Alternative Selection Methods Research Articles

Benefits by Luck: A Study of Lotteries as a Selection Method for Government Programs

Lotteries could be a just and cost-effective procedure to distribute government benefits in contexts of oversubscription, with the added benefit of allowing assessments of the impact of government programs. However, little is known about whether there is public support for employing lotteries in this context. We examine support for a lottery that selects recipients of new government-built housing units in Brazil and find that neither lotteries participants nor the general public supports their use. We arrive at this result by employing a three-pronged multi-method approach. Quasi-experimental analysis of an original survey of lottery participants reveals that support not only is tepid, but substantially higher among winners than non-winners. In-depth interviews suggest that applicants believe lotteries miss the most deserving beneficiaries. A general population survey experiment reveals that lotteries are not perceived as just or efficient when compared to alternative beneficiary selection methods. While sometimes normatively desirable, the use of lotteries for government programs enjoys limited popular support.

Thrombectomy Outcomes for Anterior Circulation Stroke in the 6-24 h Time Window Solely Based On NCCT and CTA: ASingle Center Study.

Since perfusion imaging may be unavailable in smaller hospitals, alternative imaging selection methods for acute ischemic stroke can improve outcomes and optimize resources. This study assessed the safety and effectiveness of using imaging criteria other than DEFUSE3 and DAWN for thrombectomy beyond 6 h from symptom onset in patients stroke in the anterior circulation. This is aretrospective, single-center analysis of consecutive patients with large vessel occlusion in the anterior circulation undergoing thrombectomy. Patients were categorized into two groups based on the collateral status (moderate collaterals and good collaterals). Among 198 patients, 106 (54%) met the inclusion criteria and were analyzed. Good collateral status was observed in 78 (74%) patients. Patients with good collaterals showed significantly lower mRS scores at discharge and at 90days compared to their counterparts with moderate collateral status (4 (3-4) vs.4 (4-5); p = 0.001 and 2 (0-4) vs.6 (3-6); p < 0.001, respectively). More patients with good collateral status achieved favorable outcomes at 90days compared to those with moderate status (48 (61.5%) vs.5 (17.9%); p < 0.001). Good collaterals were an independent predictor of good clinical outcomes at 90days (OR = 1.31, 95% CI: 1.13-1.53, p < 0.001). Selecting patients for endovascular treatment of acute ischemic stroke using non-contrast CT and CT angiography shows 90-day outcomes similar to the DAWN and DEFUSE-3trials. Using collateral status on CT angiography can predict favorable outcomes after mechanical thrombectomy in resource-limited settings where perfusion imaging is unavailable.

Simulating multi-scale optimization and variable selection in species distribution modeling

Species distribution modeling (SDM) is a fundamental tool in theoretical and applied ecology. However, relatively little is known about the performance of different approaches for scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. Modelers often struggle to optimize a tradeoff between ecological relevance, model robustness, complexity, and overfitting. In this paper, we investigated several methods designed to optimize spatial scale and variable selection in SDMs, in each case evaluating model fitness, parsimony and predictive performance. We used a simulation approach to produce a large pool of alternative underlying habitat relationships that reflect a broad range of realistic habitat associations. We also compared several different modeling algorithms, including logistic regression with a generalized linear model (GLM), Lasso and Elastic-Net Regularized GLMs (GLMNet), and random forest (RF), as well as alternative variable and scale selection methods. We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best predictors based on all criteria of our model performance assessment and across all attributes of the simulated underlying relationship, including nonlinearity and interaction. We had expected machine learning approaches, such as random forest, to perform better in these more complex forms of species-environment relationships. GLM using dredge variable selection was also the method that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR) which performed equivalently. The simulation experiment presented here provides a robust assessment of simulated multi-species distribution model performance, complexity and fidelity. By simulating a large range of potential habitat relationships with varying spatial scale, effect sizes, linearity, and interactions, we comprehensively evaluated model performance across gradients of complexity of the underlying relationships and violations of classical statistical assumptions. This study provides a valuable assessment and a broader example of the power and utility of controlled simulation experiments in habitat relationships and other ecological spatial predictive modeling.

