Data Files Research Articles

An Electrochemical Screening Reactor Kit for Rapid Optimization of Electrosynthesis Applications

Electrosynthetic processes powered by renewable energy present a viable solution to decarbonize chemical industry, while producing essential chemical products for modern society. However, replacing well‐established thermocatalytic methods with renewable‐powered electrosynthesis requires cost‐efficient and highly optimized systems. Current optimization of electrolysis conditions towards industrial applications involving scalable electrodes is time consuming highlighting the necessity for the development of electrochemical setups for aimed at rapid and material efficient testing. Thus, we introduce a 3D printed electrochemical screening reactor designed for rapid optimization of relevant electrochemical parameters, utilizing electrode and membrane materials comparable to those in scalable electrolyzers. The reactor comprises eight individual two‐compartment cells that can be operated simultaneously and independently. To evaluate the reactor’s ability to provide meaningful insights on scalable cell designs, trends were compared with data from conventional scalable systems for electrochemical hydrogenations (EChH), demonstrating fast and accurate parameter optimization with the screening reactor. A detailed description of the reactor design and construction data files using open‐source tool are provided, enabling easy modification for anyone. We believe this screening reactor will be a valuable tool for the scientific community, for facilitating the discovery of reactions with customized electrode designs and rapidly improving conditions in established large‐scale electrolyzers.

Assessing water resource vulnerability based on remote sensing data-enhanced SWAT+ and High-Resolution precipitation data

Climate change and human activities have significantly impacted the global water cycle, increasingly threatening water security across various sectors. By improving the performance of hydrological models in simulating the water balance, including precipitation and evapotranspiration, we can better support decision-making for regional water resource management and the deployment of climate change adaptation measures. This study aims to enhance the simulation performance of the vegetation growth module in the SWAT + model and reduce its uncertainty by dynamically updating the model’s data files with satellite-derived leaf area index and phenology data. Additionally, this study modified the weather station allocation mechanism in SWAT + to fully utilize the high-resolution meteorological data grids. The simulation results show that the enhanced SWAT + model achieved a Nash-Sutcliffe Efficiency (NSE) between 0.84 and 0.90 and a PBIAS of less than 6 % for daily streamflow simulations at four hydrological stations in the Weihe River Basin. The enhanced SWAT + model’s water balance simulation results for the Weihe River Basin were used to analyze the vulnerability of water resources within the basin. The results indicate that the Qinling region, benefiting from its well-developed forest ecosystems, has the lowest green water vulnerability. The overall blue water vulnerability of the basin reached 1.12, suggesting that during periods of insufficient precipitation or intense evaporation, blue water resources may not meet the demands of agriculture, industry, ecology, and residential use. Among these, agricultural water use exhibits the highest vulnerability. Efficient irrigation technologies can be employed to improve irrigation water use efficiency, and the use of treated reclaimed water for agricultural irrigation can be promoted to reduce the demand for fresh blue water resources.

A data encryption and file sharing framework among microservices-based edge nodes with blockchain

A data encryption and file sharing framework among microservices-based edge nodes with blockchain

The Government Policy with its Historical Background Through the Effort to Prevent the Jobless of Timor-Leste in Providing Job Management Opportunities in the State and Foreign Countries (A Qualitative and Quantitative Mixed Method Research Analysis on Re

Abstract: The research study with entitled is about the Government Policy with its State Historical Background through the effort to prevent the jobless of Timor-Leste in providing job Management opportunities in the state and foreign countries.” The objective of this research is to identify and search on the historical and economical background of the state in providing job opportunities to young people are employed in internal and foreign countries to fix their family’s economic conditions to support the state in reducing the unemployment and poverty in the country itself as a way to settle down the problems and obtain the solution. About this, the government of Timor-Leste has created a bilateral and multilateral cooperation agreement in the country through signing the mutual of understanding (MOU) with the foreign company’s organization in abroad. The economic problems and other matters are settled with the effort hard of the state’s government by providing funds to minimize and reduce the poverty and the economic problems of the living people in the state in the current day and the future. The method of this study is qualitative and quantitative mixed methods, observing, paraphrasing, and permutation in library research through collecting the facts by using online libraries, software, and hard book references. Some of the data files presented in this writing to complete this analytical research. The finding of this research is the jobless of the country are mostly employed by the government through mutual cooperation through bilateral and multilateral cooperation to reduce the unemployment in the internal country and abroad. From the total population are not finalized their studies in the school 30,7% and from the above population, 16,8% do their learning in the higher education level. Amongst the young between 14-24 years old do not access the education, unemployed, and training and it is hard for them to get the job competition. The total labor forces in 2009, there are 430,200 people in East Timor and the unemployment are 18% since 2010.

