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Raising awareness through Virtual Experiencing (RAVE) and a Cross-Cultural Perspective on Raising Awareness through Virtual Experiencing (C-RAVE) approaches are used in this article to promote awareness of concerns relating to linguistic stereotyping. Virtual Reality (VR) technology has completely changed several facets of modern life, including instruction, training and entertainment. We give a summary of the techniques employed to increase awareness of how stereotypes might distort our vision. The patterns of the answers provided by the participants demonstrate that the reported gender of the speaker in addition to the accent (native vs. non-native), has an impact on the respondents' assessments of performance. Additionally, we were able to show that the discussions and ideas produced by these response patterns led to a rise in self-awareness of language and stereotyping-related problems. The paper gives a general description of RAVE and examines how it may improve education, create empathy and foster good social change. Focus on a critique of our methodologies and place our work within a larger debate of strategies and efforts for how educational institutions might actively contribute to combating (language) discrimination and bias in many ways. The relevance of Raising Awareness by Virtual Reality and Cross-Cultural Raising of Awareness by Virtual Reality in fostering inclusion and understanding in a society that is becoming more sustainable globally connected is highlighted in the article’s conclusion.

Artificial intelligence (AI) is an efficient technique frequently used to tackle practical issues in various engineering and medical fields. Apart from different areas, water desalination is essential in surviving humans, animals, and plants. It is a crucial industry for a sustainable planet, and we cannot live without water. As a result, engineers and researchers are quite active in this area. Recent studies based on Elsevier’s Scopus database demonstrate how automation using emerging technologies has enhanced the traditional desalination process. AI offers enormous potential to address numerous problems and real-time optimization procedures. According to Elsevier’s Scopus database, water desalination has extensively used AI optimization tools, especially after 2010, such as artificial neural networks (ANN), genetic algorithms (GA), fuzzy logic, and natural swarm global optimization techniques to improve water quality and quantity. Finally, it was determined that AI technologies would surely pave the way for better operation, water resource management, and process automation in the water sector in an increasingly unstable climate.

Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms.

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single “in-the-wild” image. Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from “in-the-wild” images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce the first method that is able to reconstruct photorealistic render-ready 3D facial geometry and BRDF from a single “in-the-wild” image. To achieve this, we capture a large dataset of facial shape and reflectance, which we have made public. Moreover, we define a fast and photorealistic differentiable rendering methodology with accurate facial skin diffuse and specular reflection, self-occlusion and subsurface scattering approximation. With this, we train a network that disentangles the facial diffuse and specular reflectance components from a mesh and texture with baked illumination, scanned or reconstructed with a 3DMM fitting method. As we demonstrate in a series of qualitative and quantitative experiments, our method outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image, that are ready to be rendered in various applications and bridge the uncanny valley. [ABSTRACT FROM AUTHOR]

Solar energy is abundant, and technological advances have made solar energy systems more affordable than ever before. Using photovoltaic (PV) systems could significantly reduce our reliance on fossil fuels, and facilitate sustainable energy uses. Solar power utilities, such as self-compacting disposal bins could be used to enhance waste management processes. This is particularly important in Canada, where $3.3 billion was spent on waste management systems in 2016. In this study, solar irradiance and climatic conditions at eight locations on a University campus in Regina, Saskatchewan, are studied. Results suggest that solar utilities with automatically adjusting PV receivers could increase energy capture between 18.7 – 27.5%. Temporally, solar irradiance was similar in June and July, but lower in August. Statistical analysis found that some locations tended to be more susceptible to shadow effects. The results highlight the importance of spatial allocations of these small smart disposal bin systems. Regression analysis found that temperature was the most significant factor when relating climate to solar irradiance. The use of smart disposal bins fits well with the University's 2020–2025 Strategic Plan of reduction in ecological footprint. [ABSTRACT FROM AUTHOR]