Search

Nano-PCMs, which contain nanostructured materials, can enhance the low thermal conductivity of phase change materials (PCMs). It is crucial to predict the precise thermal conductivity of nano-PCM to assess the heat transfer during phase change procedures such as melting and solidification. In this study, artificial neural network (ANN) and response surface method (RSM) were used to develop a model for predicting the thermal conductivity of carbon-based nano-enhanced phase change materials (NEPCMs) using 482 experimental samples collected from various datasets. The carbon-based nano-particles were SWCNTs, MWCNTs, graphite, graphene, and (CNFs). Six input parameters were considered with varying temperature, mass fractions, sizes, thermal conductivities of PCMs and nano-particles, and the phase of PCM. The ANN was designed using a multi-layered feed-forward structure and Levenberg–Marquardt back-propagation algorithm with one hidden layer consisting of eight neurons. The transfer function was varied to achieve optimal performance. It was revealed that the predictive capacity of the ANN model is greater than that of the RSM model based on their corresponding the coefficient of determination (R2) and root mean square error (RMSE) values. The developed ANN achieved a R2 value of 0.99, while the RSM method model achieved an R2 value of 0.79.

This study describes the development and validation of an instrument aimed at measuring the faculty’s academic optimism in higher education. The data were collected from 211 faculty members who graduated from Iranian, Indian, Australian, United States, and British universities. At first, a question pool was developed to operationalize experts’ perceptions about academic optimism in their academic environments. In the second stage, the face and content validity of the scale were examined. Then, a pilot test was deployed to clarify the construct ambit and settled the measure and the meaning of academic optimism, through statistical methods. Then, an exploratory factor analysis was performed on 29 items, followed by a confirmatory factor analysis, which result in an ultimate scale of 23 statements in three factors including professionalism, academic emphasis & collective trust. The Cronbach’s alpha coefficient was applied and showed a reliable scale (r= 0.83). The final model represented very good fit (GoF =0.73), and the psychometric properties, such as discriminant, convergent and factorial validity, as well as reliability was assessed. Lastly, the implications of this tool for future research directions are discussed.

Osteoarthritis (OA) of the knee is a major health problem in the society. Iranian Traditional Medicine (ITM) or Persian Medicine (PM) as a branch of complementary medicine has been practiced in Iran for many centuries. An herbal medication known as squill oxymel has been used by PM physicians for OA. Our aim is to investigate the effect of squill oxymel on OA of the knee joint. Eighty eight patients were assigned to receive a placebo or squill oxymel syrup (10 ml each morning on empty stomach) for 8 consecutive weeks. Acetaminophen tablets were considered as the rescue medicine. Ultimately, 43 patients in the placebo group and 40 patients in the treatment group completed the trial and were included in the statistical analysis. Patients were followed for 4 weeks after cessation of treatment. The Knee injury and Osteoarthritis Outcome Score (KOOS) questionnaire and Visual Analog Scale (VAS) were considered as the main outcome measures. Laboratory tests including AST, ALT, BUN, Cr plus inflammatory tests including WBC, ESR, and CRP with specific tests i.e. IL6 and SOD at the beginning and the end of intervention were measured. The results showed the positive effect of treatment on the outcome of knee pain (p=0.04) and daily activity (p=0.01) of KOOS after Cessation of treatment. On the other hand, VAS decreased in both treatment and placebo groups while it showed significance intra-group and showed no significance between the two groups. After 4 weeks of cessation of treatment, the positive effect of the squill oxymel on the treatment group continued in some of the subscales of KOOS, including symptoms, knee pain and daily activities, but stopped in the placebo group. In general, both clinically and statistically significant improvement was observed after cessation of treatment. Squill oxymel syrup showed promising results in management of knee OA but future researches with larger sample size and longer duration are necessary.

In this research, liquid-liquid equilibrium (LLE) data were experimentally obtained for the ternary systems of (water + carboxylic acid + dipropyl ether) at T = 298.2 K and P = 101.3 kPa. The carboxylic acids used in this study were isobutyric acid, valeric acid, and isovaleric acid. All these systems are according to Treybal classification, Type-2 systems because the two binary subsystems are partially miscible. The lowest distribution coefficients and separation factors were calculated for isobutyric acid (40 and 329, respectively). The authenticity of the experimental equilibrium data was identified from Hand and Othmer-Tobias correlations. The experimental tie-line data were correlated by using the nonrandom two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) activity coefficient models. RMSD values are between 0.0112 and 0.0155 for the NRTL model, and are between 0.0083 and 0.0153 for UNIQUAC model.

