The Air Quality Influences of Vehicular Traffic Emissions

The number of automobiles has been steadily increasing in cities as a consequence of rapid urbanization and economic growth. It has been widely reported that vehicular emissions are strongly correlated with the level of urban air pollution. The major primary air pollutants that are linked to direct emissions from on‐road vehicles include soot (black carbon), carbon monoxide (CO), and nitric oxide (NO). Human exposure to these air pollutants is of health concern. Therefore, it is important to investigate air pollutants of traffic origin (e.g., BC, CO, and NO) in ambient air at different locations of cities and to assess the effects of vehicles on the urban air quality. With this goal in mind, we carried a systematic study in Singapore (the fourth most densely populated country in the world) with concurrent measurements of BC, NO, and CO in ambient air at four different locations having variations in traffic flows and meteorology. We then assessed the relationship between traffic flows and prevailing levels of the three air pollutants, and studied the association of these air pollutants among each other and with diverse meteorological conditions. The major outcomes of the study are discussed

Influence of Biomass Burning on Temporal and Diurnal Variations of Acidic Gases, Particulate Nitrate, and Sulfate in a Tropical Urban Atmosphere

The present study investigated the temporal and diurnal distributions of atmospheric acidic gases (sulphur dioxide (SO2), nitrous acid (HONO), and nitric acid (HNO3)) and those of particulate nitrate (NO3-) and sulfate (SO42-) through a comprehensive field campaign during the largest smoke haze episode in Singapore, a representative country in Southeast Asia (SEA). To identify the atmospheric behavior of these pollutants during the smoke haze period, the data generated from the measurement campaign were divided into three distinct periods: prehaze, during haze, and posthaze periods. The 24 hr average data indicated that ambient SO2, HONO, and HNO3 during the smoke haze episodes increased by a factor ranging from 1.2 to 2.6 compared to those during the prehaze and posthaze periods. Similarly, in the case of particulates SO42- and NO3-, the factor ranged from 2.3 to 4.2. Backward air trajectories were constructed and used to find the sources of biomass burning to the recurring smoke haze in this region. The air trajectory analysis showed that the smoke haze episodes experienced in Singapore were influenced by transboundary air pollution, caused by severe biomass burning events in the islands of Indonesia

Statistical Review Evaluation of 5G Antenna Design Models from a Pragmatic Perspective under Multi-Domain Application Scenarios

Antenna design for the 5G spectrum requires analysis of contextual frequency bands, design of miniaturization techniques, gain improvement models, polarization techniques, standard radiation pattern designs, metamaterial integration, and substrate selection. Most of these models also vary in terms of qualitative & and quantitative parameters, which include forward gain levels, reverse gain, frequency response, substrate types, antenna shape, feeding levels, etc. Due to such a wide variety in performance, it is ambiguous for researchers to identify the optimum models for their application-specific use cases. This ambiguity results in validating these models on multiple simulation tools, which increases design delays and the cost of deployments. To reduce this ambiguity, a survey of recently proposed antenna design models is discussed in this text. This discussion recommended that polarization optimization and gain maximization are the major impact factors that must be considered while designing antennas. It is also recommended that collocated microstrip slot antennas, fully planar dual-polarized broadband antennas, and real-time deployments of combined slot antenna pairs with wide-band decoupling are very advantageous. Based on this discussion, researchers will be able to identify optimal performance-specific models for different applications. This discussion also compares underlying models in terms of their quantitative parameters, which include forward gain levels, bandwidth, complexity of deployment, scalability, and cost metrics. Upon referring to this comparison, researchers will be able to identify the optimum models for their performance-specific use cases. This review also formulates a novel Antenna Design Rank Metric (ADRM) that combines the evaluated parameters, thereby allowing readers to identify antenna design models that are optimized for multiple parameters and can be used for large-scale 5G communication scenarios

Single Server Interdependent Queueing Model using Baileys Bulk Service rule

In this paper, we consider the single server queueing system having Baileys bulk service rule with phase wise. In this model various system characteristics like probability that the system emptiness, variability of the system size and the coefficient of variation are obtained. DOI: 10.17762/ijritcc2321-8169.150520

Fast Algorithm Development for SVD: Applications in Pattern Matching and Fault Diagnosis

The project aims for fast detection and diagnosis of faults occurring in process plants by designing a low-cost FPGA module for the computation. Fast detection and diagnosis when the process is still operating in a controllable region helps avoiding the further advancement of the fault and reduce the productivity loss. Model-based methods are not popular in the domain of process control as obtaining an accurate model is expensive and requires an expertise. Data-driven methods like Principal Component Analysis(PCA) is a quite popular diagnostic method for process plants as they do not require any model. PCA is widely used tool for dimensionality reduction and thus reducing the computational e�ort. The trends are captured in prinicpal components as it is di�cult to have a same amount of disturbance as simulated in historical database. The historical database has multiple instances of various kinds of faults and disturbances along with normal operation. A moving window approach has been employed to detect similar instances in the historical database based on Standard PCA similarity factor. The measurements of variables of interest over a certain period of time forms the snapshot dataset, S. At each instant, a window of same size as that of snapshot dataset is picked from the historical database forms the historical window, H. The two datasets are then compared using similarity factors like Standard PCA similarity factor which signi�es the angular di�erence between the principal components of two datasets. Since many of the operating conditions are quite similar to each other and signi�cant number of mis-classi�cations have been observed, a candidate pool which orders the historical data windows on the values of similarity factor is formed. Based on the most detected operation among the top-most windows, the operating personnel takes necessary action. Tennessee Eastman Challenge process has been chosen as an initial case study for evaluating the performance. The measurements are sampled for every one minute and the fault having the smallest maximum duration is 8 hours. Hence the snapshot window size, m has been chosen to be consisting of 500 samples i.e 8.33 hours of most recent data of all the 52 variables. Ideally, the moving window should replace the oldest sample with a new one. Then it would take approximately the same number of comparisons as that of size of historical database. The size of the historical database is 4.32 million measurements(past 8years data) for each of the 52 variables. With software simulation on Matlab, this takes around 80-100 minutes to sweep through the whole 4.32 million historical database. Since most of the computation is spent in �nding principal components of the two datasets using SVD, a hardware design has to be incorporated to accelerate the pattern matching approach. The thesis is organized as follows: Chapter 1 describes the moving window approach, various similarity factors and metrics used for pattern matching. The previous work proposed by Ashish Singhal is based on skipping few samples for reducing the computational e�ort and also employs windows as large as 5761 which is four days of snapshot. Instead, a new method which skips the samples when the similarity factor is quite low has been proposed. A simpli�ed form of the Standard PCA similarity has been proposed without any trade-o� in accuracy. Pre-computation of historical database can also be done as the data is available aprior, but this requires a large memory requirement as most of the time is spent in read/write operations. The large memory requirement is due to the fact that every sample will give rise to 52�35 matrix assuming the top-35 PC's are sufficient enough to capture the variance of the dataset. Chapter 2 describes various popular algorithms for SVD. Algorithms apart from Jacobi methods like Golub-Kahan, Divide and conquer SVD algorithms are brie y discussed. While bi-diagonal methods are very accurate they suffer from large latency and computationally intensive. On the other hand, Jacobi methods are computationally inexpensive and parallelizable, thus reducing the latency. We also evaluted the performance of the proposed hybrid Golub-Kahan Jacobi algorithm to our application. Chapter 3 describes the basic building block CORDIC which is used for performing rotations required for Jacobi methods or for n-D householder re ections of Golub-Kahan SVD. CORIDC is widely employed in hardware design for computing trigonometric, exponential or logarithmic functions as it makes use of simple shift and add/subtract operations. Two modes of CORDIC namely Rotation mode and Vectoring mode are discussed which are used in the derivation of Two-sided Jacobi SVD. Chapter 4 describes the Jacobi methods of SVD which are quite popular in hardware implementation as they are quite amenable to parallel computation. Two variants of Jacobi methods namely One-sided and Two-sided Jacobi methods are brie y discussed. Two-sided Jacobi making making use of CORDIC has has been derived. The systolic array implementation which is quite popular in hardware implementation for the past three decades has been discussed. Chapter 5 deals with the Hardware implementation of Pattern matching and reports the literature survey of various architectures developed for computing SVD. Xilinx ZC7020 has been chosen as target device for FPGA implementation as it is inexpensive device with many built-in peripherals. The latency reports with both Vivado HLS and Vivado SDSoC are also reported for the application of interest. Evaluation of other case studies and other datadriven methods similar to PCA like Correspondence Analysis(CA) and Independent Component Analysis(ICA), development of efficient hybrid method for computing SVD in hardware and highly discriminating similarity factor, extending CORDIC to n-dimensions for householder re ections have been considered for future research