Low-adhesion culture selection for human iPS cell-derived cardiomyocytes

Despite progress in generating cardiomyocytes from pluripotent stem cells, these populations often include non-contractile cells, necessitating cardiomyocyte selection for experimental purpose. This study explores a novel cardiomyocyte enrichment mechanism: low-adhesion culture selection. The cardiac cells derived from human induced pluripotent stem cells were subjected to a coating-free low-adhesion culture using bovine serum albumin and high molecular weight dextran sulfate. This approach effectively increased the population of cardiac troponin T-positive cardiomyocytes. Similar results were obtained with commercially available low-adhesion culture dishes. Subsequently, we accessed the practicality of selection of cardiomyocytes using this phenomenon by comparing it with established methods such as glucose-free culture and selection based on puromycin resistance genes. The cardiomyocytes enriched through low-adhesion culture selection maintained autonomous pulsation and responsiveness to beta-stimuli. Moreover, no significant differences were observed in the expression of genes related to subtype commitment and maturation when compared to other selection methods. In conclusion, cardiomyocytes derived from pluripotent stem cells were more low-adhesion culture resistant than their accompanying non-contractile cells, and low-adhesion culture is an alternative method for selection of pluripotent stem cell-derived cardiomyocytes.

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

Incorporating test-taking engagement into the item selection algorithm in low-stakes computerized adaptive tests

In low-stakes assessment settings, students’ performance is not only influenced by students’ ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students’ true ability levels may lead to the selection of less informative items and thereby contaminate item selection and ability estimation procedures. To date, researchers have developed various approaches to detect and remove disengaged responses after test administration is completed to alleviate the negative impact of low test-taking engagement on test scores. This study proposes an alternative item selection method based on Maximum Fisher Information (MFI) that considers test-taking engagement as a secondary latent trait to select the most optimal items based on both ability and engagement. The results of post-hoc simulation studies indicated that the proposed method could optimize item selection and improve the accuracy of final ability estimates, especially for low-ability students. Overall, the proposed method showed great promise for tailoring CATs based on test-taking engagement. Practitioners are encouraged to consider incorporating engagement into the item selection algorithm to enhance the validity of inferences made from low-stakes CATs.

Security and performance implications of BGP rerouting-resistant guard selection algorithms for Tor

Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks.In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.

P-257 Transcriptomic signature of live birth revealed in cumulus cells of women undergoing an ICSI cycle for infertility treatment

