Traditional Measures Research Articles

The Southeast Florida Surface Urban Heat Island and Hispanic Ethnic Exposure Inequalities

abstract: Urban heat islands (UHI) present serious health risks as urban growth continues and thermal conditions warm. However, UHI studies on urban agglomerations are comparatively fewer while presenting notable challenges not encountered when examining individual cities. This analysis exploring the Southeast Florida urban agglomeration surface UHI (SUHI) quantified SUHI intensity (SUHII) using five measurement approaches. Multiple regression illuminated the influence of demographic and socioeconomic variables on respective SUHII indicators. Since prior urban heat analyses considered His-panics broadly and Southeast Florida features a diverse Hispanic population exceeding two million, a Hispanic ethnicity analysis was performed granularly examining exposure differences. Results demonstrated positive statistical correlation yet noteworthy numerical disparities between SUHII indicators, while demographic and socioeconomic variables explained 40-73 percent of observed variation. Collectively, Hispanics were disproportionately exposed to higher intensity with Cubans, Hondurans, Nicaraguans, and Other Central Americans exceedingly at risk. Results underscore a need to advance beyond traditional SUHII measurement approaches when analyzing urban agglomerations and emphasize the importance of distinguishing ethnicities when conducting urban-environmental studies in places with diverse Hispanic populations.

Analyses of Morphological Differences between Geographically Distinct Populations of Gymnodiptychus dybowskii

To study the morphological differences between and the evolutionary mechanisms driving the differentiation of geographically distinct populations of Gymnodiptychus dybowskii, 158 fish were collected from the Turks River and the Manas River in Xinjiang from 2020 to 2021 with the approval of the Academic Ethics Committee. The morphological characteristics of the fish were assessed using classical fish ecology methods such as traditional morphometric measurements and the framework approach. The results showed that the morphological characteristics of the populations in the Turks River and Manas River were significantly different; a one-way ANOVA revealed 22 highly significant differences (p < 0.01) and 1 significant difference (p < 0.05) among the 33 morphological traits of the observed populations, and a principal component analysis revealed that there was no overlap between the two populations of G. dybowskii. The main characteristics associated with principle component 1 were the terminus of the dorsal fin to the ventral origin of the caudal fin (D—F), the dorsal origin of the caudal fin to the origin of the anal fin (E—H), and the insertion of the pectoral fin to the terminus of the pectoral fin (J—K); the main factors associated with principal component 2 were the body height (BD), the terminus of the dorsal fin to the insertion of the pelvic fin (D—I), the caudal peduncle height (CPH), and the tip of the snout to the last end of the frontal maxilla (A—B); and the main traits associated with principle component 3 were the terminus of the anal fin to the origin of the anal fin (G—H), the body width (BW), the insertion of the pelvic fin to the terminus of the pelvic (I—L), the insertion of the pectoral fin to the terminus of the pectoral fin (J—K), and the insertion of the pelvic fin to the insertion of the pectoral fin (I—J). An OPLS-DA revealed that the two populations could be wholly separated and that the intergroup growth traits of the Manas River population were different and significantly greater than those of the Turks River population. The discriminant functions of the Turks River and Manas River populations of G. dybowskii were as follows: YT = −432.033 + 1787.748X1 + 826.517X2 + 249.002X3 + 1183.050X4 + 554.934X5 + 999.296X6 + 627.428X7; YM = −569.819 + 2041.044X1 + 344.942X2 + 333.737X3 + 940.512X4 + 348.222X5 + 1167.770X6 + 1015.904X7. According to a coefficient of variation analysis, a total of nine traits, namely, EI/BL, C-D/BL, E-F/BL, F-H/BL, H-I/BL, C-J/BL, D-I/BL, D-H/BL, and D-F/BL, had a CD > 1.28, indicating that the differences in these nine traits had reached the subspecies level. The results showed that G. dybowskii significantly differed between the two geographically distinct populations in the Turks River and the Manas River and have differentiated to the subspecies level. This study provides a basis for a better investigation of the population structure of highland endemic fishes and the mechanisms by which they diverged and lays a foundation for developing and utilizing germplasm resources from endemic fishes in Xinjiang.

