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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.

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.

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]

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]

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]

The local optimum and stagnation state information of the population seriously affects the performance of differential evolution (DE) algorithm.An advanced DE algorithm with population state processing measures was proposed to address the above two issues.When the population falled into the local optimum, the individuals in the population were learned randomly by LBFGS method to improve the global quality of the solution, and Gaussian mutation was employed to trigger new individuals to jump out of local optimum.As for the stagnation state, the covariance matrix of the population was applied to reorganize the target individuals based on the rotation of the spatial coordinates to suppress the stagnation state of the population and enhance the global search ability of the algorithm.In addition, a new selection strategy was designed, which built an external archive to store abandoned individuals after greedy selection.When the trial individual was inferior to the target individual, the algorithm no longer generated the next generation with greedy selection strategy, but made reasonable intelligent selection around the external archive to ensure that the algorithm converges to the global optimum.Compared with eight state-of-the-art DE algorithms on 29 benchmark functions, the experimental results show that the proposed algorithm has better performance in terms of the solution accuracy and convergence speed.

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]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• 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.

[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.

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.

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]

Copyright of Continuing Professional Education: Theory & Practice is the property of Continuing Professional Education: Theory & Practice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The present study aimed to investigate the impact of Information Technology on employment using fuzzy rule-based systems. The reason for discussing occupation/employment is related to high rate of unemployment in Iran. In this regard, it was expected that information technologies could provide new jobs for educated job seekers. Fuzzy logic has been adopted in this research due to the linguistic, qualitative nature of the assessment indicators of strategies. The conceptual model of research revealed that information technology plays a dual role in job creation. In the short term, the information technology reduced the employment, however in the long time, increased the employment. In order to achieve the goal of strategy choices with the logic close to human intelligence, an 8-step process was proposed and designed. The results of fuzzy inference, indicated a better compliance with the existing realities as well as systems of human thought and the feasibility of available strategies to researcher- something not existed in the classical methods of strategy assessment.

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.

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]

Sorghum, a major crop for income generation and food security in West and Central Africa, is predominantly grown in low-input farming systems with serious soil phosphorus (P) deficiencies. This study (a) estimates genetic parameters needed to design selection protocols that optimize genetic gains for yield under low-phosphorus conditions and (b) examines the utility of introgressed backcross nested association mapping (BCNAM) populations for diversifying Malian breeding materials. A total of 1083 BC1F5 progenies derived from an elite hybrid restorer “Lata-3” and 13 diverse donor accessions were evaluated for yield and agronomic traits under contrasting soil P conditions in Mali in 2013. A subset of 298 progenies were further tested under low-P (LP) and high-P (HP) conditions in 2014 and 2015. Significant genetic variation for grain yield was observed under LP and HP conditions. Selection for grain yield under LP conditions was feasible and more efficient than the indirect selection under HP in all three years of testing. Several of the BCNAM populations exhibited yields under LP conditions that were superior to the elite restorer line used as a recurrent parent. The BCNAM approach appears promising for diversifying the male parent pool with introgression of diverse materials using both adapted Malian breed and unadapted landrace material from distant geographic origins as donors.