A comparison of Zayandehrood River water values for agriculture and the environment

River water management is challenging not only since they are open systems with changing physical structures, but also because the water values are mostly unknown over varied sectors. If policymakers grasp water values, water management will be more efficient. This research intends to examine the values of water in agriculture, which receives the most substantial portion of water resources, with the values of water in the environment in Isfahan located in the Zayandehrood River basin of Iran. The consequences of contingent valuation and production function methods revealed that per cubic metre value of water is 13 times higher in the environment than agriculture. The government should reconsider the higher value of the environment despite it is a non‐market value. The contingent valuation model additionally proved that women exhibited 21% more willingness to pay than men in order to protect the environment; however, they are paid less by 36%.     

Experimental and numerical studies on heat transfer enhancement for air conditioner condensers using a wavy fin with a rectangular winglet

Journal of Mechanical Science and Technology - The air side thermal performance of wavy fins with rectangular winglets are studied using experimental and numerical methods. The following parameters...     

High surface area benzimidazole based porous covalent organic framework for removal of methylene blue from aqueous solutions

In this study, a highly thermally stable benzimidazole based covalent organic framework (bCOF) was synthesized by the reaction of perlin‐tetracarboxylic anhydride, 5‐aminoisophthalic acid and diaminobenzidine in polyphosphoric acid medium. The synthesized porous bCOF was identified by different techniques. From CO₂ adsorption, the micropore surface area of the bCOF was found to be 856 m² g^?1; this synthesized bCOF has a widespread netting construction, and it has various porosity. The TGA results displayed the high thermal strength of the synthesized bCOF. After characterization, the bCOF was used for the removal of methylene blue from aqueous solution, and the essential parameters such as pH of the solution, contact time and initial concentration were assessed. The maximum adsorption capacity of the bCOF for removal of methylene blue was 63.29 mg g^?1 after only 40 min contact time at a pH of 6. Two kinetics and adsorption models were used for interpretation, and the outcomes showed that the pseudo‐second‐order and Langmuir models respectively were better fitted to the results. According to the results, the novel bCOF can be applied to remove methylene blue from aqueous solution. © 2020 Society of Chemical Industry     

Scenario Assessment of Streamflow Simulation and its Transition Probability in Future Periods Under Climate Change

The effect of climate change on water resources is an important challenge. To analyze the negative effects of this phenomenon and recommend adaptive measures, it is necessary to assess streamflow simulation scenarios and streamflow transition probabilities in future periods. This paper employs the HadCM3 (Hadley Centre Coupled Model, version 3) model to generate climate change scenarios in future periods (2010–2039, 2040–2069, and 2070–2099) and under A2 emission scenarios. By introducing climatic variable time series in future periods to the IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) hydrological model, long-term streamflow simulation scenarios are produced. By fitting statistically different distributions on runoff produced by using goodness-of-fit tests, the most appropriate statistical distribution for each month is chosen and relevant statistical parameters are extracted and compared with statistical parameters of runoff in the base period. Results show that long-term annual runoff average in the three future periods compared to the period 2000–1971 will decrease 22, 11, and 65 %, respectively. ?Despite the reduction in total runoff volume in future periods compared to the baseline period, the decrease is related to medium and high flows. In low flows, total runoff volumes for future periods compared to the baseline period will increase 47, 41, and 14 %, respectively. To further assess the impact of annual average runoff on flows, it is necessary to examine the correlation of time series using streamflow transition probabilities. To compare the streamflow transition probability in each of the future periods with base period streamflow in each month, streamflow is discretized and performance criteria are used. Results show a low coefficient of correlation and high error indicators.     

Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period

The design of new water distribution networks (WDNs) is an important social problem. Failures during an operational period provoke deficits in consumption nodes thus decreasing the performance of the network. WDN performance can be defined as the ability to sufficiently secure demand and desirable pressure in nodes based on changes in design parameters. This paper focuses on the evaluation of network performance during an operational period, taking into account pipe roughness uncertainty. A network analysis is performed by generating probabilistic series of pipe roughness using Monte Carlo simulation (MCS) in the operational period of the Two-loop WDN. Results show that an increase in pipe roughness uncertainty causes a decrease in network performance in the operational period. Furthermore, the network has a desirable efficiency only in the first 10 years. Thus, the proposed design methodology that considers the uncertainty of design variables is an effective procedure to evaluate network performance.     

Energy-efficient,fast-settling,modified nested-current-mirror,single-stage-amplifier for high-resolution LCDs in 90-nm CMOS

The Internet of Things (IoT) presents opportunities to address a variety of systemic, metabolic healthcare issues. Cardiovascular disease and diabetes are among the greatest contributors to premature death worldwide. Wireless wearable continuous monitoring systems such as ECG sensors connected to the IoT can greatly decrease the risk of death related to cardiac issues by providing valuable long-term information to physicians, as well as immediate contact with emergency services in the event of a heart attack or stroke. In this report we discuss the fabrication, characterization and validation of composite fabric ECG sensors made from Nylon^® coated with reduced graphene oxide (rGO_x) as part of a self-powered wearable IoT sensor. We utilize an electronic probing station to measure electrical properties, take live ECG data to measure signal reliability, and provide detailed surface characterization through scanning electron microscopy. Finally, bonding between the layers of the composite and between composite and the Nylon^® is analyzed by Fourier transform Infrared spectroscopy. Furthermore, a low power analog front end circuit designed in 65 nm CMOS process is presented to interface the sensor with a system on chip used in a wearable IoT healthcare device.     

A Model-Based Reinforcement Learning Protocol for Routing in Vehicular Ad hoc Network

Todays by equipping vehicles with wireless technologies, Vehicular Ad Hoc Network (VANET) has been emerged. This type of network can be utilized in many fields such as emergency, safety or entertainment. It is also considered as a main component of intelligent transportation system. However, due to the nodes velocity (vehicles velocity), varying density, obstacles and lack of fixed infrastructure, finding and maintaining a route between nodes are always challenging in VANET. Any routing protocol can be effective only if the nodes can learn and adapt themselves with such a dynamic environment. One way to achieve this adaptation is using machine learning techniques. In this paper we try to reach this goal by applying Multi-Agent Reinforcement Learning (MARL) that enables agents to solve routing optimization problems in a distributed way. Although model-free Reinforcement Learning (RL) schemes are introduced for this purpose, such techniques learn using a trial and error scheme in a real environment so they cannot reach an optimal policy in a short time. To deal with such a problem, we have proposed a mode-based RL based routing scheme. We have also developed a Fuzzy Logic (FL) system to evaluate the quality of links between neighbor nodes based on parameters such as velocity and connection quality. Outputs of this fuzzy system have been used to form the state transition model, needed in MARL. Results of evaluations have shown that our approach can improve some routing metrics like delivery ratio, end-to-end delay and traffic overhead.