Evaluating the Genetic Diversity of the Plasmodium vivax siap2 Locus: A Promising Candidate for an Effective Malaria Vaccine?

Malaria is the deadliest parasitic disease in the world. Traditional control measures have become less effective; hence, there is a need to explore alternative strategies, such as antimalarial vaccines. However, designing an anti-Plasmodium vivax vaccine is considered a challenge due to the complex parasite biology and the antigens´ high genetic diversity. Recently, the sporozoite invasion-associated protein 2 (SIAP2) has been suggested as a potential antigen to be considered in vaccine design due to its significance during hepatocyte invasion. However, its use may be limited by the incomplete understanding of gene/protein diversity. Here, the genetic diversity of pvsiap2 using P. vivax DNA samples from Colombia was assessed. Through PCR amplification and sequencing, we compared the Colombian sequences with available worldwide sequences, revealing that pvsiap2 displays low genetic diversity. Molecular evolutionary analyses showed that pvsiap2 appears to be influenced by directional selection. Moreover, the haplotypes found differ by a few mutational steps and several of them were shared between different geographical areas. On the other hand, several conserved regions within PvSIAP2 were predicted as potential B-cell or T-cell epitopes. Considering these characteristics and its role in hepatocyte invasion, the PvSIAP2 protein emerges as a promising antigen to be considered in a multi-antigen-multi-stage (multivalent) fully effective vaccine against P. vivax malaria.

Rethinking the Standard-Setting Process: The Role of Intangibles

ABSTRACT This think piece looks at the traditional reporting for intangibles and concludes that the measuring and reporting of intangibles needs a structural rethinking to incorporate 21st-century technology involving new forward-looking information and methods to provide information that is consistent with current capabilities (e.g., apps, bots, multiple databases, artificial intelligence). Traditional measurement methods, first published by Fra Luca Pacioli, satisfied business needs for centuries, but they limit modern external stakeholders’ ability to evaluate and compare firms’ current performance or predict their future performance. The traditional concepts of articulation, consolidation, and valuation of intangibles are inadequate, and sometimes blatantly misleading. Further, empirical research has ignored the emergence of new circumstances in business operations and accounting technology. The goal of this think piece is to discuss the traditional backward-looking approach to financial reporting for intangibles and to outline some considerations for developing a new system.

Enhancing Cognitive and Readiness Skills: An Integrated Assessment of Perceptual and Memory Abilities for SLD students

Abstract: This study explores how cognitive and readiness skills in students with Specific Learning Disabilities (SLD) can be improved through an integrated assessment of their perceptual and memory abilities. SLD students often struggle with processing information, retaining knowledge, and preparing for tasks in academic environments. Recognizing the crucial link between perceptual and memory abilities is essential for tailoring interventions to support these students effectively. Utilizing a comprehensive assessment framework, the research aims to elucidate how perceptual and memory abilities influence cognitive processing and readiness skills in SLD students. It employs a variety of methodologies, including traditional psychometric measures and innovative approaches such as virtual reality simulations and cognitive task analyses, to offer a holistic perspective By integrating multiple methodologies, the study seeks to uncover the intricate interplay between perceptual and memory abilities and their impact on cognitive readiness among SLD students. The findings are expected to guide the development of targeted interventions and educational practices tailored to their unique needs. The implications of this research extend beyond academia, potentially informing the design of training programs and cognitive enhancement interventions for SLD students. By addressing their specific challenges, the study aims to foster inclusivity and equity in education. This research highlights the importance of assessing perceptual and memory abilities in SLD students to enhance their cognitive readiness and academic outcomes. By identifying factors contributing to their cognitive challenges, educators and practitioners can devise more effective strategies to support their learning and development.

