Architectural Framework Research Articles

Development of the regulatory framework for landscape architecture

Good regulation is anchored on sound professional basis, guarantees quality and protects heritage, while leaving room for design creativity and public interest. This review of the regulatory system for landscape architecture over the last 40 years aims to show how far these guarantees have been achieved. Landscape architecture as a technical and scientific discipline is subject to engineering regulations and legislation related to several areas of heritage protection. The legal instruments for the scope of tasks of the profession, which is diverse and multiscale, are presented in terms of the strategic framework, the spatial, local and architectural-technical planning, the protection of natural and cultural heritage as well as the regulatory system for the professional license.

Automatic Colorizing Black and White Image System Using Deep Learning

Colorization involves transforming a grayscale (black-and-white) image into a colorized version that reflects the semantic color tones of the original input. Over recent years, this process has garnered substantial attention, with significant advancements achieved by researchers in the field. The technique has numerous applications, such as in medical imaging and the restoration of historical artifacts. Various methods have been developed to address this challenge, leveraging technologies like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). These models differ in their architectural frameworks and are evaluated using diverse datasets. This paper explores some of these methodologies, comparing the performance of generative models with traditional deep neural networks, while also highlighting existing limitations. Additionally, it provides a concise overview of past and current progress in the domain of image colorization, drawing on contributions from numerous researchers and studies. a widely recognized deep learning algorithm. The proposed model processes grayscale images as input and predicts their corresponding colors based on training data. The Lab color space is employed in this approach, where the model receives the L channel (lightness) as input and predicts the ab channels (color information) as output. A subset of the ImageNet dataset was used for training and evaluation, consisting of 39,604 randomly selected images, divided into 80% for training and 20% for testing.

Advances and Applications of Carbon Capture, Utilization, and Storage in Civil Engineering: A Comprehensive Review

This paper thoroughly examines the latest developments and diverse applications of Carbon Capture, Utilization, and Storage (CCUS) in civil engineering. It provides a critical analysis of the technology’s potential to mitigate the effects of climate change. Initially, a comprehensive outline of CCUS technologies is presented, emphasising their vital function in carbon dioxide (CO2) emission capture, conversion, and sequestration. Subsequent sections provide an in-depth analysis of carbon capture technologies, utilisation processes, and storage solutions. These serve as the foundation for an architectural framework that facilitates the design and integration of efficient systems. Significant attention is given to the inventive application of CCUS in the building and construction industry. Notable examples of such applications include using carbon (C) in cement and promoting sustainable cement production. Economic analyses and financing mechanisms are reviewed to assess the commercial feasibility and scalability of CCUS projects. In addition, this review examines the technological advances and innovations that have occurred, providing insight into the potential future course of CCUS progress. A comprehensive analysis of the environmental and regulatory environments is conducted to evaluate the feasibility and compliance with the policies of CCUS technology deployment. Case studies from the real world are provided to illustrate effectiveness and practical applications. It concludes by emphasising the importance of continued research, policy support, and innovation in developing CCUS technologies as a fundamental component of sustainable civil engineering practices. A tenacious stride toward carbon neutrality is underscored.

Real-time instance segmentation of recyclables from highly cluttered construction and demolition waste streams.

Efficient recycling strategies are crucial for mitigating the adverse environmental impacts of escalating construction and demolition waste (CDW). While automated identification via deep-learning is a promising direction, localizing CDW recyclables is uniquely challenging due to significant clutter and compositional complexity. Recognizing that accurate and fast localization is a strong prerequisite for swift robotic action, this study provides a comprehensive assessment of state-of-the-art (s.o.t.a) real-time instance segmentation of recyclables from complex CDW streams. Unlike previous studies that simplify the real-world conditions, this study curates and employs a high-quality CDW instance segmentation dataset tailored to capture often-ignored domain intricacies such as deformation, contamination, intricate class distinctions, scale variations, and high levels of clutter, ensuring practical applicability. The results show that even advanced networks struggle with complexity of real CDW streams due to high clutter, with segmentation accuracy not surpassing 50%. To address this, the study proposes integrating patch-based inferencing techniques, which help the model focus on cluttered regions more effectively, boosting overall performance by a notable 12.9%. Additionally, to enhance the zero-shot capabilities of s.o.t.a prompt-based real-time segmentation for identifying CDW recyclables, a simple yet effective domain-transfer framework is proposed, substantially increasing number of correct mask predictions by 68%. This study serves as a practical reference for applying deep-learning tools in automated environmental management tasks such as waste sorting and suggests the best architectural framework for practical use.

