Studying the Influence of Illumination Design in Quality Inspection on Vaccine Vials Production Line Using Computer Vision

In the industrial context, there are key factors that directly affect the system’s efficiency. Higher demands for both quantity and quality in today’s market call for constant research and development of technologies for automating production and quality control. Machine vision is a solution to increase speed and accuracy in defect detection. However, applications from machine vision are only effective if there is good data input. This is the reason why a machine vision system, needs high-quality input images from a well-designed illumination system. These illumination systems are designed to highlight faults in products. Therefore, the images obtained will provide optimized data for easier image processing thus directly increase the processing speed, accuracy, and overall system performance. To achieve this goal, this paper presents a few approaches to enhance and optimize images by implements illumination techniques into a miniature model of pharmaceutical bottle assembly line using machine vision as the inspector block. In this paper, we will evaluate the critical needs of using customize illumination system for quality inspection on an assembly line.

Representation of the Phenomenon “Trust” in Medical Advertising Discourse

The problem of creating institutional trust in modern Russian society is considered on the example of advertising activities of companies that produce and promote drugs on the consumer market. In order to identify techniques that contribute to the effective impact on the recipient – the formation of a trusting attitude towards the advertised product, the commercials shown on Russian television from 2010 to 2020 were analyzed. Using the methods of content analysis, discourse and stylistic analysis, elements of component and distributive analysis, it has been established that professional participants in medical advertising discourse use such techniques as the use of toponyms, lexical units borrowed from military discourse, terms, words with positive or negative connotations, presentation of statistical data, construction of a first-person statement, rhetorical questions. It was found that the mention of the country of origin of the drug, which is authoritative for the Russian consumer, has a positive effect on the confidence of patients in this drug; military vocabulary evokes associations with speed, accuracy, direction, strength and testifies to the effectiveness of the drug; medical, chemical, biological terms, statistical data objectify the transmitted information; rhetorical questions, self-narrative and others contribute to the establishment of close contact with the consumer.

Compiling method of automobile fault diagnosis information

Automobile fault diagnosis is the most technical content in automobile maintenance. The speed, accuracy and convenience of diagnosis directly affect the maintenance efficiency. Automobile fault diagnosis information is an important reference for finding and solving automobile faults. High quality information is the key to improve technical personnel's judgment and solving faults. In this paper, the methods and examples of fault diagnosis information compilation are given to guide the technical data writers to compile effective fault diagnosis information for automobiles.

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

Model and Verification of Medical English Machine Translation Based on Optimized Generalized Likelihood Ratio Algorithm

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase’s part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation.

Post-error slowing reflects the joint impact of adaptive and maladaptive processes during decision making

Errors and their consequences are typically studied by investigating changes in decision speed and accuracy in trials that follow an error, commonly referred to as "post-error adjustments". Many studies have reported that subjects slow down following an error, a phenomenon called "post-error slowing" (PES). However, the functional significance of PES is still a matter of debate as it is not always adaptive. That is, it is not always associated with a gain in performance and can even occur with a decline in accuracy. Here, we hypothesized that the nature of PES is influenced by one's speed-accuracy tradeoff policy, which determines the overall level of choice accuracy in the task at hand. To test this hypothesis, we investigated post-error adjustments in subjects performing the same task while they were required to either emphasize speed (low accuracy) or cautiousness (high accuracy) in two distinct contexts (hasty and cautious contexts, respectively) experienced on separate days. Accordingly, our data indicate that post-error adjustments varied according to the context in which subjects performed the task, with PES being solely significant in the hasty context. In addition, we only observed a gain in performance after errors in a specific trial type, suggesting that post-error adjustments depend on a complex combination of processes that affect the speed of ensuing actions as well as the degree to which such PES comes with a gain in performance.

Consistent Individual Tendencies in Motor Speed-Accuracy Trade-Off

The literature of Speed-Accuracy Trade-Off (SAT) in motor control has evidenced individuality in the preference to trade different aspects (mean, variance) of spatial and temporal errors. Nonetheless, to the best of our knowledge, how robust this preference is has not been properly tested. Thirty participants performed nine conditions with different time and spatial criteria over two days (scanning). In-between these scanning conditions, individuals performed a practice condition that required modifications of the individuals’ preferences in SAT. Through Bayesian analyses, we found that, despite individuals demonstrating changes during practice, decreasing movement time, they did not modify how they performed the scanning conditions. This is evidence for a robust SAT individual tendency. We discuss how such individuality could modify how individuals perform within/between SAT criteria, and what this means for interpretation of results.

The time-course of distractor-based activation modulates effects of speed-accuracy tradeoffs in conflict tasks

The cognitive processes underlying the ability of human performers to trade speed for accuracy is often conceptualized within evidence accumulation models, but it is not yet clear whether and how these models can account for decision-making in the presence of various sources of conflicting information. In the present study, we provide evidence that speed-accuracy tradeoffs (SATs) can have opposing effects on performance across two different conflict tasks. Specifically, in a single preregistered experiment, the mean reaction time (RT) congruency effect in the Simon task increased, whereas the mean RT congruency effect in the Eriksen task decreased, when the focus was put on response speed versus accuracy. Critically, distributional RT analyses revealed distinct delta plot patterns across tasks, thus indicating that the unfolding of distractor-based response activation in time is sufficient to explain the opposing pattern of congruency effects. In addition, a recent evidence accumulation model with the notion of time-varying conflicting information was successfully fitted to the experimental data. These fits revealed task-specific time-courses of distractor-based activation and suggested that time pressure substantially decreases decision boundaries in addition to reducing the duration of non-decision processes and the rate of evidence accumulation. Overall, the present results suggest that time pressure can have multiple effects in decision-making under conflict, but that strategic adjustments of decision boundaries in conjunction with different time-courses of distractor-based activation can produce counteracting effects on task performance with different types of distracting sources of information.