Water absorption characterization of boron compounds-reinforced PLA/flax fiber sustainable composite.

Biocomposites are widely used in construction, packaging, and automotive applications such as seatbacks, door panels, headliners, and dashboards, as well as industrial composting. The purpose of this study is to look into the effects of three different boron compounds (borax boric, acid combines, zinc borate, and ulexite) on the mechanical and microstructural properties of flax fiber/PLA biocomposites at different water uptake times. 7 different biocomposites were studied for this purpose: control, 3UF, 5UF, 3ZBF, 3BxBcF, 5BxBcF, and 5ZBF. Extrusion was used to create homogenous chopped flax fiber-reinforced PLA biocomposites, which were then injection molded. Alkali treatment on flax fiber surfaces was applied to improve interfacial adhesion between fiber and matrix. Water uptake tests were performed at room temperature for soaking times of 24, 50, 168, 240, 330, 480, 550, 600, and 750 h. The addition of boron compounds increases water gain from 4.4 % to 6.1 %, according to sorption results. The tensile elongation at break values of the composites increased slightly after short-term water absorption. SEM images showed that alkali-treated flax fibers and boron compounds dispersed uniformly in the PLA matrix. After 750 h of immersion, the addition of boron fillers to PLA/flax composite increased Young's Modulus and flexural modulus by about 50 % and 72 %, respectively, in comparison to the control composite sample. The addition of boric acid: borax combines into the PLA/flax composite slowed the rate of decline in tensile and flexural strength after various immersion times. Finally, using MINITAB software, the experimental results were subjected to a one-way analysis of variance (ANOVA).

Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium.

Objectives: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. Methods: Preoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected. Results: The most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points. Conclusions: AI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.