Abstract Study question Is the transcriptome of cumulus cells surrounding an oocyte that was fertilised and transferred predictive of live birth? Summary answer Live birth is associated with a distinct transcriptomic signature in cumulus cells isolated from an oocyte that was fertilised and transferred. What is known already The selection of the best embryo for transfer is currently based on standard morphological assessment, which is intrinsically subjective, and time-lapse monitoring, which fails to show benefit in predicting pregnancy. Alternative non-invasive selection methods are in development including analysis of cumulus cells (CCs) isolated from oocytes that resulted in an eligible embryo for transfer. However the majority of the existing studies were conducted using outdated methodologies such as transcriptomic analysis with PCR and microarrays. Moreover, the studies associating CCs’ transcriptome with clinical and embryological outcomes lack consensus due to non-homogenous cohorts while the outcome of live birth is inadequately explored. Study design, size, duration CC samples (n = 17), collected between 2018 and 2021 and biobanked, were retrospectively selected for RNA sequencing on the basis of their embryo transfer outcome. Thus, we analysed CCs associated with an oocyte that resulted in pregnancy (n = 6), no pregnancy (n = 7), live birth (n = 5) and pregnancy without live birth (n = 5). Differential gene expression analysis identified the differentially expressed genes (DEGs) between the groups in relation to pregnancy and live birth. Participants/materials, setting, methods The samples were retrieved from subfertile couples undergoing controlled ovarian stimulation and intracytoplasmic sperm injection with subsequent unbiopsied fresh or frozen embryo transfer at the IVF centre of University Hospital Zurich. Maternal age was below 43 years. Libraries from CCs were prepared using the Smart-seq2 pipeline and the RNA sequencing was performed on the NovaSeq 6000 system. The DESeq2 workflow was conducted and the threshold for the adjusted p-values (FDR) was set to 0.05. Main results and the role of chance No DEGs emerged when comparing the transcriptomic profiles of cumulus cells between the “no pregnancy” and “pregnancy” groups. However, 139 DEGs were identified in the comparison between the “pregnancy without live birth” and “live birth” groups. 28 DEGs were familiar and relevant to competent cumulus-oocyte-complexes: CTGF (fold change: 4.38, FDR: 0.005), SERPINE2 (fc: 3.08, FDR: 0.0002), PCK1 (fc:3.04, FDR: 0.0001), HHIP (fc:2.69, FDR: 0.006), HS3ST (fc:2.45, FDR: 0.009) and BIRC5 (fc: -2.69, FDR 0.0001) among others. Functional enrichment analysis of DEGs revealed that the incidence of live birth is associated with promotion of pathways such as runt-related transcription factor 1 (RUNX1/2) signalling, glycosaminoglycan biosynthesis and sonic hedgehog signalling (SHH), and reduction in pathways such as glycosphingolipid biosynthesis and pyrimidine metabolism in cumulus cells. Limitations, reasons for caution We reveal the transcriptome associated with live birth in CCs analysed with RNAseq on a limited number of samples. Consolidation of these findings via utilisation of a validation prospective cohort is pending. Wider implications of the findings CCs’ transcriptome could inform the development of non-invasive biomarkers to predict the chance of live birth across a group of embryos eligible for transfer. The transcriptomic findings inspire further explorations in the field of cumulus-oocyte bidirectional communication and establishment of oocyte competence. Trial registration number N/A

Multi-stage biomedical feature selection extraction algorithm for cancer detection

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained; in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced.

Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique

In order to improve classification accuracy and lower future computation and data collecting costs, feature selection is the process of choosing the most crucial features from a group of attributes and removing the less crucial or redundant ones. To narrow down the features that need to be analyzed, a variety of feature selection procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), and MI are six alternative feature selection methods used in this study. The provided dataset is aggregated using four rank aggregation strategies: "rank aggregation," "Borda Count (BC) methodology," "score and rank combination," and "unified feature scoring" based on the outcomes of the six feature selection method (UFS). These four procedures by themselves were unable to generate a clear selection rank for the characteristic. To produce different ranks of traits, this ensemble of aggregating ranks is carried out. For this, the bagging method of majority voting was applied.

Results of using climate-oceanological predictors in a Random Forest model for the forecast of stock abundance dynamics of pink salmon runs in Western Kamchatka

An improvement of the forecast of pink salmon runs is one of the most actual problems in management of salmon stocks of Kamchatka. Algorhythm of Random Forest with the use of iterative selection of most important climate-oceanological predictors seem to be promising. Three authenticly influental predictors have been found for the model of Random Forest at the error of the forecast of 28%. Verification of the obtained results used alternative methods of selection of the best predictors. Obtained results have demonstrated possibility of the use of considered climate-oceanological predictors in modeling the forecasts of stock abundance dynamics of pink salmon in Western Kamchatka.

