Ranked List Research Articles

PC26. Predicting a Successful Plastic Surgery Residency Applicant - The Linear Rank Model

PURPOSE: Linear rank modeling (LRM) is an algorithm that uses relevant inputs to model expert judgment. Our institution previously described how LRM improves consistency and fairness in resident selection. We have utilized LRM over the last five years to assist with ranking integrated plastic surgery residency (PRS) applicants. The primary aim of this study was to determine if LRM predicts applicant match success. Our secondary aim was to assess if LRM demonstrates bias against groups of underrepresented gender or self-identified race categories. METHODS: The LRM scores for applicants who applied to a single institution between 2018-2022 were calculated. Data was collected on applicant demographics, Step 1 & 2 scores, number of publications, extracurricular activity scores, letter of recommendation scores, global intuition rank, and match success. RESULTS: The mean LRM score of 231 applicants was 59. With every one-point increase in LRM score, there was a significant 8.3% increase in the likelihood of applicant match success (p <.001). An algorithm was developed to estimate the probability of match success based on the LRM score. No significant difference in average LRM scores was found between gender (p =.285) or self-identified race groups (p =.137). CONCLUSION: LRM is a valuable tool to help create rank lists by providing an objective assessment of residency applicants. This model can rank applicants based on a holistic set of criteria and estimate an applicant’s probability of successfully matching into an integrated PRS residency.

The Role of Neurology Residency Programs Presence on Social Media During Residency Application (P10-2.001)

<h3>Objective:</h3> To determine if social media presence of neurology residency programs has an impact on applicant9s program perception and selection. <h3>Background:</h3> During the peak of the pandemic, interviews were held virtually. There was an increase in neurology residency program presence on social media. Out of 160 programs, 110 programs have a presence on Twitter, Instagram or Facebook. Most of residency-run accounts were created after March 1st, 2020. <h3>Design/Methods:</h3> The survey has 10 questions and was distributed through twitter accounts, involving @NMatch2023, and medical schools through clerkship directors. Participation is optional. <h3>Results:</h3> Over the course of 3 days, 30 responses were collected. All the participants used both social media to learn about programs with the majority (56.7%) using both Twitter and Instagram. Most (46.7%) used their own personal accounts while 30% used both personal and professional accounts. 60% are 4^th year medical students and the remaining are post-graduate. Most (43.3%) reported a moderate impact of social media presence on program selection and no respondent stated that it had no impact. 50% responded that they are more likely to apply to a program with a social media profile while 46.7% stated that it did not matter. 60% of the participants reported that social media presence had a small impact on their rank list. Most of the respondents (53.3%) were American AMGs and 43.3% were non-American IMGs. <h3>Conclusions:</h3> While all the participants used social media to learn about neurology residency programs and reported that it did have some impact on which programs they selected to apply to, it did not seem to be a crucial deciding factor on residency applications. This may suggest that social media profiles are used primarily to gather information about programs. The small sample size is a limitation of this study and gathering more responses is underway. <b>Disclosure:</b> Dr. Reyes has nothing to disclose. Dr. Rehim has nothing to disclose. Dr. Zobel has nothing to disclose.

NSRGRN: a network structure refinement method for gene regulatory network inference.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

Prioritising environmental invasive weeds of European concern for classical biological control: A reanalysis

AbstractInvasive alien plant species (IAPs) are causing significant negative impacts on agricultural production, threatened native species and ecosystems, the services they provide and public health thereby affecting European biodiversity and its economy. IAPs invade all types of natural and managed habitat and their impacts, through increased numbers and area invaded, are growing exponentially. Current control options in Europe are largely limited to manual and chemical control, which is high cost, short‐term in effectiveness and with regards to chemical control declining in public acceptability. Globally, classical biological control (CBC) is widely and successfully used to manage many IAPs. CBC aims to redress the ecological imbalance caused by the IAPs, generally being released without their natural enemies. The steps are to select, risk assess potential specific natural enemy biocontrol agents of the IAP (from the IAP's native range) and follow regulatory approval prior to releasing them to ecologically suppress their abundance. CBC is not widely used in Europe. Only five active programmes exist. In this paper, we apply an existing framework to develop a ranked list of environmental IAPs named in the EU Regulation on Invasive Species for biocontrol. We used a scoring system based on existing knowledge on the IAPs impacts, the amount of effort needed to deliver a CBC programme targeting them and the feasibility and likelihood of success of such programmes. We identify 16 IAPs in Europe for which CBC has relatively high potential and discuss existing knowledge that can underpin any future investments in such activities against each of these IAPs. The top three species being Pontederia crassipes, Pistia startiotes and Acacia saligna. This research should support decision‐making on the instigation of future CBC programmes against environmental IAPs in Europe. We set this analysis in the context of other operational and regulatory constraints on developing CBC programmes against environmental IAPs in Europe.

