Type Of Architecture Research Articles

Developing Computational Thinking in the City through an OnLIFE Education and Digital Citizenship perspective

Digital citizenship is about the citizenship of the algorithmic, datatified, connected, and sensorized world of metaverses, multiverses, big data, and artificial intelligence. It is the expression of a new type of architecture that promotes the emergence of a computational ecology made of people, data, algorithms, sensors, forests, weather, viruses, and cities. This connection challenges us to a new cognitive policy in the field of Education. The objective of the article is to discuss ways of knowing and producing knowledge related to the development of computational thinking in K-12 Education to understand how this language is promoted and produced in an immersion path in the city, understood as a territory of hybridizations. The experiences in the city are part of research conducted at the International Research Group on Digital Education, GPe-dU UNISINOS/CNPq, developed from inventive, sympoietic, interventionist, and gamified pedagogical practices. As a research method, it appropriates the intervention-research cartographic method for the production and analysis of data. The results presented are based on elements present in the reticular and connective epistemologies, in the theory of inventive cognition, and in the concepts of transorganic connective act, and atopic inhabitancy. The results indicate that computational thinking is being leveraged in the co-articulation between human and non-human entities, from a citizen OnLIFE Education perspective, contributing to its interdisciplinary and transversal understanding, as well as pointing to the emergence of an ecological cognitive policy in education.

HAPSEA: Hydraulically Amplified Soft Electromagnetic Actuator for Haptics

Novel actuators that can provide squeeze, vibration, and localized forces while remaining soft and comfortable are essential for next-generation augmented reality applications. Despite this need, there are currently few soft actuator topologies that can provide high forces and high bandwidths at low voltages and temperatures. In this work, we present a new type of soft electromagnetic actuator architecture for haptics. These low-cost, easy-to-manufacture, and conformal actuators are composed of a coil, magnet, thin-film material, and water. Adding a thin ferromagnetic sheet further enables the creation of a latching actuator variant, which can improve force output while reducing power consumption. Each actuator combines low voltage (up to 2 V), high-bandwidth electromagnetics with hydraulics to amplify force output. In addition to force amplification, the hydraulics provide cooling and thermal mass, which enable the actuator to be used safely in wearables for longer durations. In this work, we characterize the output forces, frequency response, efficiency, and thermal profiles of the prototype actuators. Results from tabletop experiments show that with hydraulic amplification, a nonlatching actuator is able to exert 1.3 N of force at 4 A, and a latching actuator is able to exert up to 5.2 N when preloaded with 3.7 N of compression. Furthermore, the prototype actuator has a bandwidth of 30 Hz when operating in air. After 120 s of continuous operation (1.3 W) in ambient air (26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C), the maximum actuator temperature reaches 36 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C, making it safe for haptic applications. Using these designs, we develop a prototype wearable wristband device that can render body-grounded squeeze and vibration. By expanding on the operating principles described in this work, novel augmented reality applications can potentially become possible.

Fully-Decoupled Radio Access Networks: A Resilient Uplink Base Stations Cooperative Reception Framework

To cope with the even more urgent spectrum and energy efficiency challenge for trillion-level terminal access and data uploading in the next generation mobile communication network (6G), in this paper, we investigate the uplink transmission in an original fully-decoupled radio access networks (FD-RAN) architecture. Specifically, we propose a resilient uplink base station cooperative reception framework in FD-RAN, which is a large-scale fading based two-tier signal combination approach for the uplink transmission, including the localized signal combination at the base station and centralized signal combination at the edge cloud, respectively. Then, we formulate a weighted sum-rate maximization problem for the uplink transmission optimization, and decompose it into two subproblems. A spectrum-efficiency maximized virtual service cluster selection (SEMVS) algorithm is designed by leveraging the channel statistical information for solving subproblem one, and a fractional programming based power control (FPPC) algorithm is introduced for the power optimization of subproblem two. Compared to the typical RAN architectures with corresponding access and power control methods, simulation results demonstrate the significant performance improvements of uplink FD-RAN with the proposed solution.

