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Background: Participating in physical exercise is advantageous for maintaining optimum health, improving physical capacity, decreasing the likelihood of chronic diseases, and promoting overall wellbeing. Aim: This study aimed to find out the prevalence and factors that contribute to musculoskeletal injuries among individuals who participated in fitness activities at the gym. Methods: This cross-sectional study included 1123 gym members, both male and female, aged between 18 and 50 years, from selected fitness centers in Bangladesh. Musculoskeletal injuries were assessed using the Nordic musculoskeletal disorder questionnaire. Binary logistic regression identified the gym members' predictors of musculoskeletal injuries. Results: The highest prevalence of musculoskeletal injuries at the low back (36.6%) was seen among the eight body sites, followed by the shoulder (24.7%) and knee (17.1%). Males (aOR 2.589, CI 1.18 to 5.65) and those who go to the gym to lose weight (aOR 3.859, CI 0.91 to 16.33) and for physical fitness (aOR 1.895, CI 1.07 to 3.35) had a greater risk of musculoskeletal injury. Participants who carried out strength training exercises (aOR 4.10, CI 2.74 to 6.19) had a four-fold increased risk of musculoskeletal injuries than those who did not. Furthermore, higher adjusted odds of musculoskeletal injuries were found for the potential causes of injuries in incorrect holding (aOR 1.69, CI 1.10 to 2.60), overweight lifting (aOR 2.00, CI 1.30 to 3.08), lack of workout knowledge (aOR 3.56, CI 2.09 to 5.85), and insufficient information from the trainer (aOR 5.66, CI 1.84 to 17.39). Conclusion: Musculoskeletal injuries are highly prevalent among gym-goers in Bangladesh. The back was the most often injured area, followed by the shoulder and knee. It is important to exhibit caution and take extra care while doing strength training activities in order to avoid injury. Prior to engaging in gym-based activities, it is essential to have a thorough understanding of proper exercise knowledge. [ABSTRACT FROM AUTHOR]

This research paper presents an innovative solution for offline handwritten word recognition in Bengali, a prominent Indic language. The complexities of this script, particularly in cursive writing, often lead to overlapping characters and segmentation challenges. Conventional methodologies, reliant on individual character recognition and aggregation, are error-prone. To overcome these limitations, we propose a novel method treating the entire document as a coherent entity and utilizing the efficient you only look once (YOLO) model for word extraction. In our approach, we view individual words as distinct objects and employ the YOLO model for supervised learning, transforming object detection into a regression problematic to predict spatially detached bounding boxes and class possibilities. Rigorous training results in outstanding performance, with remarkable box_loss of 0.014, obj_loss of 0.14, and class_loss of 0.009. Furthermore, the achieved mAP_0.5 score of 0.95 and map_0.5:0.95 score of 0.97 demonstrates the model's exceptional accuracy in detecting and recognizing handwritten words. To evaluate our method comprehensively, we introduce the Omor-Ekush dataset, a meticulously curated collection of 21,300 handwritten words from 150 participants, featuring 141 words per document. Our pioneering YOLO-based approach, combined with the curated Omor-Ekush dataset, represents a significant advancement in handwritten word recognition in Bengali. [ABSTRACT FROM AUTHOR]

Fish provide an affordable and readily available source of animal protein for human consumption that plays a vital role in maintaining the livelihoods of Bangladeshi people. The study was conducted to evaluate the proximate composition of five different small indigenous fish species (SIS): viz., Glossogobius giuris, Mastacembelus pancalus, Mystus tengra, Heteropneustes fossilis, and Puntius ticto. The samples were collected from the Mathabhanga River. The proximate composition of the studied samples revealed varying proportions of moisture content (68.60 to 79.05%), crude protein (46.22 to 65.99%), crude fat (4.77 to 22.93%), ash content (12.03 to 19.22%), carbohydrate (11.22 to 15.73%), crude fiber (0.09 to 0.25%), whereas the energy value was 374.04 to 444.91Kcal/100g. Among the examined fish species, Glossogobius giuris contained significantly (P< 0.5) higher level of protein content but less amount of lipid content. Addittionally, Puntius ticto showed significantly (P< 0.05) higher lipid, ash and energy contents as compared to others. The results of the study clearly revealed that Glossogobius giuris and Puntius ticto fish species are rich in macronutrients in alliance with the tested fish species. Hence, the research findings suggest that commonly consumed small indigenous fish species (SIS) offer a valuable source of high-quality protein and essential macronutrients. This implies that the consumption of these fish can help alleviate protein and other nutrient deficiency, as well as livelihood security in Bangladesh. Further biochemical and molecular research can be conducted to analyze all the essential nutrients and genetic improvement of the fish. [ABSTRACT FROM AUTHOR]