FDDE, we conducted an in-depth study comparing it with six other advanced DE variants and four famous evolutionary algorithms using the CEC 2017, CEC 2022, and CEC 2011 benchmark sets. The experimental results demonstrate that the FDS strategy and the new scaling factor can significantly improve the performance of DE algorithms, and FDDE is significantly better than other advanced algorithm. [ABSTRACT FROM AUTHOR]

This article aims to provide a novel image paradigm for camouflaged object detection, i.e., RGB-D images. To promote the development of camouflaged object detection tasks based on RGB-D images, we construct an RGB-D camouflaged object detection dataset, dubbed CODD. This dataset is obtained by converting the existing salient object detection RGB-D datasets by image-to-image translation techniques, which is comparable to the current widely used camouflaged object detection dataset in terms of diversity and complexity. In particular, in order to obtain high-quality translated images, we design a selection strategy that takes into account the structural similarity between pre- and post-conversion images, the similarity between the appearance of objects and their surroundings, as well as the ambiguity of object boundaries. In addition, we extensively evaluate the CODD dataset using existing RGB-D-based salient object detection methods to validate the challenge and usability of the dataset. The CODD dataset will be available at: https://github.com/zcc0616/CODD-Dateset.git.

Previous works have proposed various techniques to address the premature convergence problem, where candidate solutions get trapped in local optima instead of reaching the global optimum. This has been tackled using different selection methods in metaheuristic search algorithms. However, while much of the literature focuses on either the search operators or the creation of algorithm variants, research indicates that the effectiveness of the search procedure depends on both the search operators and the selection methods. Incorporating problem-specific functional weights enhances dynamic adaptation to data patterns, reflects data relevance, and improves generalization. This paper offers an enhanced Artificial Bee Colony algorithm including functional weights and a modified selection strategy (ABC-FWMSS) to prioritize features, aiming to achieve an optimal solution and a dynamic balance between exploration and exploitation. The exploration ability of the Artificial Bee Colony is enhanced using pretrained model functional weights during the employed bee phase, while its exploitative capabilities are boosted using tournament selection and employed bee index during the onlooker bee phase. This approach dynamically balances exploration and exploitation. The proposed method achieved 96% precision on the 20 Newsgroups dataset, with the highest fitness score and a 48.8% drop in the number of selected features. [ABSTRACT FROM AUTHOR]

Yuedonghei (YDH) is the only local pig breed with full black hair among the four well-known local pig breeds originated and distributed in Guangdong province, China, which caters to the consumers’ preference of the local market of 127 million residents and thus brings a significantly above-average price. However, considerable genetic introgression (GI) has been reported for the YDH population, i.e., gene flow into YDH from other pig breeds, which is mainly due to the recent crossbreeding with several mainstream breeds for upgrading reasons. Therefore, this study aimed to evaluate the GI as well as the conservation status in the current YDH population and test the feasibility of advanced optimum contribution selection (aOCS) in alleviating GI in YDH. We first analysed the genetic diversity, ancestral structure, population structure, and phylogeny of 360 YDH relative to 782 publicly downloaded pigs of 42 Eurasian or American breeds and wild boars, based on single nucleotide polymorphism chip data. Then, we selected 304 initial YDH and stochastically simulated a practical conservation programme that spanned 10 discrete generations and implemented haplotype segment-based aOCS in every generation. The expected and observed heterozygosity of 360 YDH were 0.344 and 0.336. The linkage disequilibrium-based recent effective population size (Ne) was 32.89. Considerable GI amounting to 32.9% foreign ancestry was found in 28 lowly related YDH individuals using admixture analysis. In the simulated YDH conservation programme, the average native genomic contribution was increased from 50.4 to 71.4% while maintaining a Ne of 100 by controlling classic kinship and native kinship. Our study showed that segment-based aOCS that required only genomic data can be used to alleviate GI in the current YDH population and meanwhile increase its Ne, which provided strategic insights into the sustainable conservation of local genetic resources of livestock.

ABSTRACT: This study aimed to evaluate various selection strategies for adoption in dual-purpose (ICD), meat (ICM) and layer (ICL) breeding goals in indigenous chicken breeding programs. The ICM goal aimed to improve live weight (LW12), daily gain (ADG) and egg weight (EW12) or together with feed efficiency and antibody response. For the ICL goal, age at first egg (AFE) and egg number (EN12) or together with feed efficiency and antibody response were targeted. In the ICD goal, the objective was to improve LW12, ADG, AFE and EN12 or together with feed efficiency and antibody response. Highest total index responses of US$ 49.83, US$ 65.71, and US$ 37.90 were estimated in indices targeting only production traits in the ICD, ICM and ICL goals, respectively. Highest index accuracy estimates of 0.77 and 0.70 were observed in indices that considered production and feed-related traits in the ICD and ICL goals, respectively, while in the ICM goal, the highest estimate of 0.96 was observed in an index targeting only production traits. Inbreeding levels ranged from 0.60 to 1.14% across the various indices considered in the breeding goals. Targeting only production traits in the ICD, ICM and ICL goals required the least number of generations of selection of 7.46, 5.50, and 8.52, respectively, to achieve predefined gains. Generally, a strategy targeting only production traits in a goal was the most optimal but resulted to unfavorable correlated responses in feed efficiency and antibody response. Addition of feed efficiency or/and antibody response in a goal was, however, not attractive due to the decline in total index response and accuracy and increase in inbreeding levels and number of generations of selection. Considering the feed availability and disease challenges in the tropics, choice of including feed efficiency or/and antibody response in the ICD, ICM and ICL goals should depend on targeted production system, resource availability to support breeding activities and magnitude of correlated responses on these traits when not included in the goals.

