Relative Bias Research Articles

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint.

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Characterizing the spatial structure and aliasing effect of ocean tide loading on InSAR measurements

Ocean tide loading (OTL) displacements, shown as long-wavelength errors in Interferometric Synthetic Aperture Radar (InSAR), must be considered in large-scale applications. Despite efforts to explore the impacts of OTL on InSAR, most studies use individual interferograms and simple metrics, which fail to characterize the spatial structure of OTL. Moreover, the OTL contribution to InSAR time series remains relatively unexplored. The aliasing effect and related biases due to OTL, which are common to space-geodetic time series, are primarily theoretical with few practical observations for InSAR. This study comprehensively explores the statistical properties of OTL and their impacts on InSAR measurements, using the Southwest United Kingdom and Northwest France as study areas. Spatially, OTL artifacts on interferograms exhibit an escalating magnitude along the principal direction that aligns with the coastline's orientation. Temporally, the aliasing effect originating from OTL introduces periodic signals with prominent 15/64-day cycles into the Sentinel-1 InSAR time series, causing high velocity biases (up to ∼1 cm/yr) and uncertainties (up to ∼5 mm/yr) for short time spans. Applying OTL correction mitigates the noise level in the displacement time series, leading to a 16% improvement in accuracy, as validated against the Global Navigation Satellite System (GNSS). The study proposes the “overlapping effect” concept, which links InSAR tropospheric delay errors and OTL effects. It underscores the importance of accurate error assessment and removal. Neglecting this interaction may result in a 13% underestimation of the tropospheric error correction efficacy.

The relative bias of psychometric testing

The relative bias of psychometric testing

Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling

Fish recruitment prediction is one of the most challenging topics in fisheries science. The recruitment of arabesque greenling in northern Hokkaido, Japan, which has been annually assessed for population size, greatly fluctuates. Whether the cause of fluctuation is environment or overfishing is controversial. We use a machine learning method for predicting the recruitment of arabesque greenling. Biological, fisheries-related, and environmental factors were included in the predictive models as feature variables. A gradient boosting model (GBM) showed better predictive performance compared with a simple hockey-stick stock-recruitment curve (HS), linear regression model (LRM), generalized additive model (GAM), and a random forest model (RFM) in terms of relative bias and relative root mean square error for recruitment prediction in the last 5 years. The most influential feature for GBM was spawning stock biomass in the last year, followed by the fishing rate for older fish and recruitment at the last year. The sea temperatures (STs) at the depth of 0, 50, 100, and 200 m were unimportant predictors in GBM. The difference in important predictors among models suggests the importance of nonlinearity and incorporating multiple variables simultaneously. This study highlights the potential usefulness of GBM for fish recruitment forecast and thereby sustainable fisheries management.

Relative age and biological maturity-related selection biases in male youth soccer across different competitive levels within a national association

ABSTRACT Objectives and Methods This study aimed to examine whether biological maturation and relative age selection biases existed and varied by level of competition (regional, national, and international) in Under-15 soccer players (n = 951) within the Swedish Football Association’s male player pathway. A secondary aim was to examine the relationship between relative age and body height, body weight, predicted adult height, percentage of predicted adult height (PAH%), maturity Z-score, and biological age to chronological age offset. Results The results showed a significant bias (p < 0.001), ranging from trivial-to-small in favour of relatively older players, with the most notable increase between the regional and national levels. There were also significant moderate-to-large biases in favour of early maturing players (p < 0.001), increasing in magnitude with levels of competition. PAH% (p < 0.001) and body weight (p = 0.014) showed the strongest differences across selection levels, where the bias compared to regional level was 0.23 standard deviations (SD) for PAH% at national level and 0.41 SD at international level, while body weight appeared to be particularly related to international team selection (0.36 SD in bias). Relative age showed a moderate positive correlation with PAH% (r = 0.38), but only trivial correlations with all the other biological and physical variables examined (r=-0.05–0.11). Conclusions The lack of association between relative age and the estimates of biological maturity timing and the additional physical characteristics suggests that relative age and biological maturity are distinct constructs. We encourage critical examination of how associations select young players for national talent programmes; current practices significantly diminish the chances of selection for those who are late maturing and relatively younger.

