CHARACTERIZATION OF SEED DEFECTS IN HIGHLY SPECULAR SMOOTH COATED SURFACES

Many smooth, highly specular coatings such as automotive paints are subjected to considerable performance demands as the customer expectations for appearance of coatings are continually increasing. Therefore it is vital to develop robust methods to monitor surface quality online. An automated visual assessment of specular coated surface that would not only provide a cost effective and reliable solution to the industries but also facilitate the implementation of a real-time feedback loop. The scope of this thesis is a subset of the inspection technology that facilitates real-time close loop control of the surface quality and concentrates on one common surface defect the seed defect. This machine vision system design utilizes surface reflectance models as a rational basis. Using a single high-contrast image the height of the seed defect is computed; the result is obtained rapidly and is reasonably accurate approximation of the actual height

Recognizing Criminal Intent through Facial Expressions

Artificial intelligence is developing rapidly as a result of recent advances in the identification of image and emotion patterns in human facial features. On the other hand, the rise in crime and wrongful imprisonment are causing society to disintegrate. Artificial intelligence has greatly aided in the development of our contemporary society. Knowing the issue and the appropriate set of instruments to address it is an essential skill, and having the ability to apply those tools effectively elevates one to the rank of supreme being in the cosmos. False imprisonment is an issue that requires attention. Recognizing the facts,1 in 20 criminal prosecutions in the US alone end in an incorrect conviction. Innocent persons who have been unfairly convicted make up 1% of US jail populations, or about 20,000 people, according to the Innocent Project group. Facial emotion detection and datasets gathered based on the study "Criminality in the face" can be used by artificial intelligence to help lessen the problem of false convictions. According to a Kinesics survey, specific body motions and movements can be used as a kind of non-verbal communication. By Ray Birdwhistell in 1952, the phrase was first used. As the proverb "Face is the index of mind" states, facial expressions are an important aspect of non-verbal communication. Analyzing a person's face might provide insight into the circumstances around a criminal suspect

MORBID IMAGES IN DON DELILLOS WHITE NOISE: A STUDY.

Don DeLillos well-kept secret mind of fiction White Noise highlights suspense and intelligence of his writing. Postmodernist elements of his fiction reveal, the superhero exist only as myths in the modern world; we are natures elements, a technologically oriented people on the other hand caught in the sieve of history. This study focuses on an industrial accident that occurs at a small Midwestern town in which the total town is evacuated. Jacks family is also evacuated. The highlight of the history is Union Carbide disaster in India that killed over two thousand people and injured thousands more. Carbide is one of the most poisonous gases, which is injurious to health. It is made from rail car released as dark clouds of chemical spills in the science laboratory. Particularly, it seems timelier and frightening the environments, which is rendering American numbness. The toxic nature of Carbide results in man- made disasters, which threatens the society and the survival of nature. Here, DeLillo explores death as a result of chemical poisoning which forms the major theme of the novel, however death is inevitable and annihilation that seizes everyones consciousness

Enhancing the Thermal and Mechanical Properties of Organic-Inorganic Hybrid Nanocomposite Films Based on Poly Lactic Acid/OMMT Nano Clay

Abstract: Organic (PLA) inorganic (OMMT nano clay) hybrid nanocomposite films were fabricated using poly lactic acid (PLA) with various weight percentages (1-3wt%) of organically modified montmorillonite (OMMT) nano clay by means of one step solvent casting method. The thermal, mechanical and water absorption properties were determined as per standard testing methods to determine the optimum percentage of OMMT nano clay within the nanocomposite was investigated. The surface morphology of the organic-inorganic hybrid nanocomposite films was analyzed through XRD, SEM, and TEM surface analytical techniques. The incorporation of OMMT clay in to PLA matrix is found to have enhanced the thermo-mechanical properties. The water absorption and solubility test results also support the data from thermo-mechanical tests. The 2 wt % OMMT clay loaded PLA films showed the best results among all. The obtained results showed that the thermal, mechanical and water absorption properties could be increased significantly with the optimum incorporation of OMMT nano clay in a PLA matrix, in comparision wih the neat PLA

Endodontic Management of Mandibular First Molar with Middle Distal Canal: A Case Report

The knowledge of variations in root canal morphology is critical for a successful endodontic treatment. This article presents the endodontic management of a unique case of mandibular molar with middle distal canal which is quite uncommon

Solving a Boundary Value Problem via Fixed-Point Theorem on ®-Metric Space

In this paper, we prove the fixed-point theorem for rational contractive mapping on ®-metric space. Additionally, an Euclidean metric space with a binary relation example and an application to the first-order boundary value problem are given. Moreover, the obtained results generalize and extend some of the well-known results in the literature.The authors thank the Basque Government for its support of this work through Grant IT1207-19

Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis

Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder

Fixed-Point Theorems for Nonlinear Contraction in Fuzzy-Controlled Bipolar Metric Spaces

In this paper, we introduce the concept of fuzzy-controlled bipolar metric space and prove some fixed-point theorems in this space. Our results generalize and expand some of the literature’s well-known results. We also provide some applications of our main results to integral equations.The authors thank the Basque Government for its support of this study through grant IT1555-22

A Metabolism Toolbox for CAR T Therapy

The adoptive transfer of T cells expressing chimeric antigen receptors (CARs) through genetic engineering is one of the most promising new therapies for treating cancer patients. A robust CAR T cell-mediated anti-tumor response requires the coordination of nutrient and energy supplies with CAR T cell expansion and function. However, the high metabolic demands of tumor cells compromise the function of CAR T cells by competing for nutrients within the tumor microenvironment (TME). To substantially improve clinical outcomes of CAR T immunotherapy while treating solid tumors, it is essential to metabolically prepare CAR T cells to overcome the metabolic barriers imposed by the TME. In this review, we discuss a potential metabolism toolbox to improve the metabolic fitness of CAR T cells and maximize the efficacy of CAR T therapy