Temperature Effect on Frictional Properties of HMA at Different Polishing Stages

Both short-term and long-term variations have been observed in measured friction on pavement surface. These variations have been attributed to different factors, such as traffic, rainfall and temperature. Due to the fact that Hot Mix Asphalt (HMA) pavement surface and rubber tires are viscoelastic materials, it is believed that temperature may affect the measured frictional properties. Some researchers have found this effect to be significant; whereas, others have not. Therefore, the effect of temperature on the measured pavement friction remains to be further studied. This paper provides the results of a laboratory study aimed at evaluating the effect of temperature on the measured frictional properties of the HMA surface. The British Pendulum Tester (BPT) was used to measure friction of HMA surfaces at different polishing stages and different temperatures. Statistical analyses were performed to quantify the effect of temperature on the measured friction numbers. The main conclusion of this paper is that temperature is statistically significant in affecting the measured friction values. However, for agencies who desire to record skid number (SN) at a reference temperature for a long-term monitoring purpose, this paper provides a method for converting SN at a given temperature to the SN at the reference temperature

The impact of Servant Leadership on Organizational Trust: The Mediating Role of Organizational Culture

This study aims to examine the impact of servant leadership on organizational trust and mediating role of organizational culture for the mentioned relationship for employees in the Kuwaiti Ministry of Higher Education. A survey questionnaire was used as the main instrument for data collection. A total of 285 questionnaires were distributed among the Kuwaiti Ministry of Higher Education employees. In total, 248 valid questionnaires to analysis were returned equivalent to 87% response rate. Data analysis was conducted with the help of PLS-SEM to determine the level of relationships among servant leadership, organizational trust, and organizational culture. According to the obtained findings, there is a positive impact of servant leadership on organizational trust, and organizational culture has a partially mediated role in the relationship between servant leadership and organizational trust. The study findings motivate future studies to carry out studies of the same caliber in other sectors to obtain different perspectives

Modeling Thermal Conductivity, Thermal Diffusivity and Specific Heat of Asphalt Concrete Using Beta Regression and Mixture Volumetrics

The main objective of this paper is to develop predictive models using Beta regression for laboratory-prepared hot mix asphalt (HMA) specimens' thermal properties, including thermal conductivity (TC), thermal diffusivity (TD) and specific heat (SH). Thirty such specimens were prepared while varying the mixture's nominal maximum aggregate sizes (NMAS) and gradation coarseness. The widely used Transient Plane Source (TPS) method was employed to determine the thermal properties of the asphalt concrete. Only one type of asphalt binder was used for preparing all specimens. The air void volume (Va) and the effective binder volume (Vbe) were calculated for each mixture. To this end, the multiple linear regressions and the non-linear beta regressions were employed. Laboratory work resulted in hundred and fifty (150) data points. Three nominal maximum aggregate sizes, two gradation coarseness levels, five replicates and five different locations of measurements to ensure accuracy and repeatability in the obtained results. In conclusion, using Va and Vbe as predictors provided reliable predictive models for the thermal properties of different asphalt mixtures. The distribution of Va and Vbe was identified, and synthetic data was created to evaluate the accuracy of the models. Apart from R2 values, beta regression was more reliable to predict thermal properties of asphalt mixtures than multiple linear regression

Modeling Asphalt Pavement Frictional Properties using Different Machine Learning Algorithms

The objective of this work is to use some machine learning algorithms and test its efficiency in developing models to predict Locked Wheel Skid Trailer (LWST) values from Dynamic Friction Tester (DFT) and Circular Texture Meter (CTM) measurements conducted on asphalt pavement surfaces. For this prediction, three models were developed using DFT measurements at different speeds starting from 20km/h (12.5 mph) up to 64 km/h (40 mph) and then same DFT measurements as combination with Mean Profile Depth (MPD) and the last model used the International Friction Index (IFI) parameters (F60 and SP). The machine learning techniques includes two supervised learning algorithms: the Multi-Layer Perceptron (MLP) type of Artificial Neural Networks (ANN) and M5P tree model. In addition to one lazy algorithm called the K Nearest Neighbor (KNN) or Instance-Based Learner (IBL). The results showed that MLP models are the best in terms of the correlation coefficient that resulted in 81% prediction power using DFT parameters. Additionally, it was shown that the result of tree models was close to ANN but with much simpler regression. However, KNN models were recommended for LWST prediction of similar data characteristics and it is expected that this algorithm will be more efficient as the training data set becomes larger

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.Comment: 6 pages, Accepted and to appear in ISQED 202

TECHNICAL FACTORS AND USING OF COMPUTERIZED PHYSICIAN ORDER ENTRIES (CPOE) IN HEALTHCARE ORGANIZATIONS

Abstract Computerized Physician Order Entries (CPOE) i

The effect of Instagram on millennials consumer’s purchase intentions in the fashion industry

The purpose of the current research is to explore the impact of Instagram pages on consumers’ purchasing intentions among millennials in the fashion industry in Jordan. This study uses a quantitative, cause-effect, and cross-sectional approach. Online surveys were used to collect data from 212 respondents through different social media tools. The collected data was analyzed by SPSS software and Smart PLS to test the research hypothesis. Results show that bloggers’ recommendations significantly affect eWOM and engagement; usefulness information significantly affects eWOM and engagement; while trust insignificantly affects eWOM and engagement; brand familiarity insignificantly affects eWOM and engagement; participation and socialization insignificantly affect eWOM and engagement. Finally, useful information, eWOM, and engagement significantly affect consumers buying intention on Instagram. The study gives new information about the influence of Instagram pages on consumers' intentions. Therefore, this research expands the knowledge about factors that affect customers’ buying intentions. Since the study is a quantitative cross-sectional conducted on fashion industry Instagram users through an online survey in Jordan, which may limit its generalization to other industries and countries, therefore, the study suggests applying similar studies to online users of different ages, industries, and countries. Marketers can use Instagram to contact, promote, advertise, and sell their products by developing strong relationships with their customers through different social media tools. Using social media tools for marketing and selling reduces paperwork, printed advertisement, and transportation, which positively affects corporate social responsibility and reduces the consumption of energy and pollution

Freeze and Thaw Effect on Asphalt Concrete Mixtures Modified with Natural Bentonite Clay

This study aimed to investigate the effect of freeze and thaw (FT) cycles on the performance of asphalt concrete (AC) mixtures modified by partial replacement of mineral filler of the aggregate with natural bentonite clay (NBC) in order to reduce damage that occurs due to rapid FT cycles within the pavement structure. After exposure to FT cycles, AC mixture stability is reduced and becomes lower than minimum requirements, which leads to earlier damage of pavement. In order to enhance the AC mixture’s abilities to sustain severe FT cycles, this study used NBC amounts as a substitute for mineral filler by weight of its portion of the total aggregate: 5%, 10%, 15%, and 20%. Marshall stability, flow, and FT cycles were tested, and interior damage degree was assessed by a nondestructive test called ultrasonic pulse velocity (UPV). The results revealed the viability of combining NBC with asphalt mixtures for the purpose of improving the mixtures’ properties, particularly in environments where asphalt pavement is exposed to alternating FT cycles. The results also revealed that replacement of filler with NBC by 5% in AC mixtures reduced the damage caused over 8 continuous weeks of rapid FT cycles by 13%, which, in future applications, would reduce maintenance cost and prolong the pavement’s service life