Architecture Model Research Articles

Liquid plugs in narrow tubes: application to airway occlusion

We study liquid plugs in the pulmonary airways based on the two-phase axisymmetric weighted residual integral boundary-layer model of Dietze et al. (J. Fluid Mech., vol. 894, 2020, A17), which was originally developed to study liquid films coating the inner surface of a cylindrical tube in interaction with a core gas flow. The augmented form of this model, which was never applied beyond a proof of concept, allows for the representation of liquid pseudo-plugs. Here, we demonstrate its predictive power vs experiments and direct numerical simulations, in terms of the dynamics of plug formation and the characteristics of developed liquid plugs, such as their shape, flow field, speed and length, as well as the associated wall stresses and their spatial derivatives. In particular, we show that the augmented model allows us to establish a direct continuation path from travelling-wave solutions (TWS) to travelling-plug solutions (TPS). We then apply the model to predict mucus plugs in the conducting zone of the tracheobronchial tree, based on the lung architecture model of Weibel. We proceed by numerical continuation of travelling-state solutions in terms of the airway generation, whereby we impose the wavelength of the linearly most-amplified convective instability (CI) mode or that of the absolute instability (AI) mode. We identify the critical airway generation for liquid-plug formation (TWS/TPS transition), maximum potential for wall-stress-induced epithelial cell damage and CI/AI transition, and investigate how these phenomena are affected by the main control parameters, i.e. airway orientation vs gravity, air flow rate, mucus properties and airway size.

A Software Architecture Model to Manage Heterogeneous Data using Edge Computing in 5G Environment

With the improvement of 5G communication service, the need for edge computing has become more apparent. The Mobile Edge Computing service (MEC) has become one of the best solutions for mobile User Equipment (UE) data processing. MEC provides more context-aware local processing of UE's data. Meanwhile, the MEC needs to manage the heterogeneous nature of UEs and the type of data format they provide. Therefore, a Heterogeneous Data Service Bus is developed to manage different types of data formats and organize them in efficient manner. We propose a software architecture model to address the management of data heterogeneity within computing edges. The architecture discussion showcases its applicability and proper handling of heterogeneous data in the Edge Computing Environment.

AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction

The goal of click-through rate (CTR) prediction in recommender systems is to effectively work with input features. However, existing CTR prediction models face three main issues. First, many models use a basic approach for feature combinations, leading to noise and reduced accuracy. Second, there is no consideration for the varying importance of features in different interaction orders, affecting model performance. Third, current model architectures struggle to capture different interaction signals from various semantic spaces, leading to sub-optimal performance. To address these issues, we propose the Adaptive Graph Interaction Network (AdaGIN) with the Graph Neural Networks-based Feature Interaction Module (GFIM), the Multi-semantic Feature Interaction Module (MFIM), and the Negative Feedback-based Search (NFS) algorithm. GFIM explicitly aggregates information between features and assesses their importance, while MFIM captures information from different semantic spaces. NFS uses negative feedback to optimize model complexity. Experimental results show AdaGIN outperforms existing models on large-scale public benchmark datasets.

Computational modeling of Cs3Sb2I9-based novel architecture under WLED illumination for indoor photovoltaic applications

Computational modeling of Cs3Sb2I9-based novel architecture under WLED illumination for indoor photovoltaic applications

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP).

Evolution and development of complex floral displays.

Flowering plants - angiosperms - display an astounding diversity of floral features, which have evolved in response to animal pollination and have resulted in the most species-rich plant clade. Combinations of macroscale (e.g. colour, symmetry, organ number) and microscale (e.g. cell type, tissue patterning) features often lead to highly elaborate floral displays. Most studies have focused on model species with simple floral displays to uncover the genetic and evolutionary mechanisms involved in flower evolution, yet few studies have focused on complex floral displays. Here, we review current knowledge on the development and evolution of complex floral displays. We review gene regulatory networks involved in four developmental pathways contributing to overall floral display (inflorescence architecture, organ identity, flower symmetry and flower colour) in classical plant models. We then discuss how evolutionary modification of one or more of these pathways has resulted in the production of a range of complex floral displays. Finally, we explore modular systems in which multiple pathways have been modified simultaneously, generating the most elaborate floral displays.

A novel blockchain-based system for improving information integrity in building projects from the perspective of building energy performance

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Cost-Effective Dust Detection on Solar PV Panels through Deep Learning: A Step towards Automated Maintenance Systems

Abstract: Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy it produces. Detecting and mitigating dust accumulation is therefore crucial for optimizing solar energy production. While various techniques exist for detecting dust to schedule cleaning, many of these methods uses licensed software like MATLAB, which can be financially burdensome. This study proposes the use of modified pre-trained ResNet-50 model architecture with an adjusted fully connected layer for binary classification. An experimental setup was installed utilizing a single 75 Wp panel with an inclination maintained at 30-degree angle. The fine dirt particles were artificially introduced and datasets of images of clean and dusty panels were collected from five different sides were taken, to compensate for the surface reflectance from the PV panel due to camera angles. Those datasets were used to train and test the model and the accuracy achieved was 90%. The model's ability to detect dust with minimal false positives ensures more efficient maintenance scheduling. This research demonstrates the potential of AI-driven dust detection systems to enhance the operational efficiency of solar PV installations.

Floodwater Extraction from UAV Orthoimagery Based on a Transformer Model

In recent years, remote sensing has experienced a significant transformation due to rapid advancements in deep learning technology, which have greatly outpaced traditional methodologies. This integration has attracted substantial interest within the academic community. To address the complex challenges of extracting data on intricate water bodies during disaster scenarios, this study developed a post-disaster floodwater body dataset and an enhanced multi-scale transformer model architecture. Through end-to-end training, the precision of the model in extracting floodwater contours has been significantly improved. Additionally, by utilizing the vast amounts of unannotated data in remote sensing through an unsupervised pre-training task, the model’s backbone network has been fortified, greatly enhancing its performance in remote sensing applications. Experimental analyses have shown that the multi-scale transformer-based algorithm for floodwater contour extraction proposed in this study is not only widely applicable but also excels in delivering precise segmentation results in complex environments. This refined approach ensures that the model adeptly handles the intricacies of floodwater body delineation, providing a robust solution for accurate extraction, even in disaster-stricken areas. This innovation represents a substantial leap forward in remote sensing, offering valuable insights and tools for disaster management and environmental monitoring.

Improving information theory of context-aware phrase embeddings in HR domain

The object of the study is context-aware phrase representations. The growing need to automate candidate recruitment and job recommendation processes has paved the way for the utilization of text embeddings. These embeddings involve translating the semantic essence of text into a continuous, high-dimensional vector space. By learning context-aware rich and meaningful representations of phrases within the human resource domain, the efficacy of similarity searches and matching procedures is enhanced, which contributes to a more streamlined and effective recruitment process. However, existing approaches do not take into account the context when modeling phrases. This necessitates the improvement of information technology analysis in this area. In this paper, it is proposed to mark the beginning and end of phrases in the text using special tokens. This made it possible to reduce the requirements for computing power by calculating all phrase representations present in the text simultaneously. The effectiveness of the improvement was tested on a new dataset to compare and evaluate the models in the task of modeling phrases in the field of human resources management. The proposed approach to modeling phrase representations with regard to context in the field of human resources management leads to an improvement in computational efficiency by up to 50 % and an increase in accuracy by up to 10 %. The architecture of the machine learning model for creating context-aware phrase representations is developed, which is characterized by the presence of blocks for taking into account phrase boundaries. Experiments and comparisons with existing approaches have confirmed the effectiveness of the proposed solution. In practice, the proposed information analysis technology can be used to automate the process of identifying and normalizing candidates' skills in online recruiting

Effect of Architecture and Inference Parameters of Artificial Neural Network Models in the Detection Task on Energy Demand

Artificial neural network models for the task of detection are used in many fields and find various applications. Models of this kind require adequate computational resources and thus require adequate energy expenditure. The increase in the number of parameters, the complexity of architectures, and the need to process large data sets significantly increase energy consumption, which is becoming a key sustainability challenge. Optimization of computing and the development of energy-efficient hardware technologies are essential to reduce the energy footprint of these models. This article examines the effect of the type of model, as well as its parameters, on energy consumption during inference. For this purpose, sensors built into the graphics card were used, and software was developed to measure the energy demand of the graphics card for different architectures of YOLO models (v8, v9, v10), as well as for different batch and model sizes. This study showed that the increase in energy demand is not linearly dependent on batch size. After a certain level of batch size, the energy demand begins to decrease. This dependence does not occur only for n/t size models. Optimum utilization of computing power due to the number of processed images for the studied models occurs at the maximum studied batch size. In addition, tests were conducted on an embedded device.