A Text Analytics Framework for Supplier Capability Scoring Supported by Normalized Google Distance and Semantic Similarity Measurement Methods

AbstractThe unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, they do not lend themselves well to computational analysis. The objective of this work is to propose framework to enable automated classification and ranking of manufacturing suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics framework is supported by a formal thesaurus that uses Simple Knowledge Organization System (SKOS) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.

Omics data integration facilitates target selection for new antiparasitic drugs against TriTryp infections.

Introduction: Trypanosoma cruzi, Trypanosoma brucei, and Leishmania spp., commonly referred to as TriTryps, are a group of protozoan parasites that cause important human diseases affecting millions of people belonging to the most vulnerable populations worldwide. Current treatments have limited efficiencies and can cause serious side effects, so there is an urgent need to develop new control strategies. Presently, the identification and prioritization of appropriate targets can be aided by integrative genomic and computational approaches. Methods: In this work, we conducted a genome-wide multidimensional data integration strategy to prioritize drug targets. We included genomic, transcriptomic, metabolic, and protein structural data sources, to delineate candidate proteins with relevant features for target selection in drug development. Results and Discussion: Our final ranked list includes proteins shared by TriTryps and covers a range of biological functions including essential proteins for parasite survival or growth, oxidative stress-related enzymes, virulence factors, and proteins that are exclusive to these parasites. Our strategy found previously described candidates, which validates our approach as well as new proteins that can be attractive targets to consider during the initial steps of drug discovery.

Abstract 3351: Identification of novel biomarkers for high grade serous ovarian cancer screening via extracellular vesicle proteomic profiling

Abstract Introduction: Ovarian cancer is a silent killer that rarely shows symptoms and therefore is detected at later stages when treatments are less effective. Developing non-invasive blood tests for pre-symptomatic screening and early detection is crucial. Recent studies support the paradigm that high grade serous carcinomas (HGSOCs), the most common and deadliest form of ovarian cancers, arise from the epithelial cells within the fimbriated end of the fallopian tubes (FT). The goal of this study was to uncover novel protein biomarkers associated with tumor-derived extracellular vesicles (EVs) originating from the FT epithelial cells to advance liquid-based assays for the early detection of HGSOC. We hypothesize that EV-associated proteins (exo-proteins), i.e., lineage biomarkers, shared between the progenitor cells residing in the FT and tumor cells residing in cancerous tissue will allow for detection of the earliest forms of HGSOC. Methods: Differential ultracentrifugation was used to enrich EVs from FT and HGSOC cell lines (n=3 &amp; n=5) and healthy FT and HGSOC tissue explants (n=6 &amp; n=15), respectively. Unbiased proteomic profiling of EVs was performed by LC-MS/MS. Results: We report for the first time 985 proteins that compose the FT/HGSOC EV core proteome. We used bioinformatic pipelines to identify high-quality candidate exo-biomarkers for further validation. We selected exo-proteins that were predicted to have at least one transmembrane domain which could serve as an antigen for immunoaffinity capture. We prioritized proteins that showed a log2 fold-change ≥ -0.58 to identify potential biomarkers present in FT-associated EVs which were elevated as the disease progresses to HGSOC. A ranked list of 45 predicted transmembrane exo-proteins was screened using Simple Western assays for expression in FT and HGSOC cell line-derived EVs. Expression of the top 6 candidate biomarkers (i.e., ACSL4, IGSF8, ITGA2, ITGA5, ITGB3 and MYOF) were confirmed in FT and HGSOC tissue sample by immunohistochemistry using tissue microarray (n=100). Next, we analyzed plasma samples from a small case-control study using our ExoProfile chip (e.g., PMID 31293733) and found these 6 exo-biomarkers, plus folate receptor alpha exhibited higher levels in the HGSOC plasma relative to matched healthy controls. We performed receiver operating characteristic (ROC) analyses and found that all 6 FT/HGSOC shared exo-biomarkers exhibit an overall classification performance ranging from 0.85-0.98 as reflected by the area under the curve (AUC). Furthermore, when we used a linear combination of IGSF8 and ITGA5 based on logistic regression analysis, we achieved a sensitivity of 80% at 99.8% specificity. Conclusion: Validated lineage-associated exo-protein biomarkers can be used in liquid-based assays to detect ovarian cancer while localized to the FT/ovary and when patient outcomes are more favorable. Citation Format: Camille V. Trinidad, Harsh B. Pathak, Shibo Cheng, Rashna Madan, Mihaela E. Sardiu, Leonidas E. Bantis, Yong Zeng, Andrew K. Godwin. Identification of novel biomarkers for high grade serous ovarian cancer screening via extracellular vesicle proteomic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3351.

