Factors influencing consumers’ attitude towards online shopping in Rangpur City

Nowadays Bangladesh is called a “DIGITAL BANGLADESH”. The propensity of online shopping and businesses is increasing these days. The digital environment spreads out not only in the country but also in Rangpur City. Over the last few years, online shopping has improved and the number of people who have access to the web has increased. A positive attitude towards online shopping and selling is being observed in Bangladesh. The young generation is much more involved in space marketing or shopping than in a place. The main aim of this research is to investigate the consumers’ attitudes toward online shopping and the factors influencing consumers towards online shopping. To meet the study objectives a quantitative survey method was employed. Researchers select 150 respondents by the technique of non-probability convenience sampling. The questionnaire of the study was developed from the previous literature. The research result shows that 60% of the respondents are male, and the rest are female between the 16-59 age groups, with a Secondary school certificate to postgraduate educational level. Most of them spend 2-4 hours on the Internet daily. The result also revealed that convenience, time-saving, website design, perceived enjoyment, and people engagement and their review rating have a significant positive relationship with consumer attitudes toward online shopping

Distributed Interdigital Capacitor (IDC) Sensing for Cable Insulation Aging and Degradation Detection

Nuclear power plants (NPPs) contain myriad power, control, instrumentation, and other types of cables. The polymer insulation and jacket materials of such cables degrade over time due to operation and environmental conditions e.g., heat, humidity, and radiation. Since the life span of NPPs may extend beyond 40-50 years regular monitoring of cable insulation and jacket polymers is critical to ensure safe and reliable operation. The agingrelated degradation of cables causes changes in the relative permittivity or dielectric constant of the insulation and jacket materials. Capacitor sensors, if properly designed and developed can measure this change and thus can provide an estimate of cable insulation health. Such low-cost sensors can be attractive for cable insulation aging detection because they can be deployed in large numbers and could potentially be wireless enabled for ease of data telemetry. Since real-life aged cable specimens are normally not available for testing cable specimens are aged under an accelerated aging environment in an oven the condition of which is governed by the modified Arrhenius equation to simulate real-life aging condition. This dissertation focuses on the study, design, and application of capacitor sensors like the interdigital capacitor (IDC) sensor and the serpentine (SRC)capacitor sensor. Typically, such a sensor applies a low frequency (kHz) AC signal on a set of driving electrodes which create localized electric fields within a material under test (MUT) e.g., cable jacket or insulation. A set of sensing electrodes that are connected to a circuit or chip measures the permittivity change in the MUT in the form of an appropriate interelectrode capacitance. The challenges with capacitor sensor design and development include achieving high sensitivity, electric field penetration depth, effects of air-gap mitigation, conformability on cylindrical surface, and conductor integrity. Furthermore, for cables containing both the jacket and the insulation it is currently not possible to measure the aging related permittivity variation of both the jacket and insulation with a single sensor because of electric field penetration depth being dependent on sensor geometry and material characteristics. This dissertation is motivated to address the above challenges. First, insights are gained from analytical model review and studies of sensors using analytical methods to understand the influence of sensor design parameters on sensitivity and electric field penetration depth. Unit-cell IDC sensors are analyzed using full-wave finite element electromagnetic (EM) simulations using Ansys Maxwell that reveal that the presence of a conducting backplane is highly beneficial in achieving both high sensitivity and electric field penetration depth. Analyses also demonstrate that extremely thin substrates are conducive from both performance and installation point of views. Experimental sensor design, fabrication, and testing are conducted considering a variety of sensor substrate materials and cables. Cable specimens with and without jackets that had undergone accelerated aging testing are measured using IDC sensors demonstrating their feasibility and applicability. To allow sensor electrode conformability, electrode integrity, and effects of airgap reduction a flexible fabric-based IDC sensor is built and tested on Okoguard Okolon and Okoguard aerial jumper cables. Okoguard Okolon cable specimens aged at 140°C show capacitance more than doubling when a sensor is placed on the CPE jacket of a cable specimen that had undergone accelerated aging from zero to 840 hours. This aging amounts to about 52.5 years of real-life field aging considering 70°C operating temperature. Tests conducted on the EPR insulation of this cable show a capacitance increase by 33% from its original state. The effects of airgap on the measured capacitance due to aging related material surface degradation is also studied that reveal the need for airgap reduction when sensors are installed on curved surfaces. Finally, the challenges of measuring thru-the-jacket insulation only permittivity variation of a cable a novel reconfigurable capacitor sensor is designed, developed, and tested. The electric field penetration depth for this sensor was changed by activating and deactivating PIN diode switches. In one instance, the sensor measures the permittivity variation of the jacket while in the next, it measures the permittivity variation of both the jacket and the insulation. By leveraging previously developed permittivity estimation models from large scale finite element simulations these two sets of measurement data are then used to evaluate the aging related permittivity variation of the insulation

BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications

Effect of plant growth regulators on growth and yield of chili (Capsicum annuum L.)

Chili (Capsicum annuum L.) is an important food additive with high medicinal value. To investigate the effect of plant growth regulators on chili, seedlings of chili were collected from the local market and grown in the experimental field of the University of Barishal, Bangladesh. Foliar spray of different degrees of plant growth regulators, Gibberellin (50 mg/l, 100 mg/l, 250mg/l, 350 mg/l GA3) and Cytokinin (50 mg/l, 100 mg/l, 250mg/l, 350mg/l Kn) were applied from 15 days of germination. Data on different growth and yield parameters were collected and analyzed statistically. The result reveals that there is a significant difference in growth and yield related traits in chili due to the application of plant growth regulators. An optimum level of PGRs application shows better performance compare with control. Plant height particularly influenced by GA3 whereas other attributes like the number of leaves, branches, flowers and fruits are greatly influenced by the application of kinetin

Generative Adversarial Networks for Data Augmentation

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.Comment: 13 pages, 6 figures, 1 table; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Influence of television programs genre on violent behaviour among young children

The purpose of this study was to examine the relationship between television program genre and aggression behaviour in primary school students. The results of the survey revealed a significant relationship between watching movies with action genre and aggression level among students (p<0.05). Finding indicated that there was no difference between mean of aggression level among children who interested in particular type of TV programs, except animation which showed a significant difference (p<0.05). In conclusion current study provides additional evidence to support that content of television programs particularly its genre is very important in shaping the children behavior. As a new perspective, focus on genre as an important element in producing of television programs could be helpful for authorities

Comparative study on job scheduling using priority rule and machine learning

Cloud computing is a potential technique for running resource-intensive applications on a wide scale. Implementation of a suitable scheduling algorithm is critical in order to properly use cloud resources. Shortest Job First (SJF) and Longest Job First (LJF) are two well-known corporate schedulers that are now used to manage Cloud tasks. Although such algorithms are basic and straightforward to develop, they are limited in their ability to deal with the dynamic nature of the Cloud. In our research, we have demonstrated a comparison in our investigations between the priority algorithm performance matrices and the machine learning algorithm. In cloudsim and Google Colab, we finished our experiment. CPU time, turnaround time, wall clock time, waiting time, and execution start time are all included in this research. For time and space sharing mode, the cloudlet is assigned to the CPU. VM is allocated in space-sharing mode all the time. We’ve achieved better for SJF and a decent machine learning algorithm outcome as well