Portfolio Optimization Based on Almost Second-Degree Stochastic Dominance

In portfolio optimization, the computational complexity of implementing almost stochastic dominance has limited its practical applications. In this study, we introduce an optimization framework aimed at identifying the optimal portfolio that outperforms a specified benchmark under almost second-degree stochastic dominance (ASSD). Our approach involves discretizing the return range and establishing both sufficient and necessary conditions for ASSD. We then propose a three-step iterative procedure: first, identifying a candidate portfolio; second, assessing its optimality; and third, refining the discretization scheme. Theoretical analysis guarantees that the portfolio identified through this iterative process improves with each iteration, ultimately converging to the optimal solution. Our empirical study, utilizing industry portfolios, demonstrates the efficacy of our approach by consistently identifying an optimal portfolio within a few iterations. Furthermore, comparative analysis against other decision criteria, such as mean-variance, second-degree stochastic dominance, and third-degree stochastic dominance, reveals that ASSD generally leads to portfolios with higher out-of-sample average excess returns but also entails increased variations and risks. This paper was accepted by Agostino Capponi, finance. Funding: C. Luo acknowledges financial support from the National Natural Science Foundation of China [Grant 72101070] and the Zhejiang Provincial Natural Science Foundation of China [Grant LY23G010001]. P. Chen acknowledges financial support from the National Natural Science Foundation of China [Grant 72401253]. P. Jaillet acknowledges financial support from the Office of Naval Research [Grant N00014-18-1-2122 and N00014-24-1-2470] and the Air Force Office of Scientific Research [Grant FA9550-23-1-0182 and Grant FA9550-23-1-0190]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01092 .

Labor-Replacing Automation and Finance

We examine the effects of adoption of labor-replacing automation technology on corporate financing. Empirically, using Chinese firm-level panel data on deployment of industrial robots as an example of such automation, we find that robot adoption increases leverage and reduces cost of debt. We hypothesize that the underlying reason is that being a substitute for labor, automation provides a hedge against fluctuations in labor costs. A model based on this hedging argument delivers additional testable predictions concerning determinants of the relation between automation and corporate financing. These relations are borne out in the data, providing support for the proposed mechanism. Our evidence is inconsistent with alternative channels behind the observed relations. This paper was accepted by Victoria Ivashina, finance. Funding: X. Cheng acknowledges support from the Humanities and Social Science Foundation of the Ministry of Education in China [Grant 23YJC840004] and the Fundamental Research Funds for the Central Universities of China [Grant 2722023BQ052]. E. Lyandres acknowledges support from the Coller Foundation and the Henry Crown Institute for Business Research in Israel. K. Zhou acknowledges support from the Major Project of the National Social Science Foundation of China [Grant 20&ZD103], the Key Project of the National Natural Science Foundation of China [Grant 72432005], and the Excellent Young Team Project of the Natural Science Foundation of Guangdong Province of China [Grant 2023B1515040001]. T. Zhou acknowledges support from the National Natural Science Foundation of China [Grant 72003206]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02658 .

A Simple Approach for Measuring Higher-Order Arrow-Pratt Coefficients of Risk Aversion

Although the importance of the second-order Arrow-Pratt coefficients of risk aversion is well established, the importance of higher-order risk attitudes has only recently begun to be recognized. In this paper, we introduce a nonparametric approach to directly measure higher-order Arrow-Pratt coefficients of risk aversion in an expected utility framework using choices between compound lotteries and show how it can be easily implemented in behavioral studies. Specifically, we provide a theoretical basis for using risk apportionment to reveal the intensity of higher-order risk attitudes, and then draw upon our theoretical results to develop a simple, systematic, and generalizable procedure for eliciting higher-order Arrow-Pratt coefficients. We demonstrate our approach in a laboratory experiment and find that the modal second-order, third-order, and fourth-order Arrow-Pratt coefficients are positive and small. Further, we find that degrees of risk aversion are positively correlated across orders. Additionally, we discuss alternative implementations of our procedure. This paper was accepted by Peng Sun, behavioral economics and decision analysis. Funding: R. J. Huang acknowledges financial support from the National Science and Technology Council of Taiwan [Grants MOST 110-2410-H-008 -017 -MY3]. R. J. Huang and L. Y. Tzeng gratefully acknowledge financial support from E.SUN Bank. L. Zhao acknowledges financial support from the National Natural Science Foundation of China [Grants 72125003 and 72131003]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02300 .

