Selection Decisions Research Articles

Decoding machine learning in nursing research: A scoping review of effective algorithms.

The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms. The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s). The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains. The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research.

Implementation of MOORA and MOORSA Methods in Supporting Computer Lecturer Selection Decisions

The selection of computer science lecturers is an important process for educational institutions, requiring a balanced assessment of various criteria to find the most suitable candidates. This paper examines the implementation of Multi-Objective Optimization based on Ratio Analysis (MOORA) and its variant, namely Multi-Objective Optimization based on Ratio Analysis with a Subjective Attitude (MOORSA), as a tool to support decision making. in this case. This selection process is often complex, requiring consideration of various criteria, such as academic qualifications, teaching experience, research capabilities, and others. This research was conducted to support the decision-making process. by developing a Decision Support System (DSS) using the Multi-Objective Optimization on The Basic of Ratio Analysis (MOORA) and MOORSA methods. Many methods are used, such as SAW, AHP, Topsis and others. based on the calculation of the MOORA method, the highest result has been achieved by A1 worth 0.651819 and similarly, in the MOOSRA method the highest alternative result is A1 worth 0.592177.

PSX-4 Exploring the impact of age and body condition score on the genetic analysis of mature cow weight in American Angus cattle

Abstract Selecting sires with optimal genetic merit for mature weight (MWT) is crucial for profitability of beef producers, ensuring cost-effective production and increased economic returns per animal. The main objective of this study was to determine how different statistical approaches [e.g., including the fixed effects (FE) of age and body condition score (BCS) in the statistical model and/or pre-adjusting the weights for these effects] affect variance components estimation and genetic parameters for MWT, and their impact in the posterior selection decisions. The dataset provided by the American Angus Association (AAA) comprised 80,296 MWT records from 37,934 cows aged 2 to 15 yr. Contemporary groups (CG) were defined by concatenating herd, year, and season of measurement. Model 1 analyzed unadjusted MWT with CG and BCS as categorical FE and age as a covariable (quadratic). Model 2 was model 1 without BCS, model 3 was model 1 without age, and model 4 considered CG as the only FE. In model 5, MWT was pre-adjusted for 6 yr of age and BCS of 6, and CG was the only FE included in the statistical model. Additive genetic and permanent environment (pe) random effects were included in all statistical models except model 5, which did not include pe (only one adjusted record per animal). Variance components were calculated using REML implemented in the BLUPf90+ family of programs. Sires with at least 10 offspring (n = 1,483) were ranked according to their breeding values (EBVs) predicted for MWT, and all and the top 10% sires were used to estimate the rank correlation across models. The heritability (± SE) estimated for MWT was 0.43 (± 0.011), 0.42 (± 0.011), 0.70 (± 0.008), 0.72 (± 0.082), and 0.37 (± 0.013) for models 1, 2, 3, 4, and 5, respectively. Repeatability values were 0.64, 0.67, 0.72, and 0.76 for models 1, 2, 3, and 4, respectively. The EBV ranks were highly correlated between models 1 and 2 (Top 10%: 0.96; All: 0.99), and models 3 and 4 (Top 10%: 0.99; All: 0.99). Inflated heritabilities in models 3 and 4 suggest that the effect of age is confounded with the additive genetic effect, which will not translate to greater genetic progress. Therefore, disregarding age in the genetic evaluation of MWT is not recommended. Similarities between models 1 and 2 suggest that BCS has a relatively small impact on MWT, probably related to the relatively low genetic correlation between these traits. Models 1 and 5 cannot be directly compared, as they use slightly different response variables (body weight vs. pre-adjusted weight) and may lead to slightly different selection decisions (correlation between Top 10%: 0.85; All: 0.95). Further investigations in this field are warranted.

