Selection Process Research Articles

ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels

An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered to have noisy labels, or feature-based sampling, where samples from the same class tend to cluster together in the feature space and noisy-label samples are identified as anomalies within those clusters. Empirically, loss-based sampling is robust to a wide range of noise rates, while feature-based sampling tends to work effectively in particular scenarios, e.g., the filtering of noisy instances via their eigenvectors (FINE) sampling exhibits greater robustness in scenarios with low noise rates, and the K nearest neighbour (KNN) sampling mitigates better high noise-rate problems. This paper introduces the Adaptive Nearest Neighbours and Eigenvector-based (ANNE) sample selection methodology, a novel approach that integrates loss-based sampling with the feature-based sampling methods FINE and Adaptive KNN to optimize performance across a wide range of noise rate scenarios. ANNE achieves this integration by first partitioning the training set into high-loss and low-loss sub-groups using loss-based sampling. Subsequently, within the low-loss subset, sample selection is performed using FINE, while the high-loss subset employs Adaptive KNN for effective sample selection. We integrate ANNE into the noisy-label learning state of the art (SOTA) method SSR+, and test it on CIFAR-10/-100 (with symmetric, asymmetric and instance-dependent noise), Webvision and ANIMAL-10, where our method shows better accuracy than the SOTA in most experiments, with a competitive training time. The code is available at https://github.com/filipe-research/anne.

Decision Support System Design To Determine The Feasibility of Students Staying In The Dormitory Using The Simple Additive Weighting Method

The research aims to analyze the feasibility of students staying in the dormitory using the Simple Additive Weighting (SAW) method. The process of admitting residents is currently done manually; therefore, a system is needed to facilitate dormitory management, supported by the SAW method as a systematic approach to identify and evaluate factors influencing the feasibility of staying in the dormitory. In the SAW method, criteria weights are assigned to each factor based on their importance according to the feasibility level determined by the dormitory management. Subsequently, calculations are performed using the SAW formula to obtain the final feasibility score for each student. Based on the test results, the developed system can facilitate and expedite the dormitory management in determining the selection process for students who wish to stay in the dormitory, as well as improve decision-making accuracy.

Managers’ Productivity and Recruitment in the Public Sector

Governments face many constraints in attracting talented managers to the public sector, which often lacks high-powered incentives. In this paper, we study how a civil service reform in Chile changed the effectiveness of a vital group of public sector managers: school principals. First, we estimate principal effectiveness by using an extension of the canonical teacher value-added model. Then we evaluate the effect of the reform on principal effectiveness using a difference-in-differences approach. We find that public schools appointed more effective managers and improved their students’ outcomes after increasing the competitiveness and transparency of their selection process. (JEL D73, H83, I21, J24, J45, O15)

Theoretically informed codesign of a tailored intervention to support pressure ulcer prevention behaviours by older people living in their own homes in the UK and their lay carers: an intervention codesign study (C-PrUP)

ObjectiveTo codesign a theoretically underpinned, healthcare practitioner-mediated, tailored intervention to support housebound older patients and their lay carers to adopt pressure ulcer prevention behaviours.DesignTheoretical domains framework informed codesign.SettingOne geographical area...

A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems

Many evolutionary multi-modal multi-objective algorithms (MMEAs) have been proposed to solve multi-modal multi-objective optimization problems (MMOPs). Unfortunately, the environmental selection process causes many algorithms to place too much emphasis on solution variety in the decision space, which leads to solutions with low convergence quality. As a result, not only are all local Pareto fronts reversed, but objective values are also far lower than the global Pareto Fronts. To tackle these tasks, this paper proposes a hierarchical clustering-based MMOEA_DC_HR model that uses decision space clustering methods to group neighborhood solutions into several local clusters, preserving local Pareto Sets. And secondary clustering is performed in the objective space to select temporary populations from these local clusters to maintain the diversity of the objective space. Additionally, a hierarchical ranking method is introduced to update the convergence archive aiding in maintaining the convergence of the algorithm and controlling the quality of the Pareto Front. The test results show that this novel algorithm exhibits competitive performance in solving selected benchmark problems when compared to other cutting-edge MMEAs.