DarkMatters: A powerful tool for WIMPy analysis

We introduce a new software package, DarkMatters, which has been designed to facilitate the calculation of all aspects of indirect dark matter detection of WIMPs in astrophysical settings. Two primary features of this code are the improvement in performance compared to existing tools, and higher levels of accuracy when determining radio synchrotron emission associated with WIMP annihilations, both of which are enabled by the employment of a set of modern and novel numerical techniques. The code also includes functionality for a multi-wavelength set of output products including gamma-ray, radio and neutrino fluxes which can be saved in common formats used by the astronomical community, such as the FITS data file format. The calculations may be tailored to work with a wide range of astrophysical target structures, from dwarf galaxies to galaxy clusters, and the configuration of the underlying calculations is managed by a set of key–value dictionary entries that are easy to understand and use. The code base is publicly accessible through an online repository with a permissive MIT source code licence.

Self-Regulation for Reputation-Sensitive Buyers: SA8000 in China

Industries and firms have diverse motives for adopting self-regulatory institutions. This research develops and tests propositions about one motive—exploiting opportunities to do business with reputation-sensitive buyers—as distinct from self-regulation to defend against regulatory or activist threats. To study the adoption and effects of self-regulation for reputation-sensitive buyers, we study the SA8000 socially responsible employment certification among large firms in China in the early 2000s. Using official longitudinal industrial microdata, we test hypotheses generated by this assumed motive for self-regulation and find that (a) despite concerns about the corruptibility of certification bodies, SA8000 adopters in China exhibited higher precertification worker wages than comparable nonadopters, (b) self-regulation led to increased employment and sales to foreign markets, where reputation-sensitive buyers are concentrated, (c) the positive effect on exports was greater than the (insignificant, negatively signed) effect on domestic sales, and (d) there is no evidence that self-regulation increased worker wages beyond the initial high start. Contrasting these findings with prior research on industry self-regulation for other motives, this study highlights how both adoption patterns and downstream effects differ according to the audience for self-regulation. This paper was accepted by Olav Sorenson, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.01306 .

Playing Divide-and-Choose Given Uncertain Preferences

We study the classic divide-and-choose method for equitably allocating divisible goods between two players who are rational, self-interested Bayesian agents. The players have additive values for the goods. The prior distributions of those values are common knowledge. We consider both the cases of independent values and values that are correlated across players (as occurs when there is a common-value component). We describe the structure of optimal divisions in the divide-and-choose game and identify several cases where it is possible to efficiently compute equilibria. An approximation algorithm is presented for the case when the distribution over the chooser’s value for each good follows a normal distribution, along with a randomized approximation algorithm for the case of uniform distributions over intervals. A mixture of analytic results and computational simulations illuminates several striking differences between optimal strategies in the cases of known versus unknown preferences. Most notably, given unknown preferences, the divider has a compelling “diversification” incentive in creating the chooser’s two options. This incentive leads to multiple goods being divided at equilibrium, quite contrary to the divider’s optimal strategy when preferences are known. In many contexts, such as buy-and-sell provisions between partners, or in judging fairness, it is important to assess the relative expected utilities of the divider and chooser. Those utilities, we show, depend on the players’ levels of knowledge about each other’s values, the correlations between the players’ values, and the number of goods being divided. Under fairly mild assumptions, we show that the chooser is strictly better off for a small number of goods, whereas the divider is strictly better off for a large number of goods. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: This work was supported by the Mossavar-Rahmani Center for Business and Government, Harvard University, and the National Science Foundation [Grant DGE1745303]. Any opinions, findings, and conclusions or recommendations expressed in this material reflect the views of the authors and not necessarily those of the National Science Foundation or the Mossavar-Rahmani Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00350 .

Trends in Medicare Utilization and Reimbursement for Intertrochanteric Femur Fractures: A 21-Year Review.

