Count Data Research Articles

Impact of stage-specific limb function exercises guided by a self-management education model on arteriovenous fistula maturation status

BACKGROUND The exercise of limb function is the most economical and safe method to promote the maturation of arteriovenous fistula (AVF). However, due to the lack of a unified exercise standard in China, many patients have insufficient awareness of the importance of AVF, leading to poor effectiveness of limb function exercise. The self-management education model can effectively promote patients to take proactive health-related actions. This study focuses on the characteristics of patients during the peri-AVF period and conducts a phased limb function exercise under the guidance of the self-management education model to observe changes in factors such as the maturity of AVF. AIM To assess the impact of stage-specific limb function exercises, directed by a self-management education model, on the maturation status of AVFs. METHODS This study is a randomized controlled trial involving 74 patients with forearm AVFs from the Nephrology Department of a tertiary hospital in Sichuan Province, China. Patients were randomly divided into an observation group and a control group using a random number table method. The observation group underwent tailored stage-specific limb function exercises, informed by a self-management education model which took into account the unique features of AVF at various stages, in conjunction with routine care. Conversely, the control group was given standard limb function exercises along with routine care. The assessment involves the maturity of AVFs post-intervention, postoperative complications, and the self-management level of the fistula in both groups patients. Analyses were conducted using SPSS version 23.0. Count data were represented by frequency and percentage and subjected to chi-square test comparisons. Measurement data adhering to a normal distribution were presented as mean ± SD. The independent samples t -test was utilized for inter-group comparisons, while the paired t-test was used for intra-group comparisons. For measurement data not fitting a normal distribution, the median and interquartile range were presented and analyzed using the Wilcoxon rank sum test. RESULTS At the 8-wk postoperative mark, the observation group demonstrated significantly higher scores in AVF symptom recognition, symptom prevention, and self-management compared to the control group (P < 0.05). However, the variance in symptom management scores between the observation and control groups lacked statistical significance (P > 0.05). At 4 wk after the operation, the observation group displayed a superior vessel diameter and depth from the skin of the drainage vessels in comparison to the control group (P < 0.05). While the observation group did manifest elevated blood flow rates in the drainage vessels relative to the control group, this distinction was not statistically significant (P > 0.05). By the 8-wk postoperative interval, the observation group outperformed the control group with notable enhancements in blood flow rates, vessel diameter, and depth from the skin of drainage vessels (P < 0.01). Seven days following the procedure, the observation group manifested significantly diminished limb swelling and an overall reduced complication rate in contrast to the control group (P < 0.05). The evaluation of infection, thrombosis, embolism, arterial aneurysm stenosis, and incision bleeding showed no notable differences between the two groups (P > 0.05). By the 4-wk postoperative juncture, complications between the observation and control groups were statistically indistinguishable (P > 0.05). CONCLUSION Stage-specific limb function exercises, under the guidance of a self-management education model, amplify the capacity of AVF patients to discern and prevent symptoms. Additionally, they expedite AVF maturation and mitigate postoperative limb edema, underscoring their efficacy as a valuable method for the care and upkeep of AVF in hemodialysis patients.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

A modified co-production framework for improved cross-border collaboration in sustainable forest management and conservation of forest bird populations

The border between Canada and the United States poses jurisdictional challenges when it comes to consistently implementing science-based conservation of forests and their biological communities. Through a partnership with the Sustainable Forestry Initiative, Boreal Avian Modelling Project, and American Bird Conservancy, we developed a co-production framework to conduct research that will inform forest management practices for bird conservation in the cross-border region of Bird Conservation Region 12. Our framework first responds to the needs of resource managers and other perceived stakeholders, while investing in relationship-building for long term trust as a foundation for future partnerships with Indigenous rights holders and landowners. Our central question was: How can sustainably managed forests create and/or maintain high quality breeding habitat to support forest bird populations that are resilient to climate change? Engaging with experts in Canada and the United States, we found that the main driver for addressing our central question was our limitation in connecting bird population responses to specific forest management practices. We describe how experts are contributing avian count and forest inventory data that researchers will use to produce a requested decision-support tool. We continue to engage with land managers to link forest resource inventory data to specific forest management practices and refine the components of products by including more diverse perspectives.