Multi-trait selection for mean performance and stability in purple-fleshed sweet potato

Few purple-fleshed sweet potato (PFSP) cultivars are available for Brazilian growers, urging for the development of new and adapted cultivars. PFSP breeding programs should include multiple traits during the selection process to increase the chances of developing an adequate genotype, specially yield-related and quality-related traits. The objective was to select promising PFSP genotypes based on yield and tuberous-roots-related traits exploring genotype x environment interaction (GEI). Four experiments were carried out, in which 23 pre-selected PFSP genotypes were evaluated based on yield and quality traits, after screening among 2500 experimental genotypes. The first experiment adopted an augmented block design, with ‘Luiza’ interspersed as a control. From this first experiment, the 19 experimental genotypes with the best performance were selected for the following three experiments, which were conducted in a randomized block experimental design, with three replications each, including ‘Luiza’ as control. The best genotypes were selected in the first experiment using the multi-trait genotype-ideotype distance index (MGIDI). For the following experiments, the performance of the genotypes was assessed using deviance analysis, genotypic stability through weighted average of absolute scores (WAASB) index, and the multi-trait stability index (MTSI). In the first experiment, three factors were retained, being associated to commercial production of tuberous roots (‘K-104′, ‘K-25′ and ‘U1–29′), total production of tuberous roots (‘K-110′, ‘C-42′, ‘F-16′, and ‘U1–29′), and quality of tuberous roots (‘F-22′), respectively. Desired gains were observed for all traits. For the experiments II, III, and IV five factors were retained, being four related to quality of tuberous roots and one to yield components. The GEI was significant for yield-related traits and desired gains were observed for most traits. Due to high commercial tuberous root yield and low WAASB values, the genotypes ‘U1–29′, ‘K-98′, ‘F-22′, ‘K-57′, ‘C-36′, ‘U1–15′, ‘K-25′, and ‘U2–12′ were highly productive and stable. The genotypes selected by the MTSI index were ‘U1–15′, ‘U2–10′, ‘U1–29′, ‘K-98′, and ‘K-78′. The multi-trait selection enabled the identification of promising genotypes highlighting their strengths and weaknesses.

A robust multi-label feature selection based on label significance and fuzzy entropy

Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.

Understanding the features and effectiveness of randomized controlled trials in reducing COVID-19 misinformation: a systematic review.

This systematic review aimed to assess the features and effectiveness of individual-level randomized controlled trials targeting COVID-19 misinformation. The selection process included rigorous criteria, resulting in the inclusion of 24 individual studies from 21 papers. The majority of studies were conducted in high-income countries, with the accuracy/credibility of information as the primary outcome. Debunking and boosting interventions were the most common interventions while nudging and content labeling interventions were examined in a few studies. This study highlights that further research is needed to enhance the effectiveness of boosting strategies and to explore the impact of combined interventions. Addressing bias concerns and standardizing intervention assessment measures will contribute to the development of evidence-based approaches in this critical area.

Effect of Plant Identity in Wheat Mixtures on English Grain Aphid (Sitobion avenae) Control

ABSTRACTField experiments have demonstrated that wheat mixtures differ in their ability to regulate aphid populations. To further investigate the effectiveness of wheat mixtures (Triticum aestivum and Triticum turgidum) in controlling aphids, we conducted both laboratory and greenhouse experiments. Specifically, we assessed the associational resistance of two wheat mixtures (Florence‐Aurora with Forment, Florence‐Aurora with Montcada), and their respective monocultures, in different stages of the aphid host selection process. We analysed aphid acceptance rate, population growth, and load under different wheat treatments. Additionally, we characterised wheat aboveground biomass and nitrogen content as important functional traits for aphid resistant. Aphid acceptance decreased in plants of cv. Forment when exposed to volatiles from undamaged Florence‐Aurora plants, whereas the other tested combinations tested had no effect. Aphids performed differently in the two mixtures: Florence‐Aurora mixed with Forment significantly reduced aphid population growth and load compared to the monocultures, whereas the combination of Florence‐Aurora with Montcada wheat had no effect on aphid performance. The plant–plant interactions also modified the analysed traits. Nitrogen content of Florence‐Aurora wheat plants was reduced when mixed with Forment wheat, which may explain the lower aphid load observed in plants of cv. Florence‐Aurora when mixed with plants of cv. Forment. However, mixing wheats with similar aboveground biomass resulted in an increase in the average biomass of plants of both cultivars which could have led to a higher aphid population. The data supports the idea of right neighbour, as the benefits of wheat mixtures for aphid control were determined by the identity of the combined plants (or species). Finally, our results suggest that associating wheats with different traits may promote facilitative interactions, which in turn enhances associational resistance, whereas the combination of wheats with similar traits may result in competitive interactions that may hinder aphid control benefits.

Chemistry, Analysis, and Biological Aspects of Daprodustat, A New Hypoxia Inducible Factor Prolyl Hydroxylase Inhibitor: A Comprehensive Review.

