Moderating effect of administrative role in the relationship between the SERVQUAL dimensions and Quality Service Provisions of local government in Dhaka City

The present study aims at investigating the moderating effect of administrative role in the relationships between the service quality dimensions, logistic support and perceived quality service of local government (Dhaka City Corporation) in the context of Bangladesh. For the purpose of the study, data were collected from 222 slum dwellers of Dhaka City living in the six big slums which are Shampur, Tejgaon, Bhasantak, Korail, Kamalapur and Zurain slums to examine the moderating effect. The responses were gathered with 5 point Likert scale with response options ranging from strongly agree (5) to strongly disagree (1) through a structured questionnaire survey. Collected data were analyzed using partial least square structural equation modeling technique (PLS-SEM) with the support of the software Smart PLS 2.0 M3. The findings reveal that administrative role positively moderates the relationship between assurance dimension and perceived service quality of local government; logistic support and perceived quality service and finally, the findings reveal that administrative role has significant moderating effect in the relationship between responsiveness and perceived service equality of local government. Hence, the findings give an indication that local government should play positive administrative role to improve the services quality and make their services effective for slum dwellers. The policy makers, local government and other related stakeholders might find this study as an essential tool in designing, developing and implementing their activities directed to the slum dwellers

Long future frame prediction using optical flow informed deep neural networks for enhancement of robotic teleoperation in high latency environments

High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image-to-image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical flow components reflecting real-time control inputs are added to the standard RGB channels of the input image. We have experimented with three data sets of 20,000 input images each that were generated using our custom-designed teleoperation simulator with a 500-ms delay added between the input and target frames. Structural Similarity Index Measures (SSIMs) of 0.60 and Multi-SSIMs of 0.68 were achieved when training the cGAN with three-channel RGB image data. With the five-channel input data (incorporating optical flow) these values improved to 0.67 and 0.74, respectively. Applying Fleiss\u27 κ gave a score of 0.40 for three-channel RGB data, and 0.55 for five-channel optical flow-added data. We are confident the predicted synthetic frames are of sufficient quality and reliability to be presented to teleoperators as a video feed that will enhance teleoperation. To the best of our knowledge, we are the first to attempt to reduce the impacts of latency through future frame prediction using deep neural networks

Consumer choice behavior towards mobile phone operators in Bangladesh

Volume 2, Issue 4, October 201

Structure-aware image translation-based long future prediction for enhancement of ground robotic vehicle teleoperation

Predicting future frames through image-to-image translation and using these synthetically generated frames for high-speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure-aware SSIM-based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS-SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of \u3e 0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model

Effect of aging on the reinforcement efficiency of carbon nanotubes in epoxy matrix

The reinforcement efficiency of carbon nanotubes (CNTs) in epoxy matrix was investigated in the elastic regime. Cyclic uniaxial tensile tests were performed at constant strain amplitude and increasing maximum strain. Post-curing of the epoxy and its composite at a temperature close to the glass transition temperature allowed us to explore the effect of aging on the reinforcement efficiency of CNT. It is found that the reinforcement efficiency is compatible with a mean field mixture rule of stress reinforcement by random inclusions. It also diminishes when the maximum strain increased and this effect is amplified by aging. The decrease of elastic modulus with increasing cyclic maximum strain is quite similar to the one observed for filled elastomers with increasing strain amplitude, a phenomenon often referred as the Payne effect