РЕПУТАЦИЯ В ГОСУДАРСТВЕННЫХ ЗАКУПКАХ: ОСОБЕННОСТИ РЕГУЛИРОВАНИЯ, КЛЮЧЕВЫЕ ИНДИКАТОРЫ И ОЦЕНКИ ЭФФЕКТИВНОСТИ

The article presents a literature review on reputation factor in public procurement. Reputation is one of the tools for solving the principal-agent problem, which is typical for contractual relations. In line with the spread of New Public Management theory in public procurement, alternative methods of supplier selection are being developed, in particular, based on suppliers’ reputation. In contrast to the private sector, public buyers’ ability to consider reputation when choosing a supplier is highly regulated. It is shown that in the world practice of public procurement the reputation of suppliers is considered to varying degrees, there is no single approach to indicators, mechanisms for providing information and procedural aspects. Therefore, it is timely to analyse the arguments for and against expanding the possibilities of reputational considerations in public procurement. To answer this question, a separate block focuses on the effects of reputational considerations in public procurement in terms of barriers to entry, public procurement efficiency, and building trust between procurers and suppliers. Conclusions about the positive effects of reputational considerations are of interest to procurers who decide on the choice of supplier evaluation criteria. Relevant aspects of reputational considerations for Russian regulation concerning reputation indicators and mechanisms for providing information are identified. The article differs from existing reviews on the topic of reputation by focusing on the specificity of public procurement. An original classification of sources is presented in accordance with the following parameters: definitions and indicators of reputation, possibilities and limitations of reputational considerations and their effects in public procurement. Poorly studied aspects of interest for further research are identified.

Dynamic Production Line Re-balancing by Alternative Plans for Compensating Equipment Failures

As social uncertainty increases, there are changes in values such as safety, sustainability, and response to changes. The manufacturing industry is also required to improve its resilience by dynamically recombining production resources to respond to change. In the operation of a production line, if demand fluctuates due to changes in the external environment or customer needs, or if sudden production fluctuation such as equipment failure occurs, the production line will be stopped due to reconfiguration or equipment restoration. Therefore, the challenge is to continue production and improve productivity even when fluctuation occurs. In order to respond to the above challenges, this research aims to dynamically and quickly re-plan the production line using the existing equipment according to current equipment and production status. In this paper, failures of the production equipment, such as robots and tools, are regarded as production fluctuation factors. In order to continue production with the remaining resources in the event of equipment failure, it is necessary to change the process plan. However, since these are engineering tasks in the production preparation stage, there was the problem of not being able to flexibly change them at the manufacturing site during the production execution stage. In this development, an alternative plan pre-generation and selection method was developed to maintain production by re-allocating tasks during equipment failure. In this approach, first, multiple alternative plans for task allocation for failures in each piece of equipment are planned in advance using a process plan optimization technique. Next, production fluctuations due to equipment failures are detected. Finally, dynamic changes are possible by selecting alternative plans that maximize the throughput of the entire production system. Initial verification results are shown by comparison with the conventional method for equipment failure using simulation on a small-scale robot assembly line for automotive inverter.

Experimental study on replacing ceramsite with quartz sand in hydraulic fracturing

Hydraulic fracturing effectively enhances production in the unconventional oil and gas industry. As we all know, proppant plays a vital role in the hydraulic fracturing. In this paper, the effects of proppant type, particle size, concentration, closure pressure, and other factors are studied by laboratory experiments. Based on the actual triaxial simulation device (TSD), quartz sand, ceramic, and coated sand are used as proppant, while sandstone, coal rock, shale, and granite are selected as the stone slate to experiment. The surface of the proppant was imaged by a surface profiler (SP) and scanning electron microscope (SEM) to compare the microscopic surface characteristics before and after the experiment. The particle size macroscopic distribution was measured by Mastersizer 2000. The results and innovation are as follows: This paper investigates the proppant crushing for hydraulic fracturing and provides an alternative evaluation method for proppant selection. The crushing laws of proppant under different pressures and stone slate are compared by using the experimental device. It is reliable for quartz sand to replace ceramic in deep layer of the field application. It also overturns the previous thinking. We believe this found holds the key toward systematically optimizing the development of unconventional reservoirs.

