Online shopping isasasas buying products or services via the internet using a computer or mobile platform. It provides a different policy and environment to both consumers and suppliers for having business activities in a digital environment than a traditional way. Some countries are highly adopted and utilizing online shopping for their day to day operations. There are some differences among countries in consumers’ buying behavior toward online shopping, which may be the reason for comfortability and reliability about the procedure and policies of digital environments. Online shopping is one of the convenient and best solutions for the busy life of today’s world. This study aims to identify those options for why users select online shopping rather than traditional shopping. The research model explained and predicted the behavioral intention and attitude towards online shopping. The convenient sampling method was used to select the sample units, and the Snowball sampling method was used to identify online shopping users. The questionnaire consists of two main sections. The first section analyzes the demographic factors related to online shopping. The second section included TAM variables. among the participants, 35(56%) were males, and 27(44%) females. The majority of 31(50%) of the online shopping users were between 25 to 30 years, and the rest (6%) were above 40 years. As expected, the experience of online shopping almost 34% of the respondents have less than ten years. Whereas the rest differ in their online shopping experience, those who used online shopping for 1-3 years stood at 31%. Moreover, 24% of the respondents have 3-5 years’ experience. Only a few respondents have more than five years’ experience. It stood for 11%. This study revealed that users comprehensively supported online shopping behavior except for four hypotheses. Thus, it could be concluded that people prefer online shopping because of their knowledge, usefulness, and attitude towards online shopping. The relation between attitude and behavioral intention is strongly positive and significant.
The time and attendance systems help to monitor the employers and students working and attending time. Educational systems are struggling with the traditional system. It affects the pedagogical activities considerably. The traditional system is encountering many problems, and there is a need for a robust technological solution. This study focuses on building a two-factor prototype with RFID, IoT, and machine learning techniques. A microcontroller, GSM module, RFID tag, an RFID reader are used for first step verification. A camera with Multitask Cascaded Convolutional Network (MTCNN) model is used for a second verification. When both are okay, students will get the attendance. If it fails, parents will get a notification about the student’s attendance. When the prototype is developed as a complete system, the educational system will be getting higher advantages.
In this paper a present time developing application is used that is a UAV Antenna in aerospace technology. These antennas play a vital role in this WIMAX technology. A patch antenna is designed such that all the dimensions should be shrinked yet efficient in radiation in comb shape and this proposed antenna is used at 4.2GHz frequency range. A CST tool is used for designing and simulating our antenna all the dimensions taken for proposed antenna are comparatively less when compared to conventional models. Low return loss, gain, bandwidth and VSWR are optimized by using this design the efficiency is also enhanced by 95% which makes our antenna suitable to the UAV WIMAX applications. Surface current is also one of the major parameter which is reduced by our proposed model.
In 2004 the campus information system began to use the internet as an academic tool between students, lecturers, study programs, finance. In the system, there are several models of communication processes built, one of which is the existence of facilities for the content and values of staff, in addition to announcements, academics, finance, and college approval. To find out the causal relationship between the factors that influence the level of campus student acceptance for users. This research includes Exploration research using the Technology Acceptance Model, expenditure using AMOS software. In the Normal Ranking Table, you can see the value in column c.r. All in the recommended amount from -2.58 to 2.58. Therefore, it can be announced that the data is normally distributed, Mahalanobis d-squared assesses that there is nothing greater than the table, which means that it is not an outlier and the determinant value of the covariance matrix obtained is 51345,276 far from zero, it can be concluded that there are no multicollinearity and singularity problems in the analyzed data iso that the data is declared valid. Based on the Final Research Model, it can be accepted that the benefit variable requires acceptance of campus students. After utilizing comfort, take advantage when using. Based ion this phenomenon, it can be used, the use of "voluntary" technology that is used using codes of conduct, attitudes, and intentions in an application. In this case, it can be concluded that respondents "voluntarily" use.
Nowadays, Pyrazolone and its derivatives have gained a lot of attention due to their biological and medicinal applications. These compounds have antimicrobial, antifungal and anticancer properties. Therefore, using simple methods to prepare these compounds is important. Pyrazolone is one of the inhibitors of kinase domain containing receptor KDR or VEGFR-2. In this study, Quantitative Structure-Activity Relationship (QSAR) analysis was used to predict the inhibitory activity of new pyrazolone derivatives. Also, Bi-dimensional images were used to calculate pixels for QSAR modeling. Furthermore, the partial least squares (PLS) was used to establish a relationship between IC50-dependent variables and independent variables, i.e., pixels or hidden variables. In addition, Genetic Algorithm (GA) was used in PLS method (GA-PLS) to select the descriptors. In this method, the variables which selected to form the calibration model had negligible errors with acceptable characteristics. Pre-processing methods such as Orthogonal Signal Correction (OSC) were used to provide a suitable input for modeling as well as to improve the results of the GA (OSC-GA-PLS). Furthermore, Root Mean Squared Error of Prediction (RMSEP) was used to assess the performance of the models to predict the pIC50 of the studied compounds, the value of which was obtained equal to 0.30, 0.22, and 0.19 for PLS, GA-PLS and OSC-GA-PLS models, respectively. Finally, the proposed QSAR model was developed with the OSC-GA-PLS method to predict the inhibitory activity of the new compounds.