HIGH FREQUENCY BOOST CONVERTER EMPLOYING SOFT SWITCHING AUXILIARY RESONANT CIRCUIT

A new soft-switching boost converter is proposed in this paper. The conventional boost converter generates switching losses at turn ON and OFF, and this causes a reduction in the whole system’s efficiency. The proposed boost converter utilizes a soft switching method using an auxiliary circuit with a resonant inductor and capacitor, auxiliary switch, and diodes. Therefore, the proposed soft-switching boost converter reduces switching losses more than the conventional hard-switching converter. The efficiency, which is about 91% in hard switching, increases to about 97% in the proposed soft-switching converter. In this paper, the performance of the proposed soft-switching boost converter is verified through the theoretical analysis, simulation, and experimental results

Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over southeast India

In the present study, we have used the Weather Research and Forecasting (WRF) model to simulate the features associated with a severe thunderstorm observed over Gadanki (13.5° N, 79.2° E), over southeast India, on 21 May 2008 and examined its sensitivity to four different microphysical (MP) schemes (Thompson, Lin, WSM6 and Morrison). We have used the WRF model with three nested domains with the innermost domain of 2 km grid spacing with explicit convection. The model was integrated for 36 h with the GFS initial conditions of 00:00 UTC, 21 May 2008. For validating simulated features of the thunderstorm, we have considered the vertical wind measurements made by the Indian MST radar installed at Gadanki, reflectivity profiles by the Doppler Weather Radar at Chennai, and automatic weather station data at Gadanki.There are major differences in the simulations of the thunderstorm among the MP schemes, in spite of using the same initial and boundary conditions and model configuration. First of all, all the four schemes simulated severe convection over Gadanki almost an hour before the observed storm. The DWR data suggested passage of two convective cores over Gadanki on 21 May, which was simulated by the model in all the four MP schemes. Comparatively, the Thompson scheme simulated the observed features of the updraft/downdraft cores reasonably well. However, all the four schemes underestimated strength and vertical extend of the updraft cores. The MP schemes also showed problems in simulating the downdrafts associated with the storm. While the Thompson scheme simulated surface rainfall distribution closer to observations, the other three schemes overestimated observed rainfall. However, all the four MP schemes simulated the surface wind variations associated with the thunderstorm reasonably well. The model simulated reflectivity profiles were consistent with the observed reflectivity profile, showing two convective cores. These features are consistent with the simulated condensate profiles, which peaked around 5-6 km. As the results are dependent on initial conditions, in simulations with different initial conditions, different schemes may become closer to observations. The present study suggests not only large sensitivity but also variability of the microphysical schemes in the simulations of the thunderstorm. The study also emphasizes the need for a comprehensive observational campaign using multi-observational platforms to improve the parameterization of the cloud microphysics and land surface processes over the Indian region

PREVENTIVE EFFECTS OF THE AQUEOUS EXTRACT OF CINNAMOMUM ZEYLANICUM BARK ON DEXAMETHASONE INDUCED INSULIN RESISTANCE IN WISTAR ALBINO RATS

Objectives: To evaluate the preventive effects of aqueous extract of Cinnamomum zeylanicum bark on dexamethasone induced insulin resistance and to compare it with rosiglitazone. Methods: The animals were categorized into two series of dexamethasone (dexamethasone 4mg/kg, dexamethasone 8mg/kg series) with 5 groups in each [plain control, dexamethasone 4/8mg/kg as per series, rosiglitazone 8mg/kg and 16mg/kg, cinnamon bark extract (CZE) 250mg/kg BW]. Six animals were studied in each group. In a12 day study period, rosiglitazone and CZE groups received respective drug treatments and dexamethasone dosing (4mg/kg or 8mg/kg) was started from day 7onwards. On day 12, fasting blood, urine and post IPGTT blood samples were collected and processed for glucose, insulin and ketone estimations. Results: In both series, CZE 250mg/kg treatment showed significant reduction in mean fasting glucose and insulin compared to rosiglitazone 8mg/kg and 16mg/kg groups and dexamethasone controls (4mg/kg, 8mg/kg groups) (P<0.05). The fall in glucose and insulin levels observed with CZE treatment at 30, 60 min post IPGTT in both series were significant compared to rosiglitazone and dexamethasone treatment groups (P<0.05). Glycosuria and ketonuria were absent in CZE groups, whereas these were reduced significantly in rosiglitazone groups compared to dexamethasone groups (P<0.05). Conclusion: The aqueous extract of C. zeylanicum bark prevented the insulin resistance as evidenced by reduced fasting and post IPGTT glucose and insulin levels in steroid induced insulin resistance model. KEYWORDS: Glucose uptake; Cinnamon bark; Rosiglitazone; Hyperinsulinemia; Hyperglycemia