Type Of Architecture Research Articles

Analysis of the indoor wind environment in buildings on the Qinghai-Tibet plateau of China: A case study of the Dege Scripture Printing House

The Dege Scripture Printing House (DSPH), built in 1729, is located on the southeastern edge of the Qinghai-Tibet plateau and serves as a significant center for Tibetan Buddhism culture. Compared to typical Tibetan architecture, DSPH features unique characteristics, such as its substantial size, diverse structures, and adaptability to the local climate. The surrounding topography of DSPH primarily comprises plateaus and hilly plains, and the region experiences a continental plateau monsoon climate. This climate is characterized by cold temperatures due to high altitude, low precipitation, long sunshine hours, dry air, long winters, short summers, and intense solar radiation. To thoroughly assess the ventilation status of DSPH, a 3D scan of the building, physical environment tests, and indoor wind environment simulations were carried out. The field test results show that the unique architectural design of DSPH helps mitigate the impact of the harsh climate on indoor activities. By analyzing and evaluating the wind environment of DSPH, locally adapted climate resilience practices are summarized, and the physical properties of buildings characteristic of the Tibetan plateau are clarified. These findings provide a reference for promoting the development of local climate-adapted buildings, preserving traditional architectural culture, and promoting the integration of modern architecture with traditional culture. Furthermore, this study represents an important step toward enhancing energy efficiency and green building practices in ethnic minority settlements, contributing to sustainable development in the region.

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

Light management in Cu2ZnSnSe4 solar cells with ZnO:Al periodic sub-wavelength architectures

This study used a vacuum deposition technique to manufacture Cu₂ZnSnSe₄ (CZTSe) solar cells, providing a scalable and safe manufacturing approach for low-cost, high-performance photovoltaic technology. Additionally, nanoimprint lithography was successfully employed to fabricate two types of periodic sub-wavelength architectures on the surface of CZTSe solar cells, serving as anti-reflective layers and further enhancing their conversion efficiency. The research demonstrates that when the surface architecture of the CZTSe solar cells is Nano-hill, it exhibits superior gradient refractive index and optimal anti-reflective properties, consistent with simulation results. For CZTSe solar cells with this surface architecture, the average reflectance decreased from the pristine 9.86% to 7.34%, and at an incident light angle of 60 degrees, the reflectance dropped from 17.99% to 9.53%, demonstrating the architecture's omnidirectional anti-reflective capability. The periodic sub-wavelength architectures fabricated in this study enhanced the efficiency of the solar cells from 3.68% to 6.66%, not only improving conversion efficiency but also showcasing the potential for comprehensive anti-reflective layers, interface repair, and continuous processing. This research not only contributes to optical design but also holds significant importance for the future development of CZTS(Se) technology.

Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design.

In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-based, graph-based, and combinations of these models, accommodating both single- and multimodal model architectures. For validation, we performed a performance evaluation on the MatBench benchmarking dataset of five properties, targeting five types of regression architectures that can be designed using Matini-Net. When applied to each of the five material property datasets, the best model performance for the various architectures exhibited R2 > 0.84. This highlights the usefulness and flexibility of Matini-Net for accelerating materials discovery. Specifically, this framework was developed for researchers with limited experience in deep learning to easily apply it to research through automated feature engineering, hyperparameter tuning, and network construction. Moreover, Matini-Net improves the model interpretability by performing an importance analysis of the selected features. We believe that by employing Matini-Net, machine and deep learning can be applied more easily and effectively in various types of materials research.

Visualizing Performances Losses of Perovskite Solar Cells and Modules: From Laboratory to Industrial Scales

AbstractWhile the efficiencies of lab‐sized perovskite solar cells are continuously rising, a variety of challenges have to be overcome to realize remotely similar efficiencies in an industrial context. Any changes in the preparation process, device size, device architecture, and material type are likely to result in efficiency loss. To date, there have been no solutions that can produce large‐area modules with performance comparable to that of laboratory devices. However, depending on the type of deposition process, the dominant loss mechanisms differ significantly, which can guide the further optimization of processes and devices. In this study, a meta‐analysis of state‐of‐the‐art perovskite solar cells and modules with different preparation methods, area sizes, and material compositions, is presented. Moreover, the efficiency losses are divided into five figures of merit and they are visualized to discuss the efficiency‐limiting loss mechanisms that must be overcome for commercialization.