Fusion Analyser: a novel measurement method and software tool for dental model analysis in orthodontics

Accurate analysis of dental models is crucial for diagnosing and treating malocclusion successfully. Traditional measurement techniques, however, often fail to accurately measure teeth in crowded conditions. In this study, we introduce Fusion Analyser (FA), an innovative software tool capable of calculating key dental indicators, thereby advancing the scope of digital dental model analysis. Our unique FA framework employs segmentation discs on teeth to enhance the accuracy of the anterior Bolton’s ratio, outperforming the widely-used 3Shape software when dealing with crowded teeth. The FA framework exhibits performance commensurate with the established gold standard, as evidenced by high scores across a range of evaluation metrics, thereby validating its accuracy, stability, and efficiency. The potential of the FA framework as an auxiliary tool for dentists, particularly in diagnosing and strategising treatment plans for malocclusion, is underscored.

기업의 여유 운영 자원과 ESG 성과 간의 관계

This study aims to investigate operational management determinants for ESG (Environment, Social, and Governance) management. Market expectations for companies now include not just financial responsibility but also environmental, social, and governance responsibilities, impacting investor and customer preferences. Thus, firms are increasingly investing effort into ESG activities for sustainable management. However, additional performance management regarding ESG activities may burden companies beyond traditional measures like productivity and profit. From this perspective, the study aims to show how firms’ operational slack resources in capacity and supply chain affects ESG performance indicators from 2011 to 2021 using the panel regression method. Based on our results, we can suggest that firms with surplus capacity resources tend to achieve better ESG performance as they can accommodate additional tasks. Conversely, a surplus cash conversion cycle in the supply chain, indicating liquidity for ESG activities, may hinder ESG performance. Overall, companies with capacity slack resources and faster cash conversion cycles generally have the capability to invest in ESG initiatives, highlighting the importance of resource management in sustainable business practices.

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

How Education Shapes Indigenous Health Inequalities in the USA and Mexico.

Indigenous peoples around the world face significant health disparities relative to the dominant groups in their countries, yet the magnitude and patterns of health disparities vary across countries. We use data from the National Health Interview Survey and Mexican Family Life Survey to examine the health of Indigenous peoples in Mexico and American Indians and Alaska Natives in the USA and to evaluate how they fare relative to the majority populations in their countries (non-Indigenous Mexicans and non-Hispanic Whites, respectively). We assess disparities in self-rated health and activity limitations, with a focus on how Indigenous health disparities intersect with educational gradients in health. Regression analyses reveal three primary findings. First, Indigenous health disparities are larger in the USA than in Mexico. Second, differences in educational attainment account for most of the differences between Indigenous and non-Indigenous populations in Mexico, but less than half in the USA. Third, in both countries, health is moderated by educational attainment such that between-group disparities are largest at the highest levels of education. However, for Indigenous Mexicans there is a "cross-over" in which Indigenous Mexicans report better health at the lowest level of education. Overall, this study finds a weak relationship between education and Indigenous health, and raises the question about the validity of using traditional measures of SES in Indigenous contexts.

FARESHARE: An open-source apparatus for assessing drinking microstructure in socially housed rats

Social factors have been shown to play a significant and lasting role in alcohol consumption. Studying the role of social context on alcohol drinking is important to understand the factors that contribute to the initiation or maintenance of casual and problematic alcohol use, as well as those that may be protective. A substantial body of preclinical research has shown that social environment such as housing conditions and social rank plays an important role in alcohol consumption and preference, though the extent of these effects have been obfuscated by methodological differences and technical challenges. Robust individual differences in alcohol intake in socially housed animals are difficult to track when animals share a common fluid source. Commercial solutions are prohibitively expensive and are limited by proprietary software and hardware (including caging systems). Here we describe FARESHARE, an affordable, open-source solution for tracking fluid consumption in socially housed rats. The device uses RFID and custom hardware to individually measure and record each rat’s fluid consumption and licking microstructure. Each bout is also timestamped such that the circadian effects of drinking behaviour may be analysed. We provide a validation showing the operation of the device in a two-bottle-choice alcohol-drinking paradigm over a nine-day period in four group-housed female rats. We show that FARESHARE is able to capture traditional measures such as daily intake and preference, as well as circadian effects, microstructure, and individual variations in drinking.

