Urban Regeneration: What Are the Architectural Trends?

Many cities suffer overpopulation and the presence of obsolete underutilised areas which sometimes are located in central city areas. Urban regeneration is an effective tool to “breathe life” into such spaces. Urban regeneration has become common in the last few decades; there are many successful cases of implemented or planned urban regeneration worldwide. The Authors of this paper study such best practices in order to identify current architectural trends in urban regeneration, which contribute to the creation of more green, resilient, sustainable, safe, accessible urban areas. The case studies shown in this article are the winning projects of the C40 Reinventing Cities competition, which is a global competition that was established to accelerate the development of decarbonised and resilient urban regeneration across the globe. This paper contributes to the knowledge through highlighting five architectural trends which are the most common and crucial for facilitating positive urban regeneration processes

Elements contributing to the environ-urban dimension of a smart city concept

Smart city concept concerns urban innovations based on but not limited by the wide application of IoT. Whereas, four dimensions encompass various elements of smart cities: governance dimension, environ-urban dimension, socio-institutional dimension, and techno-economic. The research focuses on the score of factors contributing to the environ-urban dimension. Based on the previous research of the authors, where the elements of the studied dimension were listed according to their importance, this paper uses QCA methodology in order to understand which elements should be proceeded first among the element of similar high importance when driving cities to the smart development. Fourteen cities in which smart practices are being realised, were retrieved among a top hundred cities around the globe from the heatmap which shows the places people like the most. The paper concludes with the recommendations what elements are able to fill environ-urban dimension effectively, that can support cities which have recently started the way to the smart city concept

ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion

Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, typically by adjusting the prediction thresholds using manually annotated examples. In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation. Our new method ACTC finds good per-relation thresholds efficiently based on a limited set of annotated tuples. Additionally to a few annotated tuples, ACTC also leverages unlabeled tuples by estimating their correctness with Logistic Regression or Gaussian Process classifiers. We also experiment with different methods for selecting candidate tuples for annotation: density-based and random selection. Experiments with five scoring models and an oracle annotator show an improvement of 7% points when using ACTC in the challenging setting with an annotation budget of only 10 tuples, and an average improvement of 4% points over different budgets

Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have primarily focused on detecting outliers and their permanent removal - a process that is likely to over- or underfilter the dataset. In this work, we propose AGRA: a new method for learning with noisy labels by using Adaptive GRAdient-based outlier removal. Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process. By comparing the aggregated gradient of a batch of samples and an individual example gradient, our method dynamically decides whether a corresponding example is helpful for the model at this point or is counter-productive and should be left out for the current update. Extensive evaluation on several datasets demonstrates AGRA's effectiveness, while a comprehensive results analysis supports our initial hypothesis: permanent hard outlier removal is not always what model benefits the most from.Comment: Accepted for ECML PKDD 202

Additive manufacturing of concrete wall structures

3D concrete printing is a perspective technology for sustainable construction and realization of sophisticated architectural projects. The current research proposes the thermal engineering calculation of wall structure based on the 3D printed concrete element of a total thickness of 150 mm with the internal air layer about 75 mm. The 3D printing mixture was designed with the addition of perlite as filler in the dosage of 8 % by weight of cement. The printing process was performed by the 3D printer of Contour Crafting type through the nozzle with a size of 20 mm. The thermal engineering calculation was implemented for the A++ energy consumption class. The wall structure based on the 3D printed concrete element with perlit has the thermal resistance comparable with one for wall structures based on brick and aerated concrete. The total thickness of the designed wall structure with 3D printed concrete element decreased by 100 mm and 50 mm in comparison with wall structures based on brick and aerated concrete, respectively. In addition to the thermal engineering calculations, the visual assessment of the surface quality of 3D printed concrete wall elements was performed

Development of monitoring of water bodies ecological status by the example of small lakes in the North-western Ladoga region

By the example of the Suuri Lake (0.37 km2) situated in the North-Western Ladoga region, modern aspects of monitoring the ecological state of water bodies are generalized, including 1) assessment of the rates of mass transfer processes in water ecosystems and the factors affecting them; 2) assessment of the integrated properties of water bodies and their ecosystems based on hierarchical schemes summarizing information about the state of subsystems and their properties in the form of composite indices. The results of the study in 2019 are visualized. Quantitative estimates of the chemical and biological composition and physical properties of the aquatic ecosystem, mass transfer rates, factors influencing them are obtained; the values of the integral indicators for the subsystem and their properties (productivity, water quality, stability) and the integral indicators of the systems and their integrative properties as a whole (ecological status, ecological wellbeing) are estimated. The temporal dynamics of the processes, component composition and complex properties of the aquatic ecosystem are investigated