Physical modeling of the effect of shape, blockage, and flow variability on scour in culvert outlets.

The widespread use of culverts has prompted researchers to focus on developing precise designs to prevent their failure caused by scouring at the culvert outlet. This study employed physical modelling to investigate alternation in culvert outlets under different conditions, including variations in culvert shape, blockage, and flow discharge during steady and unsteady flow conditions. Box and circular culverts were examined with 0%, 15%, and 30% blockage rates at the culvert inlet. For unsteady flow conditions, two hydrographs were generated, each with nine distinct flow discharges, while for steady flow conditions, flow rates of up to 14 l/s and 22 l/s were used. The sediment and flow conditions were carefully selected to ensure clear water throughout the experiments. According to the study results, the scour profile exhibited more growth in the circular culvert compared to the box culvert across all cases. Furthermore, an increase in flow rate led to an increase in the scour hole dimension, and the scouring increased with a rise in hydrograph stepwise. However, when the degree of blockage was increased, a strictly proportional increase in scour depth was not observed across all cases. The results and data presented in this research can be used by other researchers in addition to being used by hydraulic designers.

Assessment of Hygiene Practices, Awareness, and Water Consumption Regarding Covid-19 Among Children in a Refugee Camp.

INTRODUCTION: At the outbreak of infectious diseases, the response of different communities to the disease varies, and children are most affected by the collective anxiety and grief that consequently arises. In this research, the behavior of children and their parents in terms of hygiene and precautions before and during the COVID-19 pandemic was investigated. METHODOLOGY: The focus of the present research was on sanitation facilities, particularly access to end-use of water for hand washing. The research was conducted in Barika Camp, Kurdistan, Iraq and 311 parents and children were interviewed. A data collection team consisting of two females and one male was responsible for gathering data, primarily from women who served as the main respondents. Questionnaires consisted of three main parts: demography, COVID-19 pandemic effects, and sanitary shelter specifications. RESULT: The results demonstrated that the behavior of refugees during the COVID-19 pandemic regarding the priority of child protection, type of disinfectants, and water consumption has significantly altered. These changes mainly depended on the women's age and education level. DISCUSSION: Overall results showed that in 61.09% of the participants, the number of hand washes and in 58.58%, the washing time increased, leading to water shortage in the refugee camp.

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms.

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.