General Architecture Research Articles

DCG-GAN: design concept generation with generative adversarial networks

Abstract Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG.

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

Strategies and Practices of Intelligent Imputation in Data Mining Based on Contact Number Evaluation

Abstract This paper proposes the general architecture of a multi-scale classification system based on the intelligent imputation method of multi-scale classification. SVM is used to transform the storage structure model of the object, obtain the trained support vector set with corresponding weights, measure the similarity between two data points based on the Hausdorff distance, realize the construction of a similarity matrix, adopt the idea of mean value, blur the data information, and improve the mechanism of on-scale imputation. The bicubic difference method is used as the theoretical basis of scale-down extrapolation, and the scale-down extrapolation algorithm DAMSC is established. Based on the quaternionic linkage number, the dataset data are preprocessed, and the data eigenvalue weights are calculated at the same time. The multi-scale classification validity index evaluates the model’s accuracy in classification and its performance in both upward and downward scale extrapolation. When the categorized data is 7, the MSCVI metric of the model attains a peak of 0.92254 in the segmentation dataset run results, which is more advantageous than other metrics. In the regression test of the model’s imputation results, the correlation coefficient of bus passengers is 0.99, the adjusted R-square is 0.97043, and the coefficient of the regression equation is 1.042, and the model in this paper is valid and reliable for the imputation of passenger drop-off points.

Tech-Business Analytics in Tertiary Industry Sector

Purpose: Tech-business analytics play a crucial role in the tertiary industry sector by enabling data-driven decision-making and providing analytical insights to enhance customer experiences, improve operational performance, and drive corporate growth. This sector, which includes industries such as healthcare, finance, education, and professional services, benefits from tech-business analytics through the analysis of vast data sets like patient records, financial transactions, student performance, and customer interactions. By identifying patterns, trends, and insights within these data sets, businesses can make more informed decisions. Design/Methodology/Approach: The tertiary industry sector employs an organized, iterative approach to tech-business analytics, which involves defining business issues, collecting and cleansing data, analysing this data, interpreting the results, and communicating these findings to stakeholders. Through this process, businesses are empowered to make informed decisions that support their growth and development. Findings/Result: The report talks about how Tech Business Analytics in the Tertiary Industry Sector will have controlled the expansion itself from its evolution to this point. Originality/Value: A description of how business analytics varies from tech business analytics in the tertiary industry sector. For technical purposes, there is also a generic architecture that examines 30 recently published Tech Business Analytics in Tertiary Industry sector research projects. Paper Type: Exploratory Analysis.

A Histological and Histochemical study of small intestine in adult stage of cats.

This study was performed to detect histological & histochemical facts of the bowel in adult cats.was approved on six mature cats the body masses it between 3 to 3.5 Kg, which occur in College of Veterinary Medicine, University of Baghdad. Killing euthanized by injection of an overdose of sodium pentobarbital intra-cardiac. Tissue sections were treated, sectioned and exposed to specific dyes to detected histological details.small intestine of adult cats is lined by simple columnar epithelium with goblet cells. The general architecture was like to other mammals with little individualities: the shape of the villi different from leaf-similar in the duodenum ,pointed in jejunum &leaf-formed in the ileum due to the type nutrition, the Brunner’s gland was current in the initial portion and caudal part duodenum and initial part of the jejunum act to neutralize the acidity of the food coming from the stomach with foods.The paneth cells have few numbers with rather epithelial cells in duodenum. Peyer's spot was current at the termination of jejunum and ileum. Goblet cells were able to secrete a high amount of slimy mucous into the lumen of the bowel ,.Carbohydrate profile small intestine shown the found of non-sulfated acidic mucin in the crypts and intestinal glands while the a neutral & sulfated acidic mucins found in the villi & superficial epithelium.

