Study of the deposition and characterisation of a 5,10,15,20-tetrakis-(4-sulphonatophenyl)porphyrin Co(II) layer at gold surfaces in alkaline solution

In this paper, the electrodeposition of Co(II)TSPor at gold electrodes is described. It was found that this deposition is initially controlled by kinetic parameters leading to a nearly 100% coverage of the electrode surface. However, once formed a reorganisation of the layer occurs. Experimental evidence that Co(II)TSPor is deposited was provided by using Raman spectroscopy.     

Case–Based Reasoning for Assessing Intelligent Transportation Systems Benefits

Existing transportation planning modeling tools have critical limitations with respect to assessing the benefits of intelligent transportation systems (ITS) deployment. In this article, we present a novel framework for developing modeling tools for quantifying ITS deployments benefits. This approach is based on using case–based reasoning (CBR), an artificial intelligence paradigm, to capture and organize the insights gained from running a dynamic traffic assignment (DTA) model. To demonstrate the feasibility of the approach, the study develops a prototype system for evaluating the benefits of diverting traffic away from incident locations using variable message signs. A real–world network from the Hartford area in Connecticut is used in developing the system. The performance of the prototype is evaluated by comparing its predictions to those obtained using a detailed DTA model. The prototype system is shown to yield solutions comparable to those obtained from the DTA model, thus demonstrating the feasibility of the approach.     

Testing and modeling of a novel FRP-encased steel–concrete composite column

A composite column consisting of steel, concrete and fiber reinforced polymer (FRP) is presented and assessed through experimental testing and analytical modeling. The composite column utilizes a glass FRP (GFRP) composite tube that surrounds a steel I-section, which is subsequently filled with concrete. The GFRP tube acts as a stay-in-place form in addition to providing confinement to the concrete. This study investigates the behavior of the proposed composite columns under axial loading. A total of seven specimens were tested. The influence of concrete shrinkage on the compressive behavior of the composite columns was also investigated. Significant confinement and composite action resulted in enhanced compressive behavior. The addition of a shrinkage reducing agent was found to further improve the compressive behavior of the composite columns. An analytical model was developed to predict the behavior of the composite columns under axial loading.     

Wireless Power Transfer and In-Vehicle Networking Integration for Energy-Efficient Electric Vehicles

This paper presents the wireless power transfer (WPT) technology based on inductive coupling and the design challenges of a hybrid energy harvester (EH) circuit as a promising solution to promote the energy efficiency of the electric vehicles (EVs). The design methodologies of ultra-low power (ULP) electronic module based on low leakage conditioning and processing device are detailed based on nanoscale transistor technology so that the WPT and hybrid EH can be implemented for self-powered devices in EVs. Moreover, in-vehicle network design verification based on a new power-aware behavioral model formulation and extraction for high-speed and ULP transceivers that enables the transient prediction of power and ground currents and voltages when multiple drivers are simultaneously switching for signal-power integrity evaluation. The derivation of the proposed model is based on the analysis and extension of the input/output buffer information specification (IBIS). The analysis of the previous IBIS and Mpilog modeling approaches is followed by a new model formulation along with a well-designed characterization and parametric extraction procedure.     

Conditioning specific positive states to unfamiliar flavours influences flavour liking

Food products can have significant influence on consumers’ affective states. Flavours can be used to evoke affective states, and affective information has been shown to influence how much of a beverage is consumed. In this study we investigated whether novel flavours could be conditioned to two different specific positive states (“feeling active” and “feeling relaxed”). We associated flavours with these states by using different types of positive film clips and tested how this influenced liking and different behavioural attributes using implicit measures, in particular self-focus and activity. Participants consumed one of two unfamiliar flavoured drinks (one liked and one neutral) for five consecutive days while watching film clips evoking active, relaxed, or neutral states. We examined the levels of activity and self-focus evoked by the conditioned drinks using implicit measures. Activity was higher in the active film clip condition than in the neutral control condition. For the liked flavour, positive conditioning reduced initial liking, compared to the control condition. The pattern was different for the neutral flavour. Results show that it is possible to condition flavours to induce specific positive states and that this process affects product liking.     

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.     

Physical study of Sm doped borochromate glass system

Sm³⁺ doped borochromate glasses of composition [xSm₂O₃-74.5B₂O₃-25Li₂O-(0.5 − x)Cr₂O₃] with x = 0, 0.1, 0.2, 0.3, 0.4 and 0.5 mol% have been prepared by conventional quenching melting method. The XRD profile confirm the amorphous nature of the glass samples. The density measurements were made using Archimedes’ principle of the prepared samples. Molar volume V_M, rare-earth ion concentration N and ionic radius r_p as well as field strength F have been estimated from the density measurements. The average optical basicity, Λ_th, electronegativity χ_2av and electronic polarizability α²⁻ were calculated for the prepared glass samples. Influence of rare earth ions on structural behavior in borochromate glasses have been investigated using infrared spectroscopy (IR). The structural changes have been analyzed with increasing rare earth concentration. Partial BO₃ ↔ BO₄ conversion as a function of rare earth concentration was observed. From the relative peak areas of BO₃ and BO₄ in structural groups the ratio N₄ has been calculated. ESR spectra were recorded at room temperature. The obtain ESR signals were used to estimate the paramagnetic susceptibility (χ).     

An Improved DeepNN with Feature Ranking for Covid-19 Detection

The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time.