The energy consumption loss is high particularly in manufacturing processes involving heating furnaces. Moreover, the mandatory constraints in continuous heating stage bring difficult challenges to production scheduling. To improve the production efficiency and reduce the energy consumption in a hybrid flow shop with continuous and discrete processing stages, this study developed an adaptive selection multi-objective optimization algorithm with preference (ASMOAP). The mandatory constraints of continuous processing stage are transformed into one of the optimization objectives, which is defined as maximum excess value of adjustment time in this paper. A multi-objective optimization scheduling model with the makespan, energy consumption, and maximum excess of adjustment time is established. The optimization preference is designed in the proposed multi-objective optimization algorithm. The maximum excess of adjustment time is set as the multi-objective optimization preference. Three adaptive selection strategies are designed for the algorithm based on the proportions of outstanding and preference individuals to eliminate constraint conflicts. Presented results prove that the proposed algorithm could effectively solve hybrid flow shop scheduling problem considering discrete and continuous processing stages with limited time. It can be applied to obtain a better feasible solution while improving the efficiency and reducing the energy consumed in practical production processes. [ABSTRACT FROM AUTHOR]

Introduction: Planted forests are of great significance in reducing wind erosion and controlling degraded land, and are the main measure to improve the ecological environment in arid and semi-arid areas. Afforestation is mainly based on tall trees, but forest belts consume a large amount of water, bringing significant pressure to the environmental carrying capacity. While shrubs generally consume less water than do trees, it remains understudied what role shrubs play in configuration of shelterbelts in arid areas. Methods: In this study, we conducted wind tunnel simulation experiments with the common used afforestation tree species Populus popularis and shrub species Salix psammophila. We set up single- or double-belt pure forest and mixed tree/ shrub configurations, to analyze their effects on the wind speed, flow field, and wind prevention efficiency of different forest belt types. Results: The results showed that: 1) the wind erosion resistance of the double-belt shelter forest was stronger than that of a single-belt forest. 2) Shelterbelts consisting of trees had a strong resistance to airflow disturbances, but their protective effect was reflected mainly in the canopy layer, with poor near-surface protection. 3) Shelterbelts consisting of shrubs had a good protective effect within effective height, and the wind speed in the double-shrub forest belt was 77% of that in the single-tree forest belt. 4) The wind prevention efficiency within the shrub forest belt was better than that of the tree forest belt, with wind prevention efficiency exceeding 40% in most double-shrub forests. Shrubs as a supplement to trees could increase the minimum wind prevention efficiency by up to 30%. Discussion: Therefore, the role of shrubs should be emphasized in afforestation in arid areas, especially in areas that need near-surface protection. [ABSTRACT FROM AUTHOR]

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) based on average reward reinforcement learning (ZCSAR) evolves solutions to optimize average reward per time step. However, initial experimental results have shown that, in some cases, the performance of ZCSAR oscillates heavily during the learning period, or cannot reach the optimum during the testing period. In this paper, we modify the selection strategies in ZCSAR to improve its performance, under conditions of minimal changes of ZCSAR. The proposed selection strategies take tournament selection method to choose parents in Genetic Algorithm (GA), and take roulette wheel selection method to choose actions in match set and to choose classifiers for deletion in both GA and covering. Experimental results show that ZCSAR with the new selection strategies can evolve more promising solutions with enough parameter independence, and also with slighter oscillation during the learning period. [ABSTRACT FROM AUTHOR]

The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population's capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm's performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms. [ABSTRACT FROM AUTHOR]

Multiple importance sampling (MIS) is an increasingly used methodology where several proposal densities are used to approximate integrals, generally involving target probability density functions. The use of several proposals allows for a large variety of sampling and weighting schemes. Then, the practitioner must choose a given scheme, i.e., sampling mechanism and weighting function. A variance analysis has been proposed in Elvira et al (2019, Statistical Science 34: 129-155), showing the superiority of the balanced heuristic estimator with respect to other competing schemes in some scenarios. However, some of their results are valid only for two proposals. In this paper, we extend and generalize these results, providing novel proofs that allow to determine the variance relations among MIS schemes. [ABSTRACT FROM AUTHOR]

Summary: Heuristic algorithms are widely used to solve multi‐objective test case prioritization (MOTCP) problems. However, they perform differently for different test scenarios, which conducts difficulty in applying a suitable algorithm for new test requests in the industry. A concrete hyper‐heuristic framework for MOTCP (HH‐MOTCP) is proposed for addressing this problem. It mainly has two parts: low‐level encapsulating various algorithms and hyper‐level including an evaluation and selection mechanism that dynamically selects low‐level algorithms. This framework performs good but still difficult to keep in the best three. If the evaluation mechanism can more accurately analyse the current results, it will help the selection strategy to find more conducive algorithms for evolution. Meanwhile, if the selection strategy can find a more suitable algorithm for the next generation, the performance of the HH‐MOTCP framework will be better. In this paper, we first propose new strategies for evaluating the current generation results, then perform an extensive study on the selection strategies which decide the heuristic algorithm for the next generation. Experimental results show that the new evaluation and selection strategies proposed in this paper can make the HH‐MOTCP framework more effective and efficient, which makes it almost the best two except for one test object and ahead in about 40% of all test objects. [ABSTRACT FROM AUTHOR]

