Derivation of a Retinoid X Receptor Scaffold from Peroxisome Proliferator‐Activated Receptor γ Ligand 1‐Di(1H‐indol‐3‐yl)methyl‐4‐trifluoromethylbenzene

PPARγ agonist DIM‐Ph‐4‐CF ₃ , a template for RXRα agonist (E)‐3‐[5‐di(1‐methyl‐1H‐indol‐3‐yl)methyl‐2‐thienyl] acrylic acid: DIM‐Ph‐CF₃ is reported to inhibit cancer growth independent of PPARγ and to interact with NR4A1. As both receptors dimerize with RXR, and natural PPARγ ligands activate RXR, DIM‐Ph‐4‐CF₃ was investigated as an RXR ligand. It displaces 9‐cis‐retinoic acid from RXRα but does not activate RXRα. Structure‐based direct design led to an RXRα agonist.

     

Hotspot management using a hybrid heat sink with stepped pin-fins

A hybrid heat sink design with microchannels and stepped pin-fins is introduced for the hotspot-targeted thermal management of microprocessors. The thermal and hydraulic performance were assessed numerically and compared to that of a hybrid heat sink with uniform pin-fins. Both hybrid heat sinks were designed to have two zones using rectangular microchannels above the processor’s background area and an array of pin-fins (stepped and uniform pin-fins) over the hotspot area. Conjugate heat transfer analysis was performed with the entire heat sink as the computational domain by solving the three-dimensional Navier-Stokes and energy equations. The hybrid heat sink with stepped pin-fins exhibited remarkable improvement in the temperature uniformity at the hotspot as compared to the one with uniform pin-fins, along with ample improvements in the thermal resistance, maximum temperature rise at the hotspot, and pumping power. A parametric investigation was also performed for the hybrid heat sink with uniform pin-fins to find an optimum geometry based on two geometric parameters: the ratio of the diameter of the pin-fins to their pitch and the total number of pin-fins in the array. The results revealed improvements in the thermal performance, but the pumping power was increased.     

An accurate and simplified small signal parameter extraction method for GaN HEMT

In this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20-element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch-off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance C_ds is also observed in the ohmic region (for smaller V_DS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.     

On using experimentally estimated wall shear stresses to validate numerically predicted results

The objective of this investigation was to assess the use of experimentally estimated wall shear stresses to validate numerically predicted results. The most commonly cited haemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is wall shear stress (WSS). WSS can be determined from the product of the viscosity of the fluid and the wall shear rate. Numerically, the wall shear rate is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall. Experimentally, the wall shear rate is estimated by applying a curve-fit to near-wall velocity measurements and evaluating the shear rate at a specific distance from the wall. When estimating the wall shear rate from the laser Doppler anemometry (LDA) point velocity measurements, large differences between the experimentally estimated and numerically predicted WSSs were introduced. It was found that the estimated WSS distributions from the experimental results are highly dependent on the curve-fitting method used to calculate the wall shear rate. However, the velocity profiles for both the experimental and numerical investigations show extremely good comparison. It is concluded that numerical models should be validated using unprocessed LDA point velocity measurement and not estimated WSS values.     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA, and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine the most effective model for predicting data. The stock closing price of BRIC country data was obtained from Yahoo Finance. That data dates from 01 November 2019 to 11 December 2020, then divided into two categories--training data and test data. Training data covers 01 November 2019 to 02 December 2020. Seven days (03 December 2020 to 11 December 2020) of data was tested to determine the accuracy of the models using training data as a reference. To measure the accuracy of the models, we obtained the means absolute percentage error (MAPE) and mean square error (MSE). Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price (BVSP) while MAPE (0.50) and MSE (579272.65) with Holt-Winters (smaller than ARIMA and SutteARIMA), model SutteARIMA was found most appropriate to predict the stock prices of Russia (IMOEX.ME), India (BSESN), and China (SSE) when compared to ARIMA and Holt-Winters. MAPE and MSE with SutteARIMA: Russia (MAPE:0.7; MSE:940.20), India (MAPE:0.90; MSE:207271.16), and China (MAPE: 0.72; MSE: 786.28). Finally, Holt-Winters predicted the daily forecast values for the Brazil stock price (BVSP) (12 December to 14 December 2020 i.e., 115757.6, 116150.9 and 116544.1), while SutteARIMA predicted the daily forecast values of Russia stock prices (IMOEX.ME) (12 December to 14 December 2020 i.e., 3238.06, 3241.54 and 3245.01), India stock price (BSESN) (12 December to 14 December 2020 i.e.,. 45709.38, 45828.71 and 45948.05), and China stock price (SSE) (11 December to 13 December 2020 i.e., 3397.56, 3390.59 and 3383.61) for the three time periods.     

Hybrid Computational Modeling for Web Application Security Assessment

Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era. This has led to digital revolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remains a serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses as well. Most of the data breaches are financially motivated, especially in the healthcare sector. The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web. Therefore, security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyber-attacks. The aim of this work is to provide efficient and effective healthcare web application security assessment. The study has worked with the hybrid computational model of Multi-Criteria Decision Making (MCDM) based on Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS) under the Hesitant Fuzzy (HF) environment. Hesitant fuzzy sets provide effective solutions to address decision making problems where experts counter hesitation to make a decision. The proposed research endeavor will support designers and developers in identifying, selecting and prioritizing the best security attributes for web applications’ development. The empirical analysis concludes that Robustness got highest priority amongst the assessed security attributes set followed by Encryption, Authentication, Limit Access, Revoke Access, Data Validation, and Maintain Audit Trail. The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the web application security. The study also establishes guidelines which the developers can refer for the identification and prioritization of security attributes to build more secure and trustworthy web-based applications.     

Analytical-geometrical percolation network model for piezoresistivity of hybrid CNT–CB polymer nanocomposites using Monte Carlo simulations

International Journal of Mechanics and Materials in Design - A 3D Monte Carlo simulation and percolation network model for hybrid nanocomposites reinforced by carbon nanotubes (CNTs) and carbon...     

Development of a low stress RF MEMS double-cantilever shunt capacitive switch

Microsystem Technologies - In this paper, a novel RF MEMS shunt capacitive switch with application in the Ka frequency band is proposed. The spring design and the step structure added to the beam...     

Lonsdaleite diamond growth on reconstructed Si (100) by hot-filament chemical vapor deposition (HFCVD)

In this paper, the growth of Lonsdaleite diamond using hot-filament chemical vapor deposition (HFCVD) on flashed and reconstructed Si (100) is reported. Surface morphology studies using scanning electron microscopy (SEM) show that the film is composed of decahedron and icosahedron diamond particles. The X-ray diffraction (XRD) pattern has a strongest peak at 47° and a peak at 41°, which is indicative of Lonsdaleite nature of the grown diamond film. The Raman spectrum of the film shows a broadened diamond peak at wave number of 1,329 cm⁻¹, which has shifted towards the peak position corresponding to Lonsdaleite nature of the diamond (1,326 cm⁻¹).