Saturated pool boiling over vertical tubes plays a critical role in heat transfer systems such as the Passive Residual Heat Removal (PRHR) units used in advanced nuclear power plants. There is a significant difference in bubble dynamics at the tube surface when the tube orientation changes from horizontal to vertical, leading to substantial variations in temperature and HTC along the length and radial directions. The review contains a detailed evaluation of how heat transfer occurs through saturated pool boiling on individual vertical tubes, annular tubes, and vertical tube bundle configurations; in addition to an assessment of the key parameters that influence this process, which include surface roughness, tube geometry, tube coating, pressure, nanofluid concentration, heat flux, and the pitch-to-diameter ratio, were all evaluated and analyzed systematically. In addition, empirical correlations are tabulated with experimental uncertainties and correlation accuracy to assess the applicability of these models under varying conditions. Finally, the essential research gap and future research directions are outlined.
BiOBr-based nanocomposites are emerging as efficient photocatalysts owing to their narrow bandgap and layered structure. This review highlights recent progress in CIP degradation using modified BiOBr systems, emphasizing how synthesis routes—particularly solvothermal and hydrothermal—tailor optical response and morphology. The influence of operational variables, including initial CIP concentration, dopant type/content, semiconductor coupling, pH, and coexisting ions, is critically compared. Noteworthy results show that CdSe/Se/BiOBr and S/BiOBr achieved 100
Whole Slide Imaging (WSI) has revolutionized modern pathology by enabling high-resolution digitization of tissue specimens, often exceeding 100,000 × 100,000 pixels, thereby supporting enhanced diagnostic interpretation and telepathology workflows. However, automated analysis of such gigapixel-scale data remains challenging due to computational complexity, staining variability, and morphological heterogeneity across samples. Deep learning-driven segmentation techniques particularly Fully Convolutional Networks (FCN), U-Net, and Mask R-CNN have demonstrated significant advances in tumor detection, cellular boundary delineation, and metastasis identification, with state-of-the-art studies reporting Dice similarity coefficients typically ranging from 0.85 to 0.92 in nuclei segmentation tasks. This review provides a focused and comprehensive overview of segmentation-oriented deep learning methodologies specifically designed for WSI in pathology. We summarize the current workflow including image digitization, patch extraction, data annotation, preprocessing strategies such as stain normalization and ROI enhancement, model selection, post-processing optimization, and clinical integration. By consolidating recent developments and outlining persistent gaps including limitations in labeled datasets, generalizability, hyperparameter sensitivity, and real-time deployment barriers this review offers practical insights to accelerate translation of deep learning segmentation into routine pathology practice. The work is intended as a valuable resource for clinicians, biomedical researchers, and developers engaged in computational pathology and precision diagnostics.
In tribal and climate-vulnerable regions, limited access to agricultural technologies and resource constraints often hinder crop diversification, despite its recognised role in enhancing sustainability, resilience, and food security. While technology adoption is known to influence farm diversification, evidence specific to millet-based systems in India’s tribal areas remains scarce. This study examines the determinants of crop diversification and the role of agricultural technology adoption among millet farmers in Koraput district, Odisha, a climate-sensitive and predominantly rainfed tribal region. Using primary data from 500 farm households, two composite indices were constructed: the crop diversification index (CDI), derived from Simpson’s index, and the agricultural technology intensity index (ATII), capturing physical, chemical, and strategic mechanisation dimensions. A fractional heteroscedastic probit model was applied, controlling for socio-economic and institutional factors. Results indicate that ATII has a statistically significant and positive association with CDI. Operational landholding size and age are also positively related to diversification, although their effects are comparatively modest. The heteroscedastic specification further shows that technology adoption, family size, and institutional barriers influence the variability of diversification outcomes, highlighting the role of unobserved heterogeneity. Moreover, the instrumental variable diagnostics demonstrate that correcting for endogeneity strengthens the estimated causal impact of ATII on CDI. Findings emphasise the need for promoting location-specific technologies, strengthening extension services, and enhancing targeted institutional support to scale up diversification and enhance climate resilience in millet-based farming systems.
The present study investigates the geoenvironmental characteristics and mobility of heavy metals in gold mine tailings collected from a mining region in western India. The tailings were subjected to comprehensive physical, mineralogical, and geochemical characterization, including particle size distribution, XRD-based mineralogy, XRF elemental composition, ICP-OES based total heavy metal analysis, and sequential extraction to evaluate metal speciation and environmental risk. Results indicate that the tailings are fine-grained (< 45 μm) with a slightly alkaline nature. Mineralogical analysis revealed quartz, feldspars, gypsum, and pyrite as the dominant phases. Elevated concentrations of potentially toxic elements, particularly As (4330 mg/kg), Pb (3130 mg/kg), Zn (6000 mg/kg), Cu (1900 mg/kg), and Cd (42.5 mg/kg), were observed, exceeding regulatory threshold limits for reuse given in Indian standards (MoEF CC, 2025). Sequential extraction results showed that despite high total metal concentrations, a substantial fraction of metals especially As and Ni was associated with the residual fraction, suggesting limited immediate mobility. In contrast, Zn, Cd, Cu, and Pb exhibited notable proportions in labile fractions, indicating higher environmental sensitivity. Risk index (in particular risk assessment code) identified Zn, Cd, and Pb as priority contaminants, while Ni posed the least risk. Overall, the study highlights the importance of metal speciation-based assessment for sustainable management of gold mine tailings.