Sustainable collaboration between manufacturer and supplier has emerged as a crucial supply chain decision for increasing business efficiency. In this study, a theoretical framework is proposed that uses practice‐based view to analyze key performance indicators (KPIs) for developing sustainable collaboration. Further, the current study proposes a novel three‐phase supplier evaluation–selection model to assess the incumbent suppliers on basis of KPIs for showcasing the applicability of theoretical framework. The model uses best–worst method (BWM) in the first phase for generating weights of KPI, adopts the TODIM approach in the second phase for evaluation of suppliers, and develops a supplier classification grid in the third phase for analyzing the impact of each selection strategy to be adopted. The novelty of the study is in evaluation of suppliers based on the KPIs with consideration of desirability as well as the potentiality metric and in consideration of the selection strategies, namely, supplier retention, supplier development, and supplier switching. A case study of India's leading home appliances company is taken to demonstrate the application of the current study. The result of BWM reveals that in terms of supplier's potentiality, "quality" emerges as a strong KPI while KPI "information disclosure" gains more importance while considering supplier's desirability towards strengthening the sustainable relationship. The TODIM grid analysis result suggests that suppliers with high performance in both metrics clearly qualify as the best suppliers and must be retained, while the suppliers performing low in both areas must be switched. For suppliers with metric values in conflict with each other, a trade‐off analysis is needed. Important research and managerial implications are drawn from the validation of the proposed framework, which can be useful for researchers and practitioners. [ABSTRACT FROM AUTHOR]

The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs. [ABSTRACT FROM AUTHOR]

This review highlights the potential benefits and drawbacks of using nanocarriers as drug delivery systems. Nanocarriers have been widely utilized to enhance drug efficiency, overcome drug resistance, and reduce adverse effects. However, the interaction between nanocarriers and biological systems can lead to toxic responses. Therefore, it is crucial to carefully select optimized nanocarriers to minimize toxicity and maximize efficiency. Every type of nanocarrier has its own advantages and disadvantages. Hybrid nanocarriers have been engineered to address the limitations of existing nanocarriers and are considered more suitable for developing new formulations. The article discusses various aspects of nanocarriers, including their applicability, potential toxicity, and strategies for utilizing appropriate nanocarriers in nanoformulations. To mitigate the toxicity of nanocarriers, several approaches can be employed, such as PEGylation, coating, charge coating, and injections of free PEG; moreover, by modifying the preparation method or utilizing hybrid nanocarriers, the efficiency of drug delivery systems can be improved. Overall, the article emphasizes the importance of selecting appropriate nanocarriers and employing strategies to reduce toxicity while enhancing drug delivery efficiency. [ABSTRACT FROM AUTHOR]

To date, the traditional open thyroid surgery via a low collar incision remains the standard approach for patients undergoing thyroidectomy. However, this conventional approach will inevitably leave patients a neck scar and even cause a variety of complications such as paresthesia, hypesthesia, and other uncomfortable sensations. With the progress in surgical techniques, especially in endoscopic surgery, and the increasing desire for cosmetic and functional outcomes, various new approaches for thyroidectomy have been developed to avoid or decrease side effects. Some of these alternative approaches have obvious advantages compared with traditional surgery and have already been widely used in the treatment of thyroid disease, but each has its limitations. This review aims to evaluate and compare the different approaches to thyroidectomy to help surgeons make the proper treatment strategy for different individuals. [ABSTRACT FROM AUTHOR]

The problem of the strategy for choosing disinfectants in practical medicine is considered. The pandemic of the new coronavirus infection posed new problems for disinfectology. The expanded spectrum of disinfectants and antiseptics offered by the chemical industry in recent years requires justification for the choice in favor of any product. The goals and types of disinfection considered from current positions and the main groups of disinfectants used in Russia and their properties and spectra of activity are presented. [ABSTRACT FROM AUTHOR]

Correlation filter based tracking has shown impressive and amazing results for the object tracking domain. The types of features used in this family of trackers significantly affect the performance of the tracking process. In order to achieve the significant features, deep networks can be combined with correlation trackers. In this work, the principal sample-based learning of deep networks has been proposed for correlation filter tracking. Usual training of deep networks always takes all sample frames for backpropagation, which is not ideal for large tracking video collections. To overcome these shortcomings, a novel sample selection strategy is devised while back-propagating the network loss or error, and hence the proposed method is named as Principal Sample-based Learning of Deep Network (OT-PSLDN) for correlation filter based object tracking. The proposed OT-PSLDN method is evaluated with standard performance criteria on benchmark datasets, namely Object Tracking Benchmark 2013 (OTB 2013), OTB 2015, Visual Object Tracking 2017 (VOT 2017), and VOT 2018. The experimental results show that the proposed method constantly exceeds the state-of-the-art methods in terms of qualitative and quantitative performance measures. [ABSTRACT FROM AUTHOR]