Facilitating Model Training With Automated Techniques

Automating artificial intelligence (AI) model training has emerged as a significant challenge in the field of automation. The complete pipeline from raw data to model deployment poses the need to define robust processes that ensure the efficiency of the services that expose the models. This paper introduces a generic architecture for automating data preparation, training of models, selection of models, and deployment of models as web services for third-party consumption using Microsoft Azure Machine Learning’s (AzureML) CI/CD tools. We conducted a practical experiment utilizing AzureML pipelines with predefined and custom modules, demonstrating its readiness for integration into any production application. We also successfully integrated this architecture into a real-world product designed for industrial forecasting. This practical implementation demonstrates the effectiveness and adaptability of our approach, indicating its potential to address diverse training needs. Keywords and phrases: Artificial Intelligence, Automation, Optimization.

Effects of Expansionary Fiscal Policy in a Commodity-Exporting Economy: Evidence from Mongolia

Abstract This study contributes to the ongoing debate on the consequences of expansionary fiscal policy by evaluating the macroeconomic effects of various fiscal policy options in a small open economy using a dynamic stochastic general equilibrium (DSGE) model. In addition, the study emphasizes the importance of studying Mongolia, which has unique characteristics and exhibits significant research gaps regarding its fiscal policy. The general architecture of the selected DSGE model includes different types of firms and households, commodity sectors, natural resource funds, and abundant fiscal tools regarding both expenditure and revenue. Employing numerous types of fiscal policy shocks, this study reveals that an exogenous increase in government investment yields the most significant long-term economic benefits, boosting potential output by 0.3%. Among the policy options, government transfers are the least effective in promoting economic output, and existing transfer policies in Mongolia appear to exert only a modest impact on growth, instead primarily contributing to the redistribution of resources. Finally, the estimated output multipliers (except transfers) are greater than one, implying that fiscal policy instruments may be an effective tool for managing the economy in Mongolia. JEL classification numbers: E17, E62, H24, H54, H55. Keywords: Fiscal policy, Fiscal policy multiplier, Small open economy, Dynamic Stochastic General Equilibrium model, Natural Resource Sector.

A comprehensive application architecture for unlocking the potential of student feedback

This article proposes a novel solution aimed at streamlining the student feedback, enabling them to assess various aspects and activities within their institution. Different from the classical solution of collecting students feedback using a set of questions grouped in one or many forms, we designed a solution capable to empower students of all age groups to enhance their higher education experiences by openly expressing opinions about their enrolled faculties, providing valuable guidance to prospective college students. Our proposal introduces a generic application architecture designed to integrate the presentation of the main opportunities and offers from many universities with the student reviews on all aspects of these universities. Thus, we avoid a number of factors that reduce the quantity and objectivity of student feedback. Important elements in our architecture refer to reviewing and rating system as well as the search system which facilitate the use of the existing evaluations on different aspects of an university. We devised a generic architecture for a client-server application, specifying a minimum set of required functionalities. For validation, we designed and implemented a client-server application based on the generic architecture we outlined. The application offers several key advantages, including robust information security measures, and diverse communication channels for authenticated users within the platform. The application’s architecture includes an encryption system based on multiple encryption techniques. Additionally, an algorithm for search optimization within the platform have been developed, further enhancing its utility.

Efficient IoT Traffic Inference: From Multi-view Classification to Progressive Monitoring