Assessment of Architectural Typologies and Comparative Analysis of Defensive Rammed Earth Dwellings in the Fujian Region, China

Tulou, Tubao, and Zhailu all belong to the same vernacular architectural defensive rammed earth dwellings in Fujian; however, because of the similarity of their earthen defensive functions and fortress architectural systems, they are easily confused by scholars and there is still a gap in distinguishing the in-depth differences between the three. For the first time, this article develops a proper architectural typology with a comparative analysis of five aspects (origin and prototype, plan and type, building function and settlement typology, structure type and construction technology, and ethical concept and settlement model) to distinguish their architectural features. We find that they have different architectural prototypes and plan types. Tubao is a defensive dwelling with a residential function, Tulou is a defensive dwelling with a residential function, and Zhailu is a dwelling that combines living and defense. They have six different structural types. Although Tulou has the simplest structure and the thinnest rammed earth wall, its construction technology is the best. Tubao and Zhailu have more clearly defined architectural levels than Tulou, and Tubao’s hierarchy is the most significant. Therefore, we prove that despite their similarities, they are different types of buildings. This study helps to provide a methodological guide for identifying other, similar buildings and serves as a manual for the restoration and sustainable development of defensive rammed earth dwelling.

First discovery of the podocytes in the circulatory system of enigmatic Echiurids (Annelida: Thalassematidae)

Fine structure of echiurid blood vessels in the proboscis is known in details, but the circulatory system of the trunk remains studied mainly at the level of general anatomy. The investigation of the trunk circulatory system in Bonellia viridis females revealed the presence of specialized podocytes in the ring vessel and anterior part of the ventral vessel. This study allowed to describe podocytes in echiurid circulatory system for the first time. Podocytes of B. viridis are characterized by a typical cellular architecture, which is known for other bilaterians: they consist of a cell body, primary processes, and pedicels that are interconnected via specialized slit diaphragms. The presence of podocytes in the ventral and ring vessels indicates these parts of circulatory system as sites of ultrafiltration in echiurids, i.e., exactly in these palaces, the filtration of plasma occurs through the basal lamina to the body cavity.

Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies

In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). Approaches based on using Deep Learning methods have been the most coveted in recent years. Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. It is therefore able to work on data without any preprocessing. This paper studies using four types of LSTM networks to diagnose bearing faults in a classification approach. It aims to intervene on both the input parameters and the network architecture, to achieve high performance. The proposed method is carried out in two different ways. In the first case, the data inputs are raw frames of vibration signals. However, in the second case, the network inputs are pre-computed time-frequency features. The results clearly showed that LSTMs are more accurate with the latter.

Long-range over-a-meter NFC link budget with distributed large-area coils

Near-field communication is considered to have a high level of hardware security protection owing to its natural short-range wireless operation, which makes a man-in-the-middle attack impossible. Here we question this statement by demonstrating a several-meter range NFC communication channel, supported by resonance-tuned large-area distributed coils. Typical NFC antenna architectures encompass multi-turn wires, forming flat resonant coils. Being several centimeters across, those devices cannot provide reliable communication between items, situated more than a fraction of a meter apart. An appealing approach to the range extension is to enlarge the coil area, thus spreading the magnetic field over larger distances. However, in this case, the overall length of folded conducting wires becomes wavelength comparable, nevertheless, the overall size of the coil remains electrically small, considering the 13.56 MHz operation frequency. Here we demonstrate several coil designs and establish a reliable NFC channel over several-meter distances. Adaptive impedance matching is implemented to maintain an energy power transfer between resonant coils, thus further extending the communication channel and making it robust to clutter. The ability for long-range NFC communication raises security concerns in sensitive contactless operations like wireless payments, prompting the need for enhanced countermeasures due to potential hardware vulnerabilities.

