scholarly journals Development of Evaluation Method for Variability of Groundwater Flow Conditions associated with long-term topographic change and climate perturbations

2019 ◽  
Vol 26 (1) ◽  
pp. 3-14
Author(s):  
Hironori ONOE ◽  
Hiroshi KOSAKA ◽  
Toshiyuki MATSUOKA ◽  
Tetsuya KOMATSU ◽  
Ryuji TAKEUCHI ◽  
...  
Keyword(s):  
Groundwater Flow ◽  
Evaluation Method ◽  
Flow Conditions

scholarly journals Numerical Assessment of the Influence of Long-Term Topographic Change on Deep Groundwater Flow Conditions

2009 ◽  
Vol 8 (1) ◽  
pp. 40-53 ◽  
Author(s):  
Hironori ONOE ◽  
Eiji SASAO ◽  
Hiromitsu SAEGUSA ◽  
Hiroshi KOSAKA
Keyword(s):  
Groundwater Flow ◽  
Flow Conditions ◽  
Deep Groundwater ◽  
Numerical Assessment

scholarly journals A Multi-Attribute Decision Method under Uncertainty Environment Conditions—The Green Supplier Evaluation Perspective

2021 ◽  
Vol 18 (1) ◽  
pp. 344
Author(s):  
Hao Xu ◽  
Liuxin Chen ◽  
Qiongfang Li ◽  
Jianchao Yang
Keyword(s):  
Environmental Pollution ◽  
Evaluation Method ◽  
External Environment ◽  
Environmental Indicators ◽  
Supplier Evaluation ◽  
Uncertain Environment ◽  
Green Suppliers ◽  
Uncertainty Environment ◽  
Selection Of

Due to the continuous changes of political environment, consumption habits, technological progress and other factors, the external environment of enterprises is full of uncertainty. The turbulence of external environment is not conducive to the long-term operation and development of enterprises, but also brings great challenges to the selection of suppliers. This makes the competition of enterprises focus on how to choose long-term cooperation suppliers in the uncertain external environment. In addition, due to the deterioration of the global environment, governments pay more and more attention to environmental pollution, and consumers are more and more inclined to green consumption, which makes many companies pay more and more attention to environmental indicators when selecting suppliers. In the case of external environment turbulence and serious environmental pollution, the evaluation and selection of green suppliers in uncertain environment is particularly important for the long-term development of enterprises. What’s more, when the supplier’s capability gap is small, the decision-maker often hesitates among several suppliers. In this paper, the hesitant fuzzy is used to describe the hesitant psychology of decision-makers in selecting suppliers, the variance fluctuation is used to describe the characteristics of hesitant fuzzy numbers, and the probability is used to measure the uncertainty of the environment. A green supplier evaluation model under the uncertainty environment is proposed, which comprehensively evaluates the green suppliers under the uncertain environment. Furthermore, it is compared with other methods that do not consider the uncertainty and the adaptability of evaluation method and right confirmation method, so as to reflect the influence of uncertainty to green supplier evaluation and the importance of adaptability of evaluation method and right confirmation method.

scholarly journals Using Long Term Simulations to Understand Heat Transfer Processes during Steady Flow Conditions in Combined Sewers

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 570
Author(s):  
Mohamad Abdel-Aal ◽  
Simon Tait ◽  
Mostafa Mohamed ◽  
Alma Schellart
Keyword(s):  
Heat Transfer ◽  
Heat Recovery ◽  
Predictive Accuracy ◽  
Flow Conditions ◽  
Empirical Relationships ◽  
Taylor Diagram ◽  
Physical Phenomena ◽  
The Heat Transfer Coefficient ◽  
Empirical Approaches

This paper describes a new heat transfer parameterisation between wastewater and in-sewer air based on understanding the physical phenomena observed in free surface wastewater and in-sewer air. Long-term wastewater and in-sewer air temperature data were collected and studied to indicate the importance of considering the heat exchange with in-sewer air and the relevant seasonal changes. The new parameterisation was based on the physical flow condition variations. Accurate modelling of wastewater temperature in linked combined sewers is needed to assess the feasibility of in-sewer heat recovery. Historically, the heat transfer coefficient between wastewater and in-sewer air has been estimated using simple empirical relationships. The newly developed parameterisation was implemented and validated using independent long-term flow and temperature datasets. Predictive accuracy of wastewater temperatures was investigated using a Taylor diagram, where absolute errors and correlations between modelled and observed values were plotted for different site sizes and seasons. The newly developed coefficient improved wastewater temperature modelling accuracy, compared with the older empirical approaches, which resulted in predicting more potential for heat recovery from large sewer networks. For individual locations, the RMSE between observed and predicted temperatures ranged between 0.15 and 0.5 °C with an overall average of 0.27 °C. Previous studies showed higher RMSE ranges, e.g., between 0.12 and 7.8 °C, with overall averages of 0.35, 0.42 and 2 °C. The new coefficient has also provided stable values at various seasons and minimised the number of required model inputs.

scholarly journals Long-Term Monitoring of Radiocesium Concentration in Sediments and River Water along Five Rivers in Minami-Soma City during 2012–2016 Following the Fukushima Dai-ichi Nuclear Power Plant Accident

2018 ◽  
Vol 8 (8) ◽  
pp. 1319 ◽  
Author(s):  
Kiyoshi Shizuma ◽  
Wim Ikbal Nursal ◽  
Yushi Sakurai
Keyword(s):  
River Water ◽  
Nuclear Power ◽  
Gamma Ray ◽  
137Cs Concentration ◽  
Flow Conditions ◽  
High Accumulation ◽  
Ge Detector ◽  
Long Term Monitoring ◽  
Term Monitoring

Radiocesium monitoring in sediments and river water has been conducted along five rivers in Minami-Soma City during 2012–2016 to clarify the temporal changes of radiocesium contamination in these rivers. Sampling has been performed annually under normal flow conditions. Sediment and river water samples were collected from four or five sampling sites along each river. Gamma-ray measurements of sediments were performed using a low-background Ge detector and unfiltered river water was utilized to determine radiocesium concentration using a well-type Ge detector. The 137Cs concentration in sediments was highest at upstream sites and slowly decreased to downstream sites for all rivers reflecting the high radioactive contamination in the upstream area. Temporal decrease of the 137Cs concentration was observed in sediments and river water for each river. The effective half-lives were 1.3–2.1 y for sediments, and 0.9–2.1 y for river water from rivers with upstream dams. On the undammed river, the effective half-lives were 4.7 y and 3.7 y for sediment and river water, respectively. Much longer effective-half-lives might reflect the direct transfer of radiocesium from forests and plains to the river. The 137Cs concentration in riverbed was low in downstream areas, however, accumulation of 137Cs over the floodplain was observed. Rapid decrease of 137Cs contamination through rivers will put residents at ease, but high accumulation of radiocesium over floodplains should be noted for future river decontamination.

scholarly journals Customer Segmentation Through Path Reconstruction

2020 ◽  
Author(s):  
Santiago García
Keyword(s):  
Rapid Development ◽  
Customer Segmentation ◽  
Smart Phones ◽  
Indoor Environments ◽  
Flow Conditions ◽  
Gps Receivers ◽  
Current State ◽  
Different Types

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

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