Bangabandhu Sheikh Mujibur Rahman Science and Technology University

This paper has modelled and quantitatively analyzed a hollow-core PCF to evaluate its performance as a biosensor. This biosensor is specifically designed to sense four types of cancerous cells, namely Jurkat, HeLa, MCF-7, and Basal, representing the names of blood, cervical, breast, and skin cells. The designed PCF has maintained a minimum of 0.2827 numerical aperture at 1.3 µm wavelength for the four types of cells. The effective absorption and confinement loss values have been exceptionally low for this PCF. The maximum values of these two parameters are only 2.43 × 10–7 cm−1 and 1.91 × 10–8 dB/m correspondingly at 1.3 µm. At the same wavelength, this biosensor has offered a higher value of relative sensitivity for the four types of cells that vary from 88.12% to 89.65%. In addition to these traditional values of performance indices, the simple PCF structure offers a broad likelihood of implementation using the persisting fabrication process.

Breast cancer (BC) is the second most common cause of cancer-related mortality worldwide. Topoisomerase II alpha (TOP2A), a key enzyme involved in DNA replication and repair, represents an important biomarker and a promising therapeutic target in BC. This study investigated the therapeutic potential of bioactive compounds from dragon fruit (Hylocereus undatus) as inhibitors of TOP2A using computational biology approaches. A total of 72 phytochemicals were retrieved from the PubChem database and evaluated for binding affinity, toxicity, drug-likeness, and inhibitory potential against TOP2A. Among these, fifteen compounds satisfied Lipinski's rule of five and were predicted to be non-toxic and non-carcinogenic. Further molecular screening identified four compounds, Phlorizin, Beta-Glucogallin, Peoniflorin, and Alpha-Tocopherol as strong TOP2A inhibitors, with binding energies ranging from-7.4 and-9.5 kcal/mol. Thermodynamic analysis showed that all four had favorable Gibbs free energy values, indicating spontaneous binding to TOP2A. Thermodynamic analysis revealed favorable Gibbs free energy values, indicating spontaneous binding to TOP2A. Two hundred nanosecond (200 ns) molecular dynamics simulations were performed to assess the conformational stability and binding interactions of the selected compounds with TOP2A, using doxorubicin as a reference control. Throughout the simulations, all phytochemicals demonstrated stable root mean square deviation values, low fluctuations, consistent radius of gyration, and preserved key intermolecular interactions, including hydrogen bonds and hydrophobic contacts. Pharmacokinetic analysis further indicated favorable drug-like properties, while network pharmacology identified key cancer-related targets, such as TP53 and EGFR, supporting the therapeutic relevance of these compounds in BC. Overall, Phlorizin, Beta-Glucogallin, Peoniflorin, and Alpha-Tocopherol demonstrated strong in silico potential as TOP2A inhibitors, suggesting possible therapeutic applications in BC and other cancers.

The rapid urbanization and climate change has increased the surface heating of urban peri urban areas in Bangladesh. This paper analyzes the land surface temperature within Chuadanga District using Landsat 8 imageries of April 2024 and discusses the impact of vegetation, built-up surfaces and water bodies based on NDVI, NDBI and MNDWI measures. The spatial indices were as NDBI -0.26 to 0.02, NDVI 0.14 to 0.42, MNDWI -0.24 to -0.05, and LST 31.27 to 39.25 °C. There existed strong correlations among NDBI NDVI ( -0.89), NDBI NDVLST (0.68), and NDVI LST ( -0.54), which shows that urban expansion increases the surface temperature, and vegetation alleviates the temperature. To predict LST, four machine learning models were used, which are the Random Forest (RF), the Support Vector Machine (SVM), the Extreme Gradient Boosting (XGBoost), and the Artificial Neural Network (ANN). The best model in terms of r = 0.63, R 2 = 0.393, RMSE 1.71 C and MAE 1.02 C and the largest AUC of 0.8115 was Support Vector Machine. Analysis using SHapley Additive exPlanations (SHAP) found NDBI to be the strongest effectively causing surface temperature, NDVI, and MNDWI came in second and third. The Index of Urban Thermal Field Variance (-0.11 to 0.49) indicated the presence of high thermal stress that was localized in the urban cores and industrial areas that revealed the role of urbanization in amplifying the Urban Heat Island effect and the buffering effect of vegetation and water bodies. The paper has shown that remote sensing, spectral indices, and machine learning can be used to offer a solid framework of LST evaluation as well as provide arguments of evidence-based urban climate adaptation, such as LST-based zoning, nature-based mitigation plans, and policy-making in rapidly developed cities.