Machine learning-based techniques have proven to be effective in Internet-of-Things (IoT) network behavioral inference. Existing works developed data-driven models based on features from network packets and/or flows, but mainly in a static and ad-hoc manner, without adequately quantifying their gains versus costs. In this article, we develop a generic architecture that comprises two distinct inference modules in tandem, which begins with IoT network behavior classification followed by continuous monitoring. In contrast to prior relevant works, our generic architecture flexibly accounts for various traffic features, modeling algorithms, and inference strategies. We argue quantitative metrics are required to systematically compare and efficiently select various traffic features for IoT traffic inference. This article 1 makes three contributions: (1) For IoT behavior classification, we identify four metrics, namely, cost, accuracy, availability, and frequency, that allow us to characterize and quantify the efficacy of seven sets of packet-based and flow-based traffic features, each resulting in a specialized model. By experimenting with traffic traces of 25 IoT devices collected from our testbed, we demonstrate that specialized-view models can be superior to a single combined-view model trained on a plurality of features by accuracy and cost. We also develop an optimization problem that selects the best set of specialized models for a multi-view classification. (2) For monitoring the expected IoT behaviors, we develop a progressive system consisting of one-class clustering models (per IoT class) at three levels of granularity. We develop an outlier detection technique on top of the convex hull algorithm to form custom-shape boundaries for the one-class models. We show how progression helps with computing costs and the explainability of detecting anomalies. (3) We evaluate the efficacy of our optimally selected classifiers versus the superset of specialized classifiers by applying them to our IoT traffic traces. We demonstrate how the optimal set can reduce the processing cost by a factor of six with insignificant impacts on the classification accuracy. Also, we apply our monitoring models to a public IoT dataset of benign and attack traces and show they yield an average true-positive rate of 94% and a false-positive rate of 5%. Finally, we publicly release our data (training and testing instances of classification and monitoring tasks) and code for convex hull-based one-class models.

Architecture as fluid technology. The housing blocks by Corporación de la Vivienda of Chile

ABSTRACT In a general sense, architecture and technology articulate two modes of evaluation of objects and built environments; however, are there conditions that allow us to consider architectural works as technologies? This paper explores this possibility by analyzing bibliographic, documentary, and planimetric information on housing blocks developed by the state agency, Corporación de la Vivienda (CORVI), between 1953 and 1974. Using Process Tracing as a method of analysis, milestones were identified that showed the ways in which the design and production of this typology were influenced by a regime of values based on the idea of rationalization, and that the heterogeneity of its cases showed flexibility and adaptation to the ways of calculating and realizing this value. The conclusions address the causality observed between value and typology in this case, and suggest that this behavior expresses a technological condition of architecture.

Exploring the microscopic terrain of the small intestinal epithelium: a comprehensive overview of general architecture and the present understanding of intestinal stem cells

Abstract This paper provides a comprehensive overview of the microscopic landscape of the small intestinal epithelium, focusing on its general structure and the current state of knowledge regarding intestinal stem cells. The small intestine’s epithelial layer is intricately organized, comprising various cell types with specialized functions, including goblet cells, enterocytes, enteroendocrine cells, Paneth cells, microfold cells (M cells), and tuft cells. These cells collectively contribute to essential physiological processes such as digestion, absorption, and immune response regulation. The review delves into the role of intestinal stem cells, residing in the crypts, and their significance in maintaining tissue homeostasis and regeneration. Understanding the microscopic intricacies of the small intestinal epithelium is crucial for unraveling its physiological functions and exploring potential therapeutic avenues.

Immunohistochemical characterisation of the adult Nothobranchius furzeri intestine.

Nothobranchius furzeri is emerging as an exciting vertebrate organism in the field of biomedicine, developmental biology and ecotoxicology research. Its short generation time, compressed lifespan and accelerated ageing make it a versatile model for longitudinal studies with high traceability. Although in recent years the use of this model has increased enormously, there is still little information on the anatomy, morphology and histology of its main organs. In this paper, we present a description of the digestive system of N. furzeri, with emphasis on the intestine. We note that the general architecture of the intestinal tissue is shared with other vertebrates, and includes a folding mucosa, an outer muscle layer and a myenteric plexus. By immunohistochemical analysis, we reveal that the mucosa harbours the same type of epithelial cells observed in mammals, including enterocytes, goblet cells and enteroendocrine cells, and that the myenteric neurons express neurotransmitters common to other species, such as serotonin, substance P and tyrosine hydroxylase. In addition, we detect the presence of a proliferative compartment at the base of the intestinal folds. The description of the normal intestinal morphology provided here constitutes a baseline information to contrast with tissue alterations in future lines of research assessing pathologies, ageing-related diseases or damage caused by toxic agents.

