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The ease of use and advancements in drone technology is resulting in the widespread application of Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs' several applications, livestock agriculture is one of the most promising, where UAVs facilitate various operations for efficient animal management. But the field is characterized by multiple environmental, technical, economic, and strategic challenges. However, the use of advanced technological techniques like Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), advanced sensors, etc. along with the assurance of animal welfare while operating the UAVs, can lead to widespread adoption of drone technology amongst livestock farmers. This paper discusses livestock management research where UAVs monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made to elucidate different aspects and broader issues around livestock management while highlighting the associated challenges, opportunities, and prospects. This work is the first review paper on the subject matter with all the necessary information and analysis, to the best of our knowledge. Therefore, the article promises to provide interested researchers with detailed information about the field, guiding future research.

Excessive usage of social network services (SNS) can lead to exhaustion and frustration, which, in turn, can induce SNS discontinuance intention. In this study, the negative factors when using SNS are empirically tested. We constructed a research model that explores the effects of social overload, disclosure, and pattern of SNS use on SNS-induced exhaustion and frustration. We also examined the influence of exhaustion and frustration on SNS discontinuance intention. An empirical study of 170 SNS users from Bangladesh provides support for our hypotheses. Partial least squares algorithm is implemented to test the hypotheses. The result reveals significant influence of frustration on SNS discontinuance intention. The paper contributes by identifying several factors. Both theoretical and practical implications are discussed.

Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis of the challenges, the underlying recognition process, and possible future directions. In this paper, the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of the state-of-the-art datasets and approaches towards offline BHDR have been analyzed. Furthermore, several real-life applicationspecific studies, which involve BHDR, have also been discussed in detail. This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas.

The aim of this research is to explore the possibility of web usability test in 60 seconds rather than unlimited time by customers.  Usability was tested by two major testing methods: system usability scale (SUS) and NetQu@l.  60 customers as two groups were involved in the experimental design procedure. The assessment included an online shopping website where one group tested in 60 seconds and other group had 3 days to test.  Result shows that there are significance differences in SUS based testing and no significant differences in NetQu@l based testing. Altogether, these results provide further support that SUS based usability testing can be implemented in 60 seconds time frame without imposing additional cognitive load on customers.

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