Time-related Variables Research Articles

Prevalence and predictors of driver distractions in Cyprus: An observational study

ObjectiveDriver distractions are a significant contributor to road accidents and fatalities worldwide. The present research investigated the prevalence of observable driver distractions on both sides of Nicosia, as well as exploring the effect of drivers’ characteristics and time-related variables on their prevalence. MethodsRoadside observations were conducted at eight randomly selected locations in the city of Nicosia, Cyprus. Four of these sites were located in the area of Nicosia controlled by the Republic of Cyprus, and the other four were in the area that is referred to as the Turkish Republic of Northern Cyprus (TRNC) by its administration. ResultsA total of 16,070 drivers were observed, with 8,948 being in the Republic of Cyprus (South) and 7,122 in the Turkish-controlled section of Nicosia (North). A slightly higher proportion of drivers were observed driving distracted in the South (28.0%) than was found in the northern part of the city (25.7%). The most commonly observed secondary tasks in the South were using headphones (9.2%), talking to a passenger (8.1%) and using a mobile 0.4%, 3.7% talking and 2.7% texting). In contrast, the most commonly observed secondary tasks in the North were talking to passengers (10.2%), wearing headphones (8.4%) and using a mobile 0.6% for talking and 2.5% for texting). Gender-related differences were found for a number of different distractions (i.e., talking to passengers, drinking, and handheld mobile phone conversations), and age emerged as a significant predictor for most secondary tasks, including talking to passengers, smoking, hands-free mobile phone use, handheld mobile phone use, texting/keying numbers, drinking and engagement in any type of distraction (all distractions combined). The overall pattern for age was that middle-aged and older drivers were less likely to be distracted than younger drivers. ConclusionsA relatively high proportion of drivers on both sides of Nicosia were engaged in a secondary task while driving. These findings clearly indicate that younger drivers are more likely to drive distracted, which probably contributes to their higher crash rates.

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach.

In this study, six individual machine learning (ML) models and a stacked ensemble model (SEM) were used for daytime visibility estimation at Bangkok airport during the dry season (November-April) for 2017-2022. The individual ML models are random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting machine, and cat boosting. The SEM was developed by the combination of outputs from the individual models. Furthermore, the impact of factors affecting visibility was examined using the Shapley Additive exPlanation (SHAP) method, an interpretable ML technique inspired by the game theory-based approach. The predictor variables include different air pollutants, meteorological variables, and time-related variables. The light gradient boosting machine model is identified as the most effective individual ML model. On an hourly time scale, it showed the best performance across three out of four metrics with the ρ = 0.86, MB = 0, ME = 0.48km (second lowest), and RMSE = 0.8km. On a daily time scale, the model performed the best for all evaluation metrics with ρ = 0.92, MB = 0.0km, ME = 0.3km, and RMSE = 0.43km. The SEM outperformed all the individual models across three out of four metrics on an hourly time scale with ρ = 0.88, MB = 0.0km, (second lowest), and RMSE = 0.75km. On the daily scale, it performed the best with ρ = 0.93, MB = 0.02km, ME = 0.27km, and RMSE = 0.4km. The seasonal average original (VISorig) and meteorologically normalized visibility (VISnorm) decrease from 2017 to 2021 but increase in 2022. The rate of decrease in VISorig is double than rate of decrease in VISnorm which suggests the effect of meteorology visibility degradation. The SHAP analysis identified relative humidity (RH), PM2.5, PM10, day of the season year (i.e., Julian day) (JD), and O3 as the most important variables affecting visibility. At low RH, visibility is not sensitive to changes in RH. However, beyond a threshold, a negative correlation between RH and visibility is found potentially due to the hygroscopic growth of aerosols. The dependence of the Shapley values of PM2.5 and PM10 on RH and the change in average visibilities under different RH intervals also suggest the effect of hygroscopic growth of aerosol on visibility. A negative relationship has been identified between visibility and both PM2.5 and PM10. Visibility is positively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The JD is strongly negatively related to visibility during winter while weakly associated positively later in summer. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and understanding the factors influencing its fluctuations.