Sliding hip screw (SHS) and intramedullary (IMN) constructs are commonly utilized treatments for intertrochanteric (IT) femur fractures. The aim of this study was to assess the economic and utilization trends in the management of IT fractures among the Medicare population over the last 21years. A review of the publicly available Medicare Part B National Summary Data File for years 2000-2021 was performed. Collected data included true physician reimbursement and utilization numbers for all CPT codes pertaining to fixation of IT fractures with either SHS or IMN. A total of 1,361,112 IMN implants and 739,032 SHS implants were billed to Medicare for intertrochanteric femur fractures during the studied timeline. In this 21-year span, utilization of IMN increased 695% (9648-76,667), while utilization of SHS decreased by 96% (94,223-4224). After adjusting for inflation, the average physician reimbursement for SHS decreased by 34%, while IMN decreased by 41%. Absolute physician reimbursement was found to be $943.36 for SHS and $999.88 for IMN constructs. Intramedullary implants are being increasingly utilized while sliding hip screw, and intramedullary construct reimbursement continues to decrease for intertrochanteric femur fracture fixation. These trends suggest that opting for a sliding hip screw may be more cost-effective when the fracture pattern allows for either construct.

Nonparametric model-assisted estimation from randomized response survey data

The randomized response technique offers an effective way for reducing potential bias resulting from nonresponse and untruthful responses when asking questions about sensitive behaviors or beliefs. The technique is also used for conducting statistical disclosure control of public use data files released by statistical agencies such as the U.S. Census Bureau. In both cases, the technique works by randomizing the actual survey responses using some known randomization model. In the case of asking sensitive survey questions, the randomization of responses is done by the survey respondents and only the randomized responses are collected, whereas in the case of disclosure control, the survey agency implements the randomization of responses after collecting the survey data and prior to releasing it for public use. This paper considers estimating the finite population mean from a survey where randomized responses are available for the study variable along with complete non-randomized auxiliary information. We define and study a class of nonparametric model-assisted estimators that make efficient use of the available auxiliary information and account for the complex survey design. The asymptotic properties of the proposed estimators are derived and a bootstrap variance estimator is presented. The finite sample performance of the proposed estimators is studied via extensive simulations accounting for a wide range of forms for the relationship between the study variable and auxiliary variable. The empirical results support the theoretical analyses and suggest that our proposed estimators are superior to existing estimators in most cases. Furthermore, the proposed methods are illustrated using real data from the 2015 U.S. consumer expenditure survey.

Analysis of stress distribution in implant-supported prostheses with custom abutments at different angles of 20 degrees: A finite element analysis

Aim: Implants may not always be placed parallel to each other. In such cases, implants can be made more parallel by adjusting the angles with custom abutments. Different cementation materials can be used for cement-retained abutments. The bonding and stress resistance values of materials used in the cementation of implant-supported prostheses vary. The aim of this study is to demonstrate the stress distribution values of restorations bonded with different cementation materials in finite element analysis. Methodology: In this study, a maxillary edentulous jawbone was designed using the SolidWorks program. Two implants were placed in the regions of teeth #14 and #16. Implant data was obtained from the Bilimplant company. A 20-degree angled custom abutment and implant-supported prosthesis were designed using the Exocad program. Standard Tesellation Language (STL) files were transferred to SolidWorks to obtain Standard for the Exchange of Product Model Data (STP) files. The created prosthesis design was then transferred to the ABAQUS program for finite element analysis. Subsequently, using 5 different cementation materials (glass ionomer cement, dual-cure resin cement - ivoclar, dual-cure resin cement - relyx, polycarboxylate cement, self-cure resin cement), stress distribution values of implant-supported prosthesis components were recorded as von Mises stress values (VMS) Results: İndicate higher stress values with glass ionomer cement and lower with polycarboxylate cement. Stress distribution increases posteriorly but doesn't significantly differ based on cement type. Lowest stress on implant screws is with glass ionomer, but highest varies. Cortical bone stress is lowest with glass ionomer cement. Conclusion: There was no significant difference in stress values among different cement types. Stress increases in implants, abutments, and cements due to forces applied posteriorly, while anteriorly, retention screw stress increases. The choice of cementation material does not significantly impact stress in implant components or surrounding tissues.

Asian American Representation in Medicine by Career Stage and Residency Specialty.

Asian American individuals are not underrepresented in medicine; however, aggregation in prior workforce analyses may mask underlying disparities. To assess representation by Asian race and disaggregated subgroups in the US allopathic medical school workforce. This cross-sectional study used Association of American Medical Colleges (AAMC) special reports, generated using the AAMC Applicant-Matriculant Data File, Student Records System, Graduate Medical Education Track Survey, and faculty roster. Participants included medical school applicants, matriculants, graduates, residents, and faculty enrolled or employed at US allopathic medical schools between 2013 and 2021. Data were analyzed between March and May 2024. Asian race or ethnic subgroup as per AAMC and US Census Bureau Office of Management and Budget criteria, including Bangladeshi American, Cambodian American, Chinese American, Filipino American, Indian American, Indonesian American, Japanese American, Korean American, Pakistani American, Taiwanese American, and Vietnamese American. Representation quotients (RQ) were used to indicate representation that was equivalent (RQ of 1), higher (RQ greater than 1), or lower (RQ less than 1) than expected representation based on US population estimates. One-way analysis of variance and linear regression models assessed mean RQ differences by career stage and over time, with Bonferroni correction for multiple comparisons. In this study, Asian American individuals accounted for 94 934 of 385 775 applicants (23%), 39 849 of 158 468 matriculants (24%), 37 579 of 152 453 graduates (24%), 229 899 of 1 035 512 residents (22%), and 297 413 of 1 351 187 faculty members (26%). The mean (SD) RQ was significantly greater among Asian American residents (3.44 [0.15]) and faculty (3.54 [0.03]) compared with Asian applicants (3.3 [0.04]), matriculants (3.37 [0.03]), or graduates (3.31 [0.06]). Upon disaggregation, RQ was significantly lower among residents and faculty in 10 of 12 subgroups. Although subgroups, such as Taiwanese American, Indian American, and Chinese American, had RQs greater than 1 (eg, Chinese American graduates: mean [SD], RQ, 3.90 [0.21]), the RQs were less than 1 for Laotian, Cambodian, and Filipino American subgroups (eg, Filipino American graduates: mean [SD], RQ, 0.93 [0.06]) at almost every career stage. No significant RQ changes were observed over time for Laotian American and Cambodian American trainees, with a resident RQ of 0 in 8 of 25 and 4 of 25 specialties, respectively. Faculty RQ increased in 9 of 12 subgroups, but Cambodian American, Filipino American, Indonesian American, Laotian American, and Vietnamese American faculty (eg, Vietnamese American faculty: mean [SD], RQ, 0.59 [0.08]) had RQs less than 1. In this cross-sectional study of Asian representation in US allopathic medical schools, Laotian American, Cambodian American, and Filipino American individuals were underrepresented at each stage of the physician workforce pathway. Efforts to promote diversity in medicine should account for these disparities to avoid perpetuating inequities.

The Importance of Unemployment Risk for Individual Savings

In this paper, we use a novel natural experiment and Norwegian tax data to quantify the causal impact of unemployment risk on individual savings. By comparing individuals who live in the same area but face different increases in risk, we show that a one-percentage-point increase in unemployment rates increases safe assets by 1.3%. Reassuringly, this effect is driven by low-tenured workers, who face the highest increase in risk. Savings in risky financial assets remain unaffected, implying a decrease in the overall risky share of individual portfolios. We use two independent approaches, relying either on cross-sectional variation or time series variation, to quantify the importance of job loss risk in accounting for higher savings during recessions. Our results suggest that unemployment risk can explain 60%–80% of the recession-induced increase in safe assets. This paper was accepted by Camelia Kuhnen, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00987 .

Cost‐Effective DNA Storage System with DNA Movable Type

AbstractIn the face of exponential data growth, DNA‐based storage offers a promising solution for preserving big data. However, most existing DNA storage methods, akin to traditional block printing, require costly chemical synthesis for each individual data file, adopting a sequential, one‐time‐use synthesis approach. To overcome these limitations, a novel, cost‐effective “DNA‐movable‐type storage” system, inspired by movable type printing, is introduced. This system utilizes prefabricated DNA movable types‐short, double‐stranded DNA oligonucleotides encoding specific payload, address, and checksum data. These DNA‐MTs are enzymatically ligated/assembled into cohesive sequences, termed “DNA movable type blocks,” streamlining the assembly process with the automated BISHENG‐1 DNA‐MT inkjet printer. Using BISHENG‐1, 43.7 KB of data files are successfully printed, assembled, stored, and accurately retrieved in diverse formats (text, image, audio, and video) in vitro and in vivo, using only 350 DNA‐MTs. Notably, each DNA‐MT, synthesized once (2 OD), can be used up to 10000 times, reducing costs to $122/MB—outperforming existing DNA storage methods. This innovation circumvents the need to synthesize entire DNA sequences encoding files from scratch, offering significant cost and efficiency advantages. Furthermore, it has considerable untapped potential to advance a robust DNA storage system, better meeting the extensive data storage demands of the big‐data era.