A new poisson-exponential-gamma distribution for modelling count data with applications

A new poisson-exponential-gamma distribution for modelling count data with applications

Hidden Variable Models in Text Classification and Sentiment Analysis

In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL), leading to GDMPCA and BLMPCA models, respectively. Besides using these priors as they generalize the Dir, importantly, we also implement a deterministic method that uses variational Bayesian inference for the fast convergence of the proposed algorithms. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, providing a computationally efficient method for inference. These two variational models offer higher flexibility while assigning each observation to a distinct cluster. We create several multitopic models and evaluate their strengths and weaknesses using real-world applications such as text classification and sentiment analysis.

A double generally weighted moving average control chart for monitoring zero-inflated Poisson processes

. The zero-inflated Poisson (ZIP) distribution is used to model count data with an excessive number of zeros. In this article, we present a double generally weighted moving average control chart for monitoring ZIP processes (regarded as ZIP-DGWMA chart). The run-length distribution of the proposed chart is evaluated by performing Monte Carlo simulations. A comparison study with the ZIP-GWMA chart indicates that the ZIP-DGWMA chart is more effective for shifts in the zero-inflated parameter p and for small shifts in both of the zero-inflated and Poisson parameters. The proposed chart is also applied on an example to demonstrate its application.

Improved estimators in bell regression model with application

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators.

Using multi‐scale spatial models of dendritic ecosystems to infer abundance of a stream salmonid

Abstract Understanding patterns of species abundance is essential for planning landscape‐level conservation. The complex hierarchies of dendritic ecosystems result in different levels of heterogeneity at distinct geographic scales. Species responses to dynamic environmental drivers may also vary spatially depending on their interactions with landscape features. Monitoring abundance by explicitly quantifying their spatial and temporal variation is important for strategic management. We analysed brook trout (Salvelinus fontinalis) count data collected from 173 sites in western North Carolina between 1989 and 2015. We developed a Bayesian hierarchical model that used single‐ and multi‐pass electro‐fishing data and characterized their respective capture probabilities. We quantified spatial variation using a multi‐scale process model representative of the nested stream habitats, and we investigated differences in population temporal trends and responses to seasonal weather patterns by space and life stage. Trout abundance was lower on the Atlantic slope of the Eastern Continental Divide than in the interior, on average, and the Atlantic slope juveniles were more adversely affected by high winter flows. However, Atlantic slope populations of both lifestages demonstrated positive temporal trends, whereas Interior juveniles demonstrated a negative trend. We found higher spatial variation than temporal variation in abundance when conditioned on the covariates, where the primary source of spatial heterogeneity was revealed at the segment level, compared to watershed or network levels. Our multi‐scale spatial model outperformed simpler models in abundance estimation and out‐of‐sample prediction. The inferred per‐pass capture probabilities indicated that single‐pass surveys were as efficient as multi‐pass surveys. Synthesis and applications. Our study suggested conservation priority should involve multiple criteria, including present‐day abundance, temporal trend and sensitivity to environmental drivers. Based on the inferred scale‐specific variations in trout abundance, we recommend that future surveys strategically combine single‐pass surveys with multi‐pass surveys to optimize abundance estimation. Our approach is widely applicable to other species and ecosystems occupying dendritic habitats.

Shred Central: Estimating the user benefits associated with large public skateparks

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Skateparks, often called 'wheel parks,' are becoming increasingly common within communities worldwide. Despite this growth in parks, estimates show that the development of parks needs to catch up to users. Using a count data travel cost model, we estimate the adult user benefits associated with the Lauridsen Skatepark in Des Moines, Iowa – the largest in the United States. We estimate adult user benefits to be $61 per user per day and roughly $488,000 annually. This work contributes to the literature by being the first study to use econometric techniques to estimate skatepark user benefits. Second, we develop a simple and easy-to-apply method to assist municipalities in determining community skatepark needs. Third, our results support the literature showing that skateboarders are increasingly diverse.</span></p>

2D signal estimation for sparse distributed target photon counting data

In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.