• The Yuedonghei pig (YDH) is an economically and ecologically valuable Chinese local breed. • Considerable genetic introgression (GI) exists in the current YDH population. • Advanced optimum contribution selection (aOCS) is a population management tool. • Haplotype segment-based aOCS alleviated the GI in YDH by 21.6% units in our simulation. Yuedonghei (YDH) is the only local pig breed with full black hair among the four well-known local pig breeds originated and distributed in Guangdong province, China, which caters to the consumers' preference of the local market of 127 million residents and thus brings a significantly above-average price. However, considerable genetic introgression (GI) has been reported for the YDH population, i.e., gene flow into YDH from other pig breeds, which is mainly due to the recent crossbreeding with several mainstream breeds for upgrading reasons. Therefore, this study aimed to evaluate the GI as well as the conservation status in the current YDH population and test the feasibility of advanced optimum contribution selection (aOCS) in alleviating GI in YDH. We first analysed the genetic diversity, ancestral structure, population structure, and phylogeny of 360 YDH relative to 782 publicly downloaded pigs of 42 Eurasian or American breeds and wild boars, based on single nucleotide polymorphism chip data. Then, we selected 304 initial YDH and stochastically simulated a practical conservation programme that spanned 10 discrete generations and implemented haplotype segment-based aOCS in every generation. The expected and observed heterozygosity of 360 YDH were 0.344 and 0.336. The linkage disequilibrium-based recent effective population size (N e) was 32.89. Considerable GI amounting to 32.9% foreign ancestry was found in 28 lowly related YDH individuals using admixture analysis. In the simulated YDH conservation programme, the average native genomic contribution was increased from 50.4 to 71.4% while maintaining a N e of 100 by controlling classic kinship and native kinship. Our study showed that segment-based aOCS that required only genomic data can be used to alleviate GI in the current YDH population and meanwhile increase its N e , which provided strategic insights into the sustainable conservation of local genetic resources of livestock. [ABSTRACT FROM AUTHOR]

Abstract The objective of this work was to evaluate the efficiency of early screening in the selection of sugarcane (Saccharum spp.) families for cold tolerance in a controlled environment. Fourteen sugarcane families were evaluated for leaf damage and chlorophyll content, in a controlled environment and in the field, after cold stress. The variance components and genotypic values of the families were estimated via REML/BLUP, as well as the genetic correlations between variables and environments. The heritabilities in the narrow sense and for family averages showed values above 0.23 and 0.76, respectively, and a genetic coefficient of variation above 13%, which confirmis the existence of variability and enables selection in both environments. The genetic correlations ranged from 0.59 to 0.88 and were significant for all combinations of variables and environments. The average coincidence index for the families between environments was 60% for the selected group and 85% for the unselected group. The early screening in the first stages of selection is efficient in breeding for cold tolerance in sugarcane families.

In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems. [ABSTRACT FROM AUTHOR]

Muscle-sparing vertical rectus abdominis myocutaneous (MS-VRAM) flaps are widely used in pelvic reconstruction. Aiming at optimal reconstructive outcomes, flap design and modification should be individualized to restore various kinds of defects. Summarize an empirical strategy about MS-VRAM selection for different pelvic and perineal reconstructions. Thirty patients who underwent total pelvic exenteration and pelvic reconstruction surgery from 2009 to 2017 were enrolled. The patients were divided into four groups according to the type of MS-VRAM-based flap used in the procedure: the modified long vertical flap (n = 10), the wrapping flap (n = 6), the de-epithelialized flap (n = 6), and the cork flap (n = 8). The follow-up period was 1 year after the surgery. Flap size, drainage volume, postoperative satisfaction, and complications were recorded, and postoperative photographs were collected. All of the patients achieved satisfying effect under the targeted reconstruction strategy. Of the four groups, the accurate cork flap finally acquires higher satisfaction, the shortest hospital stay, and the least total drainage volume. Meanwhile, the incidence of complications was not increased compared with the other groups. A new reconstructive strategy for pelvic reconstruction was established. Functional or non-functional reconstruction was accomplished by using various MS-VRAM flaps. Among them, the cork flap is the most economical flap to reconstruct pelvic floor defects with minimal tissue requirement and donor trauma. [ABSTRACT FROM AUTHOR]

