Sensitivity analyses of electric vehicle life cycle impacts based on consumer behaviors.

Electric vehicles (EVs) are being promoted as a viable vehicle technology to mitigate the greenhouse gas emissions associated with conventional vehicles. Previous studies have shown that the use phase of the EVs is the main contributor to EV life cycle environmental impacts. Use phase impacts depend on multiple factors such as the type and amount of electricity used to charge the batteries, driving behavior, and life span of the battery. Comprehensive life cycle assessment (LCA) studies exist on the impacts of electric vehicle. However, most studies assume no variability in user behavior. This variability could play a significant role in determining the environmental impacts of the EVs. This study aims to address some of these concerns by empirically determining the life cycle impacts of EVs based on different consumer behavior. First, EV driving and charging behaviors that have a major influence in determining battery ageing were identified from EV consumer literature and scenarios were developed to capture a variety of behaviors. These scenarios served as inputs to a battery ageing test. The experiment resulted in three unique capacity fade and energy efficiency fade distributions that were integrated into existing LCA models to determine variability in life cycle impacts. Results of the LCA model show that the environmental impacts of EVs are dependent on consumer behavior. Nevertheless, the sensitivity analyses show the severity of the impacts depend heavily on the electricity mix that is used to charge the EV in the use phase. Combining aspects of user behavior with the properties of the grid that is being used, a strategy can be developed to ensure that EVs have the lowest life cycle impact. The method to link LCA models to consumer behavior and battery ageing tests presented in this study points to opportunities for reducing the overall life cycle impact of EVs

Factors that lead to the success of inner city Hispanic students in STEM dual enrollment: a mixed methods study

The United States has been struggling to maintain its authority in a technological world. Every year thousands of skilled STEM workers come to the United States to take jobs that are not filled by our college graduates. In order to uplift STEM education in the United States intermediation is required from the early years and also to encourage participation of Hispanic students in STEM majors and careers. Over the years the high school graduation rate for Hispanic youth has increased, and their enrollment in institutions of higher education have also increased, however, their participation in STEM majors has not yet seen the same increase. Besides advanced placement (AP) credits, dual enrollment credits have become a very important part of high school curriculum. Federal government and various other organizations have been funding STEM dual enrollment programs throughout the country to promote participation of Hispanic and other minority high school students in STEM majors and careers. Although several grants are working on promoting and encouraging STEM Dual enrollment among Hispanic students, only a very small percentage of Hispanic students successfully take advantage of these opportunities. This dissertation focuses on studying factors that support the success of Hispanic high school students from inner city school districts in STEM Dual Enrollment

CREDIT RISK ANALYSIS USING MACHINE LEARNING AND NEURAL NETWORKS

A key activity within the banking industry is to extend credit to customers, hence, credit risk analysis is critical for nancial risk management. There are various methods used to perform credit risk analysis. In this project, we analyze German and Australian nancial data from UC Irvine Machine Learning repository, reproducing results previously published in literature. Further, using the same dataset and various machine learning algorithms, we attempt to create better models by tuning available parameters, however, our results are at best comparable to published results. In this report, we have explained the algorithms and mathematical framework that goes behind developing the machine learning models. We conclude with a discussion and comparision of summarizing the best approach to classify these datasets. K - Nearest Neighbors (KNN), Logistic Regression (LR), Naive Byaes Classication, Support Vector Machine (SVM), Classication Trees and Articial Neural Networks (ANN) are the machine learning models used for this report

Efficacy of standardized novel Boswellia serrata extract in the dextran sodium sulfate-induced colitis model - potential use in gut health management

Background: Objective of this study was to evaluate anti-inflammatory properties of a novel standardized Boswellia serrata extract–bsRx (developed using natural excipients and designed to have specific ratio of its major actives, viz. AKBA and BBA) in dextran sodium sulfate (DSS)-induced IBD model in BALB/c mice.Methods: Animals (BALB/c mice) in control (CL) group were administered vehicle; DSS-induced colitis group (DSS group), 2.5 % DSS; and Boswellia serrata group (BS group) received DSS, for inducing colitis, together with a novel standardized extract of Boswellia serrata (41 mg/kg, 4.1 mg/ml solution in distilled water) for 10 days. Reference group (SS group) received DSS with sulfasalazine (30 mg/kg, 3.0 mg/ml suspension in distilled water) for 10 days. Clinical assessment for disease activity index (DAI), histopathological examination and hematological assessments were performed.Results: Treatment with Boswellia serrata showed significant reduction in the DAI score on day 10 compared to the DSS group (2.49±0.93 versus 3.63±0.55, p≤0.05). Body weight (18.54±2.21 gm versus 17.05±3.53 gm) and colon length (6.8±0.9 cm versus 7.6±0.6 cm, p≤0.05) also improved in the BS group compared to DSS group, respectively. Histological scoring of colitis was lower in the BS group (10.1±1.37). There was no difference in leukotriene levels between groups (p>0.05).Conclusions: Treatment with novel Boswellia serrata extract improved colon length, DAI and histological scoring index in DSS-induced colitis in IBD mice models. Our results indicate the promising potential of novel Boswellia extract in IBD and gut health management