Unsupervised Learning-Guided Accelerated Discovery of Alkaline Anion Exchange Membranes for Fuel Cells.

Without insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V-MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2×105 candidates was proposed. The accuracy of the V-MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V-MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V-MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.

Unsupervised Learning‐Guided Accelerated Discovery of Alkaline Anion Exchange Membranes for Fuel Cells

AbstractWithout insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V‐MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2×105 candidates was proposed. The accuracy of the V‐MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V‐MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V‐MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.

MP66-20 THE VIRTUAL UROLOGY RESIDENCY INTERVIEW PROCESS: A SURVEY OF APPLICANTS TO A SINGLE INSTITUTION

You have accessJournal of UrologyCME1 Apr 2023MP66-20 THE VIRTUAL UROLOGY RESIDENCY INTERVIEW PROCESS: A SURVEY OF APPLICANTS TO A SINGLE INSTITUTION Ellen Cahill, Rachel Passarelli, Alain Kaldany, Arnav Srivastava, Sammy Elsamra, Thomas Jang, and Ji Hae Park Ellen CahillEllen Cahill More articles by this author , Rachel PassarelliRachel Passarelli More articles by this author , Alain KaldanyAlain Kaldany More articles by this author , Arnav SrivastavaArnav Srivastava More articles by this author , Sammy ElsamraSammy Elsamra More articles by this author , Thomas JangThomas Jang More articles by this author , and Ji Hae ParkJi Hae Park More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003329.20AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Though virtual residency interviews have decreased interview-related costs, they have led to challenges for applicants in making informed decisions about residency programs. We conducted a survey-based study to clarify this trade off and assess applicant understanding of residency programs. METHODS: An anonymous, web-based survey was sent to applicants of the 2022 Urology Residency Match who submitted an application to our institution (283/601). RESULTS: Of 283 applicants receiving the survey, 102 (36%) responded. On average, respondents applied to 87.4 (SD: 23) programs, received 14.6 (SD: 7) interviews and ranked 13.2 (SD: 6) programs. 82% of survey respondents matched, with a median match of program number 2 (IQR: 1-5) on their rank list. Participants chose goodness of fit within the program (38%), program location (28%), or resident happiness (23%) as the single most important factor when making their rank lists. Participants felt faculty interviews and resident happiness were well-replicated virtually, but program location was not. 44% of participants found virtual resident question and answer sessions and pre/post-interview socials helpful in assessing resident happiness. Applicant feedback included adding virtual tours, interactive maps, and “day in the life” social media posts. CONCLUSIONS: Perceived resident happiness and program location are key variables for applicants evaluating urology residency programs. Applicants found virtual events helpful in assessing resident happiness, but not program location. With the prospect of continuing virtual residency interviews going forward, urology programs should utilize social media, pre-recorded videos, and live virtual events to enhance the interview experience. Source of Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e941 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ellen Cahill More articles by this author Rachel Passarelli More articles by this author Alain Kaldany More articles by this author Arnav Srivastava More articles by this author Sammy Elsamra More articles by this author Thomas Jang More articles by this author Ji Hae Park More articles by this author Expand All Advertisement PDF downloadLoading ...

Realistic Assessment of Research Publications by Neurosurgery Residency Applicants.