The National Health and Nutrition Examination Survey (NHANES) carried out a survey between 2007-10 and found that as compared to the general population, the prevalence of anemia in chronic kidney disease (CKD) patients was twice high. Daprodustat is an investigational novel drug for the treatment of renal anemia. The objective of this study is to provide a comprehensive review of chemistry, synthesis, pharmacology, pharmacokinetic, and bioanalytical methods for the analysis of Daprodustat. To improve understanding, a review was carried out by creating a database of relevant prior research from electronic sources such as ScienceDirect and PubMed. The methodology is shown in the flowchart of the literature selection process. The drug was approved in 2020 for therapeutic purposes in Japan. It is a novel drug approved for the treatment of anemia in chronic kidney disease for oral administration. It is intended for adults who have undergone dialysis for a minimum of four months and are experiencing anemia as a result of chronic kidney disease. This review examines therapeutic, pharmacological, and analytical aspects related to the novel drug Daprodustat.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, beta). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

Heavy metal pollution in farmland soils surrounding mining areas in China and the response of soil microbial communities

Mining activities lead to significant heavy metal pollution in nearby farmland soils, affecting the soil's microbial community and functions. To comprehensively investigate the heavy metal contamination in farmland soils caused by mining activities and the impacts on soil microbial communities and functions, we collected 87 soil samples from farmlands near 29 mining sites nationwide, measured levels of cadmium, lead, copper, and zinc. Our findings revealed that 75.8% of the sampled farmlands exhibited varying degrees of heavy metal pollution. Cadmium contamination stood out, being 2.84 to 5.35 times higher compared to other metals. This pollution notably decreased microbial diversity in agricultural soils (P ≤ 0.04), causing a shift from intricate interconnected microbial co-occurrence modules to a higher number of simpler ones, indicating a fragmentation of the microbial interaction network. Additionally, heavy metal contamination led to a 10.9% increase in the importance of the heterogeneous selection process in community assembly. Despite reduced microbial alpha diversity, we observed an increase in the diversity and abundance of metal resistance genes (MRGs) and intensified microbe-MRG interaction. This suggests that microbial communities, even when altered, maintain functionality through enhanced redundancy, which probably facilitates the preservation of microbial activities. We also identified key taxa with intense connectivity in the microbial interaction networks, 58% of which have been recognized in previous studies for their predictive effects on soil health. These findings offer important insights for developing strategies to enhance soil health, such as promoting the presence of "super-connectors" in microbial networks for the maintenance of microbial community.

Prognostic Nomogram for Predicting Survival in Asian Patients With Small-Cell Lung Cancer: A Comprehensive Population-Based Study and External Verification.

The incidence of small cell lung cancer (SCLC) among Asian patients is on the rise. Nevertheless, there remains a deficiency in precise prognostic models tailored to the specific needs of this patient population. It is imperative to develop a novel nomogram aimed at forecasting the prognosis of Asian SCLC patients. The SEER database supplied data on 661 Asian SCLC patients, who were then divided into training and internal validation sets through a random selection process. In addition, we identified 212 patients from a Chinese medical institution for the purpose of creating an external validation cohort. To forecast survival, we employed both univariate and multivariate analyses. The performance of our nomogram was assessed through calibration plots, the concordance index (C-index), and decision curve analysis (DCA). Five independent prognostic factors were determined and integrated into the nomogram. C-index values for the training and internal validation cohorts were 0.774 (95% confidence interval [CI] = 0.751-0.797) and 0.731 (95%CI = 0.690-0.772), respectively. In the external validation cohort, the C-index is 0.712 (95% CI = 0.655-0.7692). Calibration curves demonstrated highly accurate predictions. When compared to the AJCC staging system, our model exhibited improved net benefits in DCA. Furthermore, the risk stratification system effectively differentiated patients with varying survival risks. We have created a novel nomogram for predicting the survival of Asian patients with SCLC. This nomogram has been subjected to external validation and has shown its superiority over the conventional TNM staging system. It offers a more precise and reliable means of forecasting the prognosis of Asian SCLC patients.

The Educational Programs in Home Hemodialysis: Scoping Review

Introduction. Home hemodialysis is an effective therapeutic option for patients with chronic kidney disease. As highlighted in the literature, its management requires good self-care abilities from the patient and adequate support for self-care from the caregiver. Therefore, the development of educational programs plays a fundamental role in patient care. Aim. The goal of this study is to map the educational programs aimed at caregivers and patients undergoing home hemodialysis treatment, in order to identify gaps in the literature regarding this focus. Methods. A Scoping Review was conducted following the Joanna Briggs Institute guidelines. Potentially relevant articles were identified through a selection process on major databases (PubMed, Scopus, CINAHL, EMBASE, Web of Science, and Google Scholar), without applying any time limits. Results. The educational programs available in the literature for home hemodialysis patients focus on both clinical and psychological aspects; training through a "practical" approach is the most commonly used strategy. Discussion and conclusions. The review highlights the crucial role that a multidisciplinary and multidimensional educational approach can provide to home hemodialysis patients. It is necessary to optimize educational strategies for this population to improve patient outcomes.