Analytical QbD for the optimization of a multimode HPLC method for the investigation of hydrochlorothiazide, diltiazem and propranolol release from 3D printed formulation

Since 3D printing technology is an emerging field in pharmaceutical technology, the present study aimed at the development of a mixed-mode liquid chromatographic method for the separation and determination of hydrochlorothiazide, diltiazem, and propranolol to investigate their in-vitro release performance from 3D printed tablets. Due to the unique properties of the mixed-mode stationary phase, the three drugs were separated in less than 8 min under isocratic elution. Method development was accomplished following the Analytical Quality by Design principles and was evaluated using risk assessment and multivariate analysis. The influences of critical method parameters on critical method attributes (were screened using a 2-level fractional factorial design and subsequently optimized through a central composite design. The method operable design region was approved by the establishment of a robust zone using Monte Carlo simulation and capability analysis. The validation of the HPLC method was performed based on the total error concept. The relative bias was varied between ─ 11.6 % and 10.5 % and the RSD values for repeatability and intermediate precision were below 4.4 % in all cases. The limits of detection (LOD) ranged between 0.17 – 0.90 μg/mL and were adequate for the specific application. The developed method was successfully applied to the analysis of the studied drugs in in-vitro drug release samples obtained from 3D-printed tablets combining the above-mentioned active pharmaceutical ingredients (APIs).

The Effects of Missing Data Handling Methods on Reliability Coefficients: A Monte Carlo Simulation Study

This study holds significant implications as it examines the impact of different missing data handling methods on the internal consistency coefficients. With Monte Carlo simulations, we manipulated number of items, true reliability, sample size, missing data ratio and mechanisms, to compare relative bias of reliability coefficients. The reliability coefficients under scrutiny in this study encompass Cronbach's Alpha, Heise &amp; Bohrnsted's Omega, Hancock &amp; Mueller's H, Gölbaşı-Şimşek &amp; Noyan's Theta G, Armor's Theta, and Gilmer-Feldt coefficients. Our arsenal of techniques includes single imputation methods like zero, mean, median, and regression imputation, as well as multiple imputation approaches like expectation maximization and random forest. We also employ the classic deletion method known as listwise deletion. The findings suggest that, for missing completely at random (MCAR) or missing at random (MAR) data, single imputation approaches (excluding zero imputation) may still be preferable to expectation maximization and random forest imputation, thereby underscoring the importance of our research.

Evaluation for serum glucose standardization in clinical laboratories of Southern China by consecutive 6 years proficiency testing based on JCTLM-recommended reference methods

Abstract Objectives The accuracy of blood glucose measurement in clinical laboratories is vital for diabetes diagnosis. Trueness Verification Plan was carried out and analyzed for evaluating the standardization of serum glucose among clinical laboratories. Methods Trueness verification samples were distributed to clinical laboratories for three days measurement, and their target values were assigned by two certified reference laboratories. The relative bias, coefficient of variation (CV), and total error (TE) for each clinical laboratory were calculated and analyzed. Moreover, the Six Sigma metrics and Quality Goal Index were utilized to reflect the measurement quality of the clinical laboratories. Results The pass rates evaluated by bias, CV, and TE ranged from 45.2 % to 64.8 %, 96.8 %–98.9 %, and 83.9 %–97.1 % over the six years. The matched systems used in clinical laboratories demonstrated better accuracy than the un-matched systems. The pass rate by bias of hexokinase method is 53.1 %–78.6 %, while the glucose oxidase method is 29.2 %–52.2 %. Overall, 74.2 %–85.7 % of clinical laboratories achieved an acceptable level (both σ&gt;3), and 35.2 %–61.4 % of laboratories reached a “world-class” level (both σ&gt;6). Conclusions The quality for serum glucose measurement has been greatly improved. However, standardization among clinical systems still needs to be further promoted.