Indoor illuminance selection procedure using fuzzy techniques

The current practice of selecting target illuminance based on the recommendations of the Illuminating Engineering Society has serious shortcomings. For example, the visual age of the observer has precise boundaries, the illuminance values are exact instead of an acceptable range, and the sudden increase of illuminance values with age is rather arbitrary. To come up with an exact output from an input that is, by its very nature, uncertain and imprecise is virtually impossible. The aim of this work is to develop an approach that overcomes the limitations of the current illuminance selection method. This work proposes an alternative method for illuminance selection based on Fuzzy techniques. Fuzzy techniques are approximations used to model phenomena or decisions for which mathematical precision is impractical. The vagueness and uncertainties of many lighting design variables suit Fuzzy logic modeling, whereby a crisp output can be obtained from a range of Fuzzy input variables. The new method involves visual age, task difficulty and task importance as input variables which lead to a target illuminance. The target illuminance is given the name “Fuzzy Illuminance” (FILL). A MATLAB digital application was developed and can be used by lighting designers. It is expected that the proposed technique and the digital application will not only resolve the limitations of the current illuminance selection procedure but will also simplify the process of the selection of illuminance design targets and on-site verification of such design targets.

Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information

Short-term building cooling load prediction plays an import role in the building energy management. The concept of similar day approach is receiving special attention as an emerging alternative instance selection method for dataset with the purpose of accuracy improvement of machine learning algorithms in cooling load prediction. However, the performance of typical machine learning algorithms integrated with different similar day selection methods have not been comprehensively assessed, particularly in case of the absence of occupancy information in dataset in most of existing buildings. This study presents a similarity-based grouping method to evaluate and optimize the dataset structure for machine-learning based short-term building cooling load prediction without measurable occupancy information in dataset. The similar day methods using time-similarity index and weather-similarity index respectively are integrated with four typical machine learning methods. The dataset is re-organized and clustered into multiple groups with different degrees of similarity for model trainings. Case studies are conducted to assess the performance of similarity-based grouping method with different similarity indexes, and the impact of lack of measurable occupancy information in dataset on the prediction accuracy. The test results show that it is not reliable to obtain higher prediction accuracy when time-similarity index is applied. The effectiveness of the weather-similarity index for similar day selection is validated to be more reliable to obtain enhanced prediction accuracy. Moreover, the dataset without occupancy information would inevitably result in significant prediction errors of machine learning algorithms, particularly in the case that the days in dataset have considerably different occupancy profiles with the target day.

Interactive Approaches to Multiple Criteria Sorting Problems: Entropy-Based Question Selection Methods

In this study, interactive approaches for sorting alternatives evaluated on multiple criteria are developed. The possible category ranges of alternatives are defined by mathematical models iteratively under the assumption that the preferences of the decision maker (DM) are consistent with an additive utility function. Simulation-based and model-based parameter generation methods are proposed to hypothetically assign the alternatives to categories. A practical approach to solve the incompatibility problem of the randomly generated parameters is developed. Based on the hypothetical assignments, the assignment frequencies of alternatives for each possible category are defined. Then, an information theoretic measure, relative entropy, is used in the selection of the alternative that will be assigned into a category by the DM. The performance of our approaches is tested on different problems with/without initial assignments and category size restrictions. The results show that relative entropy-based alternative selection methods work well in decreasing the assessment burden of DM.

Weighted Log-Rank Test for Clinical Trials with Delayed Treatment Effect Based on a Novel Hazard Function Family

In clinical trials with delayed treatment effect, the standard log-rank method in testing the difference between survival functions may have problems, including low power and poor robustness, so the method of weighted log-rank test (WLRT) is developed to improve the test performance. In this paper, a hyperbolic-cosine-shaped (CH) hazard function family model is proposed to simulate delayed treatment effect scenarios. Then, based on Fleming and Harrington’s method, this paper derives the corresponding weight function and its regular corrections, which are powerful in test, theoretically. Alternative methods of parameters selection based on potential information are also developed. Further, the simulation study is conducted to compare the power performance between CH WLRT, classical WLRT, modest weighted log-rank test and WLRT with logistic-type weight function under different hazard scenarios and simulation settings. The results indicate that the CH statistics are powerful and robust in testing the late difference, so the CH test is useful and meaningful in practice.