[Display omitted] • High-throughput screening of 192 TM@TMD catalysts for CO 2 RR. • Derivation of expressions for selectivity and ΔG in key reaction steps using SISSO. • Analyzed expressions to unveil catalyst selection strategies. The study of transition metals and various possible surface compositions has sparked great interest in low-cost materials, which exhibit high activity and selectivity in catalysis. While various single-atom catalysts loaded on transition metal dichalcogenide (TMD) substrates with excellent CO 2 reduction performance have been identified, the relationship between catalytic activity and the intrinsic properties of TMD single-atom catalysts remains unclear. Hence, a high-throughput first-principle computational approach is proposed to screen 24 transition metals anchored on 8 TMD monolayers to determine their catalytic activity in CO 2 RR. The results show that Fe@CoS 2 , Pt@TiTe 2 and Co@CoS 2 exhibit exceptional performances with low CO 2 RR limiting-potentials of −0.045 eV, 0.75 eV, and 0.54 eV, respectively, showcasing selective pathways towards formic acid (HCOOH), methane (CH 4), and methanol (CH 3 OH). Employing the Sure Independence Screening and Sparsifying Operator method(SISSO), key descriptors linking the performance of single-atom catalysts with their intrinsic features are identified, providing insights for the discovery of superior CO 2 RR catalysts. Moreover, it was observed that a feature of the anchored single atom, the difference between covalent radius and atomic radius (CR-R), is associated with multiple crucial reaction steps, exhibiting a strong linear relationship with the charge transfer of *COOH. This work not only identifies promising CO 2 RR catalysts but also establishes a predictive framework for screening catalysts based on their intrinsic properties, paving the way for future advancements in CO 2 reduction research. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate various selection strategies for adoption in dual-purpose (ICD), meat (ICM) and layer (ICL) breeding goals in indigenous chicken breeding programs. The ICM goal aimed to improve live weight (LW 12), daily gain (ADG) and egg weight (EW 12) or together with feed efficiency and antibody response. For the ICL goal, age at first egg (AFE) and egg number (EN 12) or together with feed efficiency and antibody response were targeted. In the ICD goal, the objective was to improve LW 12 , ADG, AFE and EN 12 or together with feed efficiency and antibody response. Highest total index responses of US$ 49.83, US$ 65.71, and US$ 37.90 were estimated in indices targeting only production traits in the ICD, ICM and ICL goals, respectively. Highest index accuracy estimates of 0.77 and 0.70 were observed in indices that considered production and feed-related traits in the ICD and ICL goals, respectively, while in the ICM goal, the highest estimate of 0.96 was observed in an index targeting only production traits. Inbreeding levels ranged from 0.60 to 1.14% across the various indices considered in the breeding goals. Targeting only production traits in the ICD, ICM and ICL goals required the least number of generations of selection of 7.46, 5.50, and 8.52, respectively, to achieve predefined gains. Generally, a strategy targeting only production traits in a goal was the most optimal but resulted to unfavorable correlated responses in feed efficiency and antibody response. Addition of feed efficiency or/and antibody response in a goal was, however, not attractive due to the decline in total index response and accuracy and increase in inbreeding levels and number of generations of selection. Considering the feed availability and disease challenges in the tropics, choice of including feed efficiency or/and antibody response in the ICD, ICM and ICL goals should depend on targeted production system, resource availability to support breeding activities and magnitude of correlated responses on these traits when not included in the goals. [ABSTRACT FROM AUTHOR]

Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the requirement of simultaneously using data-driven information and handcrafted information. Therefore, we propose an approach named multi-view active learning using domain-specific and data-driven document representations (MVAL4D), endeavoring to enhance the predictive capability and alleviate the requirement of labeled data. Specifically, a new view-generation mechanism is proposed to generate multiple views by simultaneously exploiting various document representations obtained using handcrafted feature engineering and by performing deep learning methods. Moreover, different from previous active learning studies in which all instances are chosen using the same selection criterion, MVAL4D adopts different criteria (i.e., confidence and informativeness) to select potentially positive instances and potentially negative instances for manual annotation. The experimental results verify the effectiveness of MVAL4D. The proposed approach can be generalized to many other text classification tasks. Moreover, it can offer a solid foundation for the ADR mention extraction task, and improve the feasibility of monitoring drug safety using social media data. [ABSTRACT FROM AUTHOR]

The article presents the results of a study of individual strategies for choosing training courses by teachers of general secondary education institutions. The article substantiates the choice of research methods and means for processing the results; describes the methodology for preparing an in-depth interview guide, its conduct, and analysis. The experiment results confirmed the interconnection between the level of demonstration of the extrinsic negative element of motivation for professional development and the Yanis coefficient calculated from the data of the content analysis of the in-depth interview. In the process of research, the educational request of teachers, which is mainly focused on updating methods of action, is specified, which indicates (whether conscious or not) the insufficient effectiveness of traditional professional technologies. The identified selection strategies (focusing on the opinion of significant individuals and accounting for characteristics of professional activity) should be considered not only when developing new training courses, but also when promoting them at the educational services market. The research team prepared relevant recommendations for the developers. Based on the results of the study, the methodological recommendations were developed for the system of postgraduate teacher education. The experiment, which is conducted in 22 educational institutions in Kyiv (Ukraine) and covers more than 1,750 teachers, requires verification in other conditions (small schools in remote regions, regional and district centres) since in the program it is not highlighted the impact of the megalopolis on the activity of teachers, and other local factors that may be significant.

In this study ten Hungarian oak provenances from Southern Romania were tested in two comparative trials 10 years after planting. The diameter at breast height (dbh) and tree height (Th) were measured in family progenies and the survival rate was determined. In both trials, the local provenances (Bals and Seaca 2) revealed superior fitness in terms of growth and adaptability traits, confirming the recommendation of using local provenances with priority in afforestation activities. Based on the measured traits, families which ranked in the first ten positions at age 3 and age 10 years were identified in both trials. The analysis of variance revealed statistically significant differences in growth traits between provenances and families in both comparative trials. Duncan’s test for 5% transgression probability revealed different results for dbh and Th in terms of provenance homogeneity in the two test sites, with a superior homogeneity in the Aramadia trial for dbh and in the Bals trial for Th. Growth performances of the Hungarian oak provenances were significantly influenced by the experimental location and by the interactions between provenance, family and locality, suggesting that maximum precaution must be taken in the choice of forest reproductive materials to be planted in different environmental conditions. The family mean heritability (h2f) was significantly lower than the individual heritability (h2i), suggesting the possible adoption of an individual selection strategy in the next stage of the current breeding programme. The juvenile-adult correlations will be made at the age of 40 years and will take advantage of the results of this study to shorten the testing period for the selection of genetically improved material in Hungarian oak.

