Change in pore size distribution of compacted soil layers and its effect on solute breakthrough curves

It is often useful to predict contaminant migration from waste containment systems, such as landfills, as part of the assessment of the overall impact of such systems on the receptor environment. In many instances, material properties, for example, those of the liner, are assumed to be constant. This study was conducted to evaluate the accuracy of considering constant material and transport parameters in the modelling of sodium and chloride breakthrough curves through a compacted soil layer using the commercial software, Pollute v.7. Experiments were conducted with three different mixtures of glass beads and varying amounts of kaolinite (30, 40 and 50% by weight). The base line hydraulic conductivity K of the samples was established using distilled water as permeant. The observed values of K were 8.2X10-11 m/s, 1.28X10-10 m/s and 1.48X10-10 m/s for the 30, 40 and 50% kaolinite, respectively. These values did not change when the permeant was changed from distilled water to 0.04 M NaCl Effective diffusion coefficient of 3.5-8.5 x 10-10 m2/s was obtained for sodium and 1.9-4 x 10-10 m2/s for chloride. These results also showed that diffusion of both ions in the soils was affected by the percentage of clay fraction. The greater the amount of clay, the lower the diffusion coefficient obtained. Moreover, the diffusion coefficient of sodium was approximately two times that of chloride and this trend was visually apparent from the shape of the breakthrough curves for Na+ and Cl-. Modelling with constant porosity overestimated the concentration of both ions. The pore size distribution of each mixture was determined from mercury intrusion porosimetry testing before and after hydraulic conductivity test. The results showed a decrease of 24%, 13% and 12% in the porosity of the 30, 40 and 50% kaolinite mixture. Sensitivity analysis carried out by decreasing the porosity of the mixture by these percentages did not alter breakthrough curves noticeably. On the other hand, sensitivity analysis based on changes in the distribution coefficient and diffusion coefficient showed a considerable change in model outputs. It was concluded that although the porosity changed during hydraulic conductivity test, it did not eliminate the discrepancy between experimental results and modelling results, In fact, the model was found to be more sensitive to change in diffusion coefficient and distribution coefficient. Therefore, more studies are required to monitor these parameters during hydraulic conductivity testin

RISK MANAGEMENT AND PARTICIPATION OF ELECTRIC VEHICLE CONSIDERING TRANSMISSION LINE CONGESTION IN THE SMART GRIDS FOR DEMAND RESPONSE

Demand response (DR) could serve as an effective tool to further balance the electricity demand and supply in smart grids. It is also defined as the changes in normal electricity usage by end-use customers in response to pricing and incentive payments. Electric cars (EVs) are potentially distributed energy sources, which support the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes, and their participation in time-based (e.g., time of use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Moreover, the smart scheduling of EV charging and discharging activities supports the high penetration of renewable energies with volatile energy generation. This article was focused on DR in the presence of EVs to assess the effects of transmission line congestion on a 33-bit grid. A random model from the standpoint of an independent system operator was used to manage the risk and participation of EVs in the DR of smart grids. The main risk factors were those caused by the uncertainties in renewable energies (e.g., wind and solar), imbalance between demand and renewable energy sources, and transmission line congestion. The effectiveness of the model in a 33-bit grid in response to various settings (e.g., penetration rate of EVs and risk level) was evaluated based on the transmission line congestion and system exploitation costs. According to the results, the use of services such as time-based DR programs was effective in the reduction of the electricity costs for independent system operators and aggregators. In addition, the results demonstrated that the participation of EVs in incentive-based DR programs with the park model was particularly effective in this regard

prediction Modeling for Design Space Exploration in Optical Network on Chip

In at least a decade chip multiprocessors (CMP) have been dominating new commercial releases due to computational advantages of parallel computing cores on a single chip. Network on Chip (NoC) has emerged as an interconnection network of CMPs. But significant bandwidth that is required for multicore chips is becoming a bottleneck in the traditional (electrical) network on chip, due to delays caused by long wires in the electric NoC. Integration of photonic links with traditional electronic interconnects proposes a promising solution for this challenge. Since there are numerous design parameters for opto-electrical network architectures, an accurate evaluation is needed to study the impact of each design parameter on network performance, and to provide the most suitable network for a given set of applications, a power or a performance goal. In this thesis, we present a prediction modeling technique for design space exploration of an opto-electrical network on chip. Our proposed model accurately predicts delay (includes network packet latency and network contention delay) and energy (includes static and dynamic energy consumption) of the network. Specifically, this work addresses the fundamental challenge of accurate estimation of desired metrics without having to incur high simulation cost of numerous configurations of the optical network on chip architecture. We reduce the number of required simulations by accurately selecting the parameters that have the most impact on the network. Furthermore, we sparsely and randomly sample the designs build using these parameters from an Optical Network on Chip (ONoC) design space, and simulate only the sampled designs. We validate our model with three different applications executing on a large set of network configurations in a large optical network on chip design space. We achieve average error rates (root relative squared error) as low as 5.5% for the delay and 3.05% for the energy consumption

Semi-Quantitative Dermal Exposure Assessment of Lead with DREAM Model in a Lead Mine in Iran

