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Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future.

Solution verification is a part of the larger framework for assessing the implementational and practical accuracy of computer models, often referred to as verification, validation and uncertainty quantification (VVUQ). Through the years different methods have been developed to estimate the error from using a discretised version of a mathematical model, implemented on a finite precision computer. Given the variety of mathematical approaches to solution verification, these methods can bring great benefits to the computational wind engineering (CWE) community. Some methods like grid convergence and residual tracking have gained popularity for estimating discretisation and iterative uncertainty and, as a result, are being applied as universal tools for solution verification and are often falsely believed to ensure comprehensive verification. This is compounded by the fact that solution verification is a computationally expensive process and is therefore often neglected or performed without consideration for the fine detail of individual simulations. In this paper, we use a case study from the Architectural Institute of Japan’s (AIJ) catalogue, as an example for CWE practitioners, which we use to demonstrate one of the most recognised metrics for spatial discretisation verification, the grid convergence index, and to study a variety of measures for iterative solution verification. We examine key conditions that must be satisfied for these methods to be applied and discuss advantages and potential issues with each of them. We conclude our study by exploring the place of solution verification in the grand scheme of VVUQ and elucidate some priority areas for future effort.

People tend to spend the majority of their time indoors. Indoor air properties can significantly affect humans’ comfort, health, and productivity. This study utilizes measurement data of indoor conditions in a kindergarten in Sofia, Bulgaria. Autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) recurrent neural network (RNN) models were developed to predict CO2 levels in the educational facility over the next hour based on 2.5 h of past data and allow for near real-time decision-making. The better-performing model, LSTM, is also used for temperature and relative humidity forecasting. Global comfort is then estimated based on threshold values for temperature, humidity, and CO2. The predicted R2 values ranged between 0.938 and 0.981 for the three parameters, while the prediction of global comfort conditions achieved a 91/100 accuracy.

Traffic-related air pollution has a significant impact on the concentration of particulate matter (PM) and nitrogen oxides (NOx) in urban areas, but there are many uncertainties associated with the modeling of PM concentration due to non-exhaust emissions. Bulgarian weather, road surfaces and traffic conditions differ significantly from the UK’s and other EU countries’ averages, which underpin many assumptions in established models. The hypothesis is that the emission factors differ from those used to calculate traffic emissions using the EMIT model. The objective of this work is to adjust the emissions for PM and the relationship between the fractions of NOx and PM using the hourly mean concentrations from road transport and urban background automatic air quality stations in Sofia, Bulgaria. Various already-published and newly developed methods are applied to local observations to derive functions and relations that better represent Bulgarian road and traffic conditions. The ADMS-Urban model is validated and evaluated by comparing pollutant concentrations from simulations using original and adjusted emissions, showing an improvement in results after applying functions and relationships derived from local observations. This work is part of our efforts to improve air quality modeling in urban areas in Bulgaria.

Obtaining predictive results at planning and conceptual design phase often represents a serious problem on which the choice of design options depends to a significant extent This paper seeks solutions to the problem using neural networks and artificial intelligence and illustrates the idea by simple examples of pre-trained neural network models.

A recently discovered RNA species on the cell surface is studied by proximity ligation.

Trichinella spiralis is an unusual parasitic intracellular nematode causing dedifferentiation of the host myofiber. Trichinella proteomic analyses have identified proteins that act at the interface between the parasite and the host and are probably important for the infection and pathogenesis. Many parasitic proteins, including a number of metalloproteins are unique for the nematodes and trichinellids and therefore present good targets for future therapeutic developments. Furthermore, detailed information on such proteins and their function in the nematode organism would provide better understanding of the parasite-host interactions. In this study we report the identification, biochemical characterization and localization of a novel poly-cysteine and histidine-tailed metalloprotein (Ts-PCHTP). The native Ts-PCHTP was purified from T. spiralis muscle larvae that were isolated from infected rats as a model system. The sequence analysis showed no homology with other proteins. Two unique poly-cysteine domains were found in the amino acid sequence of Ts-PCHTP. This protein is also the first reported natural histidine tailed protein. It was suggested that Ts-PCHTP has metal binding properties. Total Reflection X-ray Fluorescence (TXRF) assay revealed that it binds significant concentrations of iron, nickel and zinc at protein:metal ratio of about 1:2. Immunohistochemical analysis showed that the Ts-PCHTP is localized in the cuticle and in all tissues of the larvae, but that it is not excreted outside the parasite. Our data suggest that Ts-PCHTP is the first described member of a novel nematode poly-cysteine protein family and its function could be metal storage and/or transport. Since this protein family is unique for parasites from Superfamily Trichinelloidea its potential applications in diagnostics and treatment could be exploited in future.

The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed.

Isosurfaces are one of the most prominent tools in scientific data visualisation. An isosurface is a surface that defines the boundary of a feature of interest in space for a given threshold. This is integral in analysing data from the physical sciences which observe and simulate three or four dimensional phenomena. However it is time consuming and impractical to discover surfaces of interest by manually selecting different thresholds. The systematic way to discover significant isosurfaces in data is with a topological data structure called the contour tree. The contour tree encodes the connectivity and shape of each isosurface at all possible thresholds. The first part of this work has been devoted to developing algorithms that use the contour tree to discover significant features in data using high performance computing systems. Those algorithms provided a clear speedup over previous methods and were used to visualise physical plasma simulations. A major limitation of isosurfaces and contour trees is that they are only applicable when a single property is associated with data points. However scientific data sets often take multiple properties into account. A recent breakthrough generalised isosurfaces to fiber surfaces. Fiber surfaces define the boundary of a feature where the threshold is defined in terms of multiple parameters, instead of just one. In this work we used fiber surfaces together with isosurfaces and the contour tree to create a novel application that helps atmosphere scientists visualise convective cloud formation. Using this application, they were able to, for the first time, visualise the physical properties of certain structures that trigger cloud formation. Contour trees can also be generalised to handle multiple parameters. The natural extension of the contour tree is called the Reeb space and it comes from the pure mathematical field of fiber topology. The Reeb space is not yet fully understood mathematically and algorithms for computing it have significant practical limitations. A key difficulty is that while the contour tree is a traditional one dimensional data structure made up of points and lines between them, the Reeb space is far more complex. The Reeb space is made up of two dimensional sheets, attached to each other in intricate ways. The last part of this work focuses on understanding the structure of Reeb spaces and the rules that are followed when sheets are combined. This theory builds towards developing robust combinatorial algorithms to compute and use Reeb spaces for practical data analysis.

The aim of the present research was to reveal the genotype profile of the local Bulgarian Rhodopean cattle population with respect to αs1-casein, kappa-casein andβ-lactoglobulin genes, by PCR-RFLP assay and conformational 2D PAGE. According to these profiles, it is possible to determine the association between each genotype and milk qualitative and quantitative traits and to establish the position of the breed with regard to the genetic diversity other European cattle breeds. The investigation also revealed the high significance of the genetic variants of the three above-mentioned milk protein genes for: Clarification of the influence of other breeds on the Bulgarian Rhodopean cattle population; Obtaining data for genetic drift among the Bulgarian Rhodopean cattle population, the indigenous Shorthorn Rhodopean cattle population as a predecessor breed and the Jersey cow as a main improvement breed; Gathering the necessary information about preservation of this local breed, which is important for the Bulgarian biodiversity gene fund.