Work addiction risk, stress and well-being at work: testing the mediating role of sleep quality.

Introduction: Attention to work addiction risk is growing; however, more studies are needed to explore the possible impact of work addiction risk on various aspects of employees' work and life domains. Although several studies have considered the antecedents or consequences of work addiction risk, this study particularly focuses on sleep quality as a potential explanatory underlying mechanism in the relation between work addition risk and three outcome variables including stress at home, stress at work and well-being. Method: The data was collected using an online platform and participants consisted of 188 French employees who were selected using simple random sampling method. Participants responded to the survey including the Work Addiction Risk Test (WART), stress at work, well-being, and sleep quality. The data was analyzed using JASP and SPSS-26 programs. Results: The results revealed that there are significant positive relationships between work addiction risk and both stress at home and at work and negative relationships between work addiction risk and both sleep quality and well-being. In addition, the analyses of the mediation paths suggest the significant mediation role of sleep quality for the link between work addition risk and stress at work as well as the link between work addiction risk and well-being. Discussion: Given the verified mediating role of sleep quality in the relationship between work addiction, stress and wellbeing, it is recommended that organizations and companies pay particular attention to their employees' sleep quality.

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Effective conductivity of inertial flows through porous media.

We study two-dimensional incompressible inertial flows through porous media. At core (small) scale, we prove that the constitutive, nonlinear model can be rewritten into a linear one by means of a new parameter K^{★} which encompasses all the inertial effects. In natural (large-scale) formations, K^{★} is erratically changing, and we analytically compute its counterpart, which is coined generalized effective conductivity, by the self-consistent approach (SCA). In spite of its approximate nature, the SCA leads to simple results that are in good agreement with Monte Carlo simulations.

Flood occurrence analysis in small urban catchments in the context of regional variability.

An original method for analyzing the influence of the meteorological, as well as physical-geographical conditions on the flooding of stormwater in small urban catchment areas is proposed. A logistical regression model is employed for the identification of the flooding events. The elaborated model enables to simulate the stormwater flooding in a single rainfall event, on the basis of the rainfall depth, duration, imperviousness of the catchment and its spatial distribution within the analyzed area, as well as the density of the stormwater network. The rainfall events are predicted considering the regional convective rainfall model for 32 rain gauges located in Poland, based on 44 years of rainfall data. In the study, empirical models are obtained to calculate the rainfall duration conditioning the flooding of stormwater in a small urban catchment area depending on the characteristics of the examined urban basins. The empirical models enabling to control the urbanization process of catchment areas, accounting for the local rainfall and meteorological characteristics are provided. The paper proposes a methodology for the identification of the areas especially sensitive to stormwater flooding in small urban catchment areas depending to the country scale. By employing the presented methodology, the regions with most sensitive urban catchments are identified. On this basis, a ranking of towns and cities is determined from the most sensitive to flooding in small urban catchment areas to the regions where the risk of flooding is lower. Using the method developed in the paper, maximum impervious catchment area are determined for the selected regions of the country, the exceedance of which determines the occurrence of stormwater flooding.

Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities.

Changes in the hydrologic cycle due to increase in greenhouse gases cause variations in intensity, duration, and frequency of precipitation events. Quantifying the potential effects of climate change and adapting to them is one way to reduce urban vulnerability. Since rainfall characteristics are often used to design water structures, reviewing and updating rainfall characteristics (i.e., Intensity-Duration-Frequency (IDF) curves) for future climate scenarios is necessary (Reg Environ Change 13(1 Supplement):25-33, 2013). The present study regards the evaluation of the IDF curves for three case studies: Addis Ababa (Ethiopia), Dar Es Salaam (Tanzania) and Douala (Cameroon). Starting from daily rainfall observed data, to define the IDF curves and the extreme values in a smaller time window (10', 30', 1 h, 3 h, 6 h, 12 h), disaggregation techniques of the collected data have been used, in order to generate a synthetic sequence of rainfall, with statistical properties similar to the recorded data. Then, the rainfall pattern of the three test cities was analyzed and IDF curves were evaluated. In order to estimate the contingent influence of climate change on the IDF curves, the described procedure was applied to the climate (rainfall) simulations over the time period 2010-2050, provided by CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici). The evaluation of the IDF curves allowed to frame the rainfall evolution of the three case studies, considering initially only historical data, then taking into account the climate projections, in order to verify the changes in rainfall patterns. The same set of data and projections was also used for evaluating the Probable Maximum Precipitation (PMP).

Experimental assessment of level pool routing in preliminary design of floodplain storage.

Among control structures in flood management, floodplain storage represents one of the most effective measures, since it holds part of flood volume in a delimited area thus reducing the peak discharge. Sizing of floodplain storage, both on-stream and off-stream, is complex and several methodologies for preliminary design are available in literature, almost all assuming level pool reservoir routing, i.e. the water level in the floodplain is horizontal during the storage filling. Few studies examine the accuracy of that assumption. The present paper work reports an extensive experimental investigation to assess the reliability of level pool routing in the design of on-stream floodplain storages. The good agreement between numerical and experimental values during the filling phase confirmed the reliability of the hypothesis in the preliminary sizing of on-stream floodplain storage. In contrast, even significant differences can be shown during the floodplain draining, due to vegetation and bottom irregularities.