Time to sense biofield (Prana) experiences between hands: A preliminary single blinded randomized placebo controlled trial

Aims There is minimal research on the duration of biofield experiences. This preliminary study used the experiential learning practice of Master Choa Kok Sui's hands sensitisation to determine the duration to experience biofield sensations in between hands and to find the relationship between learning style preferences and biofield sensations. Methods This randomized placebo controlled, single blinded trial included 88 male and female pre-service teachers, aged 22.8±1.2 years. Participants completed a ruler drop test for reaction time, and Six Letter Cancellation test for measuring attention, learning style questionnaire for preferred method of learning, before randomization. The experimental (hands facing each other as introduced by Master Choa Kok Sui) and sham (hands facing opposite) groups practiced hands sensitisation. A semi-structured questionnaire was provided to gather information about biofield sensations and the time it took to experience these sensations between the hands. Results All (N=44) the participants in the experimental group and 13 participants in the sham group reported experiencing biofield sensations. A significant difference was noticed in experiencing magnetic (X2 = 38.247, p ≤ .001), physical sensations of energy (X2 = 12.02, p ≤ .001) and pain (X2 = 62.259, p ≤ .001) among the experimental and sham group . In the experimental group, the average time taken to first experience magnetic sensation, other biofield sensations and temperature variation was 34.84±12.97seconds, 40.28±20.96 seconds and 42.50±19.79 seconds, respectively. Minimum time taken to first experience biofield sensation was 5 seconds and lasted up to study duration of 120 seconds. There was no correlation found between reaction time, sustained attention, and the time needed to experience biofield sensations. Conclusions This study highlights importance of Master Choa Kok Sui hand sensitization in controlled setting revealing differences in experiences of various biofield sensations, showing valuable time-related insights and variability of sensation based on preferred learning.

Time to sense biofield (Prana) experiences between hands: A preliminary single blinded randomized placebo controlled trial

Aims There is minimal research on the duration of biofield experiences. This preliminary study used the experiential learning practice of Master Choa Kok Sui's hands sensitisation to determine the duration to experience biofield sensations in between hands and to find the relationship between learning style preferences and biofield sensations. Methods This randomized placebo controlled, single blinded trial included 88 male and female pre-service teachers, aged 22.8±1.2 years. Participants completed a ruler drop test for reaction time, and Six Letter Cancellation test for measuring attention, learning style questionnaire for preferred method of learning, before randomization. The experimental (hands facing each other as introduced by Master Choa Kok Sui) and sham (hands facing opposite) groups practiced hands sensitisation. A semi-structured questionnaire was provided to gather information about biofield sensations and the time it took to experience these sensations between the hands. Results All (N=44) the participants in the experimental group and 13 participants in the sham group reported experiencing biofield sensations. A significant difference was noticed in experiencing magnetic (X2 = 38.247, p ≤ .001), physical sensations of energy (X2 = 12.02, p ≤ .001) and pain (X2 = 62.259, p ≤ .001) among the experimental and sham group . In the experimental group, the average time taken to first experience magnetic sensation, other biofield sensations and temperature variation was 34.84±12.97seconds, 40.28±20.96 seconds and 42.50±19.79 seconds, respectively. Minimum time taken to first experience biofield sensation was 5 seconds and lasted up to study duration of 120 seconds. There was no correlation found between reaction time, sustained attention, and the time needed to experience biofield sensations. Conclusions This study highlights importance of Master Choa Kok Sui hand sensitization in controlled setting revealing differences in experiences of various biofield sensations, showing valuable time-related insights and variability of sensation based on preferred learning.

A time-adjusted control chart for monitoring surgical outcome variations.

Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts. A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen's Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference. Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89. Seasonal and secular trends can be controlled as potential confounders to improve signal detection in surgical outcomes monitoring over time.

Undergraduate Students’ Definitions of Sexual Violence Labels Across the Rise of the #MeToo Movement

Examining changes in perspectives of sexual violence across the #MeToo movement is critical to understanding shifts in sociocultural contexts of sexual violence; yet the literature is still nascent. In the present study, we assessed variation across the #MeToo movement in undergraduate students’ ( N = 1,028) rape myth acceptance, sexual violence experiences, identification of experiences with sexual violence labels, and self-generated definitions of sexual violence labels (e.g., sexual assault). We also examined the mediating role of the relation between timepoint across #MeToo and students’ self-generated sexual violence definitions. Text analyses revealed seven themes in students’ definitions: Activity, Chronicity, Body, Contact, Gender, Force/Harm, and Consent. Notably, there was greater representation and a higher rate of change in the Contact theme in definitions following the #MeToo movement compared to pre-#MeToo, but only among students with sexual violence history. Rape myth acceptance did not change significantly across the #MeToo movement and did not demonstrate a significant role in time-related variability in students’ definitions. These findings provide support for the role of the #MeToo movement in facilitating more inclusive perceptions of sexual violence among undergraduate students, especially among those with sexual violence histories. We hope that with increased insight into the #MeToo movement's impact on sexual violence perspectives, continued efforts of the movement and prevention initiatives can broaden their reach.

