Building Ensembles Research Articles

The Promise of Maritime Heritage for Port Cities: Challenges, Concepts and New Approaches

Maritime heritage – an ensemble of ships, ports, waterways, buildings and maritime skills – is one of the oldest and most vital forms of heritage. It has been key to the growth of port cities around the world. Maritime skills, which include the techniques and materials used to build and use watercraft, from dugout canoes to container vessels, have made it possible to transport goods, animals and people, bringing wealth to deltas and coastal areas. This article argues that maritime heritage holds cultural, historical and environmental value and can drive sustainable spatial and economic development. In the heritage sector, maritime heritage is a relative newcomer compared to built and natural monuments. However, especially in the context of climate change, it offers valuable lessons on adaptation and sustainability. Six examples from the Netherlands demonstrate how preserving maritime heritage can be compatible with climate adaptation and energy transition, including, for example, using wooden fishing vessels and tugboats in cities, towing vessels for slow tourism, and sailing vessels for combined passenger and goods transport. The article calls for additional studies of how the conservation of maritime heritage can encourage sustainable development.

НОВІ ВІДКРИТТЯ, ПРОБЛЕМИ ДОСЛІДЖЕННЯ ТА ЗБЕРЕЖЕННЯ ПІДЗЕМНОЇ СПАДЩИНИ ДАВНЬОГО ГАЛИЧА

The long history of changing ideas is considered and the hypothesis of the urban structure of ancient Halych of the 12th-13th centuries is demonstrated. according to archaeological sources. Based on the analysis of stratigraphic, planning and construction-technological features, the relative chronology of the appearance of the 16 studied relics of monumental architecture was established. The earliest monuments of the first half of the 12th century are examined in detail: an ensemble of palace buildings with a post construction of log walls, the use of a plinth and a tin roof on Kryloska Hill, a complex of churches on Tsarynka, the Church of the Savior on Karpytsia Hill, as well as other objects, on which recent discoveries have been made. The appearance of early sacred objects outside the city is explained by the gradual Christianization of the population of Halych. Analysis of the construction periodization of the Dormition Cathedral in the middle of the 12th century. allows you to review its graphic reconstructions until now and raise the question of developing a new hypothesis of the three-dimensional appearance of the building during the first construction period, from which the recently found detail of a large alabaster ornamentally decorated capital with traces of false gilding comes from. The results of architectural and archaeological soundings at the outer contours of the foundations of the Panteleimon Church confirmed the presence of regular external protrusions of the foundations, which calibrates the construction of the object together with the early paleographic dates of the early 90s of the 12th century. Part makes it possible to argue the use of a measuring foot of 30 cm. Attention is drawn to the problems of preserving a number of known architectural and archaeological objects and the search for unknown monuments by non-invasive research methods.

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

AbstractEfficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports.

Development and experimental evaluation of new building air leakage measurement methods: measurement of interior air leaks and comparison to conventional methods

Building air leaks (both through exterior and interior surfaces) can have a significant impact on energy consumption, indoor air quality, fire safety, and moisture accumulation affecting structural durability. Blower door testing has been used to measure leaks in buildings, but commonly used testing methods do not directly measure interior leaks. In this paper, new testing methods (guarded interior test and zonal multipoint pressure testing method) are presented that directly measure these interior leaks, utilizing common blower door equipment for both single and multi-point testing. These new methods are compared to conventional methods in terms of the information provided, limitations and time/effort needed. In addition, building leak measurement results are analyzed to reveal (a) coupling between power law model values (exponent and coefficient) for an ensemble of buildings, (b) the error in using single point testing when estimating low pressure leakage, and (c) how building power law models vary from low to high pressure ranges.

THE PROCESS OF FORMATION OF THE JEWISH SUBURBAN QUARTERS IN LVIV

nderstanding of formative and historical processes is basic for the comprehensive approach to the regeneration of historical city quarters, those of the ancient Jewish ghetto of Lviv in particular. According to archaeological and scientific urban studies, theneighbourhood started its development as part of the princerly “posad”, having an ancient Volyn road trading route as one of its forming factors (which survives to this day). Through the centuries jewish community has organically grown here, forming a unique ensemble of residential, religious and public buildings which naturally integrated in the spatial-planning structure of the city as it grew. One of the most prominent structures was the Great Suburban Synagogue (XVI cent.) which was the centre of ghetto’s life and spatial structure. Degradation of historical urban matter of the ghetto was caused by the tragedies of WWI and WWII, which also obliterated ghetto’s social basis and absence of any further practical and public re- consideration for the spatial-planning and historic background of the neighbourhood. Today, most of its blocks have remained a gray zone in the structure of the city center for more than 70 years. Disregard for the ghetto’s formation history and cultural background resulted in inconsistency of realised development approaches and interventions and general public unawareness of the district’s role in the history of the city. The author conducts research and visualizes the characteristic stages of the ghetto’s development, as well as the formative factors that determined it, taking 3 neighbouring historical quarters within its bounds as a basis for study. With this work, author aims at During the research, on-site study and measurements were performed, archival materials and historical iconography were analyzed, modern research works on the history of Lviv and Halychyna were analyzed too, and the obtained information was summarized. As a result, development of the historical quarters of the Jewish suburban district was characterized and structured from princerly times to present day while highlighting the quarters’ architectural and spatial-planning value.

Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks

In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to computer vision. One technique that has gained prominence in recent years is the construction of ensembles using deep CNNs. These ensembles typically involve combining multiple pretrained CNNs to create a more powerful and robust network. The purpose of this study is to evaluate the effectiveness of building CNN ensembles by combining several advanced techniques. Tested here are CNN ensembles constructed by replacing ReLU layers with different activation functions, employing various data-augmentation techniques, and utilizing several algorithms, including some novel ones, that perturb network weights. Experimental results performed across many datasets representing different tasks demonstrate that our proposed methods for building deep ensembles produces superior results.

Thermal performance of energy barrettes in an urban environment

Thermal performance of energy barrettes in an urban environment

More than anything: Advocating for synthetic architectures within large-scale language-image models

Large-scale language-image (LLI) models have the potential to open new forms of critical practice through architectural research. Their success enables designers to research within discourses that are profoundly connected to the built environment but did not previously have the resources to engage in spatial research. Although LLI models do not generate coherent building ensembles, they offer an esthetic experience of an AI infused design practice. This paper contextualizes diffusion models architecturally. Through a comparison of approaches to diffusion models in architecture, this paper outlines data-centric methods that allow architects to design critically using computation. The design of text-driven latent spaces extends the histories of typological design to synthetic environments including non-building data into an architectural space. More than synthesizing quantic ratios in various arrangements, the architect contributes by assessing new categorical differences into generated work. The architects’ creativity can elevate LLI models with a synthetic architecture, nonexistent in the data sets the models learned from.

Architectural Renovation of Industrial Buildings on the Example of Large Cities of Kazakhstan

The realities of the modern city dictate the conditions for the expansion of human habitats. A number of industrial buildings erected in the last century occupies large territories, often in the center of cities. This causes environmental and aesthetic problems. In the historical centers of large cities of Kazakhstan, the problem of industrial zones renovation is especial urgent. At the same time, it is necessary to consider the economic feasibility of such activities, their profitability, and ways to preserve the architectural ensemble of historical buildings. Moreover, one of the fundamental factors is the psychological component, that is, the need to preserver the history of industrial buildings. At the same time, it is necessary to modernize and renovate enterprises, move production out of the city, and preserve existing buildings, changing their function to a more relevant one, while forming a unique ecosystem. To resolve this issue, a government program of Kazakhstan was considered, which in every possible way contributes to the renovation policy. However, for its optimal implementation, it is necessary to consider economic aspects, select the best ways and methods of renovation, and also study foreign experience, critically compare it with domestic renovation projects and formulate recommendations necessary for the renovation of industrial buildings.

Progressive Forest: An Early Stopping Criteria For Building Ensembles

Decision forests improve their predictive power based on thecombination of various decision trees. The number of trees to be usedto achieve the best possible accuracy is not preset and has to bedetermined by a trial and error process. In many classificationproblems more trees are used than necessary.This paper introduces a new method, called Progressive Forest, thatprogressively evaluates the addition of new decision trees into adecision forest to decide when adding more trees is not longer useful.This method was incorporated into the construction schemes ofProactive Forest and Random Forest with very encouraging results.It is experimentally shown that Progressive Forest reduces the numberof trees while maintaining the accuracy of the classification.Progressive Forest can be incorporated into any scheme of constructionof ensemble, which presents similar characteristics to Random Forest.