From mouse to man:predicting biased effects of beta-blockers in asthma

This article is a Commentary on Thanawala VJ, Valdez DJ, Joshi R, Forkuo GS, Parra S, Knoll BJ, Bouvier M, Leff P and Bond RA (2015). Beta‐blockers have differential effects on the murine asthma phenotype. Br J Pharmacol 172: 4833–4846. doi: 10.1111/bph.13253. The authors reply in Bond RA (2016). Differences in asthma study models and the effectiveness of β(2)‐adrenoceptor ligands: response to Lipworth et al. Br J Pharmacol 173: 250–251. doi: 10.1111/bph.13334

A comparative pharmacokinetics study of Ashwagandha (Withania somnifera) Root Extract sustained-release capsules: an open-label, randomized, two treatment, two-sequence, two period, single-dose crossover clinical study

Background: In this open-label, randomized, balanced, two-treatment, two-sequence, two-period, crossover, single-dose oral comparative pharmacokinetics study, the pharmacokinetics, safety, and tolerability of test product ‘ashwagandha (Withania somnifera)’ root extract sustained release capsule 300 mg (Prolanza™), each containing 15 mg withanolides (administered dose: 2×15 mg) was compared with that of a reference product (organic KSM-66 ashwagandha extract [vegan] capsule, each containing 15 mg withanolides [administered dose: 2×15 mg]).Methods: Total 14 healthy men were randomized to receive either the test or the reference product as a single dose of 2 capsules in sequence, administered under fasting conditions. Plasma concentrations of total withanolides, withanolide A and 12-deoxywithastramonolide were measured using validated liquid chromatography–mass spectroscopy/mass spectroscopy.Results: The test product had higher relative absorption, better relative bioavailability, and longer elimination half-life indicating a sustained-release profile compared to reference. Specifically, the relative bioavailability of the test formulation was 12, 44, and 11 times higher for total withanolides, withanolide A and 12-deoxywithastramonolide, respectively. No adverse events were reported during the study.Conclusions: The sustained-release profile of the test product, compared to reference product, will provide more long-lasting therapeutic effects from a single daily dose (Retrospectively applied on Clinical Trials Registry - India [CTRI]. Application reference number: REF/2020/03/032408). The study reports the unique sustained release formulation of Withania somnifera (Ashwagandha) root extract. The pharmacokinetic study also reports for first time, the successful plasma estimation of withanolide A and 12-deoxywithastraamonolide, the major phytoactives of ashwagandha

Control of Neurotransmitter Release Properties by Presynaptic Calcium

Presynaptic terminals of neurons are optimized for neurotransmitter release, which is tightly controlled by presynaptic calcium. Here, we evaluate the role of calcium influx through voltage-gated calcium channels (VGCCs) in regulating the initial vesicular release probability (p) and the number of vesicles available for release by action potentials (effective RRP) at the calyx of Held synapse in mice. Two established methods of estimating effective RRP size and p reveal that both are calcium dependent. Reducing calcium influx by blocking R-type (VGCCs) or P/Q-type VGCCs also reduces EPSC amplitude via p and effective RRP size. Furthermore, activation of gamma-aminobutryic acid class B (GABAB) receptors, which reduces presynaptic calcium by regulating VGCCs without other significant effects on release, also reduces the effective RRP size and p. These findings suggest that the calcium dependence of RRP size may influence the manner in which certain neuromodulators affect neurotransmitter release

Sudden Onset Blindness in a Patient with Mixed Connective Tissue Disease

Case Presentation A 66-year-old Caucasian female recently diagnosed with mixed connective tissue disease presented with acute onset vision loss in the left eye. The patient first noted a “hazy-shower” that caused blurry vision with loss of peripheral vision. Her vision progressively worsened over a four-day period, resulting in complete blindness in the left eye and the onset of blurry vision in her right eye. She denied any eye pain, discharge, photophobia or similar symptoms in the past. The patient did note a very mild headache for four days but denied any other symptoms

Accelerating Polynomial Multiplication for RLWE using Pipelined FFT

The evolution of quantum algorithms threatens to break public key cryptography in polynomial time. The development of quantum-resistant algorithms for the post-quantum era has seen a significant growth in the field of post quantum cryptography (PQC). Polynomial multiplication is the core of Ring Learning with Error (RLWE) lattice based cryptography (LBC) which is one of the most promising PQC candidates. In this work, we present the design of fast and energy-efficient pipelined Number Theoretic Transform (NTT) based polynomial multipliers and present synthesis results on a Field Programmable Gate Array (FPGA) to evaluate their efficacy. NTT is performed using the pipelined R2SDF and R22SDF Fast Fourier Transform (FFT) architectures. In addition, we propose an energy efficient modified architecture (Modr2). The NTT-based designed polynomial multipliers employs the Modr2 architecture that achieve on average 2× better performance over the R2SDF FFT and 2.4× over the R22SDF FFT with similar levels of energy consumption. The proposed polynomial multiplier with Modr2 architecture reaches 12.5× energy efficiency over the state-ofthe-art convolution-based polynomial multiplier and 4× speedup over the systolic array NTT based polynomial multiplier for polynomial degrees of 1024, demonstrating its potential for practical deployment in future designs