A method for updating variable radius plot surveys

Variable radius plot surveys can require updated estimates when the quality of previous estimates have declined over time. We developed a statistical estimator for this purpose that is the product of two estimators, one estimator is for the updated volume-to-basal-area ratio and the other estimator is for the updated basal area. Our estimator relies only on the surveyor identifying previously measured trees and does not require the identification of ingrowth, which simplifies its application. We also propose a variance estimator that relies on the bootstrap method. We investigate the point and variance estimators under repeated sampling of stem-mapped populations that include five measurements over a period of approximately 20 years. Based on 144 sampling scenarios, our estimator demonstrated relative biases ranging between -0.3% and 0.8%. The bootstrap variance estimator tended to be deflated, with relative absolute biases ranging between -8% to 4%. When revisiting one third of the initially installed plots, our estimator obtained relative root mean squared errors within 1.6% of an independent resurvey effort that does not use the prior survey. This implies that our estimator can substantially reduce field work while producing surveys of approximately the same quality when updating prior surveys.

A Complementary Multitechnique Approach to Assess the Bias in Molecular Weight Determination of Lignin by Derivatization-Free Gel Permeation Chromatography.

The growing interest in lignin valorization in the past decades calls for analytical techniques for lignin characterization, ranging from wet chemistry techniques to highly sophisticated chromatographic and spectroscopic methods. One of the key parameters to consider is the molecular weight profile of lignin, which is routinely determined by size-exclusion chromatography; however, this is by no means straightforward and is prone to being hampered by considerable errors. Our study expands the fundamental understanding of the bias-inducing mechanisms in gel permeation chromatography (GPC), the magnitude of error originating from using polystyrene standards for mass calibration, and an evaluation of the effects of the solvent and type of lignin on the observed bias. The developed partial least-squares (PLS) regression model for lignin-related monomers revealed that lignin is prone to association mainly via hydrogen bonding. This hypothesis was supported by functional group-based analysis of the bias as well as pulse field gradient (pfg) diffusion NMR spectroscopy of model compounds in THF-d8. Furthermore, although the lack of standards hindered drawing conclusions based on functionalities, direct infusion electrospray ionization mass spectrometry indicated that the relative bias decreases considerably for higher molecular weight species. The results from pfg-diffusion NMR spectroscopy on whole lignin samples were comparable when the same solvents were used in both experiments; in addition, the comparison between results obtained by pfg-diffusion NMR in different solvents gives some additional insights into the aggregation.

Investigation on the influence of the skin tone on hyperspectral imaging for free flap surgery

Hyperspectral imaging (HSI) is a new emerging modality useful for the noncontact assessment of free flap perfusion. This measurement technique relies on the optical properties within the tissue. Since the optical properties of hemoglobin (Hb) and melanin overlap, the results of the perfusion assessment and other tissue-specific parameters are likely to be distorted by the melanin, especially at higher melanin concentrations. Many spectroscopic devices have been shown to struggle with a melanin related bias, which results in a clinical need to improve non-invasive perfusion assessment, especially for a more pigmented population. This study investigated the influence of skin tones on tissue indices measurements using HSI. In addition, other factors that might affect HSI, such as age, body mass index (BMI), sex or smoking habits, were also considered. Therefore, a prospective feasibility study was conducted, including 101 volunteers from whom tissue indices measurements were performed on 16 different body sites. Skin tone classification was performed using the Fitzpatrick skin type classification questionnaire, and the individual typology angle (ITA) acquired from the RGB images was calculated simultaneously with the measurements. Tissue indices provided by the used HSI-device were correlated to the possible influencing factors. The results show that a dark skin tone and, therefore, higher levels of pigmentation influence the HSI-derived tissue indices. In addition, possible physiological factors influencing the HSI-measurements were found. In conclusion, the HSI-based tissue indices can be used for perfusion assessment for people with lighter skin tone levels but show limitations in people with darker skin tones. Furthermore, it could be used for a more individual perfusion assessment if different physiological influencing factors are respected.

Validation of a Pseudovirus Neutralization Assay for Severe Acute Respiratory Syndrome Coronavirus 2: A High-Throughput Method for the Evaluation of Vaccine Immunogenicity.

The evaluation of coronavirus disease 2019 (COVID-19) vaccine immunogenicity remains essential as the severe acute respiratory syncytial virus 2 (SARS-CoV-2) pandemic continues to evolve and as additional variants emerge. Neutralizing antibodies are a known correlate of protection for SARS-CoV-2 vaccines. A pseudovirus neutralization (PNT) assay was developed and validated at Novavax Clinical Immunology Laboratories to allow for the detection of neutralizing antibodies in vaccine clinical trial sera. The PNT assay was precise, accurate, linear, and specific in measuring SARS-CoV-2 neutralization titers in human serum for ancestral strain and the Omicron subvariants BA.5 and XBB.1.5, with an overall geometric coefficient of variation of ≤43.4%, a percent relative bias within the expected range of -60% to 150%, and a linearity value of R2 > 0.98 for all three strains. This pseudovirus assay will be useful for the analysis of vaccine clinical trial samples to assess vaccine immunogenicity. Future work will focus on modifying the assay for emerging variants, including XBB.1.16, EG.5.1, BA.2.86, and any other variants that emerge in the ongoing pandemic.

Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine

California has a long history of wildfires that release substantial amounts of smoke, contributing to regional air pollution. This study employs advancements in Earth observation satellites, cloud computing, and the Google Earth Engine to analyze MODIS MCD19A2 Version 6 level 2, Sentinel-5P NRTI AER AI, and Sentinel-5P NRTI NO2, evaluating the impact of wildfires from 2010 to 2022 on air quality in California. Historical fire events are cross-validated using the MODIS 1-km MYD14A1 V6 dataset, and the MODIS MAIAC-derived Aerosol Optical Depth (AOD) values are validated against AERONET AOD measurements from ground-based sun photometers. To assess the analysis's uncertainty, metrics such as Mean Absolute Error, Relative Mean Bias, and Root Mean Square Error are applied. Additionally, linear regression is used to verify the correlation between satellite and ground measurements. Results show that the average monthly MODIS MAIAC AOD at 470 nm and 550 nm tends to overestimate AOD by 24% and 6%, respectively, compared to AERONET AOD values. The correlation coefficient and adjusted R-squared value range from 0.78 to 0.80 and from 0.61 to 0.65, respectively, for AOD values at 470 nm and 550 nm. During the fire season, correlations exhibit closer associations, with a coefficient above 0.78 and an adjusted R-squared value above 0.63 are observed for both wavelengths. Sentinel-5P data reveal a significant increase in NO2 concentration in the surrounding areas during the 2020 and 2021 wildfires. This study identifies historical spatiotemporal trends of air pollution attributable to wildfires in California from 2010 to 2022, informing decision-making in the development of effective prevention and mitigation programs.

Performance evaluation of multi‐precipitation products fusion based on a novel dynamic K‐nearest neighbour Bayesian ensemble calibration framework

AbstractThe fusion of multiple precipitation products can effectively improve precipitation accuracy. In order to reduce the uncertainty of traditional precipitation members and improve the applicability of Bayesian model averaging (BMA), this study proposes a dynamic ensemble calibration framework with seasonality and real‐time capability, namely dynamic K‐nearest neighbour BMA (DKBMA), for the integration calibration and comparison of ERA5 reanalysis products and three satellite precipitation products (CMORPH, 3B42RT and 3B42V7). The application results of the DKBMA in the Yujiang River basin, Southern China, during the period of 2011–2016 show that (1) the DKBMA can overcome the problem of ERA5 error interference on the seasonal scale, reduce the systematic bias of ensemble precipitation members at coastal sites and demonstrate strong robustness at different altitudes. (2) Compared with the traditional BMA, the DKBMA has significantly improved accuracy, especially in capturing extreme precipitation events. The correlation coefficient has increased from 0.793 (traditional BMA) to 0.841 (DKBMA), the root‐mean‐square error has decreased from 35.61 (traditional BMA) to 30.95 (DKBMA), and the absolute value of relative bias has decreased from 62.80% (traditional BMA) to 49.94% (DKBMA). The proposed DKBMA in this study can provide a new solution for the fusion of multi‐source precipitation products in data‐scarce regions.

Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning

Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to streamline it, full ultra-high-granularity detector simulation still proves to be difficult as it contains correlated and fine-grained information. To overcome these limitations, we propose Intra-Event Aware Generative Adversarial Network (IEA-GAN). IEA-GAN presents a Transformer-based Relational Reasoning Module that approximates an event in detector simulation, generating contextualized high-resolution full detector responses with a proper relational inductive bias. IEA-GAN also introduces a Self-Supervised intra-event aware loss and Uniformity loss, significantly enhancing sample fidelity and diversity. We demonstrate IEA-GAN’s application in generating sensor-dependent images for the ultra-high-granularity Pixel Vertex Detector (PXD), with more than 7.5 M information channels at the Belle II Experiment. Applications of this work span from Foundation Models for high-granularity detector simulation, such as at the HL-LHC (High Luminosity LHC), to simulation-based inference and fine-grained density estimation.

