Electrospun nano-fibers exhibit two significant properties: a high surface-to-volume ratio and a relatively defect-free molecular structure. Due to the high surface-to-volume ratio, electro-spun materials are well suited for activities requiring increased physical contact, such as providing a site for a chemical reaction or filtration of small-sized physical materials. However, electrospinning has many shortcomings, including difficulties in producing inorganic nanofibers and a limited number or variety of polymers used in the process. The fabrication of nanofiber bundles via electrospinning is explored in this analytical study and the relationship between all effective electrospinning parameters and the relative abundance of various fiber morphologies. Numerous variables could impact the fabrication of nanofibers, resulting in a variety of morphologies such as uniform, entangled, individual beads, beads-on-string, etc. Therefore, adequate ambient conditions and selecting the appropriate polymer and solvent for achieving a homogenous polymer solution and uniform with desired nanofiber properties for different applications of electro-spun materials are examined. Finally, the promising applications of nano-fine fibers in various fields achieved via electrospinning are studied in this paper.

Advancing bidirectional voltage source inverters in the on-board chargers of electric vehicles is vital for smart grid electric power management. These electric vehicle inverters play a crucial role in actively contributing to active/reactive power flow control in vehicle-to-grid and grid-to-vehicle applications. In this research, a proof-of-concept, model-free reinforcement learning-based agent/controller is introduced that eliminates the need for system identification or linearization techniques for control design. The goal is to dynamically control user-specified active and reactive electric vehicle on-board charger power outputs in all four quadrants for three-phase grid-connected electric vehicle voltage source inverters. A Twin-Delayed Deep Deterministic Policy Gradient deep-reinforcement learning-based controller was designed (1) to smoothly learn continuous control variables and to generate deterministic actions from the learned control policy, (2) to avoid exhaustive system identification techniques associated with nonlinearities involved in the on-board charger of electric vehicles, and (3) to ultimately track continuous active and reactive powers across all four-quadrant operation modes, ensuring safe and minimal steady-state errors. In this paper, a Markov Decision Process framework was established, encompassing the environment, actions, states, and a unique reward function. The minimal steady state error achieved in active and reactive power reference tracking across all four quadrant operation modes substantiates the trained agent’s capacity to sustain tracking accuracy and stability across a variety of operational settings. This research could potentially pave the way for any type of electric vehicle to actively play a role in smart grid management, especially through vehicle-to-grid and grid-to-vehicle capabilities.

Spinal fusion permanently connects two or more vertebrae in spine to improve stability, correct a deformity or reduce pain by immobilizing the vertebrae through pedicle screw fixation. Pedicle screws should be inserted very carefully to prevent possible irrecoverable damages to the spinal cord. Surgeons use CT/fluoroscopic images to find how to insert the screws safely. However, there is still human error, as determining precise trajectory in 3D space is difficult because of asymmetric structure of pedicle. In this study we attempt to propose a shape based method to help the surgeons to find the more accurate and safe path for screw insertion that minimizes the risk or invasiveness of the surgery using pre-operative CT images. We extracted two features for insertion paths from CT images, named “safety margin” and “pedicular screw fixation strength”. By using weighted k-means different paths were clustered and compared with each other. Results of comparison between those paths obtained from surgeon's pre-operative planning, intra operative and the proposed method proves a great improvement on the rate of success in reaching a suitable insertion trajectory by using our method. It is observed that the risk of damage in intra operative stage can be potentially high and it can be reduced considerably by using the proposed planning approach.

Electro-spun ultra-fine fibers exhibit two significant properties: a high surface-to-volume ratio and a relatively defect-free molecular structure. Due to the high surface-to-volume ratio, electro-spun materials are well suited for activities requiring increased physical contact, such as providing a site for a chemical reaction or filtration of small-sized physical materials. However, electrospinning has many shortcomings, including difficulties in producing inorganic nanofibers and a limited number or variety of polymers used in the process. The fabrication of nanofiber bundles via electrospinning is explored in this analytical study, as well as the relationship between extrinsic electrospinning parameters and the relative abundance of various fiber morphologies. Numerous variables could impact the fabrication of nanofibers, resulting in a variety of morphologies; therefore, adequate ambient conditions and selecting the appropriate solvent for achieving a homogenous polymer solution and uniform electro-spun materials are examined. Finally, common polymers suitable for electrospinning and the promising applications of ultra-fine fibers achieved via electrospinning are studied in this paper.