Integrating Local Science and School Science: The Benefits for Preserving Local Wisdom and Promoting Students’ Learning

<p dir="ltr"><span>Integrating local science into science learning benefits students and the culture. However, science teaching tends to focus only on the scientific content presented in the books but ignores the local culture. This paper explores local science in community cultural activities to be implemented in science learning to introduce students to cultural values following the concept of science. It identifies local science, describes the oral arguments produced by the tribal council, and analyzes the lesson plan by five science teachers in the Nagekeo district, East Nusa Tenggara. The data used in this analysis are observation and identification, interviews, and assessment sheets. The findings show that the local science in Nagekeo cultural activities that can be integrated into science learning includes the traditional unit and measurement systems, Newton's laws in their application to the traditional game, biodiversity in the traditional hunt, the concept of heat transfer, and its application in the construction of traditional houses, and vibrations, waves, and sounds in the traditional musical instrument. Recommendations from this research are embracing local science in science learning to provide meaningful learning for students and preserve local culture for each generation in the future so that it does not become extinct. </span></p><div><span><br /></span></div>

Effect of compatibilizer and polyhedral oligomeric silsesquioxane on mechanical, thermal, and morphological properties of polyamide6/poly (ethylene-co-vinyl acetate) composites

The mechanical properties and morphology of polyamide 6/maleic anhydride grafted ethylene-co-vinyl acetate (PA6/EVA-g-MA) ternary composites are considered a function of compatibilizer (EVA-g-MA) amount at a mixing combination of 20 wt% dispersed phase (combination of EVA and the compatibilizer). 1–7 wt% compatibilizer are added to PA6/EVA-g-MA, whereas the PA6 doping is kept constant at 80 wt% in the ternary composites. Furthermore, 0.5, 1, 3, and 5 wt% octa-Isobutyl POSS (OIP) is used for reinforcement. The thermal, mechanical, and morphological properties of PA6/EVA-g-MA composites have been studied through DSC, TGA, DMTA, traditional mechanical measurements, and SEM. Tensile strength and Young’s modulus declined by increasing the compatibilizer concentration. The impact strength of PA6/compatibilizer improved considerably up to 3 wt% of the compatibilizer and then decreased. TGA analysis demonstrated a considerable improvement in the thermal stabilities of the nanocomposites by raising the OIP amount. The results of DMTA, including Tg, and DSC analysis demonstrated that the inclusion of OIP in PA6/compatibilizer considerably changed the thermal properties of only PA6 in PA6/compatibilizer. Incorporating OIP into the composite up to 1 wt% improved its modulus, tensile, and impact strength.

Research on image deformation monitoring algorithm based on binocular vision

Monitoring the deformation of critical structures such as tunnel、buildings and bridges is critical to ensure the safety of infrastructures. Non-contact deformation monitoring technology based on digital imagery has emerged as a prevalent and effective monitoring method, which enables cost-effective and efficient tracking of structural deformations. This paper aims to develop an automated deformation monitoring methodology using the binocular vision. First, standard preprocessing of digital images is performed through the integration of filtering algorithms and image enhancement techniques. Furthermore, by combining the Scale Invariant Feature Transform (SIFT) feature matching algorithm, the inverse compositional gauss–newton iterative algorithm, and interpolation methodologies, the planar displacement of the monitored targets can be calculated precisely, with sub-pixel precision being achieved. Subsequently, by adopting an enhanced Semi-Global Matching (SGM) algorithm, stereo matching of binocular images were accomplished. This process yields a disparity map, from which the depth map is computed. Then, precise measurement of the vertical displacement of the monitoring target can be obtained. This enables accurate measurement of three-dimensional displacements of the monitored targets. Finally, the developed methodology undergoes a comparative assessment against traditional measurement methods and existing algorithms to validate its measurement accuracy. The results prove that the deformation measurement technique based on binocular vision proposed in this paper captures structural deformation effectively. The accuracy surpasses that of existing measurement algorithms, and its efficiency outperforms traditional measurement methods.

