Measures Of Variability Research Articles

Green Financial Policy, Resource Allocation and Corporate Environmental Responsibility

Green finance policy has emerged as a powerful driver for sustainable development worldwide, which has arisen at the top of the political agenda. Drawing on resource allocation theory, this study empirically investigates whether and how green finance policy affects corporate environmental responsibility in achieving sustainable development goals on a micro level. Taking China’s green finance reform and innovation (GFRI) pilot policy as a quasi-natural experiment, this paper employs the difference-in-differences model to investigate the impact of green finance policy on corporate environmental responsibility. The evidence shows that the GFRI policy significantly promotes corporate environmental responsibility. The results hold robust after a series of checks such as parallel trend examination, placebo test, exclusion of other policies, and alternative variable measurement. Moreover, this study explores the potential mechanism channels from the perspective of resource allocation theory. Specifically, green finance policy ultimately accelerates corporate environmental responsibility through financing capacity and environmental protection supervision. The heterogeneity analysis shows that the positive impact of the GFRI policy on corporate environmental responsibility is more pronounced for companies in areas with superior green development, strong law enforcement, and higher levels of pollution. The above findings indicate that the formal institution of government-led green financial policy can positively affect corporate environmental responsibility, with regional green development and law enforcement factors enhancing the effectiveness of these policies. Furthermore, the level of local pollution further intensifies the corporate sensibility to such policy effects. Overall, our study sheds light on the significant role of green financial policy in fostering a sustainable economy, helping reconcile the mixed evidence on the financial function of green finance policy on the firm level.

Examining effects of air pollution on photovoltaic systems via interpretable random forest model

Renewable energy plays a vital role in power generation and solar photovoltaic systems due to resource availability throughout the year. This work aims to investigate the impact of air pollutants and meteorological parameters on the performance of the photovoltaic systems locally, taking into consideration the advantages of the photovoltaic power potential of the SW part of Romania, where Craiova is located (average solar radiation intensity >1350 kWh/m2/year). This study is based on a one-year dataset provided by a sensor that monitors particulate matter concentrations, volatile organic compounds, dioxide of carbon, ozone, noise, formaldehyde and three climate parameters (temperature, pressure, and relative humidity). The research methodology applies an innovative interpretable random forest model emphasising the implications of air pollution for photovoltaic systems. The proposed machine learning model was trained to predict the particulate matter level in air based on the basic environmental variable measurements. The study presents six random forest models of varying complexity, which reach the accuracy of classification for the selected problem up to 99 %, and applies the Shapley Additive Explanations technique to interpret the decision-making model. The observation regarding the highest concentration of particulate matter occurring during cold months, which typically do not align with peak solar irradiance, underscores the importance of considering various environmental factors in solar energy planning. With its practical implications, this insight offers decision-makers valuable information about the feasibility of optimising solar energy generation despite seasonal variations in air pollution levels, directly addressing their needs and concerns.

A New Rank Estimator for Least Squares Estimation of Weibull Modulus

The Weibull distribution is widely used in reliability analysis to evaluate the failure behavior and lifetime characteristics of various systems and components. One of the most commonly used methods for estimating the parameters of the Weibull distribution is the ordinary least squares (OLS) technique, which is based on fitting a linear regression model to the transformed data. This paper proposes a new rank estimator for ordinary least squares estimation of Weibull modulus, a key parameter used as a measure of variability in the data. The new rank estimator is a quadratic function of the order statistic number, with three parameters that are optimized by Monte Carlo simulations. Using relative efficiency as a criterion, the performance of the new rank estimator is compared with three commonly used rank estimators, mean, median and hazen rank estimators, which are linear functions of the order statistic number. The results show that the new rank estimator has a significant advantage over the other rank estimators for any sample size between 3 and 150. The findings also imply that other nonlinear functions, such as cubic polynomials, could be applied to further improve the efficiency of the parameter estimators of the ordinary least squares method.

The social status adversity and health in daily life moments study: ecological momentary assessment and ambulatory health assessments to examine meaning and mechanisms

BackgroundAfrican Americans and those of lower socioeconomic status (SES) are at disproportionate risk for hypertension- and cardiovascular-disease-related mortality relative to their counterparts. Progress in reducing these disparities is slowed by the facts that these disparities are difficult to mitigate in older adults and early origins of these disparities are poorly understood. The Social Status Adversity and Health in Daily Life Moments Study aims to precisely understand the proximal cognitive-emotional mechanisms by which unique social exposures disproportionately impacting these populations influence blood pressure (BP) parameters early in the lifespan and determine which individuals are more at risk.MethodsThe study uses ecological momentary assessment (EMA) and ambulatory blood pressure (ABP) monitoring to assess race- and SES-based factors as they manifest in daily life moments alongside simultaneously manifesting cognitive-emotional states and ABP. A sample of 270 healthy African Americans between the ages of 18 and 30 is being recruited to complete two periods of 2-day, 2-night hourly ABP monitoring alongside hourly EMA assessments of socioeconomic strain, unfair treatment, and neighborhood strain during the waking hours. ABP data will be used to calculate ecologically valid measures of BP reactivity, variability, and nocturnal dipping. Other measures include actigraphy equipment worn during the monitoring period and comprehensive assessment of behavioral and psychosocial risk and resilience factors. Multilevel and multiple linear regression analyses will examine which momentary social adversity exposures and cognitive-emotional reactions to these exposures are associated with worse BP parameters and for whom.DiscussionThis is the first time that this research question is approached in this manner. The Social Status Adversity and Health in Daily Life Moments Study will identify the cognitive-emotional mechanisms by which the most impactful race- and SES-based exposures influence multiple BP parameters in African American emerging adults. Further, it will identify those most at risk for the health impacts of these exposures. Achievement of these aims will shape the field’s ability to develop novel interventions targeting reduction of these exposures and modification of reactions to these exposures as well as attend to those subpopulations most needing intervention within the African American emerging adult population.

Ability of a multi-segment foot model to measure kinematic differences in cavus, neutrally aligned, asymptomatic planus, and symptomatic planus foot types

BackgroundMulti-segment foot models (MFMs) provide a better understanding of the intricate biomechanics of the foot, yet it is unclear if they accurately differentiate foot type function during locomotion. Research questionWe employed an MFM to detect subtle kinematic differences between foot types, including: pes cavus, neutrally aligned, and asymptomatic and symptomatic pes planus. The study investigates how variable the results of this MFM are and if it can detect kinematic differences between pathologic and non-pathologic foot types during the stance phase of gait. MethodsIndependently, three raters instrumented three subjects on three days to assess variability. In a separate cohort, each foot type was statically quantified for ten subjects per group. Each subject walked while instrumented with a four-segment foot model to assess static alignment and foot motion during the stance phase of gait. Statistical analysis performed with a linear mixed effects regression. ResultsModel variability was highest for between-day and lowest for between–rater, with all variability measures being within the true sample variance. Almost all static measures (radiographic, digital scan, and kinematic markers) differed significantly by foot type. Sagittal hindfoot to leg and forefoot to leg kinematics differed between foot types during late stance, as well as coronal hallux to forefoot range of motion. The MFM had low between-rater variability and may be suitable for multiple raters to apply to a single study sample without introducing significant error. The model, however, only detected a few dynamic differences, with the most dramatic being the hallux to forefoot coronal plane range of motion.Significance: Results only somewhat aligned with previous work. It remains unclear if the MFM is sensitive enough to accurately detect different motion between foot types (pathologic and non-pathologic). A more accurate method of tracking foot bone motion (e.g., biplane fluoroscopy) may be needed to address this question.

Discriminative validity and interpretability of the mood and feelings questionnaire

BackgroundUsing the Mood and Feelings Questionnaire (MFQ) to differentiate between depression severity levels remains unexplored. We explored the discriminative validity of the MFQ to identify an optimal cut-off MFQ score to distinguish between subthreshold-to-mild and moderate-to-severe depression severity levels. MethodsAn observational cross-sectional design was used in a sample (N = 67) of help-seeking youth (ages 13 to 18, inclusive) experiencing depressive symptoms. The MFQ was administered verbatim to youth by a research analyst over the phone. Youth were then grouped into subthreshold-to-mild or moderate-to-severe depression severity categories based on scores received on the Kiddie Schedule for Affective Disorders and Schizophrenia-Depression Rating Scale. Receiver Operating Characteristic curve analyses were conducted, with area under the curve (AUC) and Youden Index (J) as primary indices. We hypothesized that the lower limit of the 95 % confidence interval for the area under the curve would be ≥0.70. ResultsThe primary analysis yielded an AUC of 0.85 (95 % CI: 0.763–0.947) and an optimal cut-off of ≥43 (J = 0.60, positive predictive value = 91.4 %, negative predictive value = 62.5 %, sensitivity = 72.7 %, specificity = 87.0 %). LimitationsOur study collected a small sample, and as such cannot identify how subgroup classification (e.g., based on race or gender) may moderate outcomes. Further, unknown measurement error of the predictor and reference variable measures can bias the estimates. ConclusionsOur preliminary findings highlight the potential for the MFQ to support clinical decision-making relevant to adolescents experiencing varying severities of depressive symptoms in secondary care settings.

A meta-analysis of international experience and performance: exploring the role of global mobility and national sentiments

PurposeInternational experience has long been recognized as a crucial determinant for firms’ knowledge management in the existing literature. However, within a global context, the relationship between international experience and the performance of multinational enterprises is intricate and remains ambiguous. While the impact of people mobility has been extensively studied, limited understanding exists regarding how global mobility of people and evolving external environments reshape the relationship. This study aims to integrate existing empirical evidence on this relationship and examines the contingencies posed by environmental factors.Design/methodology/approachThis study conducted a multilevel meta-analysis based on a sample of 231 effect sizes collected from 167 articles to systematically examine the international experience–performance relationship, considering the moderating effect of the global mobility of people and the rise of national sentiments (including authoritarianism and protectionism). A two-stage procedure comprising Hedges-Olkin-type meta-analysis and random-effects meta-analytic regression was adopted.FindingsThe findings demonstrate a predominantly positive international experience–performance relationship that varies across studies owing to differences in research design, variable measurements and firm characteristics. The relationship is positively moderated by the global mobility of people, yet the positive effect is contingent upon the level of national sentiments within home countries. The beneficial effect of inbound mobility on this relationship is attenuated by authoritarianism and protectionism, while the effect of outbound mobility is positively influenced by authoritarianism and less affected by protectionism.Originality/valueThis study offers novel theoretical insights into multinationals’ knowledge accumulation in the internationalization process. It contributes to the existing literature by presenting an integrated framework elucidating the international experience–performance relationship. Building upon the knowledge-based view, it integrates environmental dynamics and national sentiments to investigate the performance implications of multinationals’ international experience, thereby providing valuable practical insights for effective global knowledge management.

Imbalanced temporal states of cortical blood-oxygen-level-dependent signal variability during rest in episodic migraine

BackgroundMigraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients.MethodsTwo independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman’s rank correlation was calculated to test associations with clinical parameters.ResultsResting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort.ConclusionEpisodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.

Objective acceptability indexes

Based on the financial efficiency and leverage effect, this study classifies the measures of reward, risk, and performance into “simple” and “compound” types. We generalize three problems of observation inconsistency, overleverage deception, and dynamic manipulation behind classical simple measures (such as variability, LPM, VaR, and regression-based measures), and two problems of observation inconsistency and leverage variance behind traditional drawdown-based compound measures. We employ an axiomatic approach to answer the question of what undesirable influences measures should avoid, and complement the axiomatic framework with objective acceptability indexes by separating useless influences that do not add value for investors. We also develop new compound measures to solve the general problems with empirical evidence.

Virtual reality exposure effect in acrophobia: psychological and physiological evidence from a single experimental session

In recent years, virtual reality (VR) has gained attention from researchers in diverse fields, particularly in therapy of phobias. Currently, virtual reality exposure therapy therapy (VRET) is considered a promising cognitive-behavioral therapy technique. However, specific psychological and physiological responses of VR users to virtual exposure in such a context are still only vaguely explored. In this experimental study, we mapped VR exposure in a height environment in people with a moderate fear of heights–acrophobia. Thirty-six participants were divided into experimental and control groups–with and without psychological guidance during exposure. Participants' subjective level of anxiety was examined, and objective physiological response was captured via heart rate variability (HRV) measurement. Psychological assessments recorded an anticipated rise in participant anxiety following exposure to height; nevertheless, no distinctions were observed in self-reported anxiety concerning psychological guidance. Notably, objective physiological measures revealed that VR exposure prompts physiological responses akin to real-world scenarios. Moreover, based on the analysis of heart rate variability, participants who received psychological guidance were identified as better at compensating for anxiety compared to those without such support. These findings support VRET as a promising tool for psychotherapy and advocate for psychological guidance as beneficial in reducing anxiety and managing stress during exposure. The results may help improve our understanding of anxiety during exposure to phobic stimuli.

Combining environmental-socio-economic data with volunteer geographic information for mapping flood risk zones in Zhengzhou, Henan Province, China

Due to the occurrence of extreme weather phenomena, flooding caused by precipitation has become a frequent, widespread, and highly destructive natural disaster. For densely populated urban areas, efficient identification of people's emergency rescue needs and mapping of high flood risk areas are particularly important for decision makers to formulate disaster relief strategies. In this study, we employed a machine learning approach along with volunteer geographic information (VGI)1 data and environmental-socio-economic indicators to map the flood risk area in Zhengzhou. Firstly, the sentiment analysis tool based on artificial intelligence platform initially identified the public's emergency rescue needs. Secondly, the crowd-sourcing approach and geographical approach were used to evaluate the VGI data quality and the variable importance measures (VIMs)2 method to optimize the evaluation indicators. Lastly, the flood risk area was classified in four categories: flood environmental susceptibility, flood-induced risk, flood exposure risk, and flood mitigation capacity, subsequently resulting in a comprehensive risk map of Zhengzhou. The results showed that the accuracy of flood risk assessment increased from 85.7 % to 92.4 % after VGI quality evaluation. The most important indicators affecting flood risk assessment were population density, river system and nighttime light intensity. The comprehensive flood risk map better reflected the spatial distribution of high-risk areas in Zhengzhou. It allows emergency responders to quickly identify areas that require urgent attention.

Examining individual and contextual predictors of disability in Chinese older adults: A machine learning approach

BackgroundThere is a large gap of understanding the determinants of disability, especially the contextual characteristics. Therefore, this study aimed to examine the important predictors of disability in Chinese older adults based on the social ecological framework. MethodsWe used the China Health and Retirement Longitudinal Study to examine predictors of disability, defined as self-report of any difficulty for six activity of daily living items. We restricted analytical sample to older adults aged 65 or above (N=1816). We considered 44 predictors, including personal-, behavioral-, interpersonal-, community-, and policy-level characteristics. The built-in variable importance measure (VIM) of random forest and SHapley Additive exPlanations (SHAP) were applied to assess key predictors of disability. A multilevel logit regression was further used to examine the associations of individual and contextual characteristics with disability. ResultsThe mean age of study sample was 72.62 years old (standard deviation: 5.77). During a 2-year of follow-up, 518 (28.52 %) of them developed into disability. Walking speed, age, and peak expiratory flow were the top important predictors in both VIM and SHAP. Contextual characteristics such as humidity, PM2.5, temperature, normalized difference vegetation index, and landscape also showed promise in predicting disability. Multilevel logit regression showed that people with male gender, arthritis, vision impairment, unable to finish semi tandem, no social activity, lower grip strength, and higher waist circumference, had much higher risk of disability. ConclusionDisability prevention strategies should specifically focus on multilevel factors such as individual and contextual characteristics, although the latter is warranted to be verified in future studies.

Audit Quality Evidence from Indonesia: A Systematic Literature Review

The audit quality produced by the auditor can be a benchmark in their work. This research aims to uncover factors influencing audit quality, especially in Indonesia. Articles were obtained using Google Scholar, and the publication period was between 2018-2023. The systematic literature review method uses Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR). The results show that 157 articles are accredited by SINTA (Science and Technology Index) and registered with GARUDA (Garba Rujukan Digital). This study identifies basic theory, independent variables, intervening/moderating variables, variable measurement indicators, population and samples, sampling methods, data sources, measurement scales, data processing, respondent information, and auditors. Finally, the implications of this research are to provide direction and guidance for future research regarding audit quality in Indonesia for academics and researchers, government and professional associations, and practicing auditors.

Pengaruh Kepemimpinan, Beban Kerja dan Diklat Terhadap Kinerja Karyawan dengan Lingkungan Kerja sebagai Variabel Intervening pada Perumda Air Minum Tirta Kepri

This research aims to find out: (1) whether leadership, workload, and training have an effect on the work environment; (2) whether the work environment, leadership, workload and training have an effect on employee performance; and (3) whether leadership, workload and training influence employee performance through the work environment. This research was conducted at Perumda Air Minum Tirta Kepri with a population of 121 respondents from Perumda Air Minum Tirta Kepri. Data was collected using a questionnaire method with a questionnaire instrument. Variable measurement was carried out using a Likert scale. The research results found that leadership had a negative and insignificant effect on the environment; workload has a positive and significant effect on the work environment; training has a positive and significant effect on the work environment; work environment has a positive and significant effect on employee performance; leadership has a positive and significant effect on employee performance; workload has a negative and significant effect on employee performance; training has a positive and significant effect on employee performance; work environment has a negative and insignificant influence in mediating leadership on employee performance; work environment has a positive and significant influence in mediating workload on employee performance; The work environment has a positive and insignificant influence in mediating training on employee performance at Perumda Air Minum Tirta Kepri.

Quantifying the Variability of “Fixed-Width” Buffers on Harvested Lands in Western Oregon and Washington

Abstract In contemporary forest management, buffers of unharvested trees are left along streams to protect riparian and aquatic ecosystems. Buffer regulations often focus on specific minimum width requirements, which aid in straightforward regulation and application, but minimum widths also suggest buffered edges are uniform and contain little variability. Conceptual papers suggest that alternative buffer configurations may offer greater flexibility in landscape-level protection, increase forest complexity, and enhance aquatic and riparian biodiversity. However, before considering alternatives to fixed-width buffers, it is necessary to quantify the inherent variability in current buffer practices present on the landscape. In this study, we used aerial imagery to quantify variability of buffer widths in two hundred randomly selected recently harvested units on managed land in Oregon and Washington with both fish-bearing and non-fish-bearing sections of stream. Wider buffers on larger streams had a greater magnitude of variability, but when normalized by stream size, variation was greater in smaller streams, and overall, variation ranged from 25% to 50% of the mean width. Despite local variability, buffer widths rarely fell below 9.14 m (30 ft). The variation quantified here provides an initial measure of variability to inform future management, given emerging interest in variable retention buffers. Study Implications: In contemporary forest management, riparian and aquatic habitat protection usually relies on minimum buffer width regulations. Minimum requirements create the perception that buffered edges are uniform distances from streams containing little variability, so conceptual papers have suggested alternative buffer configurations with variable retention edges to enhance ecological benefits. However, there are several reasons why, in practice, buffer widths may vary substantially from minimum sizes within and across harvest units under current forest management, but this potential variation has not been quantified. Before considering alternative riparian management options, it is necessary to quantify and understand the inherent variability in current practices. By utilizing high-resolution aerial imagery and digital elevation models, we quantified buffer width variation. Using a set of two hundred recently harvested units, we demonstrate notable buffer width variation across managed Pacific Northwest forests. We attribute the variation in buffer widths to the presence of road crossings, tributary junctions, underlying valley slope and slope variation, and flexibility in regulations that may be overlooked in broad evaluations of strict minimum widths. Understanding fundamental information about buffer width variability provides information about current practices and provides a standard against which proposed increases in variability may be compared.

The impact of executives' green experience on environmental, social, and governance (ESG) performance: Evidence from China

Corporate Environmental, Social, and Governance (ESG) performance reflects the alignment of firms with national strategies and macroeconomic development principles, and serves as a crucial foundation for achieving sustainable development. Investigating the factors driving corporate ESG performance has significant theoretical and practical implications. This study utilizes data from Shanghai and Shenzhen A-share listed companies in China from 2010 to 2022 and theoretically analyzes and empirically tests the effects and mechanisms of executives' green experience on overall ESG performance and performance on each element of ESG. The results indicate that executives' green experience enhances the efficiency of corporate green patent innovation, thereby improving environmental performance. In addition, green experiences foster environmental investment and charitable donations, leading to better social performance. Executives with green experience also attract green investors and analysts' attention, thereby improving corporate governance and ultimately positively affecting overall corporate ESG performance. The robustness of these findings is confirmed through alternative variable measurements, Propensity Score Matching(PSM) Tests, Entropy Balancing Tests, Instrumental Variable Tests, and controlling for the impact of significant public health events. Further analysis revealed that the positive effects of executives' green experiences on corporate ESG performance are more pronounced in non-heavily polluting corporations, non-state-owned corporations, and firms where executives have higher power. This study enriches research on the consequences of executives' green experiences and the determinants of corporate ESG performance, extending the boundaries of how executive characteristics influence micro-level corporate outcomes. It also provides valuable references for regulatory bodies promoting ESG practices and decision-making by capital market participants.

