Brain cancer is one of the most dangerous cancer types in the world, and thousands of people are suffering from malignant brain tumors. Depending on the level of cancer, early diagnosis can be a lifesaver. However, thousands of scans must be studied in order to classify tumor types with high accuracy. Deep learning models can handle that amount of data and they can present results with high accuracy. It’s already known that deep learning models can give different results depending on dataset. In this paper, the effectiveness of some of the deep learning models on 2 different publicly available MRI (Magnetic Resonance Imaging) brain tumor datasets is examined. The reason for choosing this topic is that we are trying to find the best solution to classify tumors in the datasets. Different deep learning models are used separately on preprocessed datasets with the CLAHE preprocessing variable to extract features from images and classify them. Datasets are shuffled randomly for 80% training, 10% validation, and 10% testing. For finetuning, models are modified so that the output channel of the classifier is equal to the number of classes in the datasets. Results show that pre-trained and fine-tuned ResNet, RegNet, and Vision Transformer (ViT) deep learning models can achieve accuracies higher than 90% and they can be used as classifiers when a diagnosis is required.

The Forest And Buildings removed Copernicus DEM (FABDEM) represents a global DEM generated through the elimination of height biases arising due to buildings and trees in the Copernicus global 30m (GLO-30) Digital Elevation Model (DEM). Copernicus GLO-30 DEM is a Digital Surface Model (DSM) generated from edited DEM called WorldDEM, which in itself is a product generated from SAR Interferometry (InSAR) based TanDEM-X DEM. It has the potential to be used as a Digital Terrain Model (DTM) for many applications such as in engineering, environmental and hydrological studies. The current experiment evaluates the accuracy of FABDEM using ground control points (GCPs) collected through a Differential GPS (DGPS) surveys at the three experimental sites in India namely, the Dehradun site in Uttarakhand; Jaipur site in Rajasthan and Kendrapara site in Odisha. The selected three experimental sites represent varied topographic conditions in the Indian region. The FABDEM heights are converted into WGS84 heights using geoidal undulations (N) as per Earth Gravitational Model-EGM2008, which is the vertical datum for FABDEM. Statistical measures such as MAE, RMSE, and LE90 are used to assess the accuracies of FABDEM. The RMSE computed for FABDEM in the sites at Dehradun, Jaipur and Kendrapara are 5.96m, 2.77m, and 4.29m respectively. The study thus reveals that the FABDEM has relatively high accuracy in the experimental sites at Jaipur and Dehradun considering their topography. However, the accuracy is found relatively low in the alluvial plains of the Kendrapara site.

Recently there is a tremendous change throughout the world due to globalization and internationalization. In a diverse country, cultural heritage is a substantial aspect for its people of different ethnicities. Therefore, it is essential for people in a diverse country to be taught about cultural heritage as it is a part of their identity, consecutively increasing respect for each other’s social values. Museums and schools are recently trying to find better ways to do so other than visiting sites and viewing historical artifacts. With a great edutainment value, games have been known to grasp the minds of mainly young individuals. However, making specific games for each museum might be time-consuming and financially expensive. This is where the importance of a game authoring tool comes in with which any individual can make a unique experience for their fellow peers to indulge in cultural history. Recently Internet of Things (IoT) has been known to give an additional interactive method to further increase the edutainment value, but game designers have faced issues previously with properly integrating IoT. This research focuses on a cultural game authoring tool that mainly is based on sharing the story of the Kristang culture of Malacca in Malaysia and researches the best way to integrate IoT in this game authoring tool. Furthermore, it also researches whether adding IoT is beneficial in this type of maker education that utilizes serious 3D games for cultural education.

The UV-Vis absorption, fluorescence and phosphorescence spectra of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril) polypseudorotaxane (1) and polyrotaxane (2) in water and acetonitrile solutions were investigated. To achieve a deeper insight into the optical properties, the transient absorptions, lifetimes and quantum yields have been carried out on compounds 1 and 2. The transient absorption demonstrated an excited-state processes and involvement of high energy electronic states (S_n > 1). The transient absorption map in acetonitrile revealed at 210, 240, 300 and 315 nm a ground states bleaching bands (GBS), whereas at shorter wavelengths an absorption in excited states (ESA) and more than one excited state (S_n > 1). At 382 and 420 nm wavelength two negative bands appeared which were assigned to the stimulated emissions. At longer wavelengths, i.e. 605, 625 and 710 nm, other stimulated emissions appeared that are probably a result of the triplet manifold, confirming their phosphorescence properties. Additionally, the quantum yield with absolute values ​​in the range 5-25 %, and phosphorescence lifetime with values ​​in the range 1-9 μs were evaluated.

ACKNOWLEDGMENTS

This presentation was supported by a grant from the Romanian Ministry of Research, Innovation, and Digitization, CNCS-UEFISCDI, project number PN-III-P4-PCE-2021-0906

The blunt leaf dock/ broad leaved dock (Rumex obtusifolius) is a fast growing, highly competitive and as well as resistant weed. It is endemic to Austria and generally a very common weed in Europe. Rumex obtusifolius prefers nutrient-rich, moist soils. As a light germinator, it spreads easily in patchy plant stands. Its taproot can penetrate compacted, waterlogged and oxygen-poor soil layers to a depth of 2.60 meters. It is considered a pest in agriculture, both in field and pasture, because of its rapid growth, ability to vegetatively propagate from leftover roots and its extensive taproot system. The most important regulation strategy is to prevent dock plants from establishing. If plants are already present in the field, the dock population must be assessed. If there are up to two dock plants per square meter, single-stock measures with pricking out or tilling and reseeding are still helpful. If there are more than two plants per square meter, only uprooting and a dock cure will help. As a further step, it is necessary to adjust the crop rotation. The application of pesticides is possible, however, mechanical removal is preferred. The goal this study is to develop a CNN (Convolutional Neural Network) that is specially trained to identify dock plants and to capture location and position in the field/pasture. RGB photographs (approx. 3000) were collected using an unmanned arial vehicle and handheld cameras from March to August 2021 The obtained dataset contained photographs of different plants in all sizes and forms to include all phenotypes and age difference. The network was also trained to differentiate between whole plants and plant parts such as leaves.