Selection process for admission to an academic nursing programme – A meta-synthesis

BackgroundStudent nurse selection has been a topic of debate for years. Poor nursing care has been attributed to uncaring attitudes, lack of motivation and poor training and mentoring. Student nurse selection requirements and the steps involved in the process have been a revolving debate. Internationally, the recruitment and selection process are being refined to align with recommendations from the Francis Inquiry to include a values assessment. ObjectiveTo select, review and interpret evidence-based studies on student nurse selection; and develop and provide recommendations for nurse educators when selecting first-year student nurses as a holistic, integrative process fitting with caring and providing quality care. DesignQualitative meta-synthesis. Data sourcesElectronic databases were used during data collection: Ebscohost, ScienceDirect, SAGE, Cinahl and Sabinet (SAePublications). Qualitative articles published in English or Afrikaans reporting on student selection processes and experiences were included in the data collection process. Review methodsQualitative articles were systematically identified using the STARLITE principles. The articles were critically appraised for quality by employing the CASP method. ResultsFourteen articles were included. Thirteen of the articles were published in English. One PhD thesis was included, a seminal work conducted on student nurse selection in South Africa. Data analysis revealed three main themes: a reliance on the interview, alternative selection methods, and attributes as criteria for nursing selection. Within these themes, many different methods of student selection emerged. The benefits and challenges of these methods were identified, and a holistic admission process was ultimately most successful in selecting the correct student nurse. ConclusionsThe findings indicate there are many different student nurse selection processes. A holistic admission process is needed to select the best student nurse that displays caring attributes and provides quality nursing care.

Spectral variable selection for estimation of soil organic carbon content using mid‐infrared spectroscopy

AbstractThe non‐destructive and rapid estimation of soil total carbon (SOC) content with mid‐infrared spectroscopy (MIR, 4000–400 cm−1) will play a vital role in precision agriculture. However, the benefit derived from the full MIR range is compromised by multicollinearity and noise. Hence, variable selection methods have been developed to reduce the full spectrum to a few variables that contribute the most information to a property of interest. However, only a few studies have applied variable selection methods in the MIR region to estimate organic carbon content in the soil. Therefore, four variable selection methods, namely stability competitive adaptive reweighted sampling (sCARS), bootstrapping soft shrinkage (BOSS), interval combination optimization (ICO) and the interval combination optimization‐successive projections algorithm (ICO‐SPA) method were investigated to ascertain the method that identified the wavebands most sensitive to SOC in order to improve prediction accuracy. The selected variables (i.e., reduced spectrum), as well as the full MIR spectrum, were coupled with partial least squares regression (PLSR) for the model calibration of SOC. The results showed that the models based on variable selection achieved higher prediction accuracy than the full spectrum model. sCARS selected 19 variables, BOSS selected 21 variables, ICO selected 311 variables, whereas ICO‐SPA selected only 9 variables (accounting for 0.38% of all variables), while the prediction accuracy of the ICO‐SPA‐PLSR model was similar to those of the other three variable selection methods. The ICO‐SPA‐PLSR model had an Rp2 value of 0.93, RPD of 3.90 and RMSEP of 0.13%. This method identified 3450, 2920, 2767, 2000, 1800, 1765, 1600, 1560 and 927 cm−1 as the feature wavenumbers with the most useful information in accurately estimating SOC content. Therefore, the combination strategy of interval (ICO) and individual (SPA) variable selection may be a good alternative variables selection method for MIR spectroscopic data.Highlights Different spectral variable selection methods were compared for SOC estimation by MIR. ICO‐SPA variable selection was employed to improve the prediction accuracy of SOC by MIR spectra. ICO‐SPA method selected informative key spectral variables of SOC in MIR range. Interval coupled with individual variable selection can be an alternative for spectroscopic data.