Design recommendations for voluntary blink interactions based on pressure sensors

Voluntary blink control is vital in eye-controlled human–computer interaction (ECHCI). Pressure sensors, known for their low power consumption and strong resistance to interference, are suitable for blink detection. Existing research has predominantly focused on sensor materials and signal processing, and studies on the characteristics and optimized design of voluntary blink actions in ECHCI remain relatively limited. Therefore, this study presents a blink detection system based on pressure sensors and investigates the efficiency of voluntary blink actions. We examined the effects of two independent variables, blink count (single, double, and triple) and blink side (bilateral and unilateral), on recognition accuracy and three time-related variables. Subsequently, the subjective workload and evaluation experiments were conducted for each blink action. The results indicate that the blink count has a more pronounced impact on interaction efficiency and comfort, with continuous blink actions exceeding three blinks not being recommended. Bilateral blinks were perceived as more natural and comfortable than unilateral blinks. Moreover, we discovered that the participants exhibited greater attentiveness and focus when performing double bilateral blink actions, making it suitable for important commands. Based on the experimental results, the top three recommended voluntary blink actions are single bilateral blink (SB), double bilateral blink (DB), and single unilateral blink (SU). Finally, a test involving controlling the movement of a toy car using the recommended blink actions validates the usability of the actions and detection system. These findings provide valuable guidance for the design and optimization of voluntary-blink interaction systems, further enhancing this interaction method’s feasibility.

Temporal variables improve a spatiotemporal species distribution model for the non-native freshwater fish Candidia temminckii

Ecosystem conservation requires a deeper understanding of species-habitat relationships and population dynamics at a fine spatiotemporal resolution. We propose a new distribution modeling method based on a 5-year monthly survey that considers the temporal continuity of species distributions and physical habitat datasets by inputting continuous time-related variables. We employed random forests to relate the presence/absence of the non-native freshwater fish Candidia temminckii to physical habitat data at 15 sampling sites along a 1.4km spring-fed river in Japan. The proposed method outperforms all conventional methods using datasets split into a specific time period to incorporate temporality into the model. The order of variable importance and shape of the partial dependence plots of the proposed method reflect species ecology and show a gradual shift over time compared to the conventional methods. These results demonstrate the applicability of the proposed method to species distribution modeling using fine-scale spatiotemporal data.

Analyzing key elements of breathing patterns, deriving remaining variables, and identifying cutoff values in individuals with chronic respiratory disease and healthy subjects

BackgroundPulmonary physiology encompasses intricate breathing patterns (BPs), characterized by breathing frequency (Bf), volumes, and flows. The complexities intensify in the presence of interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD), especially during exercise. This study seeks to identify pivotal factors driving changes among these variables and establish cutoff values, comparing their efficacy in differentiating BPs to traditional methods, specifically a breathing reserve (BR) of 30% and a Bf of 50 bpm. MethodsScreening 267 subjects revealed 23 with ILD, 126 with COPD, 33 healthy individuals, and the exclusion of 85 subjects. Lung function tests and ramp-pattern cardiopulmonary exercise testing (CPET) were conducted, identifying crucial BP elements. Changes were compared between groups at peak exercise. The area under the receiver operating characteristic curve (AUC) analysis determined cutoff values. ResultsInspiratory time (TI) remained constant at peak exercise for all subjects (two-group comparisons, all p=NS). Given known differences in expiratory time (TE) and tidal volume (VT) among ILD, COPD, and healthy states, constant TI could infer patterns for Bf, total breathing cycle time (TTOT=60/Bf), I:E ratio, inspiratory duty cycle (IDC, TI/TTOT), rapid shallow breathing index (Bf/VT), tidal inspiratory and expiratory flows (VT/TI and VT/TE), and minute ventilation (V̇E=Bf×VT) across conditions. These inferences aligned with measurements, with potential type II errors causing inconsistencies. RSBI of 23 bpm/L and VT/TI of 104 L/min may differentiate ILD from control, while V̇E of 54 L/min, BR of 30%, and VT/TE of 108 may differentiate COPD from control. BR of 21%, TE of 0.99 s, and IDC of .45 may differentiate ILD from COPD. The algorithm outperformed traditional methods (AUC 0.84–0.91 versus 0.59–0.90). ConclusionThe quasi-fixed TI, in conjunction with TE and VT, proves effective in inferring time-related variables of BPs. The findings have the potential to significantly enhance medical education in interpreting cardiopulmonary exercise testing. Moreover, the study introduces a novel algorithm for distinguishing BPs among individuals with ILD, COPD, and those who are healthy.

Investigating demand models with more flexible elasticity functions: empirical insights from rail demand analysis

AbstractModels of rail travel demand take one of three generic functional forms: the generalised cost (GC) function where price and the various time variables are combined into a composite term through the use of appropriate values of time; the generalised journey time and fare (GJT-Fare) approach, where the time-related variables are combined into a single term and fare remains separate; and the specification of separate elasticities for all terms, termed the separate components (SC) approach. This research extends that reported by Wardman and Toner (Transportation 47:75–108, 10.1007/s11116-017-9850-7, 2020) in exploring more flexible functional forms where appropriate parameterisation of the standard GC, GJT and SC models allows them to have more general elasticity properties. Whilst the aforementioned study discounted the standard GC approach on the grounds of inferior fit and undesirable elasticity properties, the analysis reported here, based on large datasets, finds best-fitting more flexible models to have elasticity properties that resemble those of the GC approach. Indeed, the preferred functions can deliver elasticities that are somewhat different from those of the GJT-Fare approach that has long provided the basis of official rail demand forecasts in Great Britain. In addition, the study adds to the existing evidence base by providing credible and precise elasticities for GJT and fare, and importantly for the rarely estimated GC and SC elasticities, reaffirms the need of GC models to directly estimate demand consistent values of time, and indicates that the weights currently attached to headway and interchange in formulating GJT are in need of significant amendment. Although the context is rail in Great Britain, the results have relevance to demand analysis of other modes and in other countries as well as to other transport modelling approaches.

Divorce and risk of suicide attempt: a Swedish national study.

Prior research has reported an association between divorce and suicide attempt. We aimed to clarify this complex relationship, considering sex differences, temporal factors, and underlying etiologic pathways. We used Swedish longitudinal national registry data for a cohort born 1960-1990 that was registered as married between 1978 and 2018 (N = 1 601 075). We used Cox proportional hazards models to estimate the association between divorce and suicide attempt. To assess whether observed associations were attributable to familial confounders or potentially causal in nature, we conducted co-relative analyses. In the overall sample and in sex-stratified analyses, divorce was associated with increased risk of suicide attempt (adjusted hazard ratios [HRs] 1.66-1.77). Risk was highest in the year immediately following divorce (HRs 2.20-2.91) and declined thereafter, but remained elevated 5 or more years later (HRs 1.41-1.51). Divorcees from shorter marriages were at higher risk for suicide attempt than those from longer marriages (HRs 3.33-3.40 and 1.20-1.36, respectively). In general, HRs were higher for divorced females than for divorced males. Co-relative analyses suggested that familial confounders and a causal pathway contribute to the observed associations. The association between divorce and risk of suicide attempt is complex, varying as a function of sex and time-related variables. Given evidence that the observed association is due in part to a causal pathway from divorce to suicide attempt, intervention or prevention efforts, such as behavioral therapy, could be most effective early in the divorce process, and in particular among females and those whose marriages were of short duration.

Data-driven meal events detection using blood glucose response patterns

BackgroundIn the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present.MethodsThe method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as Meal or Non-Meal. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual.ResultsThe results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results.ConclusionsThe approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events.

Weather, weekday, and vacation effects on webcam recorded daily visitor numbers in the alpine winter season

Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.

Time to sense biofield (Prana) experiences between hands: A preliminary single blinded randomized placebo controlled trial.

There is minimal research on the duration of biofield experiences. This preliminary study used the experiential learning practice of Master Choa Kok Sui's hands sensitisation to determine the duration to experience biofield sensations in between hands and to find the relationship between learning style preferences and biofield sensations. This randomized placebo controlled, single blinded trial included 88 male and female pre-service teachers, aged 22.8±1.2 years. Participants completed a ruler drop test for reaction time, and Six Letter Cancellation test for measuring attention, learning style questionnaire for preferred method of learning, before randomization. The experimental (hands facing each other as introduced by Master Choa Kok Sui) and sham (hands facing opposite) groups practiced hands sensitisation. A semi-structured questionnaire was provided to gather information about biofield sensations and the time it took to experience these sensations between the hands. All (N=44) the participants in the experimental group and 13 participants in the sham group reported experiencing biofield sensations. A significant difference was noticed in experiencing magnetic (X 2 = 38.247, p ≤ .001), physical sensations of energy (X 2 = 12.02, p ≤ .001) and pain (X 2 = 62.259, p ≤ .001) among the experimental and sham group . In the experimental group, the average time taken to first experience magnetic sensation, other biofield sensations and temperature variation was 34.84±12.97seconds, 40.28±20.96 seconds and 42.50±19.79 seconds, respectively. Minimum time taken to first experience biofield sensation was 5 seconds and lasted up to study duration of 120 seconds. There was no correlation found between reaction time, sustained attention, and the time needed to experience biofield sensations. This study highlights importance of Master Choa Kok Sui hand sensitization in controlled setting revealing differences in experiences of various biofield sensations, showing valuable time-related insights and variability of sensation based on preferred learning.

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

The industrial sector in South Korea has recently undergone significant growth; however, it is also known for its hazardous workplaces. Occupational accidents have had a widespread impact across various industries; therefore, the identification of accident-influencing factors is crucial to improve workplace safety. We analyzed the occupational accident database from the Ministry of Economy and Finance to examine the influencing factors, including worker information, project details, time-related variables, and accident descriptions. Exploratory and correspondence data analyses were performed to identify patterns and relationships between variables. We applied multinomial logistic models and random forest algorithms to understand the correlation between victim status and independent variables. Results showed that 67% of all accidents occurred among workers with less than one month of employment. The multinomial regression model achieved a prediction accuracy of 97.66% with a kappa value of 0.846, outperforming the random forest model (kappa = 0.844). The receiver operating curve illustrated that the random forest had higher misclassification rates when distinguishing between injuries and fatalities. To mitigate accidents among new workers, enhanced safety training and protective measures are needed to enforce a healthy workplace. This study contributes to ongoing efforts to advance workplace safety, reduce occupational accidents, and promote a healthier working environment.

Resting pulmonary function and artery pressure and cardiopulmonary exercise testing in chronic heart failure patients in Taiwan − a prospective observational cross-sectional study comparing healthy subjects and interstitial lung disease patients

Background Restrictive ventilatory defects and elevated pulmonary artery pressure (PAP) are common in patients with chronic heart failure (CHF) and those with interstitial lung disease (ILD). However, as oxyhemoglobin desaturation seldom occurs in stable CHF patients at peak exercise, we hypothesized that the pathophysiology may be different between them. This study aimed to investigate: (1) PAP and lung function at rest, (2) pulmonary gas exchange (PGX) and breathing patterns at peak exercise, (3) mechanisms of dyspnea at peak exercise in patients with CHF compared to healthy subjects and ILD patients. Methods We consecutively enrolled 83 participants (27 with CHF, 23 with ILD, and 33 healthy controls). The CHF and ILD groups had similar functional status. Lung function and cardiopulmonary exercise tests with Borg Dyspnea Score were performed. PAP was estimated using echocardiography. Resting lung function, PAP and peak exercise data in the CHF group were compared to the healthy and ILD groups. Correlation analysis was performed to elucidate the mechanisms of dyspnea in the CHF and ILD groups. Results Compared to the healthy group, the CHF group had normal lung function, PAP at rest, and normal dyspnea score and PGX at peak exercise, whereas the ILD group had abnormal values compared to the CHF group. Dyspnea score was positively correlated with pressure gradient, lung expansion capabilities, and expiratory tidal flow in the CHF group (all p < 0.05), but inversely correlated with inspiratory time-related variables in the ILD group (all p < 0.05). Conclusion Normal lung function and PAP at rest, and dyspnea scores and PGX at peak exercise indicated that pulmonary hypertension and fibrosis were insignificant in the patients with CHF. The factors affecting dyspnea at peak exercise were different between the CHF and ILD groups. As the sample size in this study was small, large-scale studies are warranted to confirm our findings.

Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach

One of the reasons why students go to counseling is being called on based on self-reported health survey results. However, there is no concordant standard for such calls. This study aims to develop a machine learning (ML) model to predict students' mental health problems in 1 year and the following year using the health survey's content and answering time (response time, response time stamp, and answer date). Data were obtained from the responses of 3561 (62.58%) of 5690 undergraduate students from University A in Japan (a national university) who completed the health survey in 2020 and 2021. We performed 2 analyses; in analysis 1, a mental health problem in 2020 was predicted from demographics, answers for the health survey, and answering time in the same year, and in analysis 2, a mental health problem in 2021 was predicted from the same input variables as in analysis 1. We compared the results from different ML models, such as logistic regression, elastic net, random forest, XGBoost, and LightGBM. The results with and without answering time conditions were compared using the adopted model. On the basis of the comparison of the models, we adopted the LightGBM model. In this model, both analyses and conditions achieved adequate performance (eg, Matthews correlation coefficient [MCC] of with answering time condition in analysis 1 was 0.970 and MCC of without answering time condition in analysis 1 was 0.976; MCC of with answering time condition in analysis 2 was 0.986 and that of without answering time condition in analysis 2 was 0.971). In both analyses and in both conditions, the response to the questions about campus life (eg, anxiety and future) had the highest impact (Gain 0.131-0.216; Shapley additive explanations 0.018-0.028). Shapley additive explanations of 5 to 6 input variables from questions about campus life were included in the top 10. In contrast to our expectation, the inclusion of answering time-related variables did not exhibit substantial improvement in the prediction of students' mental health problems. However, certain variables generated based on the answering time are apparently helpful in improving the prediction and affecting the prediction probability. These results demonstrate the possibility of predicting mental health across years using health survey data. Demographic and behavioral data, including answering time, were effective as well as self-rating items. This model demonstrates the possibility of synergistically using the characteristics of health surveys and advantages of ML. These findings can improve health survey items and calling criteria.

Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation

Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM10 and PM2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data.

Associations between Force-Time Related Single-Leg Counter Movement Jump Variables, Agility, and Linear Sprint in Competitive Youth Male Basketball Players

Previous research has reported a strong relationship between vertical jumping, sprinting, and agility, as a reflection of lower-limb power. Unilateral analysis of this relationship has not yet been explored. This study primarily investigated the associations between single-leg countermovement jump (CMJ), sprint, and agility performances in youth basketball players. Thirty-five male basketball players from the youth category (age 15.06 ± 2.62 years, n = 32 right-limb dominant; n = 3 left-limb dominant) performed single-leg CMJ, 20 m sprint, and T-drill agility tests over two sessions. Force-time-related performance variables were measured using a single-leg CMJ test on a Kistler force plate. Significant moderate to large negative correlations were observed between single-leg CMJ variables, 20 m sprint, and T-drill agility, except for mean force for both dominant and non-dominant leg measures (r = -0.384 to -0.705). Mean power and mean force were correlated with the physical characteristics of the athletes for both legs (r = -0.389 to -0.843). Flight time and jump height were identified as the best predictor variables for both sprint and agility time in the stepwise model (R2 = 0.608 to 0.660). No statistical inter-limb differences were found during the single-leg CMJ test (p > 0.05). The study findings suggest that youth basketball players with greater single-leg jump output most likely have better sprint and agility performances. Thus, trainers and athletic performance coaches may include unilateral limb exercises in their training programs to enhance lower-limb explosive performance and reduce limb asymmetries.

Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity.

This paper aims to explore the complex dynamics and impact of vaccinations on controlling epidemic outbreaks. An epidemic transmission model which considers vaccinations and two different infection statuses with different infectivity is developed. In terms of a dynamic analysis, we calculate the basic reproduction number and control reproduction number and discuss the stability of the disease-free equilibrium. Additionally, a numerical simulation is performed to explore the effects of vaccination rate, immune waning rate and vaccine ineffective rate on the epidemic transmission. Finally, a sensitivity analysis revealed three factors that can influence the threshold: transmission rate, vaccination rate, and the hospitalized rate. In terms of optimal control, the following three time-related control variables are introduced to reconstruct the corresponding control problem: reducing social distance, enhancing vaccination rates, and enhancing the hospitalized rates. Moreover, the characteristic expression of optimal control problem. Four different control combinations are designed, and comparative studies on control effectiveness and cost effectiveness are conducted by numerical simulations. The results showed that Strategy C (including all the three controls) is the most effective strategy to reduce the number of symptomatic infections and Strategy A (including reducing social distance and enhancing vaccination rate) is the most cost-effective among the three strategies.