Mission-Based Filters in the Electronic Residency Application Service: Saving Time and Promoting Diversity

ABSTRACT Background Holistic review promotes diversity, but widespread implementation remains limited. Objective We aimed to develop a practical approach to incorporate holistic review principles in screening applicants in the Electronic Residency Application Service (ERAS) and to assess the impact on diversity. Methods Three residency programs (internal medicine [IM], pediatrics, and surgery) at McGovern Medical School developed filters to identify applicants with experiences/attributes aligned with the institutional mission. These filters were retroactively applied to each program's 2019–2020 applicant pool using built-in ERAS capabilities to group applicants by user-defined features. We compared the demographics of applicants reviewed during the cycle with those identified retrospectively through experiences/attributes filters. Results The IM, pediatrics, and surgery programs received 3527, 1341, and 1313 applications, respectively, in 2019–2020. Retrospective use of experiences/attributes filters, without scores, narrowed the IM applicant pool for review to 1301 compared to 1323 applicants reviewed during actual recruitment, while the pediatrics filters identified 514 applicants compared to 384 at baseline. The surgery filters resulted in 582 applicants, but data were missing for baseline comparison. Compared to the baseline screening approach utilizing scores, mission-based filters increased the proportions of underrepresented in medicine applicants selected for review in IM (54.8% [95% CI 52.1–57.5] vs 22.7% [20.4–24.9], P < .0001) and pediatrics (63.2% [95% CI 59.1–67.4] vs 25.3% [20.9–29.6], P < .0001). Conclusions Program directors can leverage existing ERAS features to conduct application screening in alignment with holistic review principles. Widespread implementation could have important repercussions for enhancing physician workforce diversity.

Promoting Construction Industrialisation with Policy Interventions: A Holistic Review of Published Policy Literature

By adopting the concept of ‘factory assembly followed by onsite installation,’ construction industrialisation (CI) plays an increasingly important role in sustainable urban development. CI can enhance construction quality and efficiency while reducing environmental impacts. To promote the CI, several policy interventions have been developed and implemented in different countries and regions. This study reviews the global CI promoting regulations and policies to provide a comprehensive insight into its interrelationship and development tendency. The research selects 105 publications related to practical CI policy from widely utilised databases (i.e., Web of Science and Scopus). Based on the annual publication trend analysis, geospatial distribution, and citation analysis, seven interrelated critical CI policy formulation themes are identified and examined: regulatory policies, standardised policies, promotional policies, urban design and planning policies, technological policies, managerial and educational policies, and sustainability policies. In addition, internal correlations and mutual influence among these seven classified policies are explored and discussed, which helps scholars enhance their grasp of current CI policy research and guide future research. This review provides the research community and industrial practitioners with a comprehensive understanding of various CI-promoting policies and a roadmap to CI-promoting policy development and evaluation.

Predicting Cervical Cancer Cases Resulting in Biopsies Using Machine Learning Techniques

There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer cells into different categories. This study presents a critical review of different research papers published that integrated ML methods in screening cervical cancer via different approaches analyzed in terms of typical metrics like dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the reader with an insight of Machine Learning algorithms like SVM (Support Vector Machines), k-NN (k-Nearest Neighbors), RFT (Random Forest Trees), for feature extraction and classification. This paper also covers the publicly available datasets related to cervical cancer. It presents a holistic review on the computational methods that have evolved over the period of time, in detection of malignant cells. In this paper, we are going to train our model using various machine learning techniques and all the models thus made are compared in terms of accuracy, precision and recall.

A holistic review on advanced bi-directional EV charging control algorithms

The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission. The fast development of EVs and charging facilities is driving the evolution of Internet of Vehicles (IoV) to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and Internet of Things (IoT) technologies which provide advanced bi-directional charging services and real-time data processing capability, respectively. The major design challenges of the IoEV charging control lie in the randomness of charging events and the mobility of EVs. In this article, we present a holistic review on advanced bi-directional EV charging control algorithms. For Grid-to-Vehicle (G2V), we introduce the charging control problem in two scenarios: 1) Operation of a single charging station and 2) Operation of multiple charging stations in coupled transportation and power networks. For Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the electricity market and provide ancillary services to the power grid. Besides, a case study is provided to illustrate the economic benefit of the joint optimization of routing and charging scheduling of multiple EVs in the IoEV. Last but not the least, we will highlight some open problems and future research directions of charging scheduling problems for IoEVs.