Force-controlled pose optimization and trajectory planning for chained Stewart platforms

Introduction:We study optimization methods for poses and movements of chained Stewart platforms (SPs) that we call an “Assembler” Robot. These chained SPs are parallel mechanisms that are stronger, stiffer, and more precise, on average, than their serial counterparts at the cost of a smaller range of motion. By linking these units in a series, their individual limitations are overcome while maintaining truss-like rigidity. This opens up potential uses in various applications, especially in complex space missions in conjunction with other robots.Methods:To enhance the efficiency and longevity of the Assembler Robot, we developed algorithms and optimization models. The main goal of these methodologies is to efficiently decide on favorable positions and movements that reduce force loads on the robot, consequently minimizing wear.Results:The optimized maneuvers of the interior plates of the Assembler result in more evenly distributed load forces through the legs of each constituent SP. This optimization allows for a larger workspace and a greater overall payload capacity. Our computations primarily focus on assemblers with four chained SPs.Discussion:Although our study primarily revolves around assemblers with four chained SPs, our methods are versatile and can be applied to an arbitrary number of SPs. Furthermore, these methodologies can be extended to general over-actuated truss-like robot architectures. The Assembler, designed to function collaboratively with several other robots, holds promise for a variety of space missions.

Optimization of Briquette Classification Using Deep Learning

Indonesia needs energy to meet its needs. Energy sourced from waste is called biomass briquettes. The manufacture of briquettes is still done in a traditional way, so there are product quality problems. A computational approach has been taken to base the quality of the product on certain characteristics so that the types of briquettes that are made can be classified or sorted. The research objective of this work is to determine the quality of briquettes. The approach uses deep learning methods and convolutional neural network (CNN) architecture. Classification is based on good and bad briquette products, and testing is based on the level of performance accuracy. The dataset formed consists of 5,280 images. As training data, 85% of data is used and 15% is used as test data using 300 epoch parameters, 32 batch sizes, and learning speed up to 0.001. The results of testing and evaluating the performance of briquette products with the general CNN architecture have a level of accuracy that is not optimal. The optimization results of testing the MobileNetV2 architecture with a ratio of 70:30 obtains predictions with 0.99 as the highest accuracy value and 0.73 as the lowest. The average predicted value of the model is 0.95. The computational approach is able to provide traditional communities with solutions for the process of making briquettes.

Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

Distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-Distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions), with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms’ robustness and effectiveness in both synchronous and asynchronous training modes. Moreover, by conducting a thorough comparison between synchronous and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to train neural networks in a novel gradient-independent, distributed, and asynchronous manner, delivering similar – or even improved results in Loss and Accuracy measures.

Predicting the UV Escape Fraction of the First Galaxies in the Renaissance Simulations with Machine Learning

Cosmic reionization is likely driven by UV starlight emanating from the first generations of galaxies. A galaxy’s UV escape fraction, or the fraction of photons escaping from the galaxy, is useful to quantify its contribution to reionization. However, the UV escape fraction is notoriously difficult to predict due to local environment dependency and variability over time. Using data from the Renaissance Simulations, we attempt to make predictions about the impact of the first stars and galaxies on their environments. We present a time-independent classification model using a general artificial neural network architecture to predict the UV escape fraction given other galaxy properties—namely halo mass, stellar mass, redshift, star formation rate, lookback time, and gas fraction. We find our validation accuracy to be approximately 50%–65%, depending on the data set size from each zoom-in region of the Renaissance Simulations.

Architectural solution for the ddp (diploma defense project) web application to document the examination process