Overview of convolutional neural networks architectures for brain tumor segmentation

&lt;p&gt;&lt;span lang="EN-US"&gt;Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.&lt;/span&gt;&lt;/p&gt;

Legal Framework for Social Infrastructure for Social Integration of the Roma and Their Preferences: Case of Slovenia

The EU and its Member States share responsibility for improving the living conditions and integration of the Roma into society. When developing systemic solutions to address the challenges of the Roma population, the first thing to do is to provide them with a suitable living environment. A suitable living environment for vulnerable social groups includes social housing adapted to their needs and preferences as part of the social infrastructure. In the first, theoretical part, this paper explores the existing international, EU, and Slovenian legal framework for addressing the housing needs of the Roma community. In the second, empirical part, the preferences of the members of the Roma Community regarding the type and architecture of housing, and their financial capacity regarding the type and location of accommodation are examined through a survey that was conducted in the Roma settlement “Kerinov Grm”. The research paper gives answers to the following research questions: (1) what are the preferences of the inhabitants of Roma settlements regarding the type of housing and architecture? (2) how to provide adequate housing for members of the Roma community? and (3) do the Roma take advantage of the available free non-profit housing, and if not, why? The survey shows a very low level of satisfaction with living in Roma settlements, which, in combination with the growing population, limited possibilities for settlement expansion, and specific housing preferences, poses a unique challenge to the state and local communities.

Deep Learning-Based Differential Diagnosis of Follicular Thyroid Tumors Using Histopathological Images

Deep learning systems (DLSs) have been developed for the histopathological assessment of various types of tumors, but none are suitable for differential diagnosis between follicular thyroid carcinoma (FTC) and follicular adenoma (FA). Furthermore, whether DLSs can identify the malignant characteristics of thyroid tumors based only on random views of tumor tissue histology has not been evaluated. In this study, we developed DLSs able to differentiate between FTC and FA based on 3 types of convolutional neural network architecture: EfficientNet, VGG16, and ResNet50. The performance of all 3 DLSs was excellent (area under the receiver operating characteristic curve = 0.91 ± 0.04; F1 score = 0.82 ± 0.06). Visual explanations using gradient-weighted class activation mapping suggested that the diagnosis of both FTC and FA was largely dependent on nuclear features. The DLSs were then trained with FTC images and linked information (presence or absence of recurrence within 10 years, vascular invasion, and wide capsular invasion). The ability of the DLSs to diagnose these characteristics was then determined. The results showed that, based on the random views of histology, the DLSs could predict the risk of FTC recurrence, vascular invasion, and wide capsular invasion with a certain level of accuracy (area under the receiver operating characteristic curve = 0.67 ± 0.13, 0.62 ± 0.11, and 0.65 ± 0.09, respectively). Further improvement of our DLSs could lead to the establishment of automated differential diagnosis systems requiring only biopsy specimens.

Symbolic forms in Church architecture: a case study of Saint Augustine Catholic Church Ikorodu, Lagos State

A religious building is nothing less than the symbolic representation of the tradition, values, culture and creativity levels of the special territory in which it stands. Each type of church architecture may have different symbolic qualities, but every church building transmits some symbolic messages. Human beings provide meanings to the images that are received from objects by their symbolic minds. The study examined architectural spaces in Saint Augustine Catholic Church Ikorodu, in Ikorodu Division of Lagos State Nigeria enacting from the perspective of indigenous form, art and symbols. It demonstrates the integration of cultural heritage, traditional forms, material availability, arts and symbols into the architecture of Catholic Church. The study is an historical, explanatory, qualitative using the methods of observation and focused interviews. The Church architecture illustrates how influences from indigenous and local forms and symbols have been incorporated in sacred spaces in the Catholic Church. The study expounded knowledge for the clergy and the institution of religion bodies and the enlightenment of academic expository. KEY WORDS: Catholic Church, Church Architecture, Culture, Ikorodu, Symbols.

Implementation of Chatbot for Merdeka Belajar Kampus Merdeka Program using Long Short-Term Memory

Good service can help the organization improve efficiency and effectiveness in operations. Optimal service can also improve the customer experience and provide added value to an organization that provides services. One of the services that can be optimized is the Merdeka Belajar Kampus Merdeka (MBKM) program which is a learning program organized by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek), especially MBKM services at the Institut Teknologi Telkom Purwokerto (ITTP). The problem is that the MBKM service at ITTP is not optimal due to inaccessibility to anyone and so many programs available. Thus, resulting in not optimal services provided. Therefore, this study aims to implement a Chatbot service in the MBKM program at ITTP. The method used in building a Chatbot service is the Deep Learning Long Short-Term Memory (LSTM) algorithm. LSTM is a type of artificial neural network architecture that matches text data. The results show an accuracy score of 100% and a loss of 0.121%. Meanwhile, the results of the further evaluation are in the form of average weights consisting of precision, recall, and F1-score, respectively of 100%, 100%, and 100%.

