Growth factor release from a chemically modified elastomeric poly(1,8‐octanediol‐co‐citrate) thin film promotes angiogenesis in vivo

The ultimate success of in vivo organ formation utilizing ex vivo expanded “starter” tissues relies heavily upon the level of vascularization provided by either endogenous or artificial induction of angiogenic or vasculogenic events. To facilitate proangiogenic outcomes and promote tissue growth, an elastomeric scaffold previously shown to be instrumental in the urinary bladder regenerative process was modified to release proangiogenic growth factors. Carboxylic acid groups on poly(1,8‐octanediol‐co‐citrate) films (POCfs) were modified with heparan sulfate creating a heparan binding POCf (HBPOCf). Release of proangiogenic growth factors vascular endothelial growth factor (VEGF), fibroblast growth factor 2 (FGF2), and insulin‐like growth factor 1 (IGF‐1) from HBPOCfs demonstrated an approximate threefold increase over controls during a 30‐day time course in vitro . Atomic force microscopy demonstrated significant topological differences between films. Subcutaneous implantation of POCf alone, HBPOCf, POCf‐VEGF, and HBPOCf‐VEGF within the dorsa of nude rats yielded increased vascular growth in HBPOCf‐VEGF constructs. Vessel quantification studies revealed that POCfs alone contained 41.1 ± 4.1 vessels/mm 2 , while HBPOCf, POCf‐VEGF, and HBPOCF‐VEGF contained 41.7 ± 2.6, 76.3 ± 9.4, and 167.72 ± 15.3 vessels/mm 2 , respectively. Presence of increased vessel growth was demonstrated by CD31 and vWF immunostaining in HBPOCf‐VEGF implanted areas. Data demonstrate that elastomeric POCfs can be chemically modified and possess the ability to promote angiogenesis in vivo . © 2011 Wiley Periodicals, Inc. J Biomed Mater Res Part A, 2012.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90248/1/33306_ftp.pd

Object identification and surveillance based on deep learning algorithms for quadcopters

Drone technology is evolving for the applications like surveillance, observation, rescue and control crimes, military, agriculture, civil and many more purposes. But these surveillance systems are monitored by human interaction so there may be some negligence and malfunction may happen due to lack of observation. The objective of the work was development of unmanned aerial vehicle and implementation of the deep learning, image processing tools in the quad-copter based surveillance system. Development of the quad-copter was done by using necessary components such as Arms and power distribution board with help of fasteners. And the brushless dc motors are fitted with electronic speed controllers. And the suitable propellers are fitted to the motors, from which thrust is obtained. The Arduino Uno microcontroller is used as flight controller with MPU6050. Sensor fusion concept coding was used for accelerometer and gyroscope for stable direction orientations of Aerial vehicle. Multiwii platform was used to build the flight controller for achieving desire rotation of motors as well as proper directions and speed. The receiver was installed in the quad-copter for wireless control with transmitter of 2.4 GHz range. And IP camera was used, from which the surveillance visuals are taken for monitoring. Battery of 2200mah capacity of 3 cells was used for power supply of whole system. The visuals were obtained in raspberry pi, the live video stream/images are processed with the deep learning tools i.e., Open CV, Tensor flow, yolo for effective surveillance

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

As electric vehicles (EVs) gain momentum in the shift towards sustainable transportation, the efficiency and reliability of energy storage systems become paramount. Lithium-ion batteries stand at the forefront of this transition, necessitating sophisticated battery management systems (BMS) to enhance their performance and lifespan. This research presents an innovative simulation of a 4S3P lithium-ion battery pack using MATLAB R2023b, designed to refine BMS capabilities by employing advanced mathematical modelling and computational intelligence. The simulation meticulously analyses critical operational metrics such as state of charge (SOC), state of health (SOH), temperature variations, and electrical behaviour under diverse load scenarios, offering deep insights into the intricate dynamics of lithium-ion batteries in EV applications. The results corroborate the simulation model’s accuracy in reflecting actual battery pack performance and underscore significant improvements in BMS strategies, especially concerning predictive maintenance and adaptive charging techniques. By seamlessly integrating computational intelligence into BMS, this study lays the groundwork for more durable, efficient, and intelligent energy storage systems in electric vehicles, marking a significant stride in e-mobility technology