Enhancing the low temperature oxidation performance over a Pt and a Pt–Pd diesel oxidation catalyst

The influence of hydrogen over platinum and combined platinum–palladium diesel oxidation catalysts were investigated on the oxidation kinetics of CO, HC and NO. Although H2 has been reported to have a positive effect on CO and HC oxidation as well as NO2 formation over platinum catalysts, there is still uncertainty whether this is due to the temperature rise caused by H2 oxidation or the result of a change in the reaction kinetics of CO, HC and NO oxidation by the production of intermediate species. The results have showed smaller H2 concentrations are more effective in improving the catalyst light-off temperature as well as promoting NO oxidation over both platinum and platinum–palladium catalysts. It is suggested that these benefits are a result of not only the exothermic reactions which in turn increase the local catalyst temperature but also H2 increasing the rate of reactions and the species accessibility to the catalyst active sites thus further CO, HC and NO oxidation can occur at lower catalyst temperatures

Effect of letrozole on masculinization of Siamese fighting fish (Betta splendens)

The aim of this study was to evaluate the effect of letrozole (a non-steroidal aromatase inhibitor) on masculinization of Siamese fighting fish (Betta splendens). Different doses of letrozole 50, 100, 150 and 200 ppm (mg/kg feed) were incorporated into diet and fed for periods of 30, 40 or 50 days. Immersion treatment of letrozole at selected doses (250, 500, 750 and 1,000?g/l) for 3 h each on third, fifth and eighth day-post-hatching (dph) (Trial 1) and fourth, sixth and eighth dph (Trial 2) was given.The oral administration of letrozole for 30, 40 or 50 days did not have any significant effect on the sex ratio of B. splendens. The immersion treatment of letrozole induced 100% masculinization at 500 and 1000?g/l in trial 1, while it yielded only 66.66 and 90% male population at 500 and 1000 ?g/l in Trial 2. The dietary administration of letrozole for 30 and 50 days caused tail deformities and/or rudimentary and absence of dorsal fin. The progeny testing of males from letrozole treated (both oral and immersion) groups indicate that the sex ratio of progenies of each of the males tested did not differ significantly from that of control, indicating that all those males carried XY genotype. Letrozole treatments suppressed ovarian development (atretic oocytes were common). However, the testicular development was unaffected. The study revealed that immersion treatment of letrozole was more effective in inducing masculinization of B. splendens than the oral administration of letrozole

Enhancing biopharmaceutical performance of an anticancer drug by long chain PUFA based self-nanoemulsifying lipidic nanomicellar system.

The aim of this study was to develop polyunsaturated fatty acid (PUFA) long chain glyceride (LCG) enriched self-nanoemulsifying lipidic nanomicelles systems (SNELS) for augmenting lymphatic uptake and enhancing oral bioavailability of docetaxel and compare its biopharmaceutical performance with a medium-chain fatty acid glyceride (MCG) SNELS. Equilibrium solubility and pseudo ternary phase studies facilitated the selection of suitable LCG and MCG. The critical material attributes (CMAs) and critical process parameters (CPPs) were earmarked using Placket-Burman Design (PBD) and Fractional Factorial Design (FFD) for LCG- and MCG-SNELS respectively, and nano micelles were subsequently optimized using I- and D-optimal designs. Desirability function unearthed the optimized SNELS with Temul 85% and Perm45min >75%. The SNELS demonstrated efficient biocompatibility and energy dependent cellular uptake, reduced P-gp efflux and increased permeability using bi-directional Caco-2 model. Optimal PUFA enriched LCG-SNELS exhibited distinctly superior permeability and absorption parameters during ex vivo permeation, in situ single pass intestinal perfusion, lymphatic uptake and in vivo pharmacokinetic studies over MCG-SNELS. [Abstract copyright: Copyright © 2017. Published by Elsevier B.V.

Improving the biopharmaceutical attributes of mangiferin using vitamin E-TPGS co-loaded self-assembled phosholipidic nano-mixed micellar systems

The current research work encompasses the development, characterization, and evaluation of self-assembled phospholipidic nano-mixed miceller system (SPNMS) of a poorly soluble BCS Class IV xanthone bioactive, mangiferin (Mgf) functionalized with co-delivery of vitamin E TPGS. Systematic optimization using I-optimal design yielded self-assembled phospholipidic nano-micelles with a particle size of &lt; 60 nm and &gt; 80% of drug release in 15 min. The cytotoxicity and cellular uptake studies performed using MCF-7 and MDA-MB-231 cell lines demonstrated greater kill and faster cellular uptake. The ex vivo intestinal permeability revealed higher lymphatic uptake, while in situ perfusion and in vivo pharmacokinetic studies indicated nearly 6.6- and 3.0-folds augmentation in permeability and bioavailability of Mgf. In a nutshell, vitamin E functionalized SPNMS of Mgf improved the biopharmaceutical performance of Mgf in rats for enhanced anticancer potency.</p