Classification of the qilou (arcade building) using a robust image processing framework based on the Faster R-CNN with ResNet50

ABSTRACT Qilou (arcade building) is a particular type of Chinese historical architecture combined with western and eastern building elements, which plays a significant role in the history of modern Chinese architecture. However, the recognition and classification of the qilou mainly rely on manual inspection, suppressing the cultural dissemination and protection of qilou relics. In this paper, we present a new framework that adopts multiple image processing algorithms and a deep learning network to automate qilou classification. First, image dataset of the qilou is enhanced based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Then, an improved Faster R-CNN with ResNet50 (Faster R-CNN-R) is deployed for qilou image recognition. A total of 760 images captured in Guangzhou were used for training, validation, and accuracy check of the proposed framework and several contrastive networks under the same conditions. Compared to other networks, the proposed framework works better than Faster R-CNN with VGG16 (Faster R-CNN-V) and FCOS. The accuracy of the proposed framework embedded with the Faster R-CNN-R, Faster R-CNN-V, and FCOS are 80.12%, 65.17%, and 66.35%, respectively. Based on digital images captured under different lighting conditions, the proposed framework can be used to classify nine different types of qilous, with high robustness.

EKONOMI BERBASIS KEARIFAN LOKAL: STUDI DESA WISATA SUKALASANA KABUPATEN GARUT

The village is the axis of economic development in the welfare of the community, Sukalaksana Village is a village that was originally backward and entered into a poor village in Garut Regency. Poverty occurs because based on the geographical location of the remote Sukalaksana village, traditional farmers, and the mindset of the people who want instant, the potential of the village has not been explored. This study uses qualitative methods with data collection techniques through observation, literature review, and direct interviews with the village government, tourism village managers and BUMDes Binalaksana, local communities, and the Garut Regency Community and Village Empowerment Service. The results of this study indicate that through a tourism village program based on ecology and education village, Sukalaksana Village becomes a pilot village because it can improve the economy by empowering the community in economic development. Local wisdom from typical products, rural architecture, and local culture that are used as village potentials can be developed to support the creative economy so that poverty in the village can be resolved properly as a solution in alleviating poverty in the village.&#x0D; Keywords: Economy, Local wisdom, Tourism Villages

Understanding parametric architecture within the framework of architectural typology

The article deals with the issue of understanding parametric architecture within the framework of the traditional classification of architectural objects and considers the need to develop a new architectural system that can be based on the specifics of parametric architecture form generation and types of spatial structures. Examples of parametric architectural objects within the framework of traditional architectural typology, types of architectural composition and types of spatial structures are given. The principle of working with computer methods and the need to classify parametric objects within a new system based on the traditional one is described.

Auxin plays a key role in nitrogen and plant density-modulated root growth and yield in different plant types of rapeseed