Introduction: Helicobacter pylori (H. pylori) infection has remained as a global health problem. Animal studies demonstrated the role of H. pylori oipA gene in the development of gastric cancer. The aim of this study was the cloning and expression of Helicobacter pylori oipA gene in a bicistronic vector harboring mice IL-18 gene. Materials and methods: The target gene encoding oipA was amplified from a codon-optimized clone by PCR, and then double-digested by restriction enzymes. The pIRES-Igk/mIL18/Fc plasmid was simultaneously digested by BstXI/NotI enzymes to elicit the eGFP segment. PCR product of oipA was inserted into pIRES-Igk/mIL18/Fc plasmid using T4 ligase. Transformation into DH5α strain was done. Cloning was confirmed by PCR, enzymatic digestion and sequencing. Expression of the oipA and IL-18 mRNA was assessed by means of TaqMan Real-time PCR. Results: Electrophoresis of PCR product, enzymatic digestion and sequencing showed that the H. pylori oipA gene was successfully cloned into pIRES-Igk/mIL18/Fc to generate mIL-18-pIRES2-oipA plasmid. The results of Real-time PCR confirmed the successful expression of both oipA and IL-18 in mouse macrophage cell line. Conclusion: Considering the role of oipA in pathogenesis of H. pylori and potent activity of IL-18 as a molecular adjuvant, the results of the present study showed that the expression of codon-optimized oipA gene in bicistronic vector including mouse IL-18 is successful. So, it could be considered as an appropriate genetic vaccine candidate for H. pylori in future investigations.

Surgery is the best choice of treatment for human hydatidosis. Secondary infection is one of the end points of surgery in treating the hydatidosis which results from the spillage of protoscolices into the peritoneal cavity. Many chemical scolicidal agents have been used for inactivation of the cyst’s content, but most of them are associated with adverse side effects. Some studies have reported that traditional plants might have a potential application in prevention of post-surgery infections. We searched Medline, PubMed and Google scholar to identify the potentially relevant studies on implication of traditional plants against Echinococcus granulosus protoscoleces. In this study, we have reviewed scolicidal effects of some plant species (such as Zataria multiflora, Berberis vulgaris, Allium spp. Mentha spp. and etc.) and their products (essential oils, methanolic extract, aqueous extract and etc.) on Echinococcus granulosus protoccoleces. The scolicidal activity of these herbs could be helpful in hydatid cyst surgery. However, the mechanisms by which plant extracts killed protoscolices and also their safety for human cells are unclear and needed to be more investigated.

One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been conducted to automatically detect driver distraction. Although camera-based techniques have been successfully employed to characterize driver distraction, the risk of privacy violation is high. On the other hand, physiological signals have shown to be a privacy preserving and reliable indicator of driver state, while the acquisition technology might be intrusive to drivers in practical implementation. In this study, we investigate a continuous measure of phasic Galvanic Skin Responses (GSR) using a wristband wearable to identify distraction of drivers during a driving experiment on-the-road. We first decompose the raw GSR signal into its phasic and tonic components using Continuous Decomposition Analysis (CDA), and then the continuous phasic component containing relevant characteristics of the skin conductance signals is investigated for further analysis. We generated a high resolution spectro-temporal transformation of the GSR signals for non-distracted and distracted (calling and texting) scenarios to visualize the associated behavior of the decomposed phasic GSR signal in correlation with distracted scenarios. According to the spectrogram observations, we extract relevant spectral and temporal features to capture the patterns associated with the distracted scenarios at the physiological level. We then performed feature selection using support vector machine recursive feature elimination (SVM-RFE) in order to: (1) generate a rank of the distinguishing features among the subject population, and (2) create a reduced feature subset toward more efficient distraction identification on the edge at the generalization phase. We employed support vector machine (SVM) to generate the 10-fold cross validation (10-CV) identification performance measures. Our experimental results demonstrated cross-validation accuracy of 94.81% using all the features and the accuracy of 93.01% using reduced feature space. The SVM-RFE selected set of features generated a marginal decrease in accuracy while reducing the redundancy in the input feature space toward shorter response time necessary for early notification of distracted state of the driver.

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain–computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time–frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique—namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.