Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

According to a United Nations report, the world population will increase from 7 billion to 9 billion by 2050. Further, the water stress level is more than 70% in 22 countries while in another 31 countries it is between 25% and 70%. More than 2 billion people live in these 53 countries which are all underdeveloped. Water use has increased by 1% per year since the 1980s, so global demand is expected to rise by 30% by 2050. Thus, efficient water grid management is imperative to ensure there is sufficient water for the future. Information and Communication Technology (ICT) can be used to create smart water grids to optimize water distribution, reduce waste and leakage, and resolve quality and overuse issues. In this work, a low cost, real-time, reliable and sustainable IoT based solution called SmartTubewell is proposed for smart water grid management. It is composed of two components, a sensor node installed at tube wells and an application layer on Amazon Web Services (AWS) for data analysis, storage and processing. The sensor node is based on a Raspberry Pi with integrated current and voltage sensors and a local database. The sensor data is transmitted to AWS using a cellular (GPRS) network. A comparison between the proposed system and SCADA is presented which shows that SmartTubewell has a much lower cost. A field test with multiple tube wells in Peshawar, Pakistan indicates that this is a suitable solution for developing countries

A Microscopic Traffic Flow Model Characterization for Weather Conditions

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length 800 m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather

A New Driver Model Based on Driver Response

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent

A Microscopic Traffic Model Considering Time Headway and Distance Headway

A microscopic traffic model is presented which employs differences in velocity to characterize driver behavior. The Intelligent Driver (ID) model is based on an acceleration constant which cannot capture different traffic conditions. Further, it is not based on traffic physics and so can produce inaccurate results. The proposed model is an improved ID model and both are evaluated on a 2000 m circular road. The results obtained show that the proposed model can appropriately characterize traffic flow and density. Further, the variations in flow and velocity are smoother than with the ID model. This is because the proposed model is based on actual traffic parameters rather than an unrealistic traffic exponent

A New Driver Model Based on Driver Response

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent

Terlipressin in combination with albumin as a therapy for hepatorenal syndrome in patients aged 65 years or older

Introduction and Objectives: Clinical data for older patients with advanced liver disease are limited. This post hoc analysis evaluated the efficacy and safety of terlipressin in patients aged ≥65 years with hepatorenal syndrome using data from 3 Phase III, randomized, placebo-controlled studies(OT-0401, REVERSE, CONFIRM). Patients and Methods: The pooled population of patients aged ≥65 years (terlipressin, n = 54; placebo, n = 36) was evaluated for hepatorenal syndrome reversal—defined as a serum creatinine level ≤1.5 mg/dL (≤132.6 μmol/L) while receiving terlipressin or placebo, without renal replacement therapy, liver transplantation, or death—and the incidence of renal replacement therapy (RRT). Safety analyses included an assessment of adverse events. Results: Hepatorenal syndrome reversal was almost 2-times higher in terlipressin-treated patients compared with patients who received placebo (31.5% vs 16.7%; P = 0.143). Among surviving patients, the need for RRT was significantly reduced in the terlipressin group, with an almost 3-times lower incidence of RRT versus the placebo group (Day 90: 25.0% vs 70.6%; P = 0.005). Among 23 liver-transplant-listed patients, significantly fewer patients in the terlipressin versus placebo group needed RRT by Days 30 and 60 (P = 0.027 each). Fewer patients in the terlipressin group needed RRT post-transplant (P = 0.011). More terlipressin-treated patients who were listed for and received a liver transplant were alive and RRT-free by Day 90. No new safety signals were revealed in the older subpopulation compared with previously published data. Conclusions: Terlipressin therapy may lead to clinical improvements in highly vulnerable patients aged ≥65 years with hepatorenal syndrome. Clinical trial numbers: OT-0401, NCT00089570; REVERSE, NCT01143246; CONFIRM, NCT0277071