No abstract

Confocal microscope imaging has become popular in biotechnology labs. Confocal imaging technology utilizes fluorescence optics, where laser light is focused onto a specific spot at a defined depth in the sample. A considerable number of images are produced regularly during the process of research. These images require methods of unbiased quantification to have meaningful analyses. Increasing efforts to tie reimbursement to outcomes will likely increase the need for objective data in analyzing confocal microscope images in the coming years. Utilizing visual quantification methods to quantify confocal images with naked human eyes is an essential but often underreported outcome measure due to the time required for manual counting and estimation. The current method (visual quantification methods) of image quantification is time-consuming and cumbersome, and manual measurement is imprecise because of the natural differences among human eyes’ abilities. Subsequently, objective outcome evaluation can obviate the drawbacks of the current methods and facilitate recording for documenting function and research purposes. To achieve a fast and valuable objective estimation of fluorescence in each image, an algorithm was designed based on machine vision techniques to extract the targeted objects in images that resulted from confocal images and then estimate the covered area to produce a percentage value similar to the outcome of the current method and is predicted to contribute to sustainable biotechnology image analyses by reducing time and labor consumption. The results show strong evidence that t-designed objective algorithm evaluations can replace the current method of manual and visual quantification methods to the extent that the Intraclass Correlation Coefficient (ICC) is 0.9.

Hydroforming has gained increasing attention in the manufacturing industry in recent years due to the demand for fast yet reliable production for parts, the applications of which accept a wide range of dimensional tolerances. In this study, tube hydroforming in conical dies has been analyzed. The study consists of two parts: computer simulations and experimental work. The simulation results were utilized to find the load paths which produce successful hydroforming for the selected tube specimens. Twelve load paths were identified and implemented with two friction coefficients and three pressure ranges. During the simulation process, the tubes were given an end movement that ranged from a sealing distance to twice that distance. The experimental work was implemented to verify some of the simulation results. The results showed that the best hydroforming limit was reached when the axial feeding was twice as much as the sealing distance. Also, the maximum amount of deformation rate happens shortly after the specimendie interface starts having relative motion, and it is at its slowest when the hydroforming reaches the fully-formed specimen's shape.

The path-planning algorithm is the central part of most v. The algorithm should consider fixed obstacles, furniture and building style, dynamic obstacles, humans, and pets. assistive robots encounter a challenging and complex environment with various obstacles during daily work. In addition, to maximize the service per hour, the robot has to select the optimum path. These challenges motivate the work toward an efficient path-planning algorithm that can handle complex environments. The proposed algorithm employs a designed genetic algorithm to look for the best path that maximizes the service area per hour. This genetic algorithm is then combined with a dynamic obstacle detection fuzzy system. This system relies on fuzzy membership zones. The algorithm decides whether the obstacle is dynamic or static according to speed, direction, and size. The Geno-fuzzy path planning algorithm is implemented in an assistive robot and tested in an actual environment. The algorithm implementation in a simulated environment of 100 BED hospitals in Iraq reveals a high-performance result. The test on a large scale without obstacles shows the ability of the algorithm to deal with more than 300 service points successfully. The local experiment on Webots proved the algorithm's performance to overcome dynamic obstacles and achieve safe traveling.