A decision support framework for best-fitting blockchain platform selection in sustainable supply chains under uncertainty

Despite blockchain’s potential to enhance visibility and traceability in sustainable supply chains (SCs), its adoption is complex due to the various criteria (e.g., interoperability and cost) required for the best-fitting platform selection. This study aims to investigate conflicting criteria in the blockchain technology (BT) platform selection process for decision-making under uncertainty. We propose a three-phase decision support framework to study BT adoption considering technological, organizational, and environmental contexts. In the first phase, after exploring the evaluation criteria from multiple contexts, the developed framework incorporates uncertainty and reliability to deal with the BT platform evaluation problem. Then, fuzzy cognitive map modeling, advanced by a Z-number-based inference system, is introduced to model the causal relationships between criteria. This is followed by implementing a hybrid learning algorithm to assess the impact of each criterion on adoption decisions. Finally, the fuzzy combined compromise solution embedded in the framework prioritizes BT platforms to identify the most suitable ones for sustainable SC. The findings imply that performance efficiency, implementation costs, maintainability and operability can significantly affect the BT platform selection decisions. The outcomes offer more stable, reliable, and distinguishable solutions for the proposed problem compared to the traditional approaches. The results introduce Hyperledger and R3 Corda as the best-fitting platforms for adoption based on the identified criteria.

Beyond Traditional Banking: Exploring the Influence of Technology on Depositors' Decision-Making in Bank Selection

Purpose: The banking sector is crucial for the economy, and with growing competition, banks must pinpoint factors influencing depositors' choices. This study aims to uncover how technology adoption by commercial banks plays a role in depositors' decisions. Methodology: A hybrid approached is attempted in this study to explore the phenomenon of interest. In the qualitative phase, focus group discussion was conducted on two heterogeneous groups, each with ten participants. The recorded audio was further transcribed using speech-to-text modules. A natural language processing tool has been used to identify words or phrases that are semantically related or have similar meanings to depositors’ sentiment analysis. The quantitative phase involved descriptive statistics, principal component analysis and regression analysis to explore insight numerically. Findings: Respondents expressed a generally positive sentiment toward technology in banking, influencing their selection decisions, in the qualitative phase. In the quantitative phase, significant technological aspects affecting depositors' post-selection evaluations are identified. Findings from both approaches converge on the same line. Practical implications: Findings of this study may aid decision-makers in banking industry to articulate distinct technological aspect implied in banking service delivery and better design service offerings to attract new depositors and gain competitive advantages. Originality/Value: This is the first study to focus solely on technology to explore how technology influences depositors' bank choices in the context of Bangladesh. Research Limitations: Significant parts of several conversations were sorted out for sentiment analysis. If it was possible to consider all relevant statements of the focus group discussion for sentiment analysis, it might convey a more pertinent sentiment score.

"The Best Home for This Paper": A Qualitative Study of How Authors Select Where to Submit Manuscripts.

For authors, selecting a target journal to submit a manuscript is a critical decision with career implications. In the discipline of medical education, research conducted in 2016 found that authors were influenced by multiple factors such as a journal's prestige and its mission. However, since this research was conducted the publishing landscape has shifted to include a broader variety of journals, an increased threat of predatory journals, and new publishing models. This study updates and expands upon how medical education authors decide which journal to submit to with the aim of describing the motivational factors and journal characteristics that guide authors' decision making. The authors conducted five qualitative focus groups in which twenty-two medical education authors and editors participated. During the focus groups participants were engaged in a discussion about how they select a journal to submit their manuscripts. Audio from all focus groups was transcribed. Transcripts were analyzed using codebook thematic analysis. Participants considered multiple factors when selecting a target journal. Factors included a journal's impact, the scope of a journal, journal quality, and technical factors (e.g., word limits). Participants also described how social factors influenced their process and that open access plays a role that could both encourage or deter submission. The findings describe the motivational factors and influential signals that guide authors in their journal selection decision making. These findings confirm, extend, and update journal selection factors reported in medical education and other disciplines. Notably, these findings emphasize the role of social factors, relationships and personal experiences, which were absent from previous work. Additionally, we observed increased consideration of open acces and a shift away from an emphasis on journal prestige.

Future Shorelines: A Living Shoreline Site Selection and Design Decision Support Tool that Incorporates Future Conditions Induced by Sea Level Rise

Most living shoreline site selection and design decision support tools are based upon existing environmental conditions. We developed a web-based, geospatial tool called Future Shorelines that integrates high-resolution landscape elevation data and a matrix of locally derived NOAA Interagency Sea Level Rise Scenarios to characterize future conditions of submergence and shoreline translation induced by sea level rise. Once the practitioner selects a location of interest, sea level rise scenario (e.g., high), and target year (e.g., 2050), the tool will generate plan view and cross-sectional informational graphics specific to their choices. This information can then be paired with other menu options, like parcel ownership, to facilitate the planning and construction of nature-based shoreline stabilization solutions that (1) are located where opportunities for horizontal migration are optimized, (2) remain accessible for monitoring and maintenance, and (3) perform as intended over the design life of the installation. The tool’s menu options and the user interface were informed by project partner input solicited during numerous workshops convened over the duration of the 2-year project. This coproduction created a product that was familiar to the end user and therefore increased the likelihood that it would be utilized by them during the planning and design of living shoreline projects. Although developed for use in the Indian River Lagoon, located along the east-central Florida coast, it can be seamlessly replicated for application in other coastal regions of the USA where the requisite data are available.

Word of Mouth as a Mediator between Ethnic Identification, Brand Image, and College Selection Decisions: An Empirical Investigation of Private Universities in Medan City, Indonesia

Word of Mouth as a Mediator between Ethnic Identification, Brand Image, and College Selection Decisions: An Empirical Investigation of Private Universities in Medan City, Indonesia

Chest wall resection and reconstruction for primary chest wall sarcomas: Analysis of survival, predictors of outcome, and long-term functional status

ObjectivesWe aimed to analyze survival, predictors of outcome, and the long-term functional status of patients with a diagnosis of primary chest wall sarcoma who undergo chest wall resection and reconstruction. MethodsWe analyzed a prospectively maintained database, including all patients operated on between 2008 and 2021. The primary outcome measures were overall and disease-free survival and analyses were employed to determine the risk factors for poor survival and recurrence. ResultsOne hundred thirty-nine patients were included, 55% were men. The majority (96%) had an R0 resection and 75.1% had no postoperative complications up to 30 days postprocedure; median length of hospital stay was 7 days (range, 6-10 days). Median overall and disease-free survival was 58.8 and 53.6 months, respectively. For those alive, at long-term follow-up, 80% had a Medical Research Council dyspnea score of 0 and Karnofsky index >80%. Survival and mortality rates were better in chondrosarcomas compared with nonchondromatous sarcomas (P < .05). Previous history of radiotherapy, previous history of cancer, the type of sarcoma (Ewing's or soft tissue), the need for adjuvant treatment and tumor grade were significant predictors of mortality and recurrence on univariate testing. Extended resection, a higher number of ribs removed, and the incidence of postoperative complications were significantly associated with a worse postoperative Medical Research Council dyspnea score. ConclusionsCareful patient selection and multidisciplinary decision making is crucial. This leads to clear resection margins, good overall, and disease-free survival and good functional outcomes.

Psychological well-being modulates neural synchrony during naturalistic fMRI

Psychological well-being (PWB) is a combination of feeling good and functioning efficiently, and has a significant relationship with physical and mental health. Previous research has shown that PWB is associated with improvements in selective attention, mindfulness, semantic self-images, and adaptive decision making, however, it is unclear how these differences manifest in the brain. Naturalistic stimuli better encapsulate everyday experiences and can elicit more “true-to-life” neural responses. The current study seeks to identify how differing levels of PWB modulate neural synchrony in response to an audiovisual film. With consideration of the inherent variability of the literature, we aim to ascertain the validity of the regions previously associated with PWB. We identified that higher levels of PWB were associated with heightened stimulus driven neural synchrony in the bilateral superior parietal lobule, right planum temporale, and left superior temporal gyrus, and that lower levels of PWB were associated with heightened neural synchrony in the bilateral lateral occipital cortex and precuneus. Taken together, this research suggests that there is an association between differing levels of PWB and differential neural synchrony during movie-watching. PWB may therefore have an effect on complex, multimodal processing.

Incorporating composition into life cycle assessment of steel grades

Steel is a vital material in modern industries due to its versatility and remarkable properties. In a world that is increasingly concerned about the environment, it is necessary to incorporate good practices that help reduce the environmental impact of our economies. An important aspect is evaluating the environmental impact of materials throughout their life cycle. However, calculating this impact using generic databases can be challenging, as they may lack the necessary specificity. To obtain more accurate environmental impact calculations, it is essential to consider the composition of the materials. Each steel grade, for example, can vary in the content of alloying elements, which influences its environmental impact. Therefore, particularising calculations based on compositions is essential for making informed and sustainable material selection decisions. This article presents a calculation methodology to establish a Life Cycle Inventory of steel, emphasising the specific composition and using datasets of steel production in a Basic Oxygen Furnace (BOF) developed by ecoinvent as reference. The methodology presented seeks to allow the systematic calculation of the environmental impact of any steel considering its composition, contribute to the incorporation of environmental criteria in the selection of materials, and quantify the content of critical raw materials according to the latest report published by the European Union. Likewise, a case study is included, in which the composition's influence on the environmental impact of 10 steel grades widely used in induction cooktops has been analysed.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of two future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSV) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes which enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in four generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level which was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Making the grade? A meta-analysis of academic performance as a predictor of work performance and turnover.

Many organizations assess job applicants' academic performance (AP) when making selection decisions. However, researchers and practitioners recently have suggested that AP is not as relevant to work behavior as it used to be due to factors such as grade inflation and increased differences between academic and work contexts. The present meta-analysis examines whether, and under what conditions, AP is a useful predictor of work behavior. Mean correlations (corrected for error in the criterion) between AP and outcomes were .21 for job performance (k = 114), .34 for training performance (k = 8), and -.02 for turnover (k = 20). There was considerable heterogeneity in validity estimates for job performance (80% credibility interval [.04, .37]). Moderator analyses revealed that AP is a better predictor of performance (a) for AP measures that are more relevant to students' future jobs, (b) for professor ratings of AP than for grades and class rank, (c) for samples that include applicants from the same university or from the same major, and (d) for official records of AP than for applicant self-reports. Job relevance was the strongest and most consistent moderator with operational validities in the .30s and .40s for measures that assessed AP in major-specific courses or courses in which students are evaluated on behaviors relevant to their future jobs (e.g., practicum classes). Overall, researchers and organizations should carefully consider whether and how AP is relevant to particular jobs and outcomes, as well as use designs and measures that optimize the predictive value of AP. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantifying the resistance of Australian wheat genotypes to Pratylenchus thornei based on a continuous metric from a factor analytic linear mixed model

Genetic resistance to the parasitic root-lesion nematode, Pratylenchus thornei, is one of the main management strategies cereal growers can use to minimise the impact of nematodes on winter cereal cropping. Screening of genotypes in the presence of P. thornei populations must provide reliable resistance measures that are realised under field conditions. Adoption of the latest statistical methodologies can help to better differentiate between resistant and susceptible genotypes. In this study, post-harvest P. thornei population densities were measured from a collection of 17 field experiments, with varying starting P. thornei population densities, conducted between 2011 and 2018 in locations across the northern grain growing region of eastern Australia. The experiments primarily consisted of wheat genotypes. The post-harvest P. thornei population densities were analysed across multiple environments in a linear mixed model framework, with a factor analytic structure used to model genotype by environment (G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E) interaction effects exclusively for wheat genotypes. In general, genetic correlations between environments were found to be high, indicating limited G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E interaction for resistance to P. thornei. Post-processing of results using the factor analytic selection tools (FAST) method provided a measure of the overall performance for each wheat genotype, as well as a stability measure reflecting the consistency of the resistance status across environments. The FAST method quantified genotype resistance on a continuous scale, better reflecting the nature of genetic resistance based on a quantitative variable such as nematode population density, and provided a statistically robust and informative means of aiding selection decisions for resistance to P. thornei.

Grade control drillhole spacing and mining selectivity determination using high resolution simulations applied on distinctly heterogeneous open pit mines

The grade control drillhole spacing and mining selectivity decisions are typically made using the resource model estimated from exploration and infill drilling data. Once production starts, large quantities of grade control data are collected to delineate ore and waste boundaries for ore control. In this work, a grade control drillhole spacing and mining selectivity optimization workflow is presented which allows the practitioner to use site specific knowledge to determine the most profitable ore control mining scenario. The densely gridded production data is used to simulate a ground truth block model from which scenarios of variable drillhole spacing and selectivity are evaluated against their corresponding costs to determine the maximum profit scenario. Eleven mining scenarios are evaluated using year production data from three distinctly heterogeneous mine with drillhole spacing and mining selectivity varying from 3 × 3 × 3 m (27 m3) to 30 × 30 × 15 m (13,500 m3). The profit differences from the optimum scenario varied by millions of dollars (1–8%) against the next best case depending on the heterogeneity of the deposit. Practitioners could apply this workflow to inform grade control drillhole spacing and mining selectivity decisions for different domains within the mine or multiple pits especially if distinctly heterogeneous volumes exist.

A unified model of tensile and creep deformation for use in niobium alloy materials selection and design for high-temperature applications

There is renewed interest in refractory alloys that possess higher service temperatures than incumbent Ni-based superalloys (e.g., ⪆1100 °C). This study provides a review of the high-temperature constitutive responses of Nb-alloys measured over a wide range of temperatures (≈860 °C < T < ≈1760 °C) and strain rates (≈10–9 s-1< ε˙ < ≈10–1 s-1). Nevertheless, the extant data is sparse and informed materials selection decisions require constitutive expressions to interpolate and reliably extrapolate. The Larson-Miller parameter approach to describe creep-life provides a conservative estimate of material response at the highest temperatures and lowest strain rates, whereas the Sellars-Tegart model describes both steady-state creep and high-temperature tensile test data with a single, universal equation. A minimum flow stress based on the combination of these two models is proposed for design considerations to address the overprediction of strength that can arise from applying one or the other independently. This effort highlights the fact that refractory alloys exhibit strain rate sensitive flow strengths in the temperature range of interest for applications. The roles of alloying, thermomechanical processing, and impurity levels are discussed, and highlight the fact that these advanced Nb-alloys evidence Class 1 (Class A) solute drag controlled creep behavior, except the carbide precipitation strengthened alloy, D-43. In addition, the high-temperature strengths are confirmed to be strongly correlated with alloy melting point.

A Public Mid-Density Genotyping Platform for Hexaploid Sweetpotato (Ipomoea batatas [L.] Lam).

Small public breeding programs focused on specialty crops have many barriers to adopting technology, particularly creating and using genetic marker panels for genomic-based decisions in selection. Here, we report the creation of a DArTag panel of 3120 loci distributed across the sweetpotato (Ipomoea batatas [L.] Lam) genome for molecular-marker-assisted breeding and genomic prediction. The creation of this marker panel has the potential to bring cost-effective and rapid genotyping capabilities to sweetpotato breeding programs worldwide. The open access provided by this platform will allow the genetic datasets generated on the marker panel to be compared and joined across projects, institutions, and countries. This genotyping resource has the power to make routine genotyping a reality for any breeder of sweetpotato.

Why a Little Diversity doesn't Go a Long Way: A Collective Moral Licensing Explanation for Homosocial Reproduction

AbstractDespite significant knowledge on the demographic composition of workgroups, the literature lacks group‐level theory that addresses the tendency of work groups with token levels of diversity to maintain their demographic imbalance over time. We explain this phenomenon by extending moral licensing theory to the group level, arguing that a token level of racial or gender diversity leads to the development of a collective moral credential. This credential provides psychological permission for groups to relax their moral strivings, such that they are less likely to question the influence of bias in group member selection decisions, and thus more likely to make subsequent homogenous group member additions. Additionally, we argue that the diversity climates within which groups are embedded can either magnify (i.e., in fairness‐focused diversity climates) or mitigate (i.e., in synergy‐focused diversity climates) the development of a collective moral credential. Further, we suggest that the effect of token levels of diversity on the development of a collective moral credential can be affected by the prevailing social norms for diversity. Finally, we theorize that the effects of this process can be accentuated by group use of a majority decision rule and attenuated by group use of a unanimous decision rule.

Genotypic and phenotypic characterisation of three local chicken ecotypes of Ghana based on principal component analysis and body measurements.

This study aimed to characterise three Ghanaian local chicken ecotypes, namely, Interior Savannah, Forest, and Coastal Savannah, based on morphological data and single nucleotide polymorphism (SNP) genotypes. Morphological data including body weight, shank length, body girth, back length, thigh length, beak length, comb length, and wattle length were collected from 250 local chickens. DNA isolated from blood of 1,440 local chickens was used for SNP genotyping with the Affymetrix chicken 600k SNP chip. Principal component analysis showed that Forest and Coastal Savannah birds were closely related. Generally, all three ecotypes exhibited high genetic diversity, especially birds from the Interior Savannah zone. Morphological characterisation showed that ecotype (p = 0.016) and sex (p = 0.000) had significant effects on body weight. Birds of the Interior Savannah ecotype were the heaviest (p = 0.004), with mean weights of 1.23 kg for females and 1.40 kg for males. Sex also had a strong significant effect on most of the morphological measurements, but the sex * ecotype interaction effect was not significant. Very few of the feather phenotypes previously reported to be associated with heat resistance-frizzle (2%) and naked neck (1.6%)-were found in the studied populations. It is concluded that the three local ecotypes are genetically diverse but with similar morphological features and the information provided would be useful for future selection decisions.

ANALYSIS OF PRIORITY AREAS FOR HANDLING STUNTING CASES IN SIGI REGENCY USING THE TOPSIS METHOD BASED ON WEB DASHBOARD

Stunting is a condition of growth failure in children, where a toddler has a length or height below the average. Stunting is a problem for children because it has the potential to slow down brain development with prolonged effects. Central Sulawesi Province is one of the provinces with the highest stunting prevalence rate and the area with the highest stunting rate is in Sigi Regency at 36.8%. Stunting cases are an important concern for the Sigi Regency government, especially the Health Office and Community Health Centers. To identify and determine areas that are prioritized for handling stunting cases, seven indicators are used, including the number of stunting cases, number of villages covered, number of health workers, number of integrated service posts, number of exclusive breastfeeding, percentage of clean drinking water, and percentage of proper sanitation. To support in reducing the percentage of stunting in Sigi Regency, research was conducted and a web dashboard system application was made to support priority area selection decisions using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, a best alternative method that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The results obtained in this study are the areas that are prioritized for handling stunting cases in Sigi Regency is the Sigi Biromaru area with a total of 495 stunting cases, the number of coverage villages is 18, the number of integrated service posts is 53, the number of health workers is 96, the number of exclusive breastfeeding is 35, the percentage of proper drinking water is 44%, and the percentage of proper sanitation is 84.00% with the highest preference value through the TOPSIS method analysis of 0.660.