Screening and computational characterization of novel antimicrobial cathelicidins from amphibian transcriptomic data

As cold-blooded organisms living in damp and dark environments, amphibians have evolved robust defense mechanisms to protect themselves from predators and infections. Among the wide repertoire of bioactive compounds they produce are antimicrobial peptides (AMPs), which are required as part of innate immunity. One important class of AMPs is cathelicidins, known for their broad-spectrum activity against pathogens and their immunoregulatory roles. However, despite their promising biomedical potential and the increasing availability of omics data, few cathelicidins have been studied in amphibians, mostly through conventional experimental techniques. Here, we present 210 novel cathelicidin sequences from amphibian transcriptomes, identified through a comprehensive computational pipeline, which employed HMMER and BLAST tools to screen cathelicidin domains. These sequences reveal a typical tripartite domain architecture that was confirmed by SignalP and InterProScan analysis. Phylogenetic inference with IQ-TREE classified the sequences into six categories based on evolutionary relationships. Compared to cathelicidins from other vertebrates, amphibian mature peptides exhibit longer average lengths (around 50 amino acids), fewer aromatic and hydrophobic residues, and reduced thermal stability. Furthermore, these amphibian cathelicidins were characterized for their physicochemical and biological properties, revealing significant antimicrobial potential with lower hemolytic capability, especially in anurans, which suggests a balance between their antimicrobial and hemolytic activities predicted through AMPlify, ampir, AmpGram, and HemoPI. Secondary structure estimations, including three-dimensional modeling using AlphaFold2, indicate that amphibian cathelicidins predominantly feature α-helices and coils. Some representative models also display a high α-helix composition with amphipathic topology, facilitating interactions with simulated bacterial membranes as assessed by the PPM approach. Thus, these findings highlight the functional role of cathelicidins in amphibian immunity and their promising biomedical applicability, emphasizing the importance of applying computational methods to expand the scope and reveal the diverse landscape of cathelicidins across vertebrates.

Pneumatic concrete laminar structures revisited. A singular architectural structural type

The 20th century was a time for architectural change, technical innovation and the search for affordable construction and prefabrication. In this context, pneumatic concrete laminar structures represent the advances towards a new architecture that could give answers to the challenges of the century. This work identifies the systems that were explored during that period and describes the experimental designs developed by W. Neff, H. Heifetz, H. Isler, D. B. South and D. Bini. In addition, the adaptation capability of this singular type and the reasons that caused the abandonment of these structure are also analysed.

Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

The object of the research is the application of deep learning algorithms using an improved mathematical lossless image compression method for recognizing and identifying dead trees in aerospace images. The main problem that has been solved is the archiving of images due to their large volume on disk and the possibility of their further processing by deep learning methods such as convolutional and capsule neural networks, which have shown high efficiency and accuracy in image recognition and classification tasks using the proposed new image compression method. The article presents a comparative analysis of the performance of three YOLO (You Only Look Once) models with different types of architectures, such as YOLOv5, YOLOv7 and YOLOv8, to assess the effectiveness of their work for the task of recognizing aerospace tree images obtained from satellites, drones, and aircrafts. Comprehensive analysis of YOLO models presents that model YOLO v8 turned out to be most effective with a positive accuracy of 88.2 %, a recall of 77.4 %, and a mAP50 score of 87.2 %. Moreover, the average detection time was only 0.052 seconds for each image, even though the model size remains very small – 21.5 MB. These results suggest a much better usage of time and precise identification of dead trees, and classified targets with high efficiency. From the research, there is significant prospects of global forest management especially on forest reduction and protection of ecosystems through accurate assessment on the health of forestry. The proposed approach is universal and can be used in real life conditions, providing a good compromise of the speed, accuracy and resources required for forest monitoring and management

Architectural Organization of Ecological Tourist Complexes (The South Russia Case Studies)

