Earticle

The purpose of this research is to explore whether social network sites (SNS) increases individual creativity by social network user characteristics through knowledge transfer antecedents. Date is collected through an online questionnaire from 220 SNS users and structural equation modeling is employed to analyze data. Results indicate that user characteristics including social interaction and social relationship positively were positively related to knowledge transfer antecedents namely, knowledge sharing and collective collaboration, however, social influence was positively related to only knowledge sharing. The study also introduces SNS user playfulness as a moderator between knowledge transfer antecedents and individual creativity. The study offer a new research model to explore the impact of SNS based knowledge transfer on SNS user outcome based on SNS user characteristics. The finding of study provide guidance to SNS practitioners on enhancing individual creativity.

The reconstruction of weather data is essential for various applications such as weather forecasting, climate research, and disaster preparedness. Traditionally, this task required multiple instruments to record different attributes, posing challenges for complete data reconstruction. In this study, we have proposed a simple yet effective approach based on graph embedding and independent features to reconstruct the entire family of weather attributes. Exploiting weather histories from 62 stations across diverse climate regions in Nepal, our method enables the imputation of temperature and humidity data for specific weather stations as well as all stations over a period of time. Rigorous testing and validation demonstrate the effectiveness of our approach, with key evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Our results highlight the model’s proficiency in reconstructing comprehensive weather data, offering a promising avenue for enhancing the reliability of weather-related applications. Also, the use of graph embedding techniques and independent features, our approach provides a robust framework for reconstructing historical weather data, addressing the challenges associated with incomplete or fragmented datasets.

The integration of artificial intelligence (AI) technologies, particularly ChatGPT, has garnered significant attention in the business sector. This study explores the multifaceted applications of ChatGPT in business domains including customer service, marketing and sales, human resources, data analytics and decision-making, and finance. By analyzing data from 39 selected articles out of over 104 collected from Google Scholar and IEEE Xplore, this study highlights the transformative potential of ChatGPT in enhancing operational efficiency, improving customer interactions, and supporting strategic decision-making. The benefits of ChatGPT, such as increased efficiency, cost savings, and improved customer experiences, are examined alongside the challenges, including ethical considerations, data privacy, and system integration. Future research directions are proposed to address these challenges and further explore the capabilities of ChatGPT in business contexts. This paper aims to provide a comprehensive understanding of the current state and future prospects of ChatGPT in business applications.

Image enhancement plays a critical role in image processing. This paper introduces an integrated approach using three methods: Super-Resolution Generative Adversarial Net- work (SRGAN), Convolutional Autoencoder (CAE), and Zero- Reference Deep Curve Estimation (ZeroDCE), all embedded within a web application to provide image enhancement opportunities to users. SRGAN enhances image resolution by generating high-quality images from low-resolution inputs, with improved adjustments in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. The Convolutional Autoencoder denoises impulse noise while preserving key image features. ZeroDCE automatically enhances low-light images by adjusting pixel values using Light-Enhancement curves (LE- curves), Deep Curve Estimation Network (DCE-Net), and Non- Reference loss functions. In conclusion, this paper contributes to the broader field of computer vision and image processing, providing academic and practical understanding of the performance and limitations of these three models, paving the way for further developments in the field. The code is available at https://github.com/robinson-pujara/Refinetograph.git.