Analysis of recent research and publications. The primary source of information about using Ruby on Rails is the official RoR documentation website. After researching scientific papers and textbooks on architecture, theoretical aspects that should be taken into account when developing web services were collected. Research objective. The aim of this work is to investigate existing architectural solutions for automating the work of the examination committee and to develop an architectural solution for creating a software product based on it to increase efficiency and improve the quality of the defense process. Presentation of the main research material. The main stakeholders were identified - the secretary of the commission, commission members, commission chair, academic supervisor, student undergoing defense procedures. The client-customer is considered the department. A questionnaire was proposed for teachers to determine non-functional requirements. This allowed us to better consider their needs and requirements in project development. Analysis of functional requirements (architecturally significant requirements) has been conducted. The requirements define factors that significantly influence the choice of architectural style and tools. The constraints include financial and resource aspects, as well as technical and organizational constraints, which can impact the volume, speed, and possibility of future project development. For the DDP system, the following technology stack was chosen: Ruby on Rails for backend and frontend; Postgres for the database. Additionally, a domain was purchased on the HOSTIA service - lildoc.hhos.net (domain traffic is unlimited, currently valid until 08/22/2025), andplans are in place to purchase database hosting. Github was chosen for version control. The design, class, sequence, activity, entity-relationship diagrams for theDDP system were formed and presented. The defined architecture of the DDP project: - Follows Model-View-Controller (MVC) pattern. - Components: Models, Views, Controllers, Routing, Database (PostgreSQL), User Interface, Authentication and Authorization System, Testing. Conclusions from this study and prospects for further developments in this direction. The article examines existing solutions for automating the secretary of the commission's workplace, presents the results of developing the general architecture of the DDP project based on Ruby on Rails principles and the MVC pattern. A new architectural solution for a web service to automate the work of the examination commission members is presented.

Business models for communication departments: a comprehensive approach to analyzing, explaining and innovating communication management in organizations

PurposeThe question of whether and how communication departments contribute to organizational value creation has rarely been addressed in research. Such evidence is crucial, however, as communications compete internally with other functions (e.g. marketing and human resources (HR)) for budgets and staff. This article fills the gap by applying the business model concept, an established approach from management theory and practice, to communication units.Design/methodology/approachBased on an interdisciplinary literature review, the authors propose the Communication Business Model (CBM) as a new management approach for communications. To this end, pertinent definitions, frameworks and typologies of business models are analyzed and combined with insights from corporate communications literature.FindingsThe CBM outlines the generic architecture of business models for communication departments. Such models describe the basic principles of how such a unit operates, what services and products it provides, how it creates value for an organization and what revenues and resources are allocated.Research limitations/implicationsThe approach stimulates the debate on communication units as objects of observation when researching communication management practices. Further research with appropriate empirical methods is needed to identify and study different types of business models for communications.Practical implicationsThe CBM can be used as a management tool to analyze, explain and innovate communication management in organizations. It is a fertile approach for communication practitioners to make the work of their department visible and to position themselves internally and externally.Originality/valueTransferring a well-known concept from general management to communication management enriches the value creation debate in theory and practice. It allows communication leaders to align their work with organizational goals and make it accessible to top management and other decision-makers in the organization. It also opens up new avenues for research and education.

Detection of Unauthorized Actions in Networks Using Wavelet Analysis

Signal processing techniques are used to analyze and detect network anomalies because of their ability to detect new and unknown intrusions. The paper proposes a method of modeling network signals for the detection of network anomalies, which combines wavelet approximation and the theory of system identification. To characterize the behavior of network traffic, fifteen functions are provided, which are used as input signals within the system. At the same time, it is assumed that security violations within the network can be detected by checking abnormal patterns of system functioning according to audit data.
 Despite the fact that machine learning methods have achieved significant results in detecting network anomalies, they still face the difficulty of using the implemented algorithms, in the presence of differences in the behavior of the training data and test data, which in turn leads to inefficient performance of the algorithms. This effect is exacerbated by the limitation of algorithms to detect previously unknown types of attacks due to the large number of false positives.
 The paper develops a new method of modeling network signals for detecting anomalies in networks using wavelet analysis. In particular, the general architecture of the approach consists of three components: feature analysis, modeling of normal network traffic based on wavelet approximation and prediction using ARX model, intrusion or non-intrusion decision making
 The result is evaluated using the DARPA intrusion detection dataset, which performs a comprehensive analysis of the intrusions in the dataset. Evaluation results show that this approach provides a high level of detection of both instances and types of attacks.

MachNet, a general Deep Learning architecture for Predictive Maintenance within the industry 4.0 paradigm

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup.This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets’ specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers.