Nitrate and Nitrite Level of Drinking Water and the Risk of Upper Gastrointestinal Cancers in Urban Areas of Golestan Province, Northeast of Iran

Abstract: Background & Aims: Esophageal and gastric cancers are among prevalent cancers in the world and it is believed that nitrate and nitrite contaminations of drinking water are important factors in increasing the risk of these cancers. This study was designed to determine the correlations between these factors and upper gastrointestinal cancers. Methods: In this ecologic study, mean concentrations of nitrite and nitrate of drinking waters in Golestan urban areas were obtained during 2004-2005. All patients with esophageal and gastric cancers during this period who resided in urban areas were recruited to estimate the incidence rate and Age Standardized Rate (ASR) of these cancers. The province was divided into three regions of low, intermediate and high incidence based on 33% and 66% quartiles of both cancers. Spearman Correlation Coefficient and regression line were used to analyze data. Results: Based on the results, nitrite and nitrate concentration of drinking waters in all three regions were in the standard range. There was a significant positive correlation between nitrate increase and esophageal cancer incidence (R=0.624 , P=0.013). There was no correlation between levels of nitrite in drinking water and the risk of esophageal and stomach cancer. Conclusions: It seems that there is an increased risk of esophageal cancer correlated with higher nitrate levels in drinking water. But nitrite level of drinking water has no impact on the esophageal and gastric cancer, from the ecological point of view. Further studies on food resources and drinking water of urban and rural areas are recommended to determine the effects of these factors on the upper gastrointestinal cancers. Keywords: Nitrate, Nitrite, Drinking water, Esophageal cancer, Gastric cancer, Golestan provinc

V2G Services for Renewable Integration

With the proliferation of renewable energy sources (RES) and the growing consumer demand for plug-in hybrid (PHEV) and total electric vehicles (EV), the limitations of the aging electrical grid distribution infrastructure is becoming more and more apparent. The development of better infrastructure, therefore, is at the forefront of research. The development of a smart grid, a bidirectional distribution infrastructure, will allow for two-way “communication” of power distributors and aggregators with multiple smart platforms, such as smart buildings, homes, and vehicles. The focus of this chapter is to outline the means of (electrical) vehicle to (smart) grid (V2G) interactions and how attaining a synergistic relationship is vital to improving the way power is distributed. The ability of fleets of EVs to act as a unit for excess power storage allows for the increased integration of RES into existing grid infrastructure and smart grids in the future through the bidirectional communication; providing support, giving back stored power into the grid to lessen the load felt by generation utilities, augment stochastic RES when generation is not meeting demands, lowering costs for both sellers and buyers, and above all, working toward the betterment of Earth

ZnO/1-hexyl-3-methylimidazolium chloride paste electrode, highly sensitive lorazepam sensor

The measurement of pharmaceutical compounds in biological fluids is considered an effective way to evaluate their effectiveness. On the other hand, lorazepam is a drug with good efficiency in treatment and some side effects, which measurement is very important. In this study, the ZnO nanoparticle was synthesized as an electrocatalyst by chemical precipitation method. In continuous a simple modification on paste electrode (PE) by ZnO nanoparticle (ZnO-NPs) and 1-hexyl-3-methylimidazolium chloride (HMImCl) was made and new sensor was used for sensing of lorazepam. The HMImCl/ZnO-NPs/PE showed catalytic behavior on oxidation signal of lorazepam and improved its signal about 2.17 times compared to unmodified PE. On the other hand, oxidation signal of lorazepam was reduced about 110 mV at surface of HMImCl/ZnO-NPs/PE compare to unmodified PE that confirm accelerating the electron exchange process after modification of sensor by HMImCl and ZnO-NPs as powerful catalysts. The HMImCl/ZnO-NPs/PE was used for monitoring of lorazepam in water and injection samples and results showed recovery data 98.5 to 103.5 % that are acceptable for a new sensor

Characterization of Granular Materials and Compaction Methods and Application in Design of Transportation Infrastructure

In this study, the characterization of soils and granular materials and their variation with the compaction method is investigated. Impact hammer compaction is the most prevalent method for sample fabrication of granular materials in the laboratory. Factors such as low precision of unconfined strength test and the presence of interface between layers can be downsides of this compaction method. In this research, an alternative laboratory compaction method for granular materials is proposed and studied. The effects of using Superpave gyratory compactors (SGC) on the compaction and engineering properties of unbound granular materials used in transportation infrastructure is investigated. An experimental program is performed on the specimens compacted with both the gyratory compactor and impact hammer. Unconfined compressive strength tests are conducted to investigate whether using gyratory compaction can improve the precision of this test. Furthermore, maximum dry density and optimum moisture content are determined from each compaction technique. Statistical analyses are also performed on the experimental results to compare maximum dry density, optimum moisture content, and compressive strength in the studied materials. Permanent deformation and resilient modulus testing and modeling, as pavement performance-related characteristics used in the mechanistic-empirical design of pavements, are performed on the specimens fabricated with these two procedures. Variation of these characteristics with the compaction method is studied. Moreover, filter paper test to measure the soil suction, laser particle size analyzer to obtain percent of fines content, Percometer to measure dielectric constant, Methylene blue test, and Aggregate Imaging System (AIMS) tests are used. Therefore, the effects of material properties and compaction method are both investigated on the engineering behavior. The resilient modulus model incorporating suction, moisture conditions, and stress states is studied. Moreover, prediction models for the coefficients of the resilient modulus model are developed using the performance-related properties. The prediction models for the coefficients of the permanent deformation model are also developed using the performance related properties. Additionally, an equation for estimation of compaction energy is also developed to quantify the compaction effort required using gyratory compactor, that reveals substantial difference between base course materials. The results generally have shown that gyratory compactor produces a different mechanism of compaction from the impact hammer compaction. Furthermore, the prediction of conditions of granular materials using non-destructive testing techniques is investigated using the suction and dielectric constant. CT scanning also captures the difference between structure of the specimens compacted with the two methods

Removing Barriers for Effective Deployment of Intermittent Renewable Generation

The stochastic nature of intermittent renewable resources is the main barrier to effective integration of renewable generation. This problem can be studied from feeder-scale and grid-scale perspectives. Two new stochastic methods are proposed to meet the feeder-scale controllable load with a hybrid renewable generation (including wind and PV) and energy storage system. For the first method, an optimization problem is developed whose objective function is the cost of the hybrid system including the cost of renewable generation and storage subject to constraints on energy storage and shifted load. A smart-grid strategy is developed to shift the load and match the renewable energy generation and controllable load. Minimizing the cost function guarantees minimum PV and wind generation installation, as well as storage capacity selection for supplying the controllable load. A confidence coefficient is allocated to each stochastic constraint which shows to what degree the constraint is satisfied. In the second method, a stochastic framework is developed for optimal sizing and reliability analysis of a hybrid power system including renewable resources (PV and wind) and energy storage system. The hybrid power system is optimally sized to satisfy the controllable load with a specified reliability level. A load-shifting strategy is added to provide more flexibility for the system and decrease the installation cost. Load shifting strategies and their potential impacts on the hybrid system reliability/cost analysis are evaluated trough different scenarios. Using a compromise-solution method, the best compromise between the reliability and cost will be realized for the hybrid system. For the second problem, a grid-scale stochastic framework is developed to examine the storage application and its optimal placement for the social cost and transmission congestion relief of wind integration. Storage systems are optimally placed and adequately sized to minimize the sum of operation and congestion costs over a scheduling period. A technical assessment framework is developed to enhance the efficiency of wind integration and evaluate the economics of storage technologies and conventional gas-fired alternatives. The proposed method is used to carry out a cost-benefit analysis for the IEEE 24-bus system and determine the most economical technology. In order to mitigate the financial and technical concerns of renewable energy integration into the power system, a stochastic framework is proposed for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A Non-dominated Sorting Genetic Algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy

FIBER ART IN INTERACTIONS BETWEEN CONTEMPORARY DISCIPLINES OF ARTS

Globalization has currently provided concept of arts with new approaches, the most significant of which is that art has entered a new process in an interdisciplinary dimension. Whatever is known of arts changes not only through its related definitions, meanings and contents but also with disappearance and fusion of borders between all disciplines of arts. Bridges and pluralism established between interdisciplinary arts in post modernism in1960's in particular undoubtedly caused very different and attentionattracting(interesting) products of arts to emerge. Closely interrelated with painting, sculpture and architecture since its appearance, fiber art has not only declared its own independence together with the fact that textile materials were on the agenda for their uses in disciplines of plastic arts but began to form its current identity by making leaps under the influence of countless search as well. The aim of the present study is not to define fiber art intertwined with numerous disciplines but rather assess and understand the process of its development and transformation in the light of data in the past

Voltage and power control for minimising converter and distribution losses in autonomous microgrids

Voltage source converters (VSCs) are widely used in microgrids to interface the renewable resources with the electrical network. In autonomous microgrids with the dc distribution network, the optimal dc voltage reference for the one VSC that operates in the voltage regulator mode and the optimal reference power settings of the remaining VSCs working in the power dispatcher mode have to be pre-determined. Emulating the utility grid, these settings and control modes are commonly selected such that the dc voltage is maintained within desired margins, typically ±10% around the rated value. In this study, the objective function is minimisation of the converter and distribution line losses. All the operational modes and limits of VSCs have been taken into account. Genetic algorithm has been utilised in solving the optimisation problem. Owing to limited available power from renewables, reducing the converter and distribution system losses will enhance the survivability of the microgrid and ease the cooling requirements, resulting in a more compact system. A model of a 20-bus microgrid with the dc distribution network is employed to verify the effectiveness of the presented optimisation algorithm