Enhancing brain tumor classification through ensemble attention mechanism.

Brain tumors pose a serious threat to public health, impacting thousands of individuals directly or indirectly worldwide. Timely and accurate detection of these tumors is crucial for effective treatment and enhancing the quality of patients' lives. The widely used brain imaging technique is magnetic resonance imaging, the precise identification of brain tumors in MRI images is challenging due to the diverse anatomical structures. This paper introduces an innovative approach known as the ensemble attention mechanism to address this challenge. Initially, the approach uses two networks to extract intermediate- and final-level feature maps from MobileNetV3 and EfficientNetB7. This assists in gathering the relevant feature maps from the different models at different levels. Then, the technique incorporates a co-attention mechanism into the intermediate and final feature map levels on both networks and ensembles them. This directs attention to certain regions to extract global-level features at different levels. Ensemble of attentive feature maps enabling the precise detection of various feature patterns within brain tumor images at both model, local, and global levels. This leads to an improvement in the classification process. The proposed system was evaluated on the Figshare dataset and achieved an accuracy of 98.94%, and 98.48% for the BraTS 2019 dataset which is superior to other methods. Thus, it is robust and suitable for brain tumor detection in healthcare systems.

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins.

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.

The relationships between certain physical and chemical variables and the seasonal dynamics of phytoplankton assemblages of two inlets of a shallow hypertrophic lake with different nutrient inputs.

The relationships between water discharge, temperature, total dissolved solids (TDS) conductivity, turbidity, nitrate, ammonium, phosphate and the seasonal dynamics of phytoplankton assemblages of two inlets of a shallow hypertrophic lake (Lake Manyas, Turkey) were studied between January 2003 and December 2004. The results showed that different levels of water discharge, turbidity, conductivity, TDS and nutrients could lead to the development of significantly different phytoplankton assemblages in inlets of shallow hypertrophic lakes. The multiple regression analysis identified water discharge, turbidity and water temperature as the driving factors behind the dynamics of phytoplankton biovolume in the studied inlets. The first two axes of Canonical Correspondence Analysis (CCA) explained 78% of the total variance in dominant phytoplankton species at Sigirci Inlet and 88% at Kocaçay Inlet, respectively. The purpose of this study was to determine the relationships between water discharge, temperature, conductivity, turbidity, pH, TDS, nitrate, ammonium, phosphate and the seasonal dynamics of phytoplankton assemblages of two inlets of the shallow hypertrophic Lake Manyas, Turkey by means of multivariate statistical analysis.

Spatial and seasonal variations in chlorophyll-nutrient relationships in the shallow hypertrophic lake Manyas, Turkey.

Regression and correlation analyses were used to predict responses of phytoplankton biomass (chlorophyll) (microg L(-1)) to nitrate (NO(3)) (mg L(-1)), phosphate (PO(4)) (mg L(-1)) and ammonium (NH(4)) (mg L(-1)) dynamics in the shallow hypertrophic Lake Manyas, Turkey. Nutrient concentrations showed a descending gradient with distance, while chlorophyll concentrations showed an ascending gradient with the distance from the Sigirci Inlet to the Karadere Outlet. Higher nutrient concentrations did always not coincide with higher chlorophyll concentrations. The results showed that regression models developed using seasonal data were more accurate in predicting chlorophyll concentrations than those developed using the pooled data from whole year (based on R (2) and the difference between the measured and predicted values). The findings also revealed that within a single large shallow lake, chlorophyll-nutrient relationships might show significant variations spatially. The objective of this study was to determine the seasonal and spatial variations in the relationships between chlorophyll, nitrate, phosphate and ammonium in the shallow hypertrophic Lake Manyas, Turkey.