Customer Voice on Two-Sided Platforms: The Effect of Surge Pricing on Customer Complaints

Two-sided platforms (e.g., ride-sharing, freelance labor, and lodging) match customers and service providers. Customers can voice their satisfaction level through review/rating systems, which the platforms use to monitor and screen service providers. However, customers may misdirect complaints about platform-level features toward individual service providers. We examine the extent and consequences of this issue in the context of a large ride-sharing platform. We focus on surge pricing as the platform-level feature. Our findings are as follows. First, for an average trip, surge pricing makes it 1.13 times more likely for the driver to receive a complaint (either a low rating or a formal complaint). Second, complaints negatively impact a driver’s future income. Surge-induced complaints offset about 23% of a driver’s immediate gain from the surged fare. Third, after the platform adopted a policy to cap the magnitude of surge prices, the effect of surge on customer complaints reduced. The policy also led to more usage of the platform. The results suggest a close interaction between the pricing and review systems on ride-sharing platforms. More broadly, they point to the importance for online two-sided platforms to identify and address customer complaints about platform-level features that may have been misdirected to service providers. This paper was accepted by Eric Anderson, marketing. Funding: Y. Zhu acknowledges financial support from 3M [Nontenured Faculty Grant]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.4992 .

Sabotaging material extrusion-based 3D printed parts through low-magnitude kinetic manipulation attacks

The increasing ubiquity of material-extrusion-based additive manufacturing is motivating cybersecurity researchers to explore its offensive and defensive landscape. Being a physical system, 3D printers have non-zero tolerance specifications for precision and trueness parameters. While a single-bit change in a digital data file is sufficient to fail its integrity and is easily detected through methods such as hashing, the printing process (and subsequently the printed object) remains compliant within the tolerance zone. This study systematically analyzes the material extrusion process and identifies four attack opportunities where low-magnitude kinetic cyberattacks exploit the physical process compliance zone to sabotage the printed part’s mechanical properties. The attacks are demonstrated on ASTM-compliant tensile and flexure bars through a man-in-the-middle attack scenario by hijacking the network layer communication between the 3D printer and the printer control machine. The physically stealthy attacks did not produce any evident deformation in the parts’ dimensions and mass, while the destructive tests confirm that they are still effective in modifying the tensile and bending strength by up to 25%. The effectiveness of the attacks in bypassing the defenses is assessed by implementing one of the leading detection schemes described in the current literature. The attacks were either not detected at all or detected with a significantly high false negative rate at various attack magnitudes.

A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running

Quantitative biomechanical gait analysis is an important clinical and research tool for injury and disease diagnosis and treatment. However, one major criticism is that gait analysis laboratories largely operate in isolation and there is a lack of benchmark datasets, which can be used to advance research and statistical methodologies. To address this, we present an open biomechanics dataset of n = 1798 healthy and injured, young and older adults during treadmill walking and/or running at a range of gait speeds. The full dataset is available on Figshare+ and data files are contained within a series of zipped folders with folder names representing the subject ID. Each subject ID folder contains walking and/or running data containing raw marker trajectory data along with metadata for each participant. Five tutorials are also provided, demonstrating aspects such as loading data files, sample analyses of discrete variables, and calculating joint angles from code along with covering more complex topics such as principal component analysis for dimensionality reduction, statistical parametric mapping, and conducting unsupervised clustering.

The Fairness of Credit Scoring Models

In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g., gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training data set or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance tradeoff. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Autorité de Contrôle Prudentiel et de Résolution (ACPR) Chair in Regulation and Systemic Risk, the Fintech Chair at Dauphine-PSL University, and the French National Research Agency (ANR) [MLEforRisk ANR-21-CE26-0007, Ecodec ANR-11-LABX-0047, and F-STAR ANR-17-CE26-0007-01]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03888 .