In this work, we present a comprehensive density functional theory (DFT) investigation of thallium-based novel iodo-perovskites TlMI3 (M = Ca, Sr), focusing on their structural, elastic, electronic, optical, thermodynamic, bonding, phonon, and thermoelectric characteristics. Calculations were performed using the GGA-PBEsol functional within the CASTEP framework, while thermoelectric and electronic properties were analyzed via the BoltzTraP2 code in WIEN2k. Structural stability was confirmed by negative formation energies, and the Born-Huang criteria validated mechanical stability. The optimized lattice parameters (6.02 & Aring; for TlCaI3 and 6.28 & Aring; for TlSrI3) are consistent with reported isostructural compounds. Phonon dispersion curves exhibit no imaginary frequencies, indicating vibrational stability at the Gamma point. Both compounds are indirect semiconductors with band gaps in the 2.5-2.6 eV range, and I-5p orbitals for both perovskites at EF show the maximum density of states (DOS) across PBEsol and HSE06 functionals. Charge density mapping and Mulliken population indicate mixed ionic-covalent bondings, while elastic moduli and ductility indices confirm their ductile nature in the cubic Pm 3 m phase. Optical properties show low reflectance, high conductivity, and strong absorption across the visible spectrum, suggesting excellent potential for optoelectronic and photovoltaic applications. Thermodynamic parameters were further examined under varying temperature and pressure that showed reliable stability trends. Thermoelectric calculations yielded promising ZT values of 0.81 for TlCaI3 and 0.88 for TlSrI3 at 100 K, demonstrating their suitability for low-temperature thermoelectric applications. To the best of our knowledge, this is the first detailed theoretical report on Tl-based Iodo-perovskites, that provides valuable insights into their multifunctional properties and highlights their potential for future device integration.

Background: The adoption of technology in the tourism industry has become a worldwide trend aimed at enhancing effectiveness and efficiency in this sector. Objectives: This study investigates the drivers influencing e-tourism adoption in Bangladesh by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the addition of digital literacy and vacation packages. Methodology: This research proposes and tests a conceptual model grounded in complexity and configuration theory. We employ a mixed-method analytical approach, integrating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) as a complementary tool to Structural Equation Modelling (SEM). Empirical validation was conducted using data from 297 online tourists. Results: Structural Equation Modelling (SEM) revealed that e-tourism adoption intention was significantly influenced by vacation packages, effort expectancy, performance expectancy, facilitating conditions, and digital literacy, whereas social influence did not show a significant effect. Furthermore, Fuzzy-Set Qualitative Comparative Analysis (fsQCA) identified five sufficient configurations of these causal conditions that lead to high levels of e-tourism adoption. Conclusion: The findings confirm the primary drivers of e-tourism adoption in Bangladesh and identify five specific, high-adoption pathways. Practically, these results enable e-tourism service providers and policymakers to strategically allocate resources, optimise platform design, and implement targeted digital literacy initiatives to accelerate e-tourism adoption rates in the region. Unique Contribution: This research provides a significant methodological advancement by integrating SEM and fsQCA to move beyond testing net effects and establish a detailed, configurational understanding of e-tourism adoption. Theoretically, it extends the UTAUT model by identifying new context-specific causal conditions relevant to travellers in Bangladesh. Key Recommendation: It is suggested that online travel service providers, marketers, and digital platform executives utilise the detailed findings of this study. Specifically, they should consider developing and implementing strategies that focus on optimising the five sufficient causal configurations identified by fsQCA and prioritise investments in the factors (e.g., performance expectancy, digital literacy) that significantly influence e-tourism adoption among Bangladeshi travellers.