Exploring the Impact of Social Support on Tuberculosis Treatment: A Thematic Review

Aims: Tuberculosis (TB) remains a critical global health issue, characterized by significant morbidity and mortality rates. Recent research highlights the potential of social support interventions to enhance TB treatment outcomes and improve patient quality of life. This study examines the positive effects of social support interventions on TB patients throughout their treatment process. Method: Utilizing a traditional review approach, this study applies thematic analysis to synthesize findings from the literature. The review encompasses 17 articles selected from reputable databases, including PubMed, Wiley Online Library, ProQuest, and Google Scholar, based on predefined inclusion criteria. These articles underwent quality assessment using the Critical Appraisal Skills Programme (CASP) tool. The PRISMA flowchart provided transparency in the selection process. Results: Key themes identified through the analysis include treatment adherence, psychological well-being, motivation to recover, health-related quality of life, prevention of TB transmission, successful TB treatment, and self-efficacy to recover. Conclusion: The findings suggest that social support interventions can play a crucial role in addressing the persistent challenges of TB management globally.

The Strategy of Driving School Principals as Transformational Leaders in Improving Teacher Professionalism and the Quality of Education in Elementary Schools

This research aims to describe the results of a literature review regarding the strategies of driving school principals as transformational leaders to improve teacher professionalism and the quality of education in elementary schools (SD). This research uses a Narrative Literature Review (NLR) approach with stages: identification of data sources, data selection and analysis process, synthesis and interpretation of findings. The NLR results show that there are three main strategies for driving school principals as transformational leaders in an effort to improve teacher professionalism and the quality of education. The three strategies are, namely, developing programs to increase teacher professionalism in a targeted manner, encouraging a collaborative culture and continuous learning, and providing ongoing encouragement and guidance. These strategies have specificities in terms of programs, which include the approach taken, practical activities, and monitoring and evaluation activities. These strategies encourage increased teacher professionalism and the quality of education in elementary schools. This increase will have an impact on improving the quality of learning and the quality of primary school education outcomes.

Postoperative conditions of rehabilitative interest in lung transplantation: a systematic review.

Lung transplantation is an elective treatment option for end-stage respiratory diseases in which all medical therapy options have been exhausted. The current study aimed to identify updated information on the postoperative conditions that may impair rehabilitation after lung transplantation and to provide specific considerations of their clinical relevance during the recovery process. The present study is a systematic review conducted by searching three primary databases: the United States National Library of Medicine PubMed system, Scopus, and the Cochrane Library. The databases were searched for articles published from database inception until May 2024; at the end of the selection process, 27 documents were included in the final analysis. The retrieved material identified 19 conditions of rehabilitative interest that potentially affect the postoperative course: graft dysfunction, dysphagia, postsurgical pain, cognitive impairment, chronic lung allograft dysfunction-bronchiolitis obliterans syndrome, phrenic nerve injury, delayed extracorporeal membrane oxygenation weaning, airway clearance, refractory hypoxemia, mediastinitis, reduced oxidative capacity, sternal dehiscence, coronavirus disease 2019 (COVID-19), gastroparesis, ossification of the elbow, Takotsubo cardiomyopathy, airway dehiscence, recurrent pleural effusion, and scapular prolapse. Although some patients are not amenable to rehabilitation techniques, others can significantly improve with rehabilitation.

Comparative Analysis of Decision Tree and Logistic Regression Models in Employee Recruitment and Selection for Enterprise Success

Enterprise success is determined by competent Human Resources (HR). The recruitment and selection process of employee candidates plays an important role in producing competent human resources as an effective initial selection increases the chances of finding the right candidate for a particular role. This research predicts the likelihood of a candidate being further selected in the interview phase based on behavioral and functional recruitment and selection which are important aspects of a candidate's potential fit and contribution to the enterprise. The research uses a comparison of decision tree analysis models and logistic regression to make predictions with several measurement metrics to see the accuracy and confusion matrix of each model used. Based on evaluation and validation, the decision tree analysis model is superior in prediction even though the results tend to be the same as the logistic regression model. The accuracy value of the classification using the decision tree model was 86.67% with correct prediction results of 78 data from 90 testing data and the accuracy value of the logistic regression model was 85.55% with correct prediction results of 77 data from 90 testing data. The results of the comparison of the two models show that the performance of the decision tree classifier model tends to be better.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies