A Novel Procedure for Minimizing the Volume of Water Tanks in Water Supply Systems

Due to the complexity of calculating the minimum required volume of water tanks and the associated regime of pumping water into the tank depending on the consumption pattern in the water supply systems, finding the functional dependence of these variables is a complex process. The main idea of this paper was to provide a methodology for the calculation of the minimum water tank volume considering all input variables, which could be used in a simple and applicable way in everyday water supply management and engineering. As a final product, a desktop application TankOPT was developed that is easy to run and use on a PC with a user-friendly interface for data entry (data on maximum daily consumption and the pattern of daily water consumption). A software solution was created based on a numerical model that simplifies the usual manual calculations using known spreadsheet software and solves this problem. The solution was determined with combinations of the start and duration of water pumping in the water tank, for which the minimum required volume of the tank is obtained. JavaScript programming language was used to create the app. The use and operation of the application are shown through two hypothetical examples

Discrete Emotion Effects on Lexical Decision Response Times

Our knowledge about affective processes, especially concerning effects on cognitive demands like word processing, is increasing steadily. Several studies consistently document valence and arousal effects, and although there is some debate on possible interactions and different notions of valence, broad agreement on a two dimensional model of affective space has been achieved. Alternative models like the discrete emotion theory have received little interest in word recognition research so far. Using backward elimination and multiple regression analyses, we show that five discrete emotions (i.e., happiness, disgust, fear, anger and sadness) explain as much variance as two published dimensional models assuming continuous or categorical valence, with the variables happiness, disgust and fear significantly contributing to this account. Moreover, these effects even persist in an experiment with discrete emotion conditions when the stimuli are controlled for emotional valence and arousal levels. We interpret this result as evidence for discrete emotion effects in visual word recognition that cannot be explained by the two dimensional affective space account

A novel method for classifying cortical state to identify the accompanying changes in cerebral hemodynamics

Background: Many brain imaging techniques interpret the haemodynamic response as an indirect indicator of underlying neural activity. However, a challenge when interpreting this blood based signal is how changes in brain state may affect both baseline and stimulus evoked haemodynamics. New method: We developed an Automatic Brain State Classifier (ABSC), validated on data from anaesthetised rodents. It uses vectorised information obtained from the windowed spectral frequency power of the Local Field Potential. Current state is then classified by comparing this vectorised information against that calculated from state specific training datasets. Results: The ABSC identified two user defined brain states (synchronised and desynchronised), with high accuracy (~90%). Baseline haemodynamics were found to be significantly different in the two identified states. During state defined periods of elevated baseline haemodynamics we found significant decreases in evoked haemodynamic responses to somatosensory stimuli. Comparison to existing methods: State classification - The ABSC (~90%) demonstrated greater accuracy than clustering (~66%) or 'power threshold' (~64%) methods of comparison.Haemodynamic averaging - Our novel approach of selectively averaging stimulus evoked haemodynamic trials by brain state yields higher quality data than creating a single average from all trials. Conclusions: The ABSC can account for some of the commonly observed trial-to-trial variability in haemodynamic responses which arises from changes in cortical state. This variability might otherwise be incorrectly attributed to alternative interpretations. A greater understanding of the effects of cortical state on haemodynamic changes could be used to inform techniques such as general linear modelling (GLM), commonly used in fMRI

A generalized invariant imbedding equation II

Research into fetal development and medicin

Comparison of major depression diagnostic classification probability using the SCID, CIDI, and MINI diagnostic interviews among women in pregnancy or postpartum: An individual participant data meta-analysis

Objectives A previous individual participant data meta-analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire-9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum. Methods Data accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics. Results Among fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased. Conclusion Different interviews may not classify major depression equivalently

Overestimation of Postpartum Depression Prevalence Based on a 5-item Version of the EPDS: Systematic Review and Individual Participant Data Meta-analysis

Objective:The Maternal Mental Health in Canada, 2018/2019, survey reported that 18% of 7,085 mothers who recently gave birth reported "feelings consistent with postpartum depression" based on scores >= 7 on a 5-item version of the Edinburgh Postpartum Depression Scale (EPDS-5). The EPDS-5 was designed as a screening questionnaire, not to classify disorders or estimate prevalence; the extent to which EPDS-5 results reflect depression prevalence is unknown. We investigated EPDS-5 >= 7 performance relative to major depression prevalence based on a validated diagnostic interview, the Structured Clinical Interview for DSM (SCID).Methods:We searched Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO, and the Web of Science Core Collection through June 2016 for studies with data sets with item response data to calculate EPDS-5 scores and that used the SCID to ascertain depression status. We conducted an individual participant data meta-analysis to estimate pooled percentage of EPDS-5 >= 7, pooled SCID major depression prevalence, and the pooled difference in prevalence.Results:A total of 3,958 participants from 19 primary studies were included. Pooled prevalence of SCID major depression was 9.2% (95% confidence interval [CI] 6.0% to 13.7%), pooled percentage of participants with EPDS-5 >= 7 was 16.2% (95% CI 10.7% to 23.8%), and pooled difference was 8.0% (95% CI 2.9% to 13.2%). In the 19 included studies, mean and median ratios of EPDS-5 to SCID prevalence were 2.1 and 1.4 times.Conclusions:Prevalence estimated based on EPDS-5 >= 7 appears to be substantially higher than the prevalence of major depression. Validated diagnostic interviews should be used to establish prevalence