ContextWith the increasing global food and oil security problems, improvement in rapeseed (Brassica napus L.) yield become urgent. Moreover, previous studies rarely used the semi-dwarf and compact plant type of rapeseed to improve yield potential. ObjectiveHerein, we aimed to investigate the effects of different nitrogen application rates and plant density on root growth and yield-related traits using three genotypes with different plant types to clarify various regulatory mechanisms using semi-dwarf and compact plant type mutants to achieve high yield. MethodsSplit-split-plot experiments with three nitrogen application rates (N1: N2: N3, 120: 240: 360 kg ha−1) and three plant densities (D1: D2: D3, 15 × 104: 45 × 104: 75 × 104 plants·ha−1) were conducted during the three growing seasons (2019–2022) using semi-dwarf mutant HS5sca, and HS5 (wild type) as well as their F1 hybrid HS5+/sca. ResultsWith decreasing nitrogen application rate (N3 to N2), the expression level of IAA signal response genes BnaA07. GH3 and BnaA03. IAA13 was decreased in root, while increased in shoot at the early-flowering stage. Moreover, IAA content was decreased in roots and aboveground parts, whereas soluble sugar content of root bleeding sap was increased and volume of root bleeding sap was decreased in three genotypes at early-flowering stage. Additionally, nitrogen accumulation per plant and yield per plant was decreased, while population nitrogen accumulation and population yield were increased with decreasing nitrogen application rate in three studied genotypes. On the other side, with increasing plant density, the expression of BnaA07. GH3 and BnaA03. IAA13 genes were first increased and then decreased in root tissues. In addition, root surface area, the total volume of root bleeding sap, and nitrogen accumulation were decreased, which decreased dry matter and yield per plant. However, total root area, population nitrogen accumulation, and population yield in the three genotypes were increased, and yield was the highest at D2. The population yield of HS5sca, HS5+/sca, and HS5 achieved the largest increase of 26.0%, 10.3%, and 9.5% under N2D2 in 2019, 6.9%, 8.4%, and 4.7% under N3D2 in 2020, respectively, as compared to N3D1. Moreover, the morphological indices of HS5+/sca were greater than other genotypes, with the highest population yield. ConclusionsAppropriate nitrogen application rate with plant density optimized the root architecture and aboveground type of HS5+/sca that improved auxin level through affecting the downstream auxin-related genes, as well as improved dry matter accumulation and distribution, which increased yield potential. Compared with the two parents, F1 genotype HS5+/sca was suitable for nitrogen fertilizer (240 kg ha−1) and plant density (45–75 × 104 plants ha−1). Implications+ /sca heterozygous genotypes might promote root growth under low nitrogen application rate and high plant density. A strong root system is an important guarantee of high yield; thus, this heterozygous site could use to introduce excellent hybrids in rapeseed production to further improve root traits and optimize cultivation factors to increase seed yield.

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

Acceptor Functionalization via Green Chemistry Enables High‐Performance n‐Type Organic Electrochemical Transistors for Biosensing, Memory Applications

AbstractThe organic electrochemical transistor (OECT) is one of the most versatile building blocks within the bioelectronics device toolbox. While p‐type organic semiconductors have progressed as OECT channel materials, only a few n‐type semiconductors have been reported, precluding the development of advanced sensor‐integrated OECT‐based complementary circuits. Herein, green aldol polymerization is uses to synthesize lactone‐based n‐type conjugated polymers. Fluorination of the lactone‐based acceptor endows a fully locked backbone with a low‐lying lowest unoccupied molecular orbital, facilitating efficient ionic‐to‐electronic charge coupling. The resulting polymer has a record‐high n‐type OECT performance with a high product of mobility and capacitance (µC* = 108 F cm−1 V−1 s−1), excellent mobility (0.912 cm2 V−1 s−1), low threshold voltage (0.02 V), and fast switching speed (τON, τOFF = 336 µs,108 µs). This work demonstrates two types of device architectures and applications enabled by the high performance of this n‐type OECT, i.e., an artificial synapse and a complementary amplifier for detecting α‐synuclein, a potential biomarker of Parkinson's disease. This study shows that materials that enable high gain and fast speed n‐type OECTs can be developed via a green polymerization route, and the diverse form factors that these devices take promise for exploration of other application areas.

Geochemical soil analysis of sequential ritual and residential floors from the Maya site of Holtun

This research elucidates the changing function of some of the earliest types of monumental architecture in the Maya world, the E-Group, using ICP-MS soil chemistry analysis of associated floor surfaces at the site of Holtun, Guatemala. E-groups are architectural assemblages first appearing during the Middle Preclassic period that are associated with public, ritual activities, such as marking important celestial and agricultural events. Our methodology targeted sequential, stratigraphic plaza floors in the E-Group compound at Holtun to examine changing activities through time. Residential patio surfaces were also sampled as a comparison to public, ritual activity. Our results suggest that activities related to food production and consumption are visible in sequential plaza floors of the E-Group, and the locations of some of these activities changed over time. Moreover, activities and locations revealed through soil chemical analysis also differed somewhat from those in residential patios, suggesting different patterns of use in public versus private spaces.