The Relative Age Effect presents an opportunity for Scottish football to be “better than FC Barcelona”

An overrepresentation of athletes born earlier in the year compared with those born later in the year is known as the relative age effect (RAE). A ten year analysis of professional players across ten of the top European leagues showed the existence of an RAE in the ten years from 2000 (29.3 vs 19.8%, p&lt;0.01) to 2010 (31.9 vs 18.4%, p&lt;0.01) with the RAE becoming more pronounced with time. A sample from the FC Barcelona Model (Doncaster et al., 2020. Front Sports Act Liv, 2, 91) showed a strong RAE within their male football academy in all youth groups becoming less prevalent with increasing age (Q1 Jan-Mar = 0.53 Q2 Apr-Jun = 0.27; Q3 Jul-Sep = 0.14; and Q4 Oct – Dec = 0.06 [χ_3^2= 53.6, p&lt;0.001]). Despite solutions being proposed in the literature, this indicates these solutions are not being utilised effectively within Europe’s top clubs such as FC Barcelona. Our previous work (Craig and Swinton, 2020, Eur J Sports Sci, 21, 1101-1110) identified strong relative age bias in players awarded professional contracts between 2006 and 2016 in a Scottish academy (Q1 = 0.50; Q2 = 0.26; Q3 = 0.20; and Q4 = 0.05 [χ_3^2= 40.9, p&lt;0.001]). Scottish clubs have limited resources in comparison with Barcelona meaning academy efficiency is paramount. The main study aim was therefore to assess any changes in the relative age effect over a ten-year period in an elite Scottish football academy within the academy player pool. A secondary aim was to establish if physical differences exist across each quarter to assess the impact of any potential RAE. Following ethical approval, a retrospective analysis of 512 academy players (from 2006 to 2016) from a Scottish Academy was assessed for anthropometric, physical performance and RAE impact. The range of players in each quarter was Q1 37.0-42.9% versus Q2 22.8-32.4%, Q3 11.9-26.0% and Q4 7.1-14.3%. Odds Ratio analysis indicate a greater chance of selection within the academy when assessing Q1 vs Q4 players quarter comparisons (ranging 3.2-5.2 time more likely to be part of the academy). There was no impact of time on the RAE over the ten years (p=0.3127). When controlling for age group multilevel modelling showed there were no significant differences across quarters in physical measures. Underrepresented Q4 players who transition through academy soccer can match physically their older peers. The finding that there is no improvement in the RAE with time underpins an efficiency that can be integrated. Using established interventions and corrective procedures can ensure no potential is being removed unnecessarily from the development pathway on the basis of RAE, addressing challenges in financially restricted environments such as Scottish football that resource rich environments such as FC Barcelona have not yet overcome.

Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data

BackgroundTwo propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes.MethodsWe used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE.ResultsIn the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0–0.02 vs. FS: 0.22–3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07–0.29), relative bias (homogeneity: 4.04–56.20 vs. 20–61.63; heterogeneity: 7.85–57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332–1.308 vs. 0.385–1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0–80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases.ConclusionsThese findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

A Two‐stage Bayesian Small Area Estimation Approach for Proportions

SummaryWith the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two‐stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

Development of low-cost air particulate IoT measurement using HM-3301 sensor

Abstract Air quality measurement is essentially important to create a safe and healthier environment. In this study, the accuracy and precision of the Grove HM-3301 sensor were evaluated and compared with the data taken from a standard air quality monitoring meter. Using the low-cost air particulate measuring sensor, the measuring results of the PM1, PM2.5, and PM10 concentration was fairly accurate, with a relative bias smaller than 30 %. The accuracy of the HM-3301 sensor can be improved using single-point calibration, with relative bias reduced to the limit of 13 % as tested in the particle mass concentration limit within 500 μg/m3. With improved accuracy, the air particulate IoT system using the HM-3301 sensor can be employed in practical applications to assess air quality. With a development cost of 90 USD, the PM measurement IoT system is suitable to be applied in monitoring outdoor and indoor air quality.