Neurosurgical residency applicants' prior research experience can amplify their ability to stand out to prospective neurosurgery programs. We attempted to accurately quantify the number of research publications coauthored by applicants by analyzing the publications of applicants who matched into neurosurgery in the 2021 Match. Scopus, a peer-reviewed literature database, was queried for publications by applicants who matched into neurosurgery in the 2021 Match before the finalization of rank lists. Conference papers, abstracts, and book chapters were excluded to determine an accurate average of actual publications. Descriptive statistics for resident publication data were used, with a Mann-Whitney U test used to compare research productivity between male and female residents. There were 234 positions filled by the 2021 Match, and 233 neurosurgical residents were identifiable in this study. A total of 187 residents matching from U.S. Doctor of Medicine and Doctor of Osteopathic Medicine programs were identified with 946 total publications-an average of 5.1 publications per resident. Analysis of descriptive statistics revealed type of research conducted, authorship information, most published journals, and citation data. Significant differences were found in the number of publications between male and female applicants with averages of 5.6 and 3.8 publications, respectively. Students matriculating to neurosurgery residency programs display a wide range of research productivity. Typical U.S. Doctor of Medicine and Doctor of Osteopathic Medicine applicants have coauthored a mean of 5.1 and a median of 4.0 publications. This information may assist program directors in weighing applicants' research background and give medical students interested in the field reasonable research expectations.

MP66-11 PREFERENCE SIGNALING IN THE UROLOGY MATCH: IMPACT AND USAGE TRENDS

You have accessJournal of UrologyCME1 Apr 2023MP66-11 PREFERENCE SIGNALING IN THE UROLOGY MATCH: IMPACT AND USAGE TRENDS Ralph Grauer, Daniel Ranti, Kirsten Greene, Michael Gorin, Mani Menon, and Saša Zorc Ralph GrauerRalph Grauer More articles by this author , Daniel RantiDaniel Ranti More articles by this author , Kirsten GreeneKirsten Greene More articles by this author , Michael GorinMichael Gorin More articles by this author , Mani MenonMani Menon More articles by this author , and Saša ZorcSaša Zorc More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003329.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Analyzing behavior from the implementation of signals during the American Urological Association (AUA) urology match will help clarify applicant/program signal usage and trends. METHODS: Using verified data from the match as well as survey data reported by applicants and programs, a logistic regression was performed on applicant factors associated with obtaining a residency interview: age, gender, degree (MD or DO), distribution of signals, US senior status, minority status, Latino status, IMG status, presence of a home urology program, AUA geographic section, and USMLE Step 1 score. We described signal distribution strategies stratified by program competitiveness and program behavior upon receipt of signals with respect to interviewing/ranking applicants. RESULTS: A total of 2,659 signals were sent by 553 candidates submitting rank lists for 364 positions at 142 programs. Programs received a median of 352 applications and were signaled to a median of 16 times each (IQR: 8–26). In a logistic regression predicting interview status, we found that geographic proximity (OR 3.25, 95% CI, 2.05–5.15; p=0.001), and signal status (OR 6.04, 95% CI, 3.50–10.40; p<0.001) were predictive of receiving an interview. Using multiple imputation to broadening the dataset, male gender (OR: 0.64, 95% CI, 0.45–0.92; p=0.039) and IMG status (OR: 0.35, 95% CI, 0.15–0.81; p=0.036) were negatively predictors, while MD degree (OR 2.36, 95% CI, 1.27–4.36; p=0.023), and US senior status (OR 1.91, 95% CI, 1.13–3.23; p=0.039), were positive predictors. CONCLUSIONS: We analyzed trends of the newly added signals. We reportedstrategies for signal dispersal, factors associated with obtaining interviews, and how signals informed program interviewing/ranking decisions. Source of Funding: none © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e936 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ralph Grauer More articles by this author Daniel Ranti More articles by this author Kirsten Greene More articles by this author Michael Gorin More articles by this author Mani Menon More articles by this author Saša Zorc More articles by this author Expand All Advertisement PDF downloadLoading ...

