Jahangirnagar University

The nesting and breeding ecology of the Great Egret was studied in north and northeastern Bangladesh from 2017 to 2023. Out of 199 colony sites, 111 (55.78 %) were actively breeding, 38 (19.10 %) served as night roosts, and 50 (25.12 %) were abandoned. The active breeding colony sites covered a total of 92.12 hectares, with the majority situated on private land (89.92 ha) and only a small portion on government land (2.20 ha). The highest number of active breeding colonies occurred in Naogaon (n = 22), followed by Sylhet (n = 14) and Habiganj (n = 9), while Bogura, Natore, Rajshahi, and Jamalpur each had only one (n = 1). Of the 5,755 nests counted, the largest proportion was in Naogaon (20.94%), followed by Netrokona (17.74%) and Sylhet (7.91%), with the lowest in Bogura (0.21%). Active breeding colonies were predominantly in paddy field-dominated habitats (71.17%, n = 79), with smaller proportions in wetlands (9.91%, n = 11), mixed agriculture (9.01%, n = 10), tea gardens (4.50%, n = 5), orchards (2.70%, n = 3), and homestead areas (2.70%, n = 3). Breeding occurred from April to September, with colony sizes ranging from 2 to 302 nests. Nesting occurred on 1,258 plants across 34 species, with a preference for Bambusa spp. (40.31%), followed by Mangifera indica (15.57%), Swietenia mahagoni (8.98%), and Tamarindus indica (8.06%); the average nesting height was 10.39 ± 2.41 m (range 5.2-16.41 m, n = 1,258). Clutches averaged 2-4 eggs (mean 2.78 ± 0.71, n = 18), laid over 4-9 days. The incubation period lasted 23–28 days, chicks fledged after 40-55 days, and overall breeding success was 74%. Bangladesh J. Zool. 53(3): 373-388, 2025

Long chain aliphatic ketones have gained increasing attention due to their structural diversity and broad pharmacological significance. Trewia polycarpa, a medicinal plant widely used in traditional healthcare systems of Bangladesh, remains underexplored as a source of such bioactive constituents. The n hexane extract of T. polycarpa root bark was subjected to silica gel column chromatography, leading to the isolation of a saturated aliphatic ketone. Spectroscopic and spectrometric analyses, including FTIR, ¹H-NMR, ¹³C-NMR, DEPT 135, and LC-MS/MS, in comparison with literature data, enabled the characterization and identification of the compound as hentriacontan 16 one. To investigate its potential biological relevance, molecular docking using PyRx software was performed against selected therapeutic targets, including bacterial hydrolase FphH (PDB ID: 8FTP), penicillin binding protein (6G9S), interleukin 10 (2ILK), cytochrome c peroxidase (1AEM), NF κB inducing kinase (6WPP), and the δ opioid receptor (6PT2). The compound exhibited moderate binding affinities (−5.7 to −4.8 kcal/mol), primarily driven by hydrophobic interactions. These findings represent the first report of hentriacontan 16 one from T. polycarpa and provide a foundation for future experimental bioassays, toxicity evaluations, and mechanistic studies. Subsequent research should emphasize large scale isolation and comprehensive bioassays to further assess its therapeutic potential and possible synergistic effects with other phytochemicals.

Microplastics (MPs) accumulation can potentially influence soil fertility and plant productivity. MP pollution and spatial distribution in terrestrial environments, particularly in agricultural soils surrounding the municipal solid waste (MSW) dumping site in southwest Bangladesh, is still not investigated. The objective of this study was to quantify, characterize, and assess the ecological risks of MPs in agricultural soils surrounding the MSW dumpsite of Khulna City Corporation (KCC) in southwest Bangladesh. Ten (10) composite agricultural soil samples surrounding the MSW dumping site were collected and processed for MPs and trace metals analysis. We investigated the abundance, spatial distribution, characteristics, polymer types, and ecological risk of observed MPs. The results exhibited that the mean abundance of MPs was 5567 items kg-1 (range from 1667 to 11,000 items kg-1), and the dominant shape, size, and color were found in fibers (~ 43%), < 1 mm, and blue, respectively. Polyvinyl chloride (PVC) was the dominant polymer followed by HDPE and nylon among the 14 identified polymer types across the study site's soils. Spatial analysis showed that the old dumpsite, along with agricultural soils in the vicinity of the active dumpsite, was polluted with the MPs and heavy metals. The investigated sites were classified as risk category I due to MP pollution, and thefindings indicate a decrease in MP abundance as the distance from the dumping site increases. Principal component and correlation analysis indicate different anthropogenic sources of MPs and heavy metals pollution, namely the solid waste dumpsite and improper agricultural activities (p < 0.05). The findings highlight the need for integrated management strategies to prevent microplastic pollution in agricultural soils to ensure soil health and crop productivity.

Tephritid fruit flies are major pests of fruits and vegetables in tropical regions, causing significant economic losses. Effective monitoring using male lure-based traps, such as methyl eugenol (ME) and cue-lure (CL), is essential for integrated pest management. This study evaluated the influence of lure type, trap height, storage condition, and seasonal dynamics on fruit fly captures at two sites (AERE and Jahangirnagar University) in the Savar region of Bangladesh. Methyl eugenol (ME) and cue-lure (CL) traps were deployed at three heights (2, 3, and 5 ft) over a one-year period (August 2023-July 2024). A total of 36,107 male fruit flies, representing nine Bactrocera, Dacus and Zeugodacus species, were recorded. ME traps consistently captured significantly more males than CL traps (p &lt; 0.05), confirming strong lure-specific selectivity—B. dorsalis and B. zonata dominated ME catches, whereas Z. cucurbitae and Z. tau predominated in CL traps. Trap height showed no significant effect on captures, suggesting that low-level deployment (~1-1.5 m) is adequate for field monitoring. Fruit fly abundance peaked during summer (June-July) and declined in winter (December-February), reflecting climatic influences on population activity. Lure longevity tests demonstrated that cold storage (4°C) substantially prolonged lure efficacy compared to storage at room temperature or sunlight exposure. Collectively, these findings highlight that lure type, seasonal timing, and proper storage are key determinants of trapping success, while trap height plays a minor role. The results provide practical recommendations for optimizing fruit fly surveillance and integrated pest management strategies in Bangladesh.