Recently, multi-objective evolutionary algorithms have become the most popular and efficient approach for multi-objective optimization problems involving two and three objectives. However, as the number of objectives increases, the performance of multi-objective evolutionary algorithms tends to deteriorate. This can be mainly attributed to the loss of sufficient selection pressure towards the Pareto front. To address this issue, this paper proposes a dimension convergence-based many-objective evolutionary algorithm to solve many-objective optimization problems (MaOPs). To be specific, a convergence indicator, named dimension convergence, is presented to enhance the selection pressure toward the Pareto front. When the Pareto dominance-based indicator loses the discrimination, the new indicator can further measure the convergence performance of the candidates. Moreover, a new selection strategy is designed to balance the convergence and diversity of the evolutionary process. The mating selection is applied to strengthen the selection pressure, while the environmental selection is developed to comprehensive evaluate the candidates. The proposed algorithm is tested on 36 instances of 12 many-objective benchmarks and compared with five state-of-the-art algorithms. 3 real-world problems are also used to evaluate the performance of the comparison algorithms. Experimental results show that DC-MaOEA is competitive concerning the peer algorithms.

This study draws on life history theory to rationalize how tourism enterprises make decisions and evolve during the COVID-19 pandemic. Using a case study approach, the current work improvises the house of trade-off paradox as a visual metaphoric framework that integrates three major dyadic trade-off pairs along with four organizational resource configuration aspects. This inquiry further synthesizes the wheel of selection strategy to pinpoint a mechanism in which tourism agencies mutate to adapt to a new normal based on acute environmental shocks. We further provide practical implications for operators with valuable insights germane to post-pandemic recovery. [ABSTRACT FROM AUTHOR]

Objective: We aimed to establish the reliability of technique selection strategy for ureteroileal anastomosis (Bricker vs. Wallace) by comparing perioperative outcomes, complications, and anastomotic stricture rate in a contemporary series of patients who underwent open radical cystectomy followed by reconstruction of modified Hautmann neobladder. Materials and methods: A total of 60 patients underwent radical cystectomy and modified Hautmann neobladder, of whom 30 patients (group I) with Bricker anastomotic technique were compared to 30 matched paired patients with end-to-end ureteroileal anastomosis (group II). Long-term results, including ureteroileal stricture (UIS) and postoperative complication rate at two year follow up were available. The choice of anastomosis type was successively based on chimney size, ureteral length after retro-sigmoidal tunneling and diameter of distal ureter. Postoperative complications were graded according to the Clavien-Dindo system. Results: Ureteroileal stricture rate was 6.6% in group I vs. 0% in group II, after three months (p < 0.05), while anastomotic leakage rate was 6.6% vs. 3.3% (group I vs group II) between the two groups for the same follow up period (p > 0.05). High-grade complications (Clavien III-V) were more in Bricker group as compared to Wallace group and the difference was significant (20% vs 10.3%, p = 0.03). Conclusion: Our preliminary outcomes demonstrate that this selection strategy seems to be clinically reliable, with lower incidence of postoperative complications in Wallace group.

Objective: We aimed to establish the reliability of technique selection strategy for ureteroileal anastomosis (Bricker vs. Wallace) by comparing perioperative outcomes, complications, and anastomotic stricture rate in a contemporary series of patients who underwent open radical cystectomy followed by reconstruction of modified Hautmann neobladder. Materials and methods: A total of 60 patients underwent radical cystectomy and modified Hautmann neobladder, of whom 30 patients (group I) with Bricker anastomotic technique were compared to 30 matched paired patients with end-to-end ureteroileal anastomosis (group II). Long-term results, including ureteroileal stricture (UIS) and postoperative complication rate at two year follow up were available. The choice of anastomosis type was successively based on chimney size, ureteral length after retro-sigmoidal tunneling and diameter of distal ureter. Postoperative complications were graded according to the Clavien-Dindo system. Results: Ureteroileal stricture rate was 6.6% in group I vs. 0% in group II, after three months (p < 0.05), while anastomotic leakage rate was 6.6% vs. 3.3% (group I vs group II) between the two groups for the same follow up period (p > 0.05). High-grade complications (Clavien III-V) were more in Bricker group as compared to Wallace group and the difference was significant (20% vs 10.3%, p = 0.03). Conclusion: Our preliminary outcomes demonstrate that this selection strategy seems to be clinically reliable, with lower incidence of postoperative complications in Wallace group. [ABSTRACT FROM AUTHOR]

Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the requirement of simultaneously using data-driven information and handcrafted information. Therefore, we propose an approach named multi-view active learning using domain-specific and data-driven document representations (MVAL4D), endeavoring to enhance the predictive capability and alleviate the requirement of labeled data. Specifically, a new view-generation mechanism is proposed to generate multiple views by simultaneously exploiting various document representations obtained using handcrafted feature engineering and by performing deep learning methods. Moreover, different from previous active learning studies in which all instances are chosen using the same selection criterion, MVAL4D adopts different criteria (i.e., confidence and informativeness) to select potentially positive instances and potentially negative instances for manual annotation. The experimental results verify the effectiveness of MVAL4D. The proposed approach can be generalized to many other text classification tasks. Moreover, it can offer a solid foundation for the ADR mention extraction task, and improve the feasibility of monitoring drug safety using social media data.