This article analyzes the domestic and foreign experience in design of ecological tourist complexes, their architectural organization, three architectural models of ecological tourist complexes, and identifies the main design techniques. The relevance is determined by the growing interest of society in ecology; an increase in the tourist potential of the territories of southern Russia due to changing conditions; the emergence of a new type of tourist architecture, as well as an insufficient amount of design experience.Purpose: To identify the architecture of ecological tourist complexes and develop design proposals for such complexes in the south of Russia.Methodology/approach: The analysis of formative factors: urban planning, architectural planning, functional, environmental, affecting the architecture of ecological tourist complexes, their classification by organizational structure, functional content, accessibility, social efficiency and environmental factors. The concept of an ecological tourist complex in Krasnodar is proposed.Research findings: The organization of ecological tourism in the natural environment, environmental education of the population in the process of recreation are considered. Architectural models of ecological tourist complexes are proposed: adventure, scientific and recreational elements of environmental education, their comparative analysis, and selection of the effective model for Krasnodar.

Part-Prototype Models in Medical Imaging: Applications and Current Challenges

Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges.

Image Denoising Using Deblur Generative Adversarial Network Denoising U-Net

Convolutional neural networks (CNNs) are becoming increasingly popular for image denoising. U-Nets, a type of CNN architecture, have been shown to be effective for this task. However, the impact of shallow layers on deeper layers decreases as the depth of the network increases. To address this issue, the authors propose a new image denoising method called DGANDU-Net. DGANDU-Net combines the DeblurGAN design with a U-Net architecture. This combination allows DGANDU-Net to effectively remove noise from images while preserving fine details. The authors also propose the use of two loss functions, mean square error (MSE) and perceptual loss, to improve the performance of DGANDU-Net. MSE is used to learn and improve the extracted features, while perceptual loss is used to produce the final denoised image. The authors evaluate the performance of DGANDU-Net on a variety of noise levels and find that it outperforms other state-of-the-art denoising algorithms in terms of both visual quality and two evaluation indices, including peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measure (SSIM). Specifically, for extremely noisy environments with a noise standard deviation of 75, DGANDU-Net achieves an average PSNR of 37.39[Formula: see text]dB in the test dataset. The authors conclude that DGANDU-Net is a promising new method for image denoising that has the potential to significantly improve the quality of medical images used for diagnosis and treatment.

The cryptonephridial/rectal complex: an evolutionary adaptation for water and ion conservation.

Arthropods have integrated digestive and renal systems, which function to acquire and maintain homeostatically the substances they require for survival. The cryptonephridial complex (CNC) is an evolutionary novelty in which the renal organs and gut have been dramatically reorganised. Parts of the renal or Malpighian tubules (MpTs) form a close association with the surface of the rectum, and are surrounded by a novel tissue, the perinephric membrane, which acts to insulate the system from the haemolymph and thus allows tight regulation of ions and water into and out of the CNC. The CNC can reclaim water and solutes from the rectal contents and recycle these back into the haemolymph. Fluid flow in the MpTs runs counter to flow within the rectum. It is this countercurrent arrangement that underpins its powerful recycling capabilities, and represents one of the most efficient water conservation mechanisms in nature. CNCs appear to have evolved multiple times, and are present in some of the largest and most evolutionarily successful insect groups including the larvae of most Lepidoptera and in a major beetle lineage (Cucujiformia + Bostrichoidea), suggesting that the CNC is an important adaptation. Here we review the knowledge of this remarkable organ system gained over the past 200 years. We first focus on the CNCs of tenebrionid beetles, for which we have an in-depth understanding from physiological, structural and ultrastructural studies (primarily in Tenebrio molitor), which are now being extended by studies in Tribolium castaneum enabled by advances in molecular and microscopy approaches established for this species. These recent studies are beginning to illuminate CNC development, physiology and endocrine control. We then take a broader view of arthropod CNCs, phylogenetically mapping their reported occurrence to assess their distribution and likely evolutionary origins. We explore CNCs from an ecological viewpoint, put forward evidence that CNCs may primarily be adaptations for facing the challenges of larval life, and argue that their loss in many aquatic species could point to a primary function in conserving water in terrestrial species. Finally, by considering the functions of renal and digestive epithelia in insects lacking CNCs, as well as the typical architecture of these organs in relation to one another, we propose that ancestral features of these organs predispose them for the evolution of CNCs.

