Disentangling flow and nonflow correlations via Bayesian unfolding of the event-by-event distributions of harmonic coefficients in ultrarelativistic heavy-ion collisions

The performance of the Bayesian unfolding method in extracting the event-by-event (EbyE) distributions of harmonic flow coefficients v_n is investigated using a toy model simulation, as well as simulations based on the HIJING and AMPT models. The unfolding method is shown to recover the input v_2-v_4 distributions for multiplicities similar to those observed in Pb+Pb collisions at the LHC. The effects of the nonflow are evaluated using HIJING simulation with and without a flow afterburner. The probability distribution of v_n resulting only from nonflow in HIJING is nearly a Gaussian and can be largely suppressed in the data-driven unfolding method used by the ATLAS Collaboration. The residual nonflow effects have no appreciable impact on the v_3 distributions, but are found to affect the tails of the v_2 and v_4 distributions; these effects manifest as a small simultaneous change in the mean and standard deviation of the $v_n$ distributions. For the AMPT model, which contains both flow fluctuations and nonflow effects, the reduced shape of the extracted v_n distributions is found to be independent of pT in the low pT region, similar to what is observed in the ATLAS data. The prospect of using the EbyE distribution of the harmonic spectrum aided by the unfolding technique as a general tool to study azimuthal correlations in high energy collisions is also discussed.Comment: 13 pages 17 figure

Design of a Controller for Simultaneous Control of Multiple Systems in Wireless Scenario

Wireless technology is becoming an ever-emerging part of human life with new services and products being released every month. Thus wireless communications brings huge benefits to the user or users. The used Radio Frequency (RF) Module is basically an Advanced Virtual RISC (AVR) microcontroller based communication system. The RF Module used in our project contains two units transmitter and receiver. The transmitter module converts parallel data into serial by using HT12E encoder prior to wireless transmission. The encoded data get received by receiver and converts or decodes the serial data into parallel by using HT12D decoder. After converting the data into parallel form which is made use by AVR16A micro controller to generate instructions for operation of relays connected to two different bulbs

Challenges in Recovery of Valuable and Hazardous Elements from Bulk Fly Ash and Options for Increasing Fly Ash Utilization

Beneficiation of fly ash should require for ensuring the removal of reactive elements to reduce the effect of hazardous impact on our atmosphere and can fill the demand for resources such as metals and rare earths. In this chapter, we concentrate to describe the responsible factors involve in fly ash beneficiation that has a great contribution to our environment. The purpose of the current study is to know the recovery of different minerals; maximum removal of the contaminant, reactivity and neutralization capacity of acid mine drainage (AMD) with fly ash and development of the cost‐effective method of disposal of fly ash are achieved. Different beneficiation techniques of fly ash and utilization of fly ash are explained

Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications

Measurement of elliptic and higher order flow harmonics at sNN=2.76\sqrt{s_{NN}}=2.76 TeV Pb+Pb collisions with the ATLAS Detector

The measurements of flow harmonics $v_2$-$v_6$ using the event plane and two particle correlations methods in broad $p_T$, $\eta$ and centrality ranges using the ATLAS detector at LHC are presented. ATLAS recorded about 9 $\mu \textrm{b}^{-1}$ of lead-lead collision data in the 2010 heavy ion run. The full azimuthal acceptance of the ATLAS detector in $\pm2.5$ units of pseudorapidity for charged hadrons and the large amount of data allows for a detailed study of the flow harmonics. The $p_T$, centrality and $\eta$ ranges where the two methods give consistent $v_n$ and where they disagree are discussed. It is shown that the ridge as well as the so called "mach-cone" seen in two particle correlations are largely accounted for by the collective flow. Some scaling relations in the $p_T$ dependence of the $v_n$ are also discussed

Performance Enhancement of Active Power Filter using Robust Control Strategies

The prime focus of this thesis is to report control strategies to improve the performance of single phase shunt Active Power Filter (APF). Basically, Sliding Mode (SM) control strategy and Feedback Linearization based control strategy have been applied considering their ease of implementation and robustness under external disturbances. An low cost analog SM controller is presented to reduce the steady state current error. In this method a band pass filter is used for calculating the reference source current which makes source current Total Harmonic Distortion (THD) independent of source voltage THD. Multisim based simulation method and results are presented to report the method of low cost analog implementation. To overcome the drawbacks caused by varying switching frequency, a fixed switching frequency SM controller is presented, in which Artificial Neural Network (ANN) is used to generate the reference source current. In this control strategy, a proper combination of fixed frequency sliding mode current control, ANN based fundamental source current extraction circuit and unipolar PWM increases the dynamic response of APF system and makes it adaptive under variable load and source conditions. As feedback linearization based controller improves the performance of the power electronic systems by analysing stability of the complete system, a straight forward Partial Feedback Linearization (PFL) based control strategy is presented to reduce the source current THD of single phase shunt APF. The nonlinear system dynamics of the APF has been partially feedback linearized using its average dynamic model. New control input to the linearized system is obtained considering the stability of the complete APF system. After that, control input to APF is derived by nonlinear transformation. Stability of the internal dynamics of the system is analysed considering zero dynamics of the system. A prototype of the APF system is built and the proposed controller is implemented using dSPACE 1104. Both experimental and simulation results of the PFL based control strategy are compared with exact feedback linearization of APF via SM control for validation of performance improvement. Finally the application of PFL based control strategy is extended to three phase APF by considering it as Multiple Input Multiple Output (MIMO) system and MATLAB/Simulink based simulation results are presented to validate the theory

Robust Short-term Operation of AC Power Network with Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) for conventional non-linear ACOPF results in computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this paper proposes a single-stage non-integer non-recursive RC of ACOPF, using a dual transformation, for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of defined injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled without using any affine policy. The proposed approach also includes the budget of uncertainty constraints for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the obtained setpoints on NESTA and case9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low operational cost and computation time.Comment: 16 pages, 5 figures, 5 table