An economical, environmental, and social comparison between vacuum and gravity sewers in decentralized sanitation systems, with Egypt as a case study

2015 ◽  
Vol 5 (4) ◽  
pp. 614-619 ◽  
Author(s):  
Abdelsalam Elawwad ◽  
Mostafa Ragab ◽  
Hisham Abdel-Halim
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Vacuum System ◽  
Quadratic Regression ◽  
Maintenance Costs ◽  
Rural Villages ◽  
Gravity Sewer ◽  
Sanitation Systems ◽  
Better Than

The conventional gravity sewer is the most commonly used rural sewerage system in developing countries. However, this system has many technical, economic, environmental, and social disadvantages. Vacuum sewers could serve as a good competitor as an alternative system to conventional gravity sewers. A sample of 33 rural villages with populations of <10,000 people is selected from Egypt. A statistical analysis was done using SPSS and STATISTICA software where population and area variables had the most significant effect on the calculation of investment, operation, and maintenance costs. It was found that investment costs for the vacuum system were mostly lower than for the conventional one, while operational and maintenance costs played significant roles. Prediction models were obtained based on multiple quadratic regression models. It was found that the vacuum system was economically competitive in large villages with low population densities. Environmentally and socially, the vacuum sewers proved to be better than gravity sewers.

scholarly journals 465Simplifying the Differences between Causal and Prediction Analyses

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Krista Wollny ◽  
Amy Metcalfe ◽  
Deborah McNeil ◽  
Karen Benzies ◽  
Tolulope Sajobi ◽  
...  
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Research Question ◽  
Model Development ◽  
Model Specification ◽  
Clinical Environment ◽  
Clinical Questions ◽  
Conducting Research ◽  
Future Outcomes

Abstract Focus of Presentation Multivariable regression models can be used to answer a variety of clinical questions. The two main objectives of regression models are to either 1) understand an association between one or more exposures and an outcome; or 2) predict future outcomes based on certain exposures or variables. To simplify this, we will consider the former causal analysis, and the latter prediction analysis. This presentation will explain the steps in model development and assessment using a clinical case study, highlighting the similarities and differences. This presentation is aimed at trainees. Findings The key differences between causal and prediction models include: the purpose and research questions, power calculations, variable selection, model specification, testing model fit, and the desired outcome of each model. The case study demonstrates these differences, while working through a causal and prediction model with similar clinical questions. Conclusions/Implications It is important for researchers to consider the purpose of their research question and to tailor the model accordingly. This will guide the model development and interpretation, which are different for causal and prediction analyses. Key messages A thorough understanding of the types of models available, their assumptions, and the process of model development and assessment is essential to conducting research that is valid and applicable to the clinical environment, enabling knowledge translation.

scholarly journals Predictive Structured-Unstructured Interactions in EHR Models: A Case Study of Suicide Prediction

2021 ◽  
Author(s):  
Ilkin Bayramli ◽  
Victor Castro ◽  
Yuval Barak-Corren ◽  
Emily M Madsen ◽  
Matthew K Nock ◽  
...  
Keyword(s):  
Prediction Models ◽  
Predictive Performance ◽  
Structured Data ◽  
Data Types ◽  
Risk Prediction Models ◽  
Clinical Risk ◽  
Suicide Prediction ◽  
Related Mortality ◽  
Better Than

Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p<0.001), likely due to the RF model's ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.

scholarly journals A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China

2019 ◽  
Vol 8 (11) ◽  
pp. 495 ◽  
Author(s):  
Xiong ◽  
Li ◽  
Cheng ◽  
Ye ◽  
Zhang
Keyword(s):  
Regression Models ◽  
Population Distribution ◽  
Percentage Error ◽  
Night Time ◽  
Geographically Weighted Regression Model ◽  
Southwestern Area ◽  
The Mean ◽  
Gwr Model ◽  
Better Than

Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.

STUDY ON THRIVE SCORE IN THE PROGNOSIS OF ISCHEMIC STROKE

2017 ◽  
pp. 22-24
Author(s):  
Thi Thao Nhi Tran ◽  
Dinh Toan Nguyen
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Regression Models ◽  
Cross Sectional Study ◽  
High Rate ◽  
Vascular Events ◽  
Cross Sectional ◽  
Key Words Ischemic Stroke ◽  
Outcome Mortality ◽  
Better Than

Background and Purpose: Stroke is the second cause of mortality and the leading cause of disability. Using the clinical scale to predict the outcome of the patient play an important role in clinical practice. The Totaled Health Risks in Vascular Events (THRIVE) score has shown broad utility, allowing prediction of clinical outcome and death. Methods: A cross-sectional study conducting on 102 patients with acute ischemic stroke using THRIVE score. The outcome of patient was assessed by mRankin in the day of 30 after stroke. Statistic analysis using SPSS 15.0. Results: There was 60.4% patient in the group with THRIVE score 0 – 2 points having a good outcome (mRS 0 - 2), patient group with THRIVE score 6 - 9 having a high rate of bad outcome and mortality. Having a positive correlation between THRIVE score on admission and mRankin score at the day 30 after stroke with r = 0.712. THRIVE score strongly predicts clinical outcome with ROC-AUC was 0.814 (95% CI 0.735 - 0.893, p<0.001), Se 69%, Sp 84% and the cut-off was 2. THRIVE score strongly predicts mortality with ROC-AUC was 0.856 (95% CI 0.756 - 0.956, p<0.01), Se 86%, Sp 77% and the cut-off was 3. Analysis of prognostic factors by multivariate regression models showed that THRIVE score was only independent prognostic factor for the outcome of post stroke patients. Conclusions: The THRIVE score is a simple-to-use tool to predict clinical outcome, mortality in patients with ischemic stroke. Despite its simplicity, the THRIVE score performs better than several other outcome prediction tools. Key words: Ischemic stroke, THRIVE, prognosis, outcome, mortality

Municipal wastewater treatment with pond–constructed wetland system: a case study

2005 ◽  
Vol 51 (12) ◽  
pp. 325-329 ◽  
Author(s):  
X. Wang ◽  
X. Bai ◽  
J. Qiu ◽  
B. Wang
Keyword(s):  
Constructed Wetland ◽  
Municipal Wastewater ◽  
Municipal Wastewater Treatment ◽  
Oxidation Pond ◽  
System A ◽  
Treated Effluent ◽  
Wetland System ◽  
Oxidation Ponds ◽  
Better Than

The performance of a pond–constructed wetland system in the treatment of municipal wastewater in Kiaochow city was studied; and comparison with oxidation ponds system was conducted. In the post-constructed wetland, the removal of COD, TN and TP is 24%, 58.5% and 24.8% respectively. The treated effluent from the constructed wetland can meet the Chinese National Agricultural and Irrigation Standard. The comparison between pond–constructed wetland system and oxidation pond system shows that total nitrogen removal in a constructed wetland is better than that in an oxidation pond and the TP removal is inferior. A possible reason is the low dissolved oxygen concentration in the wetland. Constructed wetlands can restrain the growth of algae effectively, and can produce obvious ecological and economical benefits.

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia

Risks ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Valentina Kravchenko ◽  
Tatiana Kudryavtseva ◽  
Yuriy Kuporov
Keyword(s):  
Regression Models ◽  
Public Procurement ◽  
Ordinary Least Squares ◽  
Economic Security ◽  
Government Procurement ◽  
Security Monitoring ◽  
Significant Relationships ◽  
Monitoring Tools ◽  
Russian Region

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.

scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

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