Although above ground biomass (AGB) inventory provides critical information for sustainable ecosystem management, credible metrics assaying AGB remain a significant challenge. Projecting AGB dynamics, can be enhanced by understanding technological processes and products, thereby isolating the “myths from reality”. Generally, “myths” tacitly presuppose map aptness, whereas “reality” create awareness to proceed with caution by exhibiting imperfections that not only enhance knowledge but create new opportunities for innovation on remedial strategies. Other than data issues (i.e., complexity, heterogeneity, scarcity, accessibility), factors influencing scientific integrity and accuracy in AGB quantification include efficacy of measurement tools, robustness of algorithm, and systemic error in generalized map products smoothened after mosaicking. Here, AGB quantification at variable scales particularly for heterogeneous landscapes are explicated, and limitations of generalization exposed. Consideration is given to the mixed pixels challenge in digital imagery with emphasis on related technology flaws that exacerbate misclassification. The effects of heterogeneity in ground dataset and contribution to product accuracy are explicated. A case study of an unadulterated detailed AGB map output from multi-platform datasets is previewed to show real map aspect prior to generalization which is ordinarily subjective. Future opportunities are advanced for transferable metrics and models that incorporate a minimum data set of key covariates and fusion of multi-platform datasets.
Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with early and accurate diagnosis being critical to improving survival rates. While deep learning has revolutionized medical image classification, current models often face significant challenges in balancing intricate local features with global features. This study presents a hybrid multi-class classification framework using the Swin Transformer and ConvNeXt model. The proposed SwinTConvNeXt-LDGF model dynamically fuses local-global features within a learnable dynamic gating network and classifies pathological images into different categories. The model was evaluated on an eight-class histopathology BreakHis dataset, achieving a test accuracy of 95.53%, a precision of 94.74%, a recall of 96.32%, and an F1 score of 95.47%. These results demonstrate the effectiveness of combining the Swin Transformer and ConvNeXt backbones within a unified, learnable dynamic training framework. The proposed approach emphasizes the strong potential of SwinTConvNeXt-LDGF to support pathologists in the real-world classification of breast cancer subtypes.
The alarming rise of antibiotic resistance has prompted the search for alternative medicines to address the crisis of microbial resistance. Over the last two decades, scientists have become increasingly interested in the new dimensions of metallic nanoparticles. In this study, we expand upon this knowledge by synthesizing antibacterial silver nanoparticles (AgNPs) using Prunus africana stem bark extract as a reducing, capping, and stabilizing agent. Using a UV-Vis spectrophotometer, the surface plasmon resonance observed at 432.5 nm indicated the formation of AgNPs. Probable vibrational stretches that are characteristic of AgNPs and the capping functional groups were identified using an FT-IR (Fourier Transform Infrared) spectrophotometer. The characteristic peaks of the XRD (X-ray Diffraction) pattern confirmed the synthesis of pure AgNPs with an average crystalline size of 17.07 nm. TEM (Transmission Electron Microscopy) analysis confirmed that the synthesized AgNPs were spherical with sizes ranging from 15.95 nm to 43.04 nm. DLS (Dynamic Light Scattering) analysis confirmed the stability of the AgNPs in solution at -12.44 mV. The synthesized AgNPs demonstrated strong antibacterial activity against four bacterial strains (Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, and Bacillus subtilis) and one fungus (Candida albicans), compared to the corresponding aqueous Prunus africana extracts and the positive control. The AgNPs showed notable antibacterial activity against MDR Pseudomonas aeruginosa and E. coli, with zones of inhibition ranging from 10.18 to 12.58 mm. The results indicated significant antibacterial and antifungal effects, highlighting the potential of green-synthesized AgNPs as effective agents in medicine.
Species augmentation is one of the methods used to promote biodiversity and prevent endangered species loss and extinction. The current work applies discrete-time optimal control theory to two models of species augmentation for predator-prey relationships. In discrete-time models, the order in which events occur can give different qualitative results. Two models representing different orders of events of optimal augmentation timing are considered. In one model, the population grows and predator-prey action occurs before the translocation of reserve species for augmentation. In the second model, the augmentation happens first and is followed by growth and then predator-prey action. The reserve and target populations are subjected to strong Allee effects. The optimal augmentation models employed in this work aim to maximize the prey (target population) and reserve population at the final time and minimize the associated cost at each time step. Numerical simulations in the two models are conducted using the discrete version of the forward-backward sweep method and the sequential quadratic programming iterative method, respectively. The simulation results show different population levels in the two models under varying parameter scenarios. Objective functional values showing percentage increases with optimal controls are calculated for each simulation. Different optimal augmentation strategies for the two orders of events are discussed. This work represents the first optimal augmentation results for models incorporating the predator-prey relationship with discrete events.