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures.

Enhanced Feature Optimization for Multiclasss Intrusion Detection in IOT Fog Computing Environments

To overcome the shortcomings of traditional security measures in IoT fog computing, The Multiclass Intrusion Detection (MCID) model is put forward. The model's goal is to improve intrusion detection by identifying and classifying different attack types. The behavioral, temporal and anomaly features are fused through SVM-BFE for obtaining the best possible selection of high worth features. Finally we use a Random Forest algorithm to robustly classify them. There is also its adaptability to the ever-changing security demands of fog computing. MCID's ability to improve the cloud security of fog computing is shown by a 4-fold cross validation, which returns performances including precision rates up to 99.43%, recall about 95% and F-measures at as much as 97.17%. Moreover there are specificity rate totals coming in over this whole range that hit close or right.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging.

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps. To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation. In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI. We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

Does the investment performance measure matter? A perspective from regulatory focus theory

An experimental study with a sample (N = 213, 49.8% male, 18-year-old or above) indicates that investment performance measures based on drawdown duration can capture investors’ preference when other investment measures focusing on risk-adjusted returns such as the Sharpe ratio fail to. Based on the result, the prevention-focused investors are found to be more sensitive to, and easily affected by the number of drawdown days than the promotion-focused investors. Performance measures based on drawdown duration can supplement traditional performance measures to capture investors’ preference.

Human coexistence with leopards and elephants: Losses and coping strategies in an Indian tiger reserve

Conflicts between humans and wildlife, particularly involving common leopards (Panthera pardus) and Asian elephants (Elephas maximus) in the Himalayan region, pose significant challenges for both communities and conservation efforts. This study fills crucial gaps in existing research, extending beyond landscape-level predictors of these conflicts. By examining patterns of losses due to leopard and elephant encounters and assessing the effectiveness of coping strategies, along with socio-economic factors associated with these conflicts in the Buxa Tiger Reserve (BTR), this study aims to inform more targeted policy approaches for forest communities. Conducting face-to-face interviews with 345 households across 10 forest villages in the BTR, the study revealed that leopards predominantly target cows during the day and goats during both day and night, while elephants raid crops, particularly during crucial agricultural phases like paddy transplantation and harvesting. Financial losses prompt the adoption of coping strategies, with traditional measures showing limited effectiveness. Visual deterrents like flashing bright LED torchlights for elephants and night lights for leopards prove more promising. Larger households with substantial livestock are more vulnerable to leopard attacks, and rice cultivation areas and the eastern division of the BTR face heightened elephant-related crop damage. The study recommends immediate measures like visual deterrents and long-term strategies such as sustainable livelihood support and habitat restoration to mitigate conflicts. Its global significance lies in advocating for tailored policies worldwide that balance conservation with livelihood protection.

Creation of a holistic admissions review process: A promising change designed to promote diversity and inclusion in nursing education

Objective: The holistic admissions process employs a review strategy designed to assess an applicant's unique life experiences together with traditional measures of academic achievement. This process considers a broad range of factors that reflect an applicant's potential for professional success in the nursing field. The purpose of this initiative was to introduce a holistic admissions process at a nursing program that has historically utilized an academic metrics-based approach.Methods: The nursing program identified goals and targets for holistic admissions as a means to evaluate the process and to make informed decisions that support the aim of increased student diversity.Results: A team-based approach was used to develop criteria for holistic admissions. Shared values for characteristics of ideal nursing applicants were discussed first, and a literature review was conducted, focusing on predictors of success in nursing school and on the nursing licensure examination. Criteria were developed aimed at capturing the shared values, predictors of success, while also allowing applicants to ‘tell their stories’ during the application process. A student-facing application and applicant faculty review process were created. Benchmarks for monitoring diversity of the student population were identified.Conclusions: Our faculty task force developed and implemented a holistic admissions review process and generated plans designed to monitor the improved diversity of the student population. Faculty reflections highlight the value of pursuing this change and moving forward with holistic admissions in our nursing program.