744 Reducing Implicit Bias in the Neurosurgery Application and Interview Process

INTRODUCTION: Each year, Neurosurgery residency programs aim to match the best and brightest applicants aspiring to join the field. Traditionally, metrics such as USMLE Step 1 and 2 scores, publications, and medical school grades have played a key role in determining who is interviewed, the interviewers’ impression of the candidates, and subsequently who is most likely to match. However, these evaluative tools are known to reflect biases in the medical and medical education systems, potentially overlooking diverse applicants well-suited for a career in neurosurgery. METHODS: During the 2020-2021 and 2021-2022 residency application cycles, our program instituted a new process for reviewing applications, conducting interviews, and generating a final rank list. Each application was reviewed by attending and resident reviewers, who were blinded to potentially biasing information including pictures and board scores. From this review, selected applicants were interviewed using a set of standardized questions, designed to evaluate specific domains essential for success in neurosurgery. Linear regression was performed to determine correlation between traditional metrics and domain-based interview scores and final rank. RESULTS: 137 applicants were interviewed. There was no significant correlation between rank and Step 1 (p = 0.47, R2 = 0.003) or Step 2 scores (p = 0.16, R2 = 0.014).Interview scores lacked statistically significant association with Step 1 scores (p=0.10, R2=.02), but were found to correlate with Step 2 scores (p = 0.02, R2 = .05). CONCLUSIONS: In our two-cycle experience using this new system, we found no correlation between applicants’ Step 1 scores and their interview scores or final rank list position. We propose this as one alternative method to historically biased tools for evaluating neurosurgery applicants.

Virtual Recruitment Effects on Matched Residents in Family Medicine: Experiences From Central Pennsylvania.

As a result of the COVID-19 pandemic, interviews during the 2021 US residency match were conducted virtually, a practice again recommended and repeated by many programs in 2022. The impact of virtual interviews on recruitment and match outcomes has recently been of interest, with results showing the virtual format to be mostly well received by applicants due to cost, travel, and scheduling benefits. Few studies have looked at pre/posttransition comparisons of applicant geographic and demographic data. We compared objective match outcomes between in-person and virtual interviews across three residency programs. We conducted a retrospective cross-sectional analysis of National Residency Matching Program data between 2015-2022 across three family medicine residency programs. Primary outcomes were fill rate, average rank position, distance from program, and percentage of underrepresented in medicine demographic status for matched applicants. We compared aggregate in-person data (2015-2019) to aggregate virtual data (2020-2022) for each program using χ2, Fisher Exact test, or 2-tailed t tests to 95% confidence. Saint Joseph Hospital in Reading, Pennsylvania, a 3-year community-based university affiliated program, had significantly more unfilled positions during virtual recruitment (P=.0058). Mount Nittany Medical Center in State College, Pennsylvania, a 3-year community based university-affiliated program, had a significant difference in distance of matched residents' current address (P=.048). Virtual interviews were not associated with significant differences in average position on rank list, average distance from permanent address zip code, or percentage of underrepresented in medicine (URiM) demographic status for matched applicants. The impact of virtual interviewing on unfilled positions and geographic data is likely site specific and generally small, as some programs had significant structural changes. Further research is needed to confirm the generalizability of these results and explore future comparisons of demographic and geographic characteristics of matched applicants pre/posttransition to the virtual format.

Outlier detection in network revenue management

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn.

Combination of symmetry point of criterion, compromise ranking of alternatives from distance to ideal solution and collaborative unbiased rank list integration methods for woodworking machinery selection for small business in Vietnam

Woodworking industry plays an important role in the development of Vietnam’s economy. The efficiency of woodworking process depends a lot on the machinery used in the woodworking process. Selecting the best option among a variety of machines is tedious and complex work. However, if the choice of machine is based only on the subjective opinion of the customer, it will lead to mistakes. That mistake is understood that the customer will choose the option that is not the best among the machines proposed by the supplier. Instead, machine selection must be based on all machine parameters. This is called multi-criteria decision making (MCDM). There are MCDM methods, when used it is necessary to know the weights of the criteria. However, there are also methods that do not need to know the weights of the criteria. CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) is a method that, when used, is required to weight the criteria. In contrast, this problem is unnecessary when using the CURLI (Collaborative Unbiased Rank List Integration) method. In this study, three kinds of machinery commonly used for small business in woodworking field were selected. The three kinds of machinery mentioned in this study include wood milling machine, wood saw machine, wood planer. The SPC (Symmetry Point of Criterion) method was used to calculate the weights of the criteria for each kind of machinery. This is the youngest method among the methods of determining the weights for the criteria, it was only found in 2023. The two methods include CRADIS and CURLI were used to rank the machinery kinds. The result showed that in all the surveyed situation, the best alternative is always determined consistently when using CRADIS and CURLI methods. Accordingly, three best alternatives with three different machinery kinds (milling machine, saw machine and planer) were found in this study

Choosing the best machine tool in mechanical manufacturing

Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine types

What really influences the development of renewable energy? A systematic review and meta-analysis.