High-Throughput FPGA-Based Inference of Gradient Tree Boosting Models for Position Estimation in PET Detectors

In modern large-scale positron emission tomography (PET) systems, transferring the digitized raw detector data at high count rates to a centralized processing unit is a challenge. Processing data on field programmable gate arrays (FPGAs) close to the detectors can reduce data early on and improve scalability of the PET system. We present and evaluate an FPGA implementation of gradient tree boosting (GTB) for 1-D position estimation of gamma interactions in the scintillator. GTB is a supervised machine learning algorithm based on building ensembles of binary decision trees. Models were trained offline and inferred in an FPGA (XC7K410T-2FFG676 Kintex-7). Input features and GTB parameters influencing both positioning performance and model size were varied while evaluating the inferred models concerning data throughput and FPGA resource consumption as well as positioning performance. We achieved throughputs per detector between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.94\times 10^{6}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.55\times 10^{6}$ </tex-math></inline-formula> gamma interactions per second. For an optimized GTB model, resource consumption could be reduced by factors of 17 and 10 to less than 1% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.51\times 10^{3}$ </tex-math></inline-formula> look-up tables) of available logic and 1.26% (20 BRAMs) of memory resources, while maintaining a positioning performance of 98.63% when compared to the model with the best positioning performance. The presented framework can be easily adapted to other photosensors and scintillator influencing.

Il complesso termale Tettuccio a Montecatini Terme. Un patrimonio da svelare

Montecatini Terme, emblematic city for the close combination between thermal baths and urban structure, has been recently enrolled in the list of the Unesco Heritage. It becames particularly urgent to deepen the knowledge, in an appropriate way, of the building ensemble that can be considered the key features in the enrollement, for the purposes of their protection and safeguard. Among these, the Tettuccio bath assumes a particular relevance with regard to the complex architectural articulation, the richness of the decorative characteristics and the variety of the materials used. For this purpose, a laser scanner survey combined with photogrammetry was carried out, thus obtaining a high level of instrumental data, integrated by accurate in-place information. This approach highlighted the materials, traditional and modern, and the decay of the building, founded by the grand duke Pietro Leopoldo and almost completely refashioned about a century ago.&#x0D;

Правый берег Исети в районе общественной территории «сад Нурова» у Царского моста в городе Екатеринбурге: история городской застройки и научных исследований 1770–2023 гг.

The Unified State Register of Cultural Heritage Objects in Ekaterinburg includes about 800 sites. In recent years, the public has become more likely to talk about the need to preserve cultural heritage, because many of these objects are in poor condition. Despite the public outcry, various parks and green areas located in historical buildings are under particular threat of extinction. One of the main problems when conducting research on such territories is a small database of archival sources, which may complicate the task of substantiating the historical value of the object. Such territories include a section of the right bank of the Iset River near the Tsarsky Bridge within the Dekabristov — Tveritin Streets of Ekaterinburg, now also known as the Nurov Garden public territory. Formed in the middle of the 18th century as commercial quarters, this space transformed into a string of private enclosed gardens of the city’s influential merchants with a rich ensemble of buildings: gazebos and pavilions, baths and berths for boats, bridges and piers. Now, having left behind the time of complete oblivion, preparations for the process of landscaping the embankment begins on the territory. The article, relying primarily on documentary archival sources, considers the history of the emergence and development of this territory, analyzes the affecting factors and summarizes the studies already conducted in this territory.

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study.

The introduction of new clinical risk scores (e.g. European System for Cardiac Operative Risk Evaluation (EuroSCORE) II) superseding original scores (e.g. EuroSCORE I) with different variable sets typically result in disparate datasets due to high levels of missingness for new score variables prior to time of adoption. Little is known about the use of ensemble learning to incorporate disparate data from legacy scores. We tested the hypothesised that Homogenenous and Heterogeneous Machine Learning (ML) ensembles will have better performance than ensembles of Dynamic Model Averaging (DMA) for combining knowledge from EuroSCORE I legacy data with EuroSCORE II data to predict cardiac surgery risk. Using the National Adult Cardiac Surgery Audit dataset, we trained 12 different base learner models, based on two different variable sets from either EuroSCORE I (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996-2016 or 2012-2016) and evaluated on holdout set (2017-2019). These base learner models were ensembled using nine different combinations of six ML algorithms to produce homogeneous or heterogeneous ensembles. Performance was assessed using a consensus metric. Xgboost homogenous ensemble (HE) was the highest performing model (clinical effectiveness metric (CEM) 0.725) with area under the curve (AUC) (0.8327; 95% confidence interval (CI) 0.8323-0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320-0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996-2011 (t-test adjusted, p = 1.67×10-6) or 2012-2019 (t-test adjusted, p = 1.35×10-193) datasets alone. Both homogenous and heterogenous ML ensembles performed significantly better than DMA ensemble of Bayesian Update models. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data.

