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Bangladesh Betar (BB), the national radio of Bangladesh has been providing extension services to the mostly uneducated Bangladeshi farming communities for the diffusion of modern farm technologies from its very beginning. This study aimed at evaluating the effectiveness of farm programs (FPs) of BB as distance education through assessing its capacity to motivate the farmers. A sample of 465 farmers was surveyed for primary data and the binary logistic regression model was used for data analysis. This study has revealed that those who listen to the FPs of BB were found more likely to adopt modern farm technologies than those who did not listen to the FPs. The concerned authority should take the necessary steps to make the farmers listen to the FPs of BB for educating and motivating them.

Background and Aims: Regulations response to COVID‐19 has increased internet addiction (IA), depression, and pornography addiction (PA) among adolescents worldwide. The objective of this nationwide study was to assess the current prevalence rate of IA, depression, and PA after the post‐COVID‐19 period among school‐going adolescents in Bangladesh. Methods: A total of 8832 male and female adolescents participated in this research. The cross‐sectional study was conducted online using a simple random sampling method. Including the sociodemographic variables, Young's IA Test (IAT‐20) Scale, Patient Health Questionnaire (PHQ‐9), and Pornography Craving Questionnaire (PCQ‐12) were used to measure IA, depression, and PA. By SPSS version 25.0, the prevalence and correlation between IA, depression, and PA were analyzed using the Chi‐square test, binary logistic regression, and a bivariate co‐relation matrix. Results: Sixty‐three percent, 76.6%, and 62.9% of the students were suffering from IA, depression, and PA respectively. Depressive and anxious symptoms were significantly associated with IA. Female students were more depressed than males. Males were more addicted to pornography than females. Students who utilized social media but didn't exercise had greater depression and PA. IA, depression, and PA were correlated. Conclusion: The research emphasizes the need for comprehensive mental health treatments, digital literacy programs, and family and teacher participation to reduce IA, depression, and PA among adolescents post‐COVID‐19. Promotion of physical exercise and supporting policies to build safer online settings for adolescents are also encouraged. [ABSTRACT FROM AUTHOR]

This study examines the exclusion of Vanessa Nakate, a Ugandan climate activist, from a group photo published by the Associated Press (AP) during the 2020 World Economic Forum. Using the muted group theory, it explores the implications of Nakate's omission as the sole Black individual in the image, shedding light on the broader issue of symbolic annihilation faced by Black women activists in Western media. Through visual and textual analysis, the study reveals the power dynamics and biases behind AP's decision to crop Nakate, fueling subsequent controversy. Furthermore, the paper examines AP's response to the backlash, analyzing their strategies to rebuild their reputation, including issuing an apology and releasing the original photograph. Plain language summary: Erasure of Black women activists from media: A review This paper looks at a photo from the 2020 World Economic Forum where a Ugandan climate activist named Vanessa Nakate was cropped out. The paper uses "muted group theory" to explain how this might have happened and what it means. It argues that Nakate was left out because she's a Black woman and that this is part of a bigger problem where the media often ignores or overlooks Black women activists. The focus is on the broader issue of symbolic annihilation experienced by Black women activists in Western media. Using visual and textual analysis, the study uncovers the power dynamics and biases influencing AP's decision to crop Nakate, sparking subsequent controversy. Additionally, the paper scrutinizes AP's response to the criticism, examining their strategies to rebuild their reputation, which include issuing an apology and releasing the original photograph. Overall, the paper is about how powerful media organizations can be and how important it is to be aware of the biases they might have. It is also about the importance of giving Black women activists a voice and making sure their stories are heard. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic has forced educational institutions to use e-learning systems. Bangladesh is no exception; many students come from underprivileged families who are not well-off. This study aimed to explore the antecedents to the underprivileged undergraduate students' intention to participate in online classes in Bangladesh through the integration of the Technology Acceptance Model, Information Systems Success Model, and Theory of Planned Behaviour. We used confirmatory factor analysis (CFA) to test the hypotheses. The non-probability sampling method was used to select 394 respondents by dint of the subjective judgment of the researchers. Using smart PLS software, the data were analyzed with Structural Equation Modeling (SEM). It was divulged that e-Learning usage intention (BI) is influenced by attitude (ATT), perceived usefulness (PU), students' online learning satisfaction (SOS) and subjective norms (SN). But perceived ease of use (PEU) and system quality (SQ), internet service quality (ISQ) and perceived behavioral control (PBC) do not influence BI. Even ISQ does not influence SOS. It was also revealed that PEU mediated attitude and PU, and PEU and SQ also influenced SOS. The study contributes to e-Learning literature by incorporating three models which may guide policymakers in understanding how to integrate students from all social classes into e-learning systems to eliminate academic digital discrimination. [ABSTRACT FROM AUTHOR]

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily-mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, facebook/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn't achieve good result in ROUGE-2. [ABSTRACT FROM AUTHOR]