Promoting renewable energy (RE) is one key strategy to increase energy security and mitigate global warming. What really influences the development of RE has aroused public attention worldwide. Numerous studies have identified and evaluated the critical influence factors (CIFs) for renewable energy development (RED); however, there seems to be no consensus among the previous studies on these CIFs and their importance level or influence direction. Given that, this study, for the first time, conducts a systematic review and meta-analysis of the CIFs for RED. With evidence from 33,119 observations in 67 studies between 2010 and 2022, 44 CIFs distributed in political, economic, environmental, social, and technological (PEEST) dimensions were selected from an international perspective. Results demonstrate that (i) 27 CIFs with statistical significance and their rank list were identified through meta-analysis. Some of them were mentioned many times in previous studies, but their significance for RED was not very high. (ii) The top three driving factors in CIFs' significance rank list were industrial infrastructure investment, R&D, and financial development, and the top three inhibiting factors were the fossil-based energy consumption structure, policy uncertainty, and population life. (iii) The publication year, country's economy, and links of the RED value chain have a moderating effect on some CIFs' influence mechanisms. This study not only contributes to the existing RED knowledge body but also provides references to policymakers and practitioners in formulating policies and good practices to promote renewable energy.

Improved risk minimization algorithms for technology-assisted review

MINECORE is a recently proposed decision-theoretic algorithm for technology-assisted review that attempts to minimise the expected costs of review for responsiveness and privilege in e-discovery. In MINECORE, two probabilistic classifiers that classify documents by responsiveness and by privilege, respectively, generate posterior probabilities. These latter are fed to an algorithm that returns as output, after applying risk minimization, two ranked lists, which indicate exactly which documents the annotators should review for responsiveness and which documents they should review for privilege. In this paper we attempt to find out if the performance of MINECORE can be improved (a) by using, for the purpose of training the two classifiers, active learning (implemented either via relevance sampling, or via uncertainty sampling, or via a combination of them) instead of passive learning, and (b) by using the Saerens-Latinne-Decaestecker algorithm to improve the quality of the posterior probabilities that MINECORE receives as input. We address these two research questions by carrying out extensive experiments on the RCV1-v2 benchmark. We make publicly available the code and data for reproducing all our experiments.

Outcomes of the 2021-2022 APDS General Surgery Recruitment Process Recommendations

In recent years, mounting challenges for applicants and programs in resident recruitment have catapulted this topic into a top priority in medical education. These challenges span all aspects of recruitment-from the time an applicant applies until the time of the Match-and have widespread implications on cost, applicant stress, compromise of value alignment, and holistic review, and equity. In 2021-2022, the Association of Program Directors in Surgery (APDS) set forth recommendations to guide processes for General Surgery residency recruitment. This work summarizes the APDS 2021-2022 resident recruitment process recommendations, along with their justification and program end-of-cycle program feedback and compliance. This work also outlines the impact of these data on the subsequent 2022-2023 recommendations. After a comprehensive review of the available literature and data about resident recruitment, the APDS Task Force proposed recommendations to guide 2021-2022 General Surgery resident recruitment. Following cycle completion, programs participating in the categorical General Surgery Match were surveyed for feedback and compliance. About 122 of the 342 programs (35.7%) participating in the 2022 categorical General Surgery Match responded. Based on available data in advance of the cycle, recommendations around firm application and interview numbers could not be made. About 62% of programs participated in the first round interview offer period with 86% of programs limiting offers to the number of slots available; 95% conducted virtual-only interviews. Programs responded they would consider or strongly consider the following components in future cycles: holistic review (90%), transparency around firm requirements (88%), de-emphasis of standardized test scores (54%), participation in the ERAS Supplemental application (58%), single first round interview release period (69%), interview offers limited to the number of available slots (93%), 48-hour minimum interview offer response time (98%), operationalization of applicant expectations (88%), and virtual interviews (80%). There was variability in terms of the feedback regarding the timing of the single first round offer period as well as support for a voluntary, live site visit for applicants following program rank list certification. The majority of programs would consider implementing similar recommendations in 2022-2023. The greatest variability around compliance revolved around single interview release and the format of interviews. Future innovation is contingent upon the ongoing collection of data as well as unification of data sources involved in the recruitment process.