Influence of large scale climate drivers on hydro-climate variability in Dwangwa River Basin, Malawi

Large-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of the Indian Ocean Dipole (IOD) on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann–Kendall trend test at α = 0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs between 20 and 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20 to 30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann–Kendall test statistics showed insignificant decrease in annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Niño and a positive IOD could cause DRB to have low water availability. Therefore, the study demonstrates that rainfall is experiencing a decreasing trend, which is driven by large-scale mechanics.

Invariant contexts reduce response time variability in visual search in an age-specific way: A comparison of children, teenagers, and adults.

Contextual cueing is a phenomenon in which repeatedly encountered arrays of items can enhance the visual search for a target item. This is widely attributed to attentional guidance driven by contextual memory acquired during visual search. Some studies suggest that children may have an immature ability to use contextual cues compared to adults, while others argue that contextual learning capacity is similar across ages. To test the development of context-guided attention, this study compared contextual cueing effects among three age groups: adults (aged 18-33 years, N = 32), teenagers (aged 15-17 years, N = 41), and younger children (aged 8-9 years, N = 43). Moreover, this study introduced a measure of response time variability that tracks fluctuations in response time throughout the experiment, in addition to the conventional analysis of response times. The results showed that all age groups demonstrated significantly faster responses in repeated than non-repeated search contexts. Notably, adults and teenagers exhibited smaller response time variability in repeated contexts than in non-repeated ones, while younger children did not. This implies that children are less efficient at consolidating contextual information into a stable memory representation, which may lead to less stable attentional guidance during visual search.

Validity of Ultra-Short-Term Heart Rate Variability Derived from Femoral Arterial Pulse Waveform in a British Military Cohort.

Various non-electrocardiogram (ECG) based methods are considered reliable sources of heart rate variability (HRV) measurement. However, the ultra-short recording of a femoral arterial waveform has never been validated against the gold-standard ECG-based 300s HRV and was the aim of this study.A validity study was conducted using a sample from the first follow-up of the longitudinal ADVANCE study UK. The participants were adult servicemen (n = 100); similar in age, rank, and deployment period (Afghanistan 2003-2014). The femoral arterial waveforms (14s) from the pulse wave velocity (PWV) assessment, and ECG (300s) were recorded at rest in the supine position using the Vicorder™ and Bittium Faros™ devices, respectively, in the same session. HRV analysis was performed using Kubios Premium. Resting heart rate (HR) and root mean square of successive differences (RMSSD) were reported. The Bland-Altman %plots were constructed to explore the PWV-ECG agreement in HRV measurement. A further exploratory analysis was conducted across methods and durations.The participants' mean age was 38.0 ± 5.3 years. Both PWV-derived HR (r = 0.85) and RMSSD (rs=0.84) showed strong correlations with their 300s-ECG counterparts (p < 0.001). Mean HR was significantly higher with ECG than PWV (mean bias: -12.71 ± 7.73%, 95%CI: -14.25%, -11.18%). In contrast, the difference in RMSSD between the two methods was non-significant [mean bias: -2.90 ± 37.82% (95%CI: -10.40%, 4.60%)] indicating good agreement. An exploratory analysis of 14s ECG-vs-300s ECG measurement revealed strong agreement in both RMSSD and HR.The 14s PWV-derived RMSSD strongly agrees with the gold-standard (300s-ECG-based) RMSSD at rest. Conversely, HR appears method sensitive.

Weed resistance prediction: a random forest analysis based on field histories

Herbicide resistance has become a major issue in recent decades. Because diagnostics is still expensive, prediction models are helping to assess risks of resistance evolution. In this paper the influence of weed management on the evolution of resistance of the grass Alopecurus myosuroides Huds to ALS-inhibitors is investigated based on field history data from two regions, Hohenlohe in Germany and Champagne in France respectively. Champagne data also comprise information on Lolium spp. Using a random forest method variable importance and performance measures were obtained for a large number of single analyses allowing for a statistical analysis of the four performance measures, type I error, type II error, AUC and accuracy. It could be shown that acceptable predictions can be obtained for training data from Hohenlohe applied to Champagne and vice versa. It turned out that in nearly all analyses false negative classifications are more frequent than false positive classifications. Based on a combined training set of A.myosuroides samples from Hohenlohe and Champagne resistance status of Lolium spp. from the Champagne dataset can be predicted with a good accuracy. This suggest that resistance evolution to ALS-inhibitors of the two grasses are closely related. This work is a first step to set a simple herbicide resistance prediction tool to the users based on field history weed management data.