Occupational exposure to toxic substances occurs in a variety of ways. The DREAM model is suggested for assessing skin exposure using preset values. The purpose of this study is to investigate the exposure of lead in workers at a mine lead using the DREAM model. This research was done in several steps. First, collect information about people and the work environment. Then design the model in Excel2016 by the authors. This research was descriptive-analytic research and included 46 miners. The DREAM model has a total of 33 variables included. In the DREAM model, exposure assessment was performed for 9 body parts at task level 2. The DREAM model was completed for 5 jobs. Jobs were in the lab, tunnel-74, tunnel-34, entrance to the tunnel and flotation workshop. The results were calculated for each of the 9 parts of the site for propagation, transfer, deposition, and potential and actual exposures, and eventually total exposures. The DREAM model, in comparison with similar methods, estimates the skin exposure level in a semi-quantitative fashion. This method has been used to estimate skin exposure in a variety of industries. This method was used to assess the skin exposure of workers in a mine, which resulted in training workers and providing personal protective equipment appropriate to the environment

Design, synthesis and Anti-cholinesterase activity of indole-Isoxazole carbohydrazide derivatives

A novel series of carbohydrazide indole-isoxazole hybrid derivatives have been synthesized. All the title compounds were characterized by 1H NMR, 13C NMR, MS and IR spectral data. The in vitro anti-cholinesterase activity of all the compounds were evaluated. Introduction: Alzheimer disease (AD) has emerged as the most prevalent age-related neurodegenerative diseases and the main cause of dementia, which is very common in elder population with high morbidity in such a manner that the daily activity of patients is completely affected by the resulting cognitive impairments. In recent years, most of therapeutic treatments for AD has focused on the inhibition of acetylcholinesterase (AChE) to increase the level of ACh in cholinergic synaptic cleft. Indole and its derivatives are very important heterocyclic compounds in drug-discovery studies that exhibit diverse range of biological activities like antimicrobial, anticancer, anti-Alzheimer and anti-platelet aggregation activity. Herein, in this study on the synthesis of bioactive compounds, we describe design, synthesis and anti-cholinesterase activity of N-benzylidene-5-(1-methyl-1H-indol-3-yl)isoxazole-3-carbohydrazide. Methods and Results: The title compounds were prepared via the 5-(1-methyl-1H-indol-3-yl)isoxazole-3-carbohydrazide which is key intermediate for the production of the desired compounds. Condensation with carbaldehydes in water and acetic acid afforded the title compounds. All the synthesized compounds were characterized by 1H NMR, 13C NMR, MS and IR spectral data. The in vitro anti-cholinesterase activity of all the compounds were evaluated. Conclusions: The target compounds were obtained from proper aldehydes and N-benzylidene-5-(1-methyl-1H-indol-3-yl)isoxazole-3-carbohydrazide condensation with good to excellent yields. The AChE and BuChE inhibition activity of the synthesized compounds were evaluated

Dynamic broadcasting in vehicular ad hoc networks

Vehicular Ad hoc Network (VANET) is a subclass of mobile ad hoc networks (MANETs). VANETs provide a variety of interesting applications. Many of these applications rely on broadcasting of messages to other vehicles. The simplest broadcasting algorithm is flooding. Because of a large number of vehicles during peak hour, blindly flooding may lead to packet collision and high contention named broadcast storm problem. This paper presents a broadcasting approach for safety messages that dynamically adjust waiting time of a vehicle according to the number of neighbor vehicles and distance to source. We evaluate the performance of our proposed approach in terms of reachability, reliability. The simulation results show our protocol introduces better performance than flooding and random waiting time protocol

Shallow Depth Factoring Based on Quantum Feasibility Labeling and Variational Quantum Search

Large integer factorization is a prominent research challenge, particularly in the context of quantum computing. This holds significant importance, especially in information security that relies on public key cryptosystems. The classical computation of prime factors for an integer has exponential time complexity. Quantum computing offers the potential for significantly faster computational processes compared to classical processors. In this paper, we propose a new quantum algorithm, Shallow Depth Factoring (SDF), to factor a biprime integer. SDF consists of three steps. First, it converts a factoring problem to an optimization problem without an objective function. Then, it uses a Quantum Feasibility Labeling (QFL) method to label every possible solution according to whether it is feasible or infeasible for the optimization problem. Finally, it employs the Variational Quantum Search (VQS) to find all feasible solutions. The SDF utilizes shallow-depth quantum circuits for efficient factorization, with the circuit depth scaling linearly as the integer to be factorized increases. Through minimizing the number of gates in the circuit, the algorithm enhances feasibility and reduces vulnerability to errors.Comment: 10 pages, 3 figure

Blockade of the Naloxone-induced Aversion in Morphine-conditioned Wistar Rats by L-Arginine Intra-central Amygdala

AbstractObjective(s)Single injection of naloxone, a selective antagonist of morphine, prior to the drug conditioning testing was used to investigate on morphine dependence.Materials and MethodsConditioning to morphine (2.5-10 mg/kg, s.c.) was established in adult male Wistar rats (weighing 200-250 g) using an unbiased procedure. Nitric oxide agents were microinjected into the central amygdala prior to naloxone-paired place conditioning testing.ResultsThe results showed that morphine produced a significant dose-dependent place preference in animals. Naloxone (0.1-0.4 mg/kg, i.p.) injections pre-testing of the response to morphine (7.5 mg/kg, s.c.) caused a significant aversion at the higher doses (0.4 mg/kg, i.p.). This response was reversed by microinjection of L-arginine (0.3-3 µg/rat, intra-central amygdala) prior to naloxone on the day of the testing. The response to L-arginine was blocked by pre-injection of NG-nitro-L-arginine methyl ester (L-NAME) (intra-central amygdala).ConclusionA single injection of naloxone on the test day of morphine place conditioning may simply reveal the occurrence of morphine dependence in rats, and that the nitric oxide in the central amygdala most likely plays a key role in this phenomenon