Bisphenol analogues (BPs) are widely used in the production of epoxy resins and poly carbonate plastics. In recent years, the demand and production capacity of BPs are growing rapidly. The occurrence of BPs other than bisphenol A (BPA) in Buriganga River water system and sediment is largely unreported. This study assesses the seasonal occurrence and spatial distribution of four bisphenol analogues bisphenol B (BPB), bisphenol E (BPE), bisphenol AF (BPAF), and bisphenol F (BPF) in water and sediment samples from ten riverine sites and quantification was performed via GC–MS. In water, rainy-season’s mean concentrations reached 3.74 μg/L for BPAF and 3.06 μg/L for BPF, while BPB and BPE remained below 1.2 μg/L. Winter levels more than doubled, peaking at 7.11 μg/L for BPF and 6.89 μg/L for BPAF, with BPB and BPE rising to 4.94 μg/L and 4.53 μg/L, respectively. Broader winter ranges (e.g., BPF 3.37–13.7 μg/L) indicate enhanced mobilization and accumulation under low flow conditions. Sediment analysis revealed mean concentrations of 64.9 μg/g (BPF), 54.2 μg/g (BPE), 45.0 μg/g (BPAF), and 24.6 μg/g (BPB), with maximum values reaching up to 159.9 μg/g, highlighting localized hotspots. Proportional composition shifted from BPAF dominance in the rainy season to BPF prevalence in winter, while sediments consistently followed the hierarchy BPF > BPE > BPAF > BPB. BPs in water exhibited significant and strong positive correlations, whereas sediment bisphenols showed no significant links. This study could be a wake-up call for the scientific community and policymakers to consider remedial measures.

Maternal and neonatal mortality in Bangladesh remains high, particularly in rural areas where access to skilled postnatal care (PNC) is limited. This study assessed urban-rural disparities in PNC utilization using data from the 2022 Bangladesh Demographic and Health Survey. A sample of 4,844 ever-married women aged 15-49 with a recent live birth (3,249 from rural areas; 1,595 from urban areas) was analyzed. Outcomes included receiving any PNC, receiving a timely PNC within two days, and receiving a PNC from a trained provider. Socio-demographic, reproductive, and healthcare determinants are analyzed using survey-adjusted weighted logistic regression. Overall, 76.6% of women received PNC, with 70.4% receiving it within 48 hours, and 24.7% from skilled providers. Utilization was consistently higher in urban than rural areas. Education, wealth, antenatal visits, and maternal employment were positively associated with PNC use, with women attending four or more antenatal visits showing nearly twice the odds of receiving timely and skilled PNC. Conversely, higher birth order and regional disparities, particularly in Rangpur, were associated with reduced access to skilled care. Chattogram showed comparatively higher coverage. Persistent inequities in PNC utilization, with rural women lagging in both timeliness and access to skilled providers, indicate targeted interventions addressing education, socio-economic inequality, and service availability are critical to improving maternal and newborn health outcomes in Bangladesh.

Accidental falls have emerged as a major public health concern, especially among individuals aged 65 and older, due to their high incidence and severe consequences. Without timely intervention, such falls can result in fractures, traumatic brain injuries, and long-term complications. As a result, considerable research has focused on developing automated fall detection systems that integrate intelligent algorithms with sensor-based data acquisition to enable rapid response and medical assistance. This study follows the PRISMA framework to conduct a systematic literature review. A comprehensive search was performed across major databases including PubMed, Google Scholar, Scopus, and IEEE Xplore using fall detection–related keywords. From an initial pool of 596 articles, duplicates were removed and strict inclusion/exclusion criteria were applied, resulting in 182 relevant articles for in-depth analysis. This review examines a wide range of Artificial Intelligence (AI) and Machine Learning (ML) approaches applied to fall detection using diverse sensor modalities, including wearable, vision-based, ambient, and multimodal systems. Additionally, it summarizes the publicly available datasets and sensor configurations used in existing studies. This broad perspective is essential to compare trade-offs across sensing modalities and support effective system development for real-world deployment. This review provides a comparative overview of AI/ML-based fall detection approaches, highlighting differences in accuracy, sensitivity, and dataset usage as reported in existing literature. In the end, many open research challenges in using AI and ML to detect falls are outlined, along with potential future perspectives.

In Bangladesh, fresh graduates face a tough job market, with far more candidates than available positions. Many traditional job search methods depend heavily on matching keywords, but these often miss the mark when it comes to truly connecting people with roles that fit their skills and career goals. This study looks at a new job recommendation system that combines different machine learning techniques, like content-based and collaborative filtering, to make job matching smarter and more accurate. By incorporating natural language processing, deep learning, and career path analysis, the system aims to offer recommendations that feel more personalized and relevant. This study explored with various machine learning models, utilizing data from employment platforms, resumes, and recruitment trends. Out of all the models, decision trees stood out, achieving an accuracy of 88