Computational Thinking: Students’ Abstraction on the Concepts of Kinematics

Abstraction is the primary key in computational thinking. This study aims to analyze students’ computational thinking skills of abstraction on the concept of kinematics. The data were collected through students’ project documents and interviews. The data is examined using a content analysis approach that emphasizes writing, verbal, or visual communication. The results revealed that students’ abstraction skills were evident in collecting data and analyzing, and recognizing patterns but were less visible in building models or simulations. Abstraction skills can be used as a foundation and framework for viewing a concept in physics not only in mathematics or formulas views but as a data iterative relationship. This research is expected to provide an overview for physics instructors to integrate computational thinking in their learning classroom

A Review of Modelling of the FCC Unit—Part II: The Regenerator

Heavy petroleum industries, including the Fluid Catalytic Cracking (FCC) unit, are among some of the biggest contributors to global greenhouse gas (GHG) emissions. The FCC unit’s regenerator is where these emissions originate mostly, meaning the operation of FCC regenerators has come under scrutiny in recent years due to the global mitigation efforts against climate change, affecting both current operations and the future of the FCC unit. As a result, it is more important than ever to develop models that are accurate and reliable at predicting emissions of various greenhouse gases to keep up with new reporting guidelines that will help optimise the unit for increased coke conversion and lower operating costs. Part 1 of this paper was dedicated to reviewing the riser section of the FCC unit. Part 2 reviews traditional modelling methodologies used in modelling and simulating the FCC regenerator. Hydrodynamics and kinetics of the regenerator are discussed in terms of experimental data and modelling. Modelling of constitutive parts that are important to the FCC unit, such as gas–solid cyclones and catalyst transport lines, are also considered. This review then identifies areas where the current generation of models of the regenerator can be improved for the future. Parts 1 and 2 are such that a comprehensive review of the literature on modelling the FCC unit is presented, showing the guidance and framework followed in building models for the unit.

Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model’s performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models’ performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

The impact of efficient insulation on thermal performance of building elements in hot arid region

The application of thermal insulation technique for buildings in hot arid region still under development and requires investigation for many aspects, especially those related to the individual elements of the construction. The present study investigates the impact of efficient insulation on the thermal performance of the following construction elements: wall, roof and foundation. The techniques used for the insulation have followed the Passive House criteria. The study introduces many benefits for passive design of the building in extreme hot climate. The work has done experimentally in Kirkuk, Iraq for two building models; one is efficiently insulated and the other is traditional. The data, which are collected in summer time, show reduction in indoor temperature of the efficient model by 8 °C in average comparing to that of traditional one. Measurements show stability in the internal wall temperatures for efficient model with an average temperature of 33 °C comparing to 42 °C for traditional one. Similarly, the insulated roof radiates less heat into the indoor than that of traditional model. Furthermore, the effect of both efficient insulated roof and canopy shading reduces the temperature of internal surface temperature of the roof by 12 °C comparing to the traditional model. A local simulation program based on ASHRAE relations has shown an energy saving in the cooling load up to 70%.

Use of Artificial Intelligence in Financial Accounting

Artificial intelligence (AI) is a huge headway in innovation that has everybody talking about its energizing guarantees in the innovation world. With regards to AI, it additionally incorporates its territories, for example, AI (ML). While AI could be portrayed as the capacity of machines to settle on shrewd human-like choices and improve over the long run, ML includes building models, generally statistical models that give prescient outcomes and can be developed. Many are not extremely educated about this area. While this is true, there is something else entirely to it from face recognition, fingerprints recognition, chat-bots, predictive business models, and sentimental analysis. Beforehand, AI joining in the product advancement was simply conceivable to the huge organizations that had the assets to recruit exceptionally qualified experts. Over the long run, AI structures with high deliberation levels have been created, and with few coding lines in any programming language of the decision, one can have the option to enter in various fields.

Modelling Buildings in CityGML LOD1: Building Parts, Terrain Intersection Curve and Address Features

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in the last decades, they are predominantly used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environment bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in Sofia city based on CityGML 2.0 standard. The model represents the buildings in LOD1 with a focus on CityGML features of related to the buildings like building part, terrain intersection curve and address. Similar building models of 18 cities provided as open datasets are explored and compared in order to extract good modeling practices. As a result, workflows for generation of 3D building models in LOD1 are elaborated and improvements in the feature modeling are proposed. Two options of building model are examined: modeling of a building as a single solid and modeling of a building with separate building parts. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

A REVIEW OF THE DISTINGUISHING FEATURES OF THE HISTORICAL BUILDINGS IN SAFRANBOLU REGION FOR THE PURPOSE OF CLASSIFICATION FOR SEMANTICALLY ENHANCED 3D BUILDING MODEL

In addition to making our daily life easier with the use of it in different areas of our lives, technology continues to be used increasingly with different applications in many sectors.With the increase of developments in the construction sector, which is the locomotive of many sectors, different applications have been used in the field of modelling. But a building needs many projects such as static, dynamic, electricity, installation, furniture, etc. While these sectors are working with different software specific to them, it has been possible to work on these building projects in a single project by the help of BIM (Building Information Modelling).In addition to its function of projecting new buildings, BIM is an important development and building model in terms of preserving historical buildings, easily creating original building details, and transferring them to future generations without deterioration. The term HBIM (Historic / Heritage Building Information Modelling) has been brought to the literature by using the BIM model in historical buildings.The known history of Safranbolu district of Karabük in Turkey dates back to 3000 years. Safranbolu, which has hosted Roman, Byzantine, Seljuk and Ottoman empires in its history, has buildings that are still preserved with their originality. These structures were built in the pre-Ottoman, the Ottoman and the Republic periods.In our study, historical buildings such as houses, commercial houses, places of worship, inns, baths, fountains, and clock towers will be examined. Building models and distinctive features were examined to classify these structures by modeling them with BIM.While the differentiation of the buildings can be made easily by the building model, the distinguishing features of the houses built in different periods or by different civilizations were also determined in order to distinguish the housing types.While structures such as baths, clock towers or inns are not in a number that can be classified, it has been observed that there are residences, businesses and places of worship that can be classified. It has been determined that it is possible to classify the buildings by their materials, building sizes and shapes, and by their other features that can be used to classify.

Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.