Traffic crash prediction model in Kano State, Nigeria: a multivariate LSTM approach

Accurate traffic crash prediction is crucial for implementing effective road safety measures. This study compares the performance of Long Short-Term Memory (LSTM) and Multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano State, Nigeria. Human and vehicle factors, including speed violation, tire burst, brake failure, sign light violation, and phone use while driving, are incorporated as covariates in the MLSTM model. An ARIMAX model is employed to investigate the effects of the covariates. The MLSTM model outperforms both the basic LSTM model and individual covariate models, emphasizing the synergistic effect of considering a broad range of factors. The ARIMAX model results reveal that speed violation is significantly positively correlated with total crashes, while other covariates show positive correlations but do not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, which include increased enforcement of traffic rules, targeted driver education and campaigns, and improvements to road infrastructure.

Control chart for detecting the scale parameter of the zero‐inflated Poisson model

AbstractWhen monitoring risk in public health, count data commonly exhibit an excessive number of zero, and the zero‐inflated Poisson (ZIP) model is often used to fit this type of data. Most previous methods for monitoring of the ZIP model have focused on the changes in the location parameter and the existence of the scale parameter and usually assumed that the scale parameter is zero in the H0 stage. However, in an objective environment, data often have certain fluctuations, meaning that the scale parameter always exists. Therefore, it is more meaningful to monitor the changes in the scale parameter on top of the predefined baseline than to monitor its existence. In this study, we derive a score test statistic based on the generalized Henderson's joint likelihood function, construct a risk‐adjusted exponentially weighted moving average (EWMA) control chart to monitor the variability of the random effects variance component in the ZIP mixed‐effects model. And the convergence property of the score test statistic is proved through derivation, which shows that the new method has theoretical reliability. The simulation results Indicate that when the scale parameter has different predefined baselines and different variation amplitudes, the proposed method is more effective than the existing RA‐ZIP and PR‐ZIP control charts. In addition, the proposed method is applied to real data from a Hong Kong hospital for online influenza surveillance to demonstrate its practicability.

279 Effect of multiple feed additives on sow inflammatory status and reproductive performance during lactation

Abstract Low grade inflammation and insulin resistance were associated with increased gut permeability in lactating sows during the farrowing transition period. The objective of the present study is to investigate the effect of 4 feed additives including monolaurin, essential oil (Cinergy FIT 3S), phytogenic prototype 1 (PT1) and 2 (PT2) on sow inflammatory status and reproductive performance. It was hypothesized that the active components in these additives will reduce sow inflammation during lactation and lead to better sow reproductive performance. A randomized block design with 5 treatments was used in the study. A total of 550 gestating sows were enrolled with entry body weight of 240.1 ± 14.6 kg and allotted to one of the following five diets: Control; monolaurin; Cinergy; PT1 and PT2. Corn-soy based lactation diet was formulated with CP of 18%. All diets were formulated to be at similar net energy and standardized ileal digestible Lysine level. Experimental diets were fed beginning approximately 4 d before farrowing and throughout the entire lactation phase (21 d). Blood was collected from 16 sows per treatment before farrowing and within 5 d post farrowing to measure inflammatory cytokine concentrations. Performance data were analyzed using a general linear model in R (version 4.0.0). Count data were analyzed using a generalized linear model with a binomial distribution. Dietary treatments did not impact average daily feed intake. Dietary treatment did not affect probability of still born or number of weaned pigs, but diet supplemented with PT1 resulted in greater (P < 0.05) probability of born alive compared with the control (Table 1). There was a tendency (P < 0.10) in treatment effect on pre-weaning mortality, with sows received PT2 supplemented diet having lowest value numerically. The concentration of cytokines IL-1α (P < 0.05), IL-1β (P < 0.01), IL-4 (P < 0.01), IL-6 (P < 0.10) and IL-10 (P < 0.10) were reduced between pre and post farrowing to a greater extent in sows fed Cinergy supplemented diet compared with the control. In conclusion, Cinergy worked best in reducing multiple inflammatory cytokine concentrations post-farrowing. Sows received PT1 supplemented diet had greater born alive and those received PT2 supplemented diet had numerically least pre-weaning mortality.

PSV-14 Determination of optimal digestible phosphorus level in lactating sows

Abstract Optimal dietary phosphorus (P) is important for sows, not only to maintain P homeostasis and prevent premature culling due to lameness and locomotion, but also to support growth of their offspring. The objective of this experiment was to determine the optimal level of digestible P in lactating sows for the best reproductive performance and piglet growth. A total of 550 gestating sows were enrolled using a randomized block design with sow parity and group as blocks. Sows with an entry body weight of 247.5 ± 29.1 kg were allotted to one of the five levels of digestible P (similar as standard total tract digestibility of P with consideration of phytase released P): 0.2%, 0.3%, 0.4%, 0.5% and 0.6%. A corn-soy-wheat middling based lactation diet was formulated with phytase inclusion (550 FTU/kg) and a fixed ratio between calcium (Ca) and digestible P of 2.5. All diets were formulated to have the same nutrient levels except 69 kcal net energy difference between 0.6% and 0.2% digestible P treatments due to slight difference in the ingredient composition. Experimental diets were fed approximately 4 d before farrowing and throughout the entire lactation (21 d). Feces were collected from 20 sows per treatment around 14 d of lactation to measure apparent total tract digestibility (ATTD) of Ca and P. Using R (version 4.0.0), performance and digestibility data were analyzed using a general linear model and a generalized linear model specifying a binomial distribution was deployed for count data. Dietary treatments did not impact sow average daily feed intake or piglet birth and weaning weights (Table 1). However, dietary treatments affected piglet livability (P < 0.05) and pre-weaning morality (P < 0.01), with 0.5% digestible P having the greatest livability and 0.4% digestible P having the least pre-weaning mortality. There was a positive linear dose effect of digestible P on probability of weaned pigs (P < 0.05). Dietary digestible P affected ATTD Ca (88.8 ± 3.1%, P < 0.01) and ATTD P (64.4 ± 6.5%, P < 0.01) in sows on d14 of lactation, with greater digestible P leading to decreased ATTD Ca and P. In conclusion, dietary digestible P level affected ATTD Ca and P during lactation. Digestible P level of 0.4% and greater led to greater piglet livability and resulted in greater probability of weaned pigs.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT)acquisition. A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensembleaveraging. The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given localROI. A new method was developed to enable local NPS from a single PCD-CTacquisition.

191 Supplementing short- and medium-chain fatty acid-based products increases the health and performance of weaned piglets

Abstract Several feed supplements have received interest for their potential to increase growth performance and health post weaning, including alternatives to high levels of zinc oxide (ZnO) such as short- and medium-chain fatty acids (SCFA, MCFA), and phytogenics. The objective of this study is to observe the growth performance and intestinal health effects from the supplementation of SCFA with and without the addition of MCFA and phytogenics. The trial was performed on a commercial farm in Segovia, Spain. Piglets (n = 192; Topigs sow x Pietrain) were weaned at 28 d. The initial body weight (BW) was 8.5 ± 1.5 kg. Two phases were fed; phase 1 (d 1-14) and phase 2 (d 15-35). Piglets were distributed evenly to 4 treatments of 8 pens, 6 pigs/pen. Four treatments were evaluated: a negative control (NC) without addition of dietary additives any other growth promoters, a positive control (PC) with 3,000 ppm ZnO in the prestarter diet, NC with 2,000 ppm SCFA, coated butyrate, in the prestarter and 1,500 ppm SCFA in the starter (SB), and NC with a mixture of SCFA, MCFA, and phytogenics supplemented at 3,000 ppm in the prestarter and 2,000 ppm in the starter (SMP). Feed intake and individual BW were measured weekly. On d 0 and 7, one piglet per pen was rectally sampled for qPCR analysis of Escherichia coli, Streptococcus suis, and Clostridium perfringens. Veterinary interventions were recorded. Statistical analysis of growth performance, E. coli, and S. suis counts data were analyzed by ANOVA and Tukey’s t-test. The number of veterinary interventions and the occurrence of a high C. perfringens load were analyzed with a chi-square test. All statistical significance was set at P ≤ 0.05. The PC had the greatest average daily gain (ADG) at d 14 with 235 g/d while the NC and the SB had statistically less ADG with 217 and 212 g/d, NC and SB, respectively (P ≤ 0.05); however, no statistical differences between treatments were present at the end of the trial (332 NC, 335 PC, 331 SB, 347 SMP, P > 0.05). The feed conversion ratio (FCR) was less for PC (1.22) and SMP (1.23) in phase 1 than NC (1.33) and SB (1.35; P ≤ 0.05). No differences were detected in phase 2 (1.36 NC, 1.31 PC, 1.36 SB, 1.27 SMP; P >0.05). Dietary interventions did not significantly affect E. coli and S. suis counts. However, SB and SMP treatments had sufficiently reduced fecal C. perfringens load and antimicrobic treatments, P ≤ 0.05 (Table 1). In conclusion, supplementation of SCFA, with or without MCFA or phytogenics, reduced C. perfringens and incidence of diarrhea. The addition of MCFA and phytogenics with SCFA, improved performance to the positive control.

Regression to the mean effect for bi-variate dispersed count data

Regression to the mean (RTM) occurs when extreme observations upon remeasurement are found closer to the population mean. Interventions are usually applied to subjects based on a cut-off point to check it effectiveness. The average difference of the pre-post observations called the total effect is the sum of RTM and intervention effects and needs to be accounted for RTM to avoid incorrect conclusions and unbiasedly estimate the intervention effect. The existing methods assume equidispersion and strict positive correlation for bivariate count data. This study considers a bivariate Poisson distribution which takes both the dispersion and negative correlation into consideration. Expressions for the total effect are derived and partitioned into RTM and intervention effects. The maximum likelihood estimators of the RTM and intervention effects are found numerically, and their asymptotic properties are verified via simulations. Data on the English premier league are used to estimate the RTM and intervention effects.

Bootstrap Confidence Intervals for the Poisson-Pranav Distribution Parameter with an Application to COVID-19 Data

The Poisson distribution is commonly applied in statistical modeling to represent the count of events, assuming that the events are independent and occur at a constant rate. This assumption, however, may not always hold true in real-life situations. The Poisson distribution may be inadequate if the underlying rate of occurrence is not constant. The mixed Poisson distribution is proposed to solve this limitation because it permits the rate parameter of the Poisson distribution to be random rather than fixed. The Poisson-Pranav distribution, which is classified as a type of mixed Poisson distribution, has been commonly utilized to analyze count data that exhibits over-dispersion over many domains. However, no research has been done into building confidence intervals for the parameter of the Poisson-Pranav distribution using the bootstrap method. Using Monte Carlo simulation, the coverage probabilities and average lengths of the percentile bootstrap (PB), basic bootstrap (BB), and biased-corrected and accelerated (BCa) bootstrap’s interval-estimation performances were compared. The bootstrap method was not appropriate for achieving the desired nominal confidence level with small sample sizes. In addition, the performance of the bootstrap confidence intervals did not differ significantly when the sample size was increased considerably. In each case studied, the BCa bootstrap confidence intervals performed better than the others. The effectiveness of bootstrap confidence intervals was demonstrated by applying them to the number of COVID-19 deaths in Belgium. The computations substantially supported the proposed bootstrap confidence intervals.

Current status of pubertal sexual characteristics development of 2 704 girls aged 6-18 years in Tongzhou District of Beijing

Objective: To understand the current status of pubertal sexual characteristics development of girls aged 6-18 years in Tongzhou District of Beijing and to compare the differences in sexual characteristics development among girls characterized as thin, normal, overweight, and obese. Methods: A cross-sectional survey was conducted among 2 844 girls aged 6-18 years in Tongzhou District of Beijing from September 2022 to July 2023. The developmental stages of breast and pubic hair were assessed on site, and menarche status was inquired. Weight and height were measured. The girls were subsequently characterized into thin, normal, overweight and obese groups. Basic information (including family and personal history) was obtained through questionnaires. Probit probability unit regression was applied to calculate the age of each Tanner stage of sexual characteristics development and the age of menarche. The χ2 test was applied to compare the counting data between two or multiple groups. Results: A total of 2 844 girls were surveyed and 2 704 girls met the inclusion criteria, resulting in a valid response rate of 95.1%. Among these girls, 1 105 (40.9%) were aged 6-9 years, 1 053 (38.9%) were aged 10-13 years, and 546 (20.2%) were aged 14-18 years. The of height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and body mass index-for-age Z-score (BAZ) were 0.46(-0.23,1.16), 0.69(-0.16,1.67), and 0.67(-0.27,1.73) respectively. The prevalences of thin, overweight, and obesity were respectively 1.7% (45/2 704), 17.3% (467/2 704), and 19.9% (538/2 704), respectively. There were 45 girls in the thin group, 1 654 girls in the normal weight group, 1 005 girls in the overweight and obesity group. The age of Tanner stage breast 2 (B2), Tanner stage pubic hair 2 (P2), and menarche was 9.0 (95%CI 8.9-9.1), 10.5 (95%CI 10.4-10.6), and 11.4 (95%CI 11.3-1.5) years, respectively. The current status of breast and pubic hair maturity in girls with pubertal development shows that 64.6% (1 211/1 874) of these girls had breast development preceding pubic hair development, 32.4% (607/1 874) had concurrent breast and pubic hair development, and 3.0% (56/1 874) had pubic hairs development preceding breast development. The interval age between B2 and B5 was 4.7 (95%CI 4.6-4.8) years, between P2 and P5 was 4.5 (95%CI 4.4-4.6) years, and between B2 and menarche was 2.4 (95%CI 2.3-2.5) years. The ages of sexual characteristics development in overweight and obese groups were earlier than that in normal and thin groups. The ages of B2 in thin, normal, overweight, and obese groups were 10.0 (95%CI 9.5-10.6), 9.3 (95%CI 9.2-9.4), and 8.6 (95%CI 8.4-8.7) years, respectively. The age of menarche in thin, normal, overweight, and obese groups were 13.1 (95%CI 12.4-13.7), 11.6 (95%CI 11.4-11.7), and 11.1 (95%CI 11.0-11.2) years, respectively. The interval ages between B2 and B5 and between P2 and P5 was 4.5 and 4.1 years, respectively in the overweight and obese groups, and those in normal group and thin group was 4.7 and 4.5 years, 4.6 and 4.7 years, respectively. Conclusions: The ages of sexual characteristics development and menarche tend in Tongzhou District of Beijing to be earlier than that being reported of Beijing's survey 20 years ago. Girls characterized as overweight and obese not only start puberty at an earlier age than girls of normal weight, but also have a shorter developmental process.

Traffic count estimation using crowd-sourced trajectory data in the absence of dedicated infrastructure

Traffic count (or link count) data represents the cumulative traffic in the lanes between two consecutive signalised intersections. Typically, dedicated infrastructure-based sensors are required for link count data collection. The lack of adequate data collection infrastructure leads to lack of link count data for numerous cities, particularly those in low- and middle-income countries. Here, we address the research problem of link count estimation using crowd-sourced trajectory data to reduce the reliance on any dedicated infrastructure. A stochastic queue discharge model is developed to estimate link counts at signalised intersections taking into account the sparsity and low penetration rate (i.e., the percentage of vehicles with known trajectory) brought on by crowdsourcing. The issue of poor penetration rate is tackled by constructing synthetic trajectories entirely from known trajectories. The proposed model further provides a methodology for estimating the delay resulting from the start-up loss time of the vehicles in the queue under unknown traffic conditions. The proposed model is implemented and validated with real-world data at a signalised intersection in Kolkata, India. Validation results demonstrate that the model can estimate link count with an average accuracy score of 82% with a very low penetration rate (not in the city, but at the intersection) of 5.09% in unknown traffic conditions, which is yet to be accomplished in the current state-of-the-art.