Exploration of Deep Learning Applications on an Autonomous Embedded Platform (Bluebox 2.0)

Indiana University-Purdue University Indianapolis (IUPUI)An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of competent processors for autonomous or self-driving car have shown amplitude of prospect, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Deployment of several mentioned ADAS safety feature using multiple sensors and individual processors, increases the integration complexity and also results in the distribution of the system, which is very pivotal for autonomous vehicles. This thesis attempts to tackle two important adas safety feature: Forward collision Warning, and Object Detection using the machine learning and Deep Neural Networks and there deployment in the autonomous embedded platform. 1. A machine learning based approach for the forward collision warning system in an autonomous vehicle. 2. 3-D object detection using Lidar and Camera which is primarily based on Lidar Point Clouds. The proposed forward collision warning model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity and separation distance to a classifier algorithm which on the basis of supervised learning model, alerts the driver of possible collision. Decision Tress, Linear Regression, Support Vector Machine, Stochastic Gradient Descent, and a Fully Connected Neural Network is used for the prediction purpose. The second proposed methods uses object detection architecture, which combines the 2D object detectors and a contemporary 3D deep learning techniques. For this approach, the 2D object detectors is used first, which proposes a 2D bounding box on the images or video frames. Additionally a 3D object detection technique is used where the point clouds are instance segmented and based on raw point clouds density a 3D bounding box is predicted across the previously segmented objects

Role for substance p-based nociceptive signaling in progenitor cell activation and angiogenesis during ischemia in mice and in human subjects

Our data highlight the role of SP in reparative neovascularization. Nociceptive signaling may represent a novel target of regenerative medicine

UR-213 Generative AI & Cybersecurity

This research project details the impact of Generative AI on Cybersecurity through both its potential enhancements and threats. Using advanced AI algorithms, this project explores how Generative AI can strengthen cybersecurity through systems like Anomaly Detection, Intrusion Detection Systems (IDS), and Malware Analysis. Also, this project addresses the growing challenges posed from Generative AI. In particular, issues surrounding Deepfake Phishing and Polymorphic Malware are discussed. Solutions to mitigate these issues are also provided to engage further understanding in the field. The goal of this research is to offer practical solutions for addressing the growing field of AI-driven cybersecurity

Increased NO2 concentration in the diesel engine exhaust for improved Ag/Al2O3 catalyst NH3-SCR activity

Increasing the NO2 availability in some aftertreatment systems enhance their performance in reducing pollutants from internal combustion (IC) engines but result in significant fuel economy and CO2 emissions penalties. The presence of NO2 in the engine exhaust gas enhances the regeneration of the Diesel Particulate Filters (DPFs) and can improve the activity of the catalysts in reducing NOx emissions in the selective catalytic reduction (SCRs) process. In this work the production and the role of the increased NO2 concentration in the Ag/Al2O3 catalyst for the SCR process of NOx removal at low exhaust gas temperatures under real engine operation has been investigated. We have increased the NO2 concentration available for the SCR process with (i) the addition of different NH3 and H2 mixtures upstream the SCR catalyst and/or (ii) by the use of a Pt based Diesel Oxidation Catalyst (DOC) in front of the Ag/Al2O3-SCR catalyst. In the case of NH3 and H2 mixtures additions, H2 enhances the NO2 production on the Ag/Al2O3 catalyst, leading in promoting the “Fast-SCR” like reaction by utilising the available NH3 mainly at low reaction temperature. The incorporation of the DOC in front of the Ag/Al2O3 showed the same effect as it enhanced the NO2 availability for the SCR process

Legal Implications of Artificial Intelligence in Criminal Justice Systems

The rapid advancement of Artificial Intelligence (AI) is transforming various sectors, including the criminal justice system, where it is increasingly utilized for tasks such as law enforcement, sentencing, and predictive policing. AI holds the potential to enhance efficiency, accuracy, and decision-making within these areas. However, its deployment also brings forth critical ethical and legal challenges. Key concerns include accountability for AI-driven decisions, algorithmic bias, transparency in AI processes, and the diminishing role of human oversight. This research paper critically examines these challenges, explores the existing legal frameworks governing AI in criminal justice, and proposes strategies to mitigate associated risks. By addressing the ethical and legal implications of AI integration in criminal justice, this paper seeks to contribute to the evolving discourse on balancing innovation with the preservation of justice and fairness