Prediction Capability of Geomagnetic Events from Solar Wind Data Using Neural Networks

Multiple neural network architectures, with different structural composition and complexity, are implemented in this study with the aim of providing multi-hour-ahead warnings of severe geomagnetic disturbances, based on in situ measurements of the solar wind plasma and magnetic field acquired at the Lagrangian point L1. First, a statistical analysis of the interplanetary data was performed to point out which are the most relevant parameters to be provided as input to the neural networks, and a preprocessing of the data set was implemented to face its heavy imbalance (the Earth’s magnetosphere is in fact mostly at rest). Then, neural networks were tested to evaluate their performance. It turned out that, in a binary classification problem, recurrent approaches are best at predicting critical events both 1 and 8 hr in advance, achieving a balanced accuracy of 94% and 70%, respectively. Finally, in an attempt at multistep prediction of the criticality of future geomagnetic events from 1–8 hr ahead, more complex neural networks, built by merging the different types of basic convolutional and recurrent architectures, have been shown to outperform single-step and state-of-the-art approaches with a balanced accuracy of at least 70%. Interestingly, the accuracy peaks at 4 hr, corresponding to the waiting time between the detection of solar drivers at L1 and the onset of the geomagnetic storm (as previously obtained by statistical investigations), suggesting that on average this is the time the Earth’s magnetosphere takes to react to the solar event.

Textile in civil construction: from tents to tensioned architectonic membranes

Textile architecture has accompanied humanity since the first man-made shelters. With rustic structures that are easy to assemble and manufacture, it was a very important resource at different times in history. This work sought to briefly list the path of textile architecture over the years, showing the similarities and basic differences between the materials. Finally, it characterizes the quality of the currently used materials, and show some works and the scenario of this type of architecture in Brazil.

Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL.

Engine modelling architecture study for hybrid electric vehicle diagnosis application

Model-based control, calibration, and diagnosis are widely used in hybrid electric vehicles (HEVs) studies. For the real-time application, the contradiction lies in the model accuracy and fast response. This problem is particularly evident in engine modeling due to its complexity. In this study, three types of engine model architectures were studied for HEV Hardware In the Loop Simulation (HILS) diagnosis application. It shows that in terms of the real-time factor, the MAP model, obtaining all engine outputs only by looking up maps, has the best real-time performance. Both Mean Value Model (MVM) and Fast Running Model (FRM), with less than 3% steady-state error and preserving the engine's dynamic characteristics, have better real-time performance than the detailed model. What is more, the real-time performance of MVM is about 14% faster than that of FRM. MVM-based and FRM-based vehicle models were verified respectively in the HILS for the fault classification using support vector machine (SVM) and for the engine's misfire fault diagnosis. Since the MVM combines multiple detailed cylinders into a single mean value cylinder through neural network training, the MVM is typically used in diagnosis situations where fast computation speed is especially important and the detailed characterization of engine processes is considered relatively unimportant, such as vehicle-level fault diagnosis applications involving control system design, thermal management system fault, and power system fault, etc. Since the FRM reduces cylinder calculations by using the “cylinder translation method”, which preserves the working process within a single-cylinder cycle, it can also be used for fault diagnosis applications requiring engine performance with a crank-angle resolution, such as cylinder pressure diagnostics, combustion diagnostics, and timing control of valve and fuel injection, etc.

Impact of Climate Change on the “Trabocchi Coast” (Italy): The Trabocco Turchino Case Study

The increasing and extreme weather phenomena observed in the Mediterranean basin are only one aspect of the problem which has broader effects on population, structures and infrastructure.Each of these aspects is itself characterized by a wide variety of issues, which are increasingly leading studies toward a multidimensional assessment of impacts (economic, social and environmental). In this study, we focus on the impact related to the increase in extreme weather events in a specific area characterized by typical vernacular architecture: the “trabocchi” of the Italian Adriatic coast, whose identification as cultural heritage is the result of historical events and social dynamics closely linked to the collective imagination and for which inclusion as intangible cultural heritage in the UNESCO World heritage List has been requested. The weather event investigation was performed considering both long-term large-scale (using the ERA5 dataset) analysis and short-term small-scale (models and ground-based sensors) analysis. The results provide an overview of the event dynamics and enhanced understanding of the area’s vulnerability factors to extreme weather phenomena, as well as emphasized the need, in order to protect the integrity of the asset, to study environment changes and to plan concrete actions aimed at conservation, including social actions, to mitigate the problem.