Deep residual architectures and ensemble learning for efficient brain tumour classification

AbstractThe prompt and accurate detection of brain tumours is essential for disease management and life‐saving. This paper introduces an efficient and robust completely automated system for classifying the three prominent types of brain tumour. The aim is to contribute for enhanced classification accuracy with minimum pre‐processing and less inference time. The power of deep networks is thoroughly investigated, with and without transfer learning. Fine‐tuned deep Residual Networks (ResNets) with depth up to 101 are introduced to manage the complex nature of brain images, and to capture their microstructural information. The proposed residual architectures with their in‐depth representations are evaluated and compared to other fine‐tuned networks (AlexNet, GoogLeNet and VGG16). A novel Convolutional Network (ConvNet) built and trained from scratch is also proposed for tumour type classification. Proven models are integrated by combining their decisions using majority voting to obtain the final classification accuracy. Results show that the residual architectures can be optimized efficiently, and a noticeable accuracy can be gained with them. Although ResNet models are deeper than VGG16, they show lower complexity. Results also indicate that building ensemble of models is a successful strategy to enhance the system performance. Each model in the ensemble learns specific patterns with certain filters. This stochastic nature boosts the classification accuracy. The accuracies obtained from ResNet18, ResNet101, and the proposed ConvNet are 98.91%, 97.39% and 95.43%, respectively. The accuracy based on decision fusion for the three networks is 99.57%, which is better than those of all state‐of‐the‐art techniques. The accuracy obtained with ResNet50 is 98.26%, and its fusion with ResNet18 and the designed network yields a 99.35% accuracy, which is also better than those of previous methods, meanwhile achieving minimum detection time requirements. Finally, visual representation of the learned features is provided to understand what the models have learned.

Laidu Manor House School. Assessment of Cultural Heritage Assets upon Closing of the School

The architecture of Courland manor houses is rich in classical monuments. Many of them form impressive building ensembles. The use of the buildings varies, historically – schools, parish boards, farm administrations, nowadays – guesthouses, private properties and still schools. One such site is in Laidi, where the existence of a school is problematic.

Feature transforms for image data augmentation

A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature.

Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets.

CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the small sample size of many medical data sets dampens performance and results in overfitting. In some medical areas, it is simply too labor-intensive and expensive to amass images numbering in the hundreds of thousands. Building Deep CNN ensembles of pre-trained CNNs is one powerful method for overcoming this problem. Ensembles combine the outputs of multiple classifiers to improve performance. This method relies on the introduction of diversity, which can be introduced on many levels in the classification workflow. A recent ensembling method that has shown promise is to vary the activation functions in a set of CNNs or within different layers of a single CNN. This study aims to examine the performance of both methods using a large set of twenty activations functions, six of which are presented here for the first time: 2D Mexican ReLU, TanELU, MeLU + GaLU, Symmetric MeLU, Symmetric GaLU, and Flexible MeLU. The proposed method was tested on fifteen medical data sets representing various classification tasks. The best performing ensemble combined two well-known CNNs (VGG16 and ResNet50) whose standard ReLU activation layers were randomly replaced with another. Results demonstrate the superiority in performance of this approach.

Filling the bottomless pit: Financing the construction of the Royal Museums of Art and History in interwar Belgium (1919–39)

ABSTRACT During the interwar years, when Belgium struggled to overcome the destructions of the First World War and the economic slump of the Great Depression, the government launched one of the most ambitious construction projects in Belgian history. At the Parc du Cinquantenaire an impressive ensemble of buildings, halls and galleries was constructed during the 1920s and 1930s to house the collections of the Royal Museums of Art and History. Drawing new evidence from the archives of the museum, newspaper articles, and parliamentary proceedings, this article will examine how the curators in chief managed to secure enough funding for these ambitious projects in times of deep economic crisis. It will be argued that this tour de force was the result of a combination of factors, including intense lobbying, putting forward convincing arguments, and deploying a pragmatic realpolitik.

КРЕАТИВНЫЙ ПОДХОД К ПРОДВИЖЕНИЮ БРЕНДА УНИВЕРСИТЕТА

The authors consider the issues of effective brand management of Industrial University of Tyumen by developing a marketing strategy aimed at identifying tools that enhance branding and allow forming vivid mass communications of potential and actual consumers with the brand idea. To build an emotional connection with the brand, the authors suggest a creative format for presenting the university’s logo using the Tesla transformer. Particular attention is paid to the issues of rational placement of the logo in the architectural and urban ensemble of educational buildings of the university. The objectives of the pilot marketing research were to study the attitude of students and staff to the logo and other brand attributes of Industrial University of Tyumen and to identify their opinions on the proposed form of creative presentation of the logo. Analysis of the results of the study showed that it is necessary to strengthen the brand of the university by expanding the range of consumers and communications, aimed at creating a brand-oriented image of the university. This requires applying best practices for identifying and influencing its target audiences as part of an impressions marketing strategy. The creative format of the university logo fully meets the formulated needs.