Reduced erbium-doped ceria nanoparticles: one nano-host applicable for simultaneous optical down- and up-conversions

This paper introduces a new synthesis procedure to form erbium-doped ceria nanoparticles (EDC NPs) that can act as an optical medium for both up-conversion and down-conversion in the same time. This synthesis process results qualitatively in a high concentration of Ce(3+) ions required to obtain high fluorescence efficiency in the down-conversion process. Simultaneously, the synthesized nanoparticles contain the molecular energy levels of erbium that are required for up-conversion. Therefore, the synthesized EDC NPs can emit visible light when excited with either UV or IR photons. This opens new opportunities for applications where emission of light via both up- and down-conversions from a single nanomaterial is desired such as solar cells and bio-imaging

RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model

Satellite imagery generation and super-resolution are pivotal tasks in remote sensing, demanding high-quality, detailed images for accurate analysis and decision-making. In this paper, we propose an innovative and lightweight approach that employs two-stage diffusion models to gradually generate high-resolution Satellite images purely based on text prompts. Our innovative pipeline comprises two interconnected diffusion models: a Low-Resolution Generation Diffusion Model (LR-GDM) that generates low-resolution images from text and a Super-Resolution Diffusion Model (SRDM) conditionally produced. The LR-GDM effectively synthesizes low-resolution by (computing the correlations of the text embedding and the image embedding in a shared latent space), capturing the essential content and layout of the desired scenes. Subsequently, the SRDM takes the generated low-resolution image and its corresponding text prompts and efficiently produces the high-resolution counterparts, infusing fine-grained spatial details and enhancing visual fidelity. Experiments are conducted on the commonly used dataset, Remote Sensing Image Captioning Dataset (RSICD). Our results demonstrate that our approach outperforms existing state-of-the-art (SoTA) models in generating satellite images with realistic geographical features, weather conditions, and land structures while achieving remarkable super-resolution results for increased spatial precision

Essential techniques for laparoscopic surgery simulation

Laparoscopic surgery is a complex minimum invasive operation that requires long learning curve for the new trainees to have adequate experience to become a qualified surgeon. With the development of virtual reality technology, virtual reality-based surgery simulation is playing an increasingly important role in the surgery training. The simulation of laparoscopic surgery is challenging because it involves large non-linear soft tissue deformation, frequent surgical tool interaction and complex anatomical environment. Current researches mostly focus on very specific topics (such as deformation and collision detection) rather than a consistent and efficient framework. The direct use of the existing methods cannot achieve high visual/haptic quality and a satisfactory refreshing rate at the same time, especially for complex surgery simulation. In this paper, we proposed a set of tailored key technologies for laparoscopic surgery simulation, ranging from the simulation of soft tissues with different properties, to the interactions between surgical tools and soft tissues to the rendering of complex anatomical environment. Compared with the current methods, our tailored algorithms aimed at improving the performance from accuracy, stability and efficiency perspectives. We also abstract and design a set of intuitive parameters that can provide developers with high flexibility to develop their own simulators

The Role of Electronic Assessment in Rising of the Level of Academic Achievement among Female Students from the Point of View of Secondary School Teachers in Hail

The study aimed to identify the role of electronic assessment in raising the level of academic achievement among female students, from the point of view of secondary school teachers in the city of Hail. The study followed the descriptive approach, using the questionnaire as a tool for collecting data. The questionnaire consisted of (45) statements, divided into four axes. They are: the role of assessment through the electronic achievement file in raising the level of academic achievement for secondary school female students, the role of assessment through electronic testing in raising the level of academic achievement for secondary school female students, the role of assessment through the electronic assignments in raising the level of academic achievement for secondary school female students, and finally The most prominent challenges facing electronic assessment to raise the level of academic achievement for female secondary school students. The study sample consisted of the secondary school teachers in the city of Hail, where their number reached (296) teachers. The study found a set of challenges facing electronic assessment, as the study recommended the necessity of working to address the problems of slow Internet speed to run programs related to electronic assessment, and paying attention to providing infrastructure such as computer laboratories and others in secondary schools, working to provide Internet lines and specialized programs, providing the immediate technical support, and interest in providing manuals for using electronic calendar methods

Eucalyptus

In Egypt, the River Red Gum (Eucalyptus camaldulensis) is a well-known tree and is highly appreciated by the rural and urban dwellers. The role of Eucalyptus trees in the ecology of Cryptococcus neoformans is documented worldwide. The aim of this survey was to show the prevalence of C. neoformans during the flowering season of E. camaldulensis at the Delta region in Egypt. Three hundred and eleven samples out of two hundred Eucalyptus trees, including leaves, flowers, and woody trunks, were collected from four governorates in the Delta region. Thirteen isolates of C. neoformans were recovered from Eucalyptus tree samples (4.2%). Molecular identification of C. neoformans was done by capsular gene specific primer CAP64 and serotype identification was done depending on LAC1 gene. This study represents an update on the ecology of C. neoformans associated with Eucalyptus tree in Egyptian environment

STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map

Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one, and some groupings might produce performance degradation due to negative interference between tasks. Furthermore, existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on hand-crafted features, specifically Data Maps, which capture the training behavior for each classification task during the MTL training. We experiment with the method demonstrating its effectiveness, even on an unprecedented number of tasks (up to 100).Comment: Accepted submission to DMLR workshop @ ICML 2