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There are two kinds of alignment systems, marked and unmarked. The glass substrate for touch panels is categorized as an unmarked work piece. Vision based glass substrate alignment (GSA) relies on the edge of the glass. Traditional GSA systems compensate first for angular and then for linear error. This reduces alignment accuracy and increases alignment time and edge detection usually takes longer than 10 ms. This study proposes an effortless edge detection method. This method is very simple and can significantly reduce the time taken to detect the edge to about 6 ms using a 1.3 megapixel image. In this study, a floating center idea is used to control the glass substrate on a high precision coplanar XXY alignment stage. According to the method, users can set the rotation center anywhere as long as it is on the working ( xy ) plane. Tolerance prognosis is also considered in this study to help the operator decide if the substrate is usable or should be rejected. The experimental results show alignment repeatability of the x, y , and θ axes to be 1 μm, 1 μm, and 5 arcsec, respectively.

Web applications, widely used by enterprises for business services, require extensive testing to ensure functionality. Performing form testing with random input data often takes a long time to complete. Previously, we introduced a model for automated testing of web applications using reinforcement learning. The model was trained to fill form fields with fixed input values and click buttons. However, the performance of this model was limited by a fixed set of input data and the imprecise detection of successful form submission. This paper proposes a model to address these limitations. First, we use a large language model with data fakers to generate a wide variety of input data. Additionally, whether form submission is successful is partially determined by GPT-4o. Experiments show that our method increases average statement coverage by 2.3% over the previous model and 7.7% to 11.9% compared to QExplore, highlighting its effectiveness.

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. This study aims to evaluate the feasibility of training a deep neural network for the segmentation and detection of metastatic brain tumors in MRI using a very small dataset of 33 cases, by leveraging large public datasets of primary tumors; Methods: This study explores various methods, including supervised learning, two transfer learning approaches, and self-supervised learning, utilizing U-net and Swin UNETR models; Results: The self-supervised learning approach utilizing the Swin UNETR model yielded the best performance. The Dice score for small brain tumors was approximately 0.19. Sensitivity reached 100%, while specificity was 54.5%. When excluding subjects with hyperintensities, the specificity improved to 80.0%; Conclusions: It is feasible to train a model using self-supervised learning and a small dataset for the segmentation and detection of small brain tumors.

The advent of intelligent technology has spurred most large companies to introduce business intelligence systems (BIS), but those with low information maturity still have a wait-and-see attitude towards BIS. In order to accelerate the introduction of BIS, this study found and analyzed the critical factors (CFs) considered by companies when introducing BIS. First, the literature on factors considered by companies to introduce BIS was reviewed. The three stages before, during, and after introduction in marketing that organizations undergo during the procurement process were developed into a three-layer hierarchy factor table. An expert questionnaire with pairwise factors was then designed and sent to senior executives in companies that had introduced BIS, and the weights of all factors were calculated by the fuzzy analytic hierarchy process (FAHP) based on the collected questionnaire data. After this, four critical factors—system function integrity, approaching corporate strategy, licensing fee, and information technology maturity—were determined objectively by using the conditions for the acceptable advantage of Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and further explored in order to help companies input fewer resources, introduce BIS efficiently, and thus increase their decision-making power.