A dataset of drone-captured, segmented images for oil spill detection in port environments

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents. Currently, there’s a scarcity of datasets employing RGB images for oil spill detection in maritime settings. This paper presents a unique, annotated dataset aimed at addressing this gap, leveraging a neural network for analysis on both desktop and edge computing platforms. The dataset, captured via drone, comprises 1268 images categorized into oil, water, and other, with a convolutional neural network trained using an Unet model architecture achieving an F1 score of 0.71 for oil detection. This underscores the dataset’s practicality for real-world applications, offering crucial resources for environmental conservation in port environments.

Network Attack Classification using Neural Network-Based Imputation Technique

Rapid technological developments have changed access to information significantly, especially in telecommunications. This growth creates new threats, such as network attacks, so detection becomes critical for network security. Leveraging machine learning algorithms to detect threats is promising, with effectiveness largely dependent on selecting relevant features optimized by the bat algorithm. Data imputation is critical in preparing data sets, and neural network-based imputation techniques demonstrate outstanding performance, achieving accuracy rates of 99.4% on validation data and 99.3% on test data. This method consistently maintains precision, recall, and scores around 98%. Models using this method also approach perfection in classifying normal and neptune labels. This imputation method can also be applied to other model architectures using autoML. Alternative models such as Light GBM, XGBoost, Random Forest, Extra Trees, and Weighted Ensemble L2 also exhibit exceptional accuracy, exceeding 99.8%.

Review of Convolutional Neural Network

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence. Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable. This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools. Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.

Magnetosheath Plasma Flow and Its Response to IMF and Geodipole Tilt as Obtained From the Data‐Based Modeling

AbstractLarge‐scale patterns of the steady‐state magnetosheath plasma flow and their dependence on the interplanetary magnetic field (IMF) have been reconstructed for the first time on the basis of large multi‐year multi‐mission pool of spacecraft observations, concurrent interplanetary data, and an empirical high‐resolution model. The flow model architecture builds upon a recently developed magnetosheath magnetic field representation by flexible expansions of its toroidal and poloidal components in a coordinate system, naturally conformed with the magnetopause and bow shock shapes. The model includes two physics‐based flow symmetry modes: the first one treats the magnetosphere as an axisymmetric unmagnetized obstacle, whereas the second mode takes into account the geodipole tilt, an important factor in the reconnection effects. The spacecraft data pool includes 1‐min average data by Themis (2007–2024), Cluster (2001–2022), and MMS‐1 (2015–2024) missions, as well as OMNI interplanetary data. The model drivers include the solar wind particle flux, IMF components, and the geodipole tilt angle. The model calculations faithfully reproduce the average plasma flow geometry and substantial effects have been found of the IMF orientation and magnitude, a principal factor that defines electromagnetic forces inside the magnetosheath. A strong dependence of the magnetosheath flow patterns on the Earth's dipole tilt indicates an important contribution of reconnection effects at the magnetopause to the solar wind particle transport around the dayside magnetosphere.

Impact of Pre-trained Weights on MobileNet Architectures for Animal Classification

This study aims to address the problem of improving animal image classification accuracy using different versions of MobileNet. Accurate animal classification plays a vital role in biodiversity protection, environmental monitoring, and agriculture. The research is significant because existing studies focus on specific models and datasets, leaving a gap in the comparative performance analysis of MobileNet versions. To address this issue, MobileNet V1, V2, and V3 models were utilized, both with and without ImageNet pre-trained weights. The models were trained on a dataset composed of 30,179 images from two sources, covering 13 animal categories. The experiment involved training the models over 10 epochs using a standard configuration of the TensorFlow framework, with accuracy serving as the primary evaluation metric.The results showed that MobileNet V3Large, with pre-trained weights, achieved the highest accuracy (97.43%), outperforming V1 and V2. Using pre-trained weights consistently enhanced performance, as models without pre-training exhibited lower accuracy and slower convergence. This study contributes by providing a comprehensive comparison of MobileNet versions in animal classification tasks, demonstrating the importance of pre-training and model architecture optimization for achieving high accuracy in image classification.

Exploring Architectural Tools for Oculus Quest 2

Virtual Reality (VR) has emerged as a pivotal technology in architectural design and education, offering immersive and interactive experiences that surpass traditional methods. This article delves into the diverse applications of VR in architecture, with a particular emphasis on the various software tools utilized in architectural modeling, visualization, and education. The study conducts a thorough examination of both professional and entry-level VR tools and platforms like Blender, Rhino, Unity, and Enscape, which are increasingly integrated into architectural workflows. By providing real-time rendering, interactive design environments, and enhanced spatial understanding, these tools are transforming the way architects and designers conceive and develop their projects. The article also explores the educational implications of VR software such as Tilt Brush, SketchUp Viewer, and Arkio, which offer accessible platforms for students and educators to engage with complex spatial concepts in an intuitive manner. Through comparative analysis, the study highlights the strengths and limitations of these tools, focusing on their usability, accessibility, and relevance to architectural practice and pedagogy. Furthermore, the integration of VR with Building Information Modeling (BIM) is identified as a significant innovation that enhances collaboration and information synchronization throughout the design and construction process. This article provides a comprehensive overview of the current state of VR software in architecture, offering insights into future trends and potential advancements that could further revolutionize the field. The findings underscore VR’s critical role in fostering creativity, improving design accuracy, and enhancing educational experiences, positioning it as a cornerstone technology in the future of architecture.

Rationalised experiment design for parameter estimation with sensitivity clustering

Quantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.

Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data

Abstract Background Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterised by left-ventricular dilatation and reduced contractility, often associated with arrhythmia and conduction disease. DCM often has a genetic component, putting family members of diagnosed patients at risk for the disease. Stratification tools to provide personalized screening advice are currently lacking. Artificial intelligence (AI) based analysis of the electrocardiogram (ECG) offers a potential solution to this challenge. Purpose Our study aims to develop and validate a novel AI-ECG model capable of prediction and early detection of DCM. Methods An AI-ECG model was developed on a United States (US) based secondary care centre dataset including 337,463 ECGs from 50,932 patients within 4 months of diagnostic echocardiography and 265,576 ECGs from 51,086 patients prior to the first diagnostic echo. The data was split 50/10/40% for training, validation, and testing, with each subset containing 9-12% DCM positive labels. The model architecture was based on a convolutional neural network with residual blocks with a modified final layer for a discrete-time survival model. The AI-ECG score was linked to changes in the median ECG within the test set to provide explainability. External validation was performed on 68,885 diagnostic ECGs (0.08% DCM positive) from the UK biobank (UKB) and a Chinese general hospital dataset with 317,854 diagnostic ECGs (2.2% DCM positive) and 76,936 predictive ECGs (1.7% DCM positive). We performed common and rare variant association studies for the AI ECG scores on quality-controlled UKB data. Results The AI-ECG score for predicted future DCM was associated with diverse ECG features (Fig. 1). In the US cohort test set, the model achieved high diagnostic accuracy with an AUC of 0.9 (95% CI 0.89-0.90) overall, and 0.89 (0.85-0.92) in a subset excluding ischemic or valvular heart disease. External datasets showed AUCs of 0.86 (0.81-0.91) in the UKB and 0.97 (96-0.97) in the Chinese dataset. The predictive performance of the AI-ECG score, adjusted for age and sex, closely approximated echocardiographic traits (LVEF and LVEDD, Fig. 2) with a concordance index (CI) of 0.79 (0.76-0.83) and 0.81 (0.77-0.85), respectively, at 10 years of follow up (FU). Combining AI-ECG scores with echocardiographic traits enhanced the CI to 0.84 (0.8-0.87). Similar findings were observed in the Chinese dataset, with a CI of 0.87 (0.85-0.89) for the AI-ECG score and 0.9 (0.88-0.92) for echocardiographic traits. Genetic analyses in UKB identified associations between the AI-ECG score and rare TTN variants as well as 25 common variant loci. Conclusion Our explainable AI ECG model for future DCM prediction demonstrates robust performance across datasets, providing comparable predictive accuracy to echocardiographic traits up to 10 years pre-diagnosis. Genetic associations support the model's validity and provide new tools for DCM genetic discovery.