Effective and congestion-aware routing is vital to the performance of network-on-chip. The efficient routing algorithm undoubtedly relies on the considered selection strategy. If the routing function returns a number of more than one permissible output ports, a selection function is exploited to choose the best output port to reduce packets latency. In this paper, we introduce a new selection strategy that can be used in any adaptive routing algorithm. The intended selection function is named Modified-Neighbor-on-Path, the purpose of that is handling the condition of hesitation happening when the routing function provides a set of acceptable output ports. In fact, number of inquiries that each router has sent to its neighbors in determined past cycles is a new parameter that can be combined with number of free slots of adjacent nodes in the latest selection function named Neighbor-on-Path. Performance analysis is performed by using exact simulation tools under different traffic scenarios. Outcomes show how the proposed selection function applied to West-first and North-last routing algorithms outperforms in average delay up to 20 percent on maximum and an acceptable improvement in total energy consumption.

In this paper, a novel modified whale optimization algorithm based on Tent chaos map and tournament selection strategy (MWOA) was presented. The aim of the improved algorithm is to reduce the possibility of the standard whale algorithm falling into local optimal. During the initialization of the population, in order to increase population diversity and randomness, MWOA cites the Tent chaos map. In the optimization process, in order to improve the development ability of the standard algorithm, the tournament selection strategy was employed to improve the algorithm accuracy. Numerical simulation and example calculation results show that the improved algorithm is superior to the standard WOA algorithm. The improved method provides a new method for truss structure optimization. [ABSTRACT FROM AUTHOR]

The cavitation states among centrifugal pumps can be mirrored by corresponding vibration features. To select the vibration feature target scientifically and objectively for monitor the cavitation states in real time, the analysis method of grey slope correlation with weight entropy was proposed in this paper to explore the relevance between cavitation and vibration features. Thus, the net positive suction head (NPSH) and vibration signal from centrifugal pumps under multiple operation conditions were captured. Moreover, the universal feature targets were extracted from the vibration signal. The grey slope correlation method was applied in the analysis of the positive and negative relevance between NPSH and the multiple operation conditions in a different stage. These feature targets are transformed into the same numerical scale by standardization process. In the end, the final comprehensive coefficient can be attached after endowing power by weight entropy method. These methods can be used to determine the feature targets which have intensive relevance with NPSH. The analysis results indicate that the kurtosis factor, variance, absolute mean, and root mean square obtained from the vibration acceleration signal have stable relevance with NPSH. These feature targets can be used for the proper detection and evaluation of cavitation states in centrifugal pumps. Therefore, the analysis method of grey slope correlation with weight entropy can be used to pre-select the feature targets based on the calculated grey incidence. This method is effective in establishing the relevance between NPSH and vibration. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm incorporating broadening selection strategy in competitive constraint handling paradigm for finding the optimum solution in constrained problems has been proposed, referred as Broadening Selection Competitive Constraint Handling (BSCCH). Although, competitive constraint handling approaches have proved to be very efficient, but they lack faster convergence due to offspring generation from random individuals. By incorporating selection strategy such as broadening selection in the competitive approach, better results are obtained and convergence rate is improved significantly. Incorporating said strategy, the BSCCH algorithm has been proposed which is generic in nature and can be coupled with various evolutionary algorithms. In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. The proposed algorithm has been evaluated using 24 benchmark functions. The mean closure performance of the BSCCH algorithm is compared against seven selected state-of-the-art algorithms, namely Differential Evolution with Adaptive Trial Vector Generation Strategy and Cluster-replacement-based Feasibility Rule (CACDE), Improved Teaching Learning Based Optimization (ITLBO), Modified Global Best Artificial Bee Colony (MGABC), Stochastic Ranking Differential Evolution (SRDE), Novel Differential Evolution (NDE), Partical Swarm Optimization for solving engineering problems - a new constraint handling mechanism (CVI-PSO) and Ensemble of Constraint Handling Techniques (ECHT). The median convergence traces have been compared with two different algorithms based on differential evolution, i:e: Ensemble of Constraint Handling Techniques (ECHT) and Stochastic Ranking Differential Evolution (SRDE). ECHT is considered to be a flagship ensemble technique till date for constrained optimization problems, whereas SRDE employs a parent selection mechanism for constrained optimization. The proposed algorithm is found to provide better solutions and achieve significantly faster convergence in most of the problems. [ABSTRACT FROM AUTHOR]

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As various disease biomarkers, drugs/biomolecules carriers and physiological function regulator, exosomes play significant roles in various physiological and pathological process. Thus, the purification and detection of some exosome are of great significance, which make it necessary for the development of the recognition elements. Aptamers with high affinity and specificity exhibited well in the above areas. In this work, a new three-step evolutionary enhanced strategy based on capillary electrophoresis (CE)-SELEX was proposed for effective aptamer selection of exosome vesicles for the first time. Natural killer cells (NK) exosome was used as the model target. And the aptamer could be obtained in five round CE selection within three steps evolution including precise nucleic acid sequence convergence (with NK exosome specific proteins as targets in round 1st and 3rd), affinity evolution (with NK exosome as target in round 2nd and 4th) and specific evolution (negative selection in round 5th). Arising from the compatibility of CE-SELEX for exosome vesicles aptamer selection, and the proposed strategy, aptamer against NK exosome had been selected with good affinity (K D value of 27.6 nM) and excellent specificity. The as proposed CE-SELEX strategy paves a novel way for recognition element selection of exosome vesicles. [Display omitted] • Aptamer of exosome vesicles was selected through CE-SELEX strategy for the first time. • Through the three-step evolutionary enhanced strategy, effective aptamer selection of NK exosome was achieved. • The proposed method and strategy provide more effective aptamers for important exosomes distinguishing from complex body fluids. [ABSTRACT FROM AUTHOR]