AI-Powered Approaches for Hypersurface Reconstruction in Multidimensional Spaces

The present article explores the possibilities of using artificial neural networks to solve problems related to reconstructing complex geometric surfaces in Euclidean and pseudo-Euclidean spaces, examining various approaches and techniques for training the networks. The main focus is on the possibility of training a set of neural networks with information about the available surface points, which can then be used to predict and complete missing parts. A method is proposed for using separate neural networks that reconstruct surfaces in different spatial directions, employing various types of architectures, such as multilayer perceptrons, recursive networks, and feedforward networks. Experimental results show that artificial neural networks can successfully approximate both smooth surfaces and those containing singular points. The article presents the results with the smallest error, showcasing networks of different types, along with a technique for reconstructing geographic relief. A comparison is made between the results achieved by neural networks and those obtained using traditional surface approximation methods such as Bézier curves, k-nearest neighbors, principal component analysis, Markov random fields, conditional random fields, and convolutional neural networks.

A modeling of complex trait phenotypic variance determinants.

Studies have now shown that the heritability of some complex traits, such as human height, can be virtually fully captured via potential use of sufficiently powered approaches that can characterize the associated collective common- and rare-variant additive genetic architecture. However, for other traits, including complex disease traits, full recovery of such narrow sense heritability would still likely fall far short of respective heritability estimates yielded from pedigree-based analyses such as twin studies. Here, it is proposed that such traits could also involve additional types of relevant architecture and underlying genetic mechanism, such that interaction of somatic variants with heritable variants may represent an underappreciated component. The theoretical model suggested predicts that some relevant heritability estimates are systematically inflated by twin studies, and that instead a significant proportion of the phenotypic variances may be explained by specialized types of heritable genotype-by-environment interaction.

Different Types of Architectures in Frontend Design and Development

Frontend architecture defines how frontend code is organized and structured for scalability, maintainability, and performance. Over time, different architectural patterns have emerged to address the challenges of modern web applications. This paper examines prominent frontend architectures, including Monolithic, Component-based, Micro Frontend, MVP (Model-View-Presenter), MVVM (Model-View-ViewModel), Server-Side Rendering (SSR), and Jamstack. By understanding the advantages, limitations, and ideal use cases of each, developers can make informed choices in their projects.

Mutation potentiates migration swamping in polygenic local adaptation.

Locally adapted traits can exhibit a wide range of genetic architectures, from pronounced divergence at a few loci to small frequency divergence at many loci. The type of architecture that evolves depends strongly on the migration rate, as weakly selected loci experience swamping and do not make lasting contributions to divergence. Simulations from previous studies showed that even when mutations are strongly selected and should resist migration swamping, the architecture of adaptation can collapse and become transient at high mutation rates. Here, we use an analytical two-population model to study how this transition in genetic architecture depends upon population size, strength of selection, and parameters describing the mutation process. To do this, we develop a mathematical theory based on the diffusion approximation to predict the threshold mutation rate above which the transition occurs. We find that this performs well across a wide range of parameter space, based on comparisons with individual-based simulations. The threshold mutation rate depends most strongly on the average effect size of mutations, weakly on the strength of selection, and marginally on the population size. Across a wide range of the parameter space, we observe that the transition to a transient architecture occurs when the trait-wide mutation rate is 10-3-10-2, suggesting that this phenomenon is potentially relevant to complex traits with a large mutational target. On the other hand, based on the apparent stability of genetic architecture in many classic examples of local adaptation, our theory suggests that per-trait mutation rates are often relatively low.