Al Turath University College

The safe human-level decision-making for expressing the autonomous vehicles and the estimation of the eco vehicles has been proposed for the motion control for the driving behavior. For efficient decision-making, the sensor-based tracking of the autonomous machine for blockchain is to be done. The sensor-based history recording management has to propose, and storing the vehicle’s traveling history must be managed. For security reasons, the blockchain is done. Self-determination theory and energy-efficient mixed-precision neural networks are used in autonomous vehicles’ decision-making, and this technique is used in making moral decisions. The self-determination theory is used in creating the vehicles traveling steps using the innovative signal delivery system of the autonomous vehicles. The energy-efficient mixed-precision neural networks are used in managing the problem that travels the signal to the vehicles using the mixed–precision neural networks. The vehicle network has been made more efficient for storing the data of the mixed precision value and its neural network from autonomous vehicles. Here 80% of the precision value is raised compared to the previous days. In previous days, 20% of the precision has been calculated in autonomous vehicles. Comparing this, 60% of the precision value has been raised in traveling history. According to these variations, 40%–50% of autonomous vehicles’ data transmission that delivers the neural network has been proposed. By improving autonomous vehicles, the efficiency of mixed precision neural networks is the decision-making for efficient precision.

A Zn-SeO2 nanocomposite solution was prepared using the two-step technique of pulsed laser ablation in liquid (PLAL): the first step was by ablation of a zinc disc, and the second step was by exposure of a selenium disc within the same ZnO solution to a laser. The properties of the resulting plasma were analyzed using optical emission spectroscopy (OES) and laser-induced breakdown spectroscopy (LIBS) to determine the electron temperature and electron density. The crystal structure was characterized using X‑ray diffraction (XRD), the surface structure was characterized using a field-effect scanning electron microscope (FE-SEM), and optical absorption measurements were used to determine the energy gap. Antibacterial activity tests against Staphylococcus aureus ATCC 25923 using the resulting particles at concentrations of 25, 50, and 100 ppm showed no obvious inhibition zones. The results indicate distinct optical and structural behavior with no antibacterial activity under the tested conditions.

Silver nanoparticles (AgNPs) were chemically synthesized and incorporated into a polyvinyl alcohol–polyvinylpyrrolidone (PVA–PVP) polymer blend using a solution casting method to create PVA–PVP/AgNPs nanocomposite films. Transmission electron microscopy (TEM) revealed uniformly dispersed spherical AgNPs with diameters ranging from 10 to 30 nm, which strongly depend on precursor concentration. X-ray diffraction (XRD) confirmed the semi-crystalline structure of the (PVA–PVP) blend, while Fourier Transform Infrared (FTIR) indicated specific interactions between the silver species and the (PVA–PVP) matrices. UV–Vis spectroscopy showed a surface plasmon resonance (SPR) band between 380 and 400 nm, confirming the successful incorporation of nanoparticles. The position and width of this band correlated with particle size and dispersion. The optical band gap, estimated from Tauc plots, decreased from 5.28 eV for the pure PVA–PVP matrix to 5.18 eV upon AgNPs addition, suggesting improved electronic transition characteristics. Broadband dielectric spectroscopy (0.1 Hz–20 MHz) indicated enhanced dielectric constant and loss at low-frequency attributable to interfacial and space-charge polarization effects. PVA–PVP/AgNPs interfaces introduced varying properties, while the conductivity spectra revealed frequency-dependent hopping processes.

A spectroscopic study of TiO2 plasma produced by a 500 mJ Nd:YAG laser is performed. Optical emission spectroscopy (OES) is used to obtain important information on the electron temperature and density. The Stark expansion method is used to calculate the electron density, which is found to be 6.44⋅1017 electrons/cm3, while the Boltzmann diagram method is used to calculate the electron temperature, which is 1.926 eV. Nanoparticles are prepared by pulsed laser ablation in liquid (PLAL) and examined using XRD and FE-SEM techniques, which revealing that the resulting nanoparticles have an average diameter of 35 nm. The absorbance test of the prepared sample shows the maximum wavelength of 373 nm, and the biological effectiveness of these particles is tested against two types of bacteria (E. coli and S. aureus) using four different concentrations, where the samples show promising results due to their nanoscale properties and high stability proven by the Zeta potential test.

Cancer stem cells (CSCs) pose a formidable therapeutic challenge as a treatment-resistant subpopulation that is critical for tumor recurrence and metastasis. A theranostic approach, unifying diagnosis and treatment in a single strategy, presents a viable pathway to overcome this obstacle. This review focuses on engineered extracellular vesicles (EVs), particularly exosomes, as an emerging and flexible platform for CSC theranostics. We examine recent advances in EV engineering that enhance their utility as both sensitive biomarkers for CSC detection and targeted delivery vehicles for anticancer agents. Furthermore, we discuss the development of synthetic EVs designed to counteract the pro‑tumorigenic functions of natural CSC‑derived EVs, which drive angiogenesis, metastasis, and therapy resistance. Finally, we evaluate the translational pathway for EV‑based theranostics, highlighting their significant clinical potential while also highlighting persisting challenges. Engineered EV platforms, by enabling simultaneous diagnosis and treatment, hold considerable promise as they leverage their dual roles as native biomarkers and customizable drug delivery systems.