ગુજરાતની મ્યુનિસિપલ કોર્પોરેશનની નાણાંકીય પરિસ્થિતિનો તુલનાત્મક અભ્યાસ

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Mathematical Modelling of a First Order Transducer

First chapter lays the ground work for this research paper. The problem is posed immediately, enabling the reader to gain a better appreciation of the material that follows. The next article explains the thought process that motivated this problem. This is followed by reviewing the available. literature in the field and on which the author has relied quite extensively. The chapter concludes by explaining the more common terminology which will be used frequently throughout the dissertation. Given a configuration of linear, passive network elements, termed filter, how faithful a reproduction of the input process is the process at the output of a first order transducer? Or, in other words, how much information is lost in a filter? This problem arises due to the necessity of using a filter in a system --- either for reasons of convenience or by force of circumstances. The paper attempts to model a transducer mathematically and to express the input and output processes statistically. In order to-do so, a meaningful measure of error should be chosen and defined. The error measure chosen is the Mean-Integral. Square Error (MISER). A very good approximation of the situation is the existence of the signal for a long period of time with respect to the time constants of the filter. The problem then lends itself to what is known as steady state analysis. This work is restricted to analyzing the pattern given a random Stationary Gaussian Markoff (SGM) process. An expression to calculate the MISER will be developed and a method to computerize the same will be indicated. However, actual numerical computations will be deferred. The expression for MISER will establish a relationship between the location of a pole and/or zero and the magnitude of MISER. The use of the SGM process as the input, enables one to describe output process in a concise manner. It also enables the results of this study to be compared with the results of others in related fields who have used the SGM process as a signal source

Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples

This research paper explores the impact of various input parameters, including Infill percentage, Layer Height, Extrusion Temperature, and Print Speed, on the resulting Tensile Strength in objects produced through additive manufacturing. The main objective of this study is to enhance our understanding of the correlation between the input parameters and Tensile Strength, as well as to identify the key factors influencing the performance of the additive manufacturing process. To achieve this objective, we introduced the utilization of Explainable Artificial Intelligence (XAI) techniques for the first time, which allowed us to analyze the data and gain valuable insights into the system's behavior. Specifically, we employed SHAP (SHapley Additive exPlanations), a widely adopted framework for interpreting machine learning model predictions, to provide explanations for the behavior of a machine learning model trained on the data. Our findings reveal that the Infill percentage and Extrusion Temperature have the most significant influence on Tensile Strength, while the impact of Layer Height and Print Speed is relatively minor. Furthermore, we discovered that the relationship between the input parameters and Tensile Strength is highly intricate and nonlinear, making it difficult to accurately describe using simple linear models

Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens

Surface roughness is a crucial factor influencing the performance and functionality of additive manufactured components. Accurate prediction of surface roughness is vital for optimizing manufacturing processes and ensuring the quality of the final product. Quantum computing has recently gained attention as a potential solution for tackling complex problems and creating precise predictive models. In this research paper, we conduct an in-depth comparison of three quantum algorithms i.e. the Quantum Neural Network (QNN), Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) adapted for regression for predicting surface roughness in additive manufactured specimens for the first time. We assess the algorithms performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) as evaluation metrics. Our findings show that the Q-Forest algorithm surpasses the other algorithms, achieving an MSE of 56.905, MAE of 7.479, and an EVS of 0.2957. In contrast, the QNN algorithm displays a higher MSE of 60.840 and MAE of 7.671, coupled with a negative EVS of -0.444, indicating that it may not be appropriate for predicting surface roughness in this application. The VQC adapted for regression exhibits an MSE of 59.121, MAE of 7.597, and an EVS of -0.0106, suggesting its performance is also inferior to the Q-Forest algorithm

Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens

In this study, we investigate the application of supervised machine learning algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM) process. A total of 31 PLA specimens were prepared, with Infill Percentage, Layer Height, Print Speed, and Extrusion Temperature serving as input parameters. The primary objective was to assess the accuracy and effectiveness of four distinct supervised classification algorithms, namely Logistic Classification, Gradient Boosting Classification, Decision Tree, and K-Nearest Neighbor, in predicting the UTS of the specimens. The results revealed that while the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1 score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC) score of 0.79, outperforming the other algorithms. This demonstrates the superior ability of the KNN algorithm in differentiating between the two classes of ultimate tensile strength within the dataset, rendering it the most favorable choice for classification in the context of this research. This study represents the first attempt to estimate the UTS of PLA specimens using machine learning-based classification algorithms, and the findings offer valuable insights into the potential of these techniques in improving the performance and accuracy of predictive models in the domain of additive manufacturing

PHOTOCATALYTIC DEGRADATION OF PHARMACEUTICAL DRUG ZIDOVUDINE BY UNDOPED AND 5 % BARIUM DOPED ZINC OXIDE NANOPARTICLES DURING WATER TREATMENT: SYNTHESIS AND CHARACTERISATION

Objective: To study the photocatalytic degradation of pharmaceutical drug zidovudine (ZDV) by synthesized undoped zinc oxide nanoparticles (ZONPs) and 5% (mole ratio) barium doped zinc oxide nanoparticles (BZONPs) during water treatment.Methods: Kinetics studies were carried out with the help of UV-Visible Spectrophotometer. High-Resolution Mass Spectrophotometry (HR-MS) was used to identify products. A photo-reactor with mercury lamp was used as an external source of light energy. Optical power meter was used for the measurement of light intensity. The particle size of the synthesized photocatalysts was identified with the help of siemens x-ray diffractometer (XRD). The surface topography of photocatalysts was done by scanning electron microscope (SEM). Transmission electron microscopy (TEM) was used for the studies of particle size and morphology.Results: Five degraded products of ZDV are identified by HR-MS. A suitable electron-hole pair mechanism is projected. XRD patterns show that the intensity of peak is slightly stronger in ZONPs. There is an increase in the rate of photocatalytic degradation of ZDV by adding different quantities of photocatalyst from 0.05 g l-1 to 0.1 g l-1. The kinetic data reveals that there is an initial increase in the values of rate constants with the increase in the concentration of ZDV. The kinetic data indicate that the values of rate constants are higher at pH = 9. There is an increase in the rate constant values with an increase in the light intensities of UV lamp.Conclusion: The rates of photocatalytic degradation of ZDV were found to be higher using 5 % (mole ratio) BZONPs as a photocatalyst.Â

Detecting Baryon Acoustic Oscillations with third generation gravitational wave observatories

We explore the possibility of detecting Baryon Acoustic Oscillations (BAO) solely from gravitational wave observations of binary neutron star mergers with third generation (3G) gravitational wave (GW) detectors like Cosmic Explorer and the Einstein Telescope. These measurements would provide a new independent probe of cosmology. The detection of the BAO peak with current generation GW detectors (solely from GW observations) is not possible because i) unlike galaxies, the GW mergers are poorly localized and ii) there are not enough merger events to probe the BAO length scale. With the 3G GW detector network, it is possible to observe $\sim \mathcal{O}(1000)$ binary neutron star mergers per year localized well within one square degree in the sky for redshift $z \leq 0.3$. We show that 3G observatories will enable precision measurements of the BAO feature in the large-scale two-point correlation function; the effect of BAO can be independently detected at different reshifts, with a log-evidence ratio of $\sim$ 23, 17, or 3 favouring a model with a BAO peak at redshift of 0.2, 0.25, or 0.3, respectively, using a redshift bin corresponding to a shell of thickness $~150 h^{-1}$ Mpc

Exposure to stress minimizes the zone of antimicrobial action: a phenotypic demonstration with six Acinetobacter baumannii strains

Aim: To phenotypically study the role of domestic environmental stress in the emergence of antimicrobial resistance in Acinetobacter baumannii. Materials and Methods: Six strains of A. baumannii were initially subjected to AST and then were exposed to various stresses (temperature, pH and random combinations). Stressed cells were subcultured and then subjected for AST. The ZOIs before and after exposure to stress were compared. Statistical analysis was done using Student t-test at p < 0.10. Results: Exposure to stresses and combination of stresses resulted in substantial reduction in the ZOIs. Stress hardening was associated with further reduction in ZOIs. Conclusion: Exposure to domestic environmental stress imparted a significant and substantial reduction in the susceptibility of A. baumannii strains to antibiotics. DOI: http://dx.doi.org/10.5281/zenodo.118415