Temperature and humidity play an extremely important role in indoor thermal comfort and industrial production. To achieve precise control of temperature and humidity in the building space environment and reduce energy consumption, it is necessary to analyse turbulence from the perspective of energy. On the basis of the basic principle of building energy enthalpy, under the premise of reasonable assumptions of various boundary conditions, four turbulence models were established for simulation. The basic characteristics, overall distribution and local characteristics of the interior energy enthalpy were compared and analysed. The theory of building energy enthalpy not only can effectively solve the influence of latent heat on the building environment, but also can provide guidance on the selection strategy of turbulence models. The proposed theory can improve the accuracy and credibility of the simulation data of building space.

Temperature and humidity play an extremely important role in indoor thermal comfort and industrial production. To achieve precise control of temperature and humidity in the building space environment and reduce energy consumption, it is necessary to analyse turbulence from the perspective of energy. On the basis of the basic principle of building energy enthalpy, under the premise of reasonable assumptions of various boundary conditions, four turbulence models were established for simulation. The basic characteristics, overall distribution and local characteristics of the interior energy enthalpy were compared and analysed. The theory of building energy enthalpy not only can effectively solve the influence of latent heat on the building environment, but also can provide guidance on the selection strategy of turbulence models. The proposed theory can improve the accuracy and credibility of the simulation data of building space. [ABSTRACT FROM AUTHOR]

Having the best solution for Vehicle Routing Problem (VRP) is still in demand. Beside, Cuckoo Search (CS) is a popular metaheuristic based on the reproductive strategy of the Cuckoo species and has been successfully applied in various optimizations, including Capacitated Vehicle Routing Problem (CVRP). Although CS and hybrid CS have been proposed for CVRP, the performance of CS is far from the state-of-art. Therefore, this study proposes a hybrid CS with Simulated Annealing (SA) algorithm for the CVRP, consisting of three improvements—the investigation of 12 neighborhood structures, three selections strategy and hybrid it with SA. The experiment was conducted using 16 instances of the Augerat benchmark dataset. The results show that 6 out of 12 neighborhood structures were the best and the disruptive selection strategy is the best strategy. The experiments’ results showed that the proposed method could find optimal and near-optimal solutions compared with state-of-the-art algorithms.

The cavitation states among centrifugal pumps can be mirrored by corresponding vibration features. To select the vibration feature target scientifically and objectively for monitor the cavitation states in real time, the analysis method of grey slope correlation with weight entropy was proposed in this paper to explore the relevance between cavitation and vibration features. Thus, the net positive suction head (NPSH) and vibration signal from centrifugal pumps under multiple operation conditions were captured. Moreover, the universal feature targets were extracted from the vibration signal. The grey slope correlation method was applied in the analysis of the positive and negative relevance between NPSH and the multiple operation conditions in a different stage. These feature targets are transformed into the same numerical scale by standardization process. In the end, the final comprehensive coefficient can be attached after endowing power by weight entropy method. These methods can be used to determine the feature targets which have intensive relevance with NPSH. The analysis results indicate that the kurtosis factor, variance, absolute mean, and root mean square obtained from the vibration acceleration signal have stable relevance with NPSH. These feature targets can be used for the proper detection and evaluation of cavitation states in centrifugal pumps. Therefore, the analysis method of grey slope correlation with weight entropy can be used to pre-select the feature targets based on the calculated grey incidence. This method is effective in establishing the relevance between NPSH and vibration.

Casein phosphopeptides (CPPs) have been used worldwide as a nutritional supplement. However, the peptide components have been unknown; as a consequence, few quantification methods of CPP in infant formula were reported. This study introduced a quantification method based on peptide marker and corresponding peptide selection strategy using a simplified model with four commercial types of CPP. The peptides from four commercial CPPs were first identified. Due to the great variety of CPPs, two marker selection strategies were adopted: on one hand, universal marker peptide VLPVPQK can be used for the quantification of all four commercial CPPs, if the CPP can be obtained as a standard. On the other hand, the specific marker peptide LYQEPVLGPV can be used for identification and quantification of commercial CPP type K content in infant formula with a fixed calculation factor. In the simplified model, the combination use of the two markers can meet most of the requirements of CPP analysis in infant formula. The method validation revealed that this was suitable for the routine analysis laboratories without proteomics backgrounds. This selection strategy was suggested for the large-scale marker peptide selection with all commercial CPPs, which can give a comprehensive solution of CPP quantification in infant formula. Abbreviations: CPP: Casein phosphopeptides; LC: Liquid chromatography; TQMS: Triple quadrupole mass spectrometry; MRM: Multiple reaction monitoring; RSD: Relative standard deviation; L*: [13C6, 15N]-leucine; SSSEE: Peptides sequence of serine-serine-serine-glutamic acid-glutamic acid [ABSTRACT FROM AUTHOR]

The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper. To extract the correct edge pixels, we divide the whole texture image into 16 = 4 × 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms. It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS strategy is also verified through a series of experiments. [ABSTRACT FROM AUTHOR]