Comprehensive tuning guide: IBM MDM, PME algorithm, services, and configurations

The research paper provides comprehensive guidance on tuning specifics to maximize the efficiency of IBM MDM, particularly in high-performance environments. It addresses several advanced topics and tuning guidelines, focusing on the architectural framework and the details required for sustained performance. Including the recommendations to Handle Long-Chained Duplicates in Suspect Duplicate Processing (SDP), Minimize Deadlocks During Duplicate Processing, and Advanced Database Configurations for High-Performance WebSphere Application Server (WAS) Java Virtual Machine (JVM) and Cache Optimization, and Thread and session management, upgrading the Java version to hava8, Garbage Collection (GC) and Heap Memory Settings, servlet caching, and reduced application logging, MDM Workbench recommendations, Services tuning, and Batch processor tuning. IBM Messaging Queue’s Max server connections and Queue Connection Factory Settings.

Dual-Network Layered Network: A Method to Improve Reliability, Security, and Network Efficiency in Distributed Heterogeneous Network Transmission

This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet diverse application needs. Therefore, dual-network layered networks, as an innovative solution, are gradually receiving attention from both academia and industry. This article first analyzes the key technical elements in the dual-network layered network architecture, including the optimization of routing algorithms, distributed consensus, reliability assurance mechanisms for packet transmission, and dynamic allocation strategies for network resources. Through in-depth research on these technologies, this article elaborates on the important role of dual-network layered networks in building efficient and stable transmission environments, providing important theoretical foundations and technical support for the construction and optimization of future network systems.

The Role of Internet of Things and Fog Computing in Smart Cities

Smart cities represent the next evolution of urban centers, integrating advanced technologies to create better living environments. These cities will feature billions of connected devices, such as sensors and actuators, generating vast amounts of data. This data typically flows to cloud servers for processing. However, traditional Big Data analytics and cloud computing have proven inefficient in some scenarios, as not all data is pertinent enough for cloud-level processing. Transmitting all data from IoT and edge devices to the cloud consumes excessive storage and network resources. Fog computing emerges as a solution by decentralizing some data processing, analytics, and storage to the network's edge, closer to the data source. This approach alleviates the load on central servers and reduces latency, ensuring timely responses. Fog computing leverages edge computing to handle immediate data processing needs, making the overall system more efficient. This paper will cover the fundamental concepts of smart cities, IoT, and fog computing. It will present the important technologies and architectural frameworks that underpin these systems. Furthermore, several case studies will illustrate the practical applications and benefits of integrating fog computing with cloud computing and IoT in smart cities. These examples will demonstrate how such integrations lead to more efficient, responsive, and sustainable urban environments.

A novel deep neural network-based technique for network embedding.

In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network's structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world.

Understanding Generative AI: From Basic Principles to Real-World Applications

This comprehensive article examines the foundational principles, technical implementations, and societal implications of generative artificial intelligence (GenAI), a transformative technology that has revolutionized the creation of synthetic content across multiple domains. The article explores the architectural frameworks underpinning GenAI, focusing on Generative Adversarial Networks (GANs) and Transformer-based models, while detailing the sophisticated training methodologies and evaluation metrics that enable their functionality. Through an in-depth analysis of real-world applications in healthcare, creative industries, and software development, this article illuminates the technology's potential to enhance human capabilities and drive innovation. The investigation extends to critical ethical considerations, addressing security concerns surrounding deepfakes, challenges in bias mitigation, and complex intellectual property issues. Furthermore, the article presents a forward-looking perspective on research opportunities, policy implications, and industry best practices, emphasizing the importance of responsible development and deployment of generative AI systems. This article contributes to the growing body of knowledge on GenAI by providing a holistic understanding of its current state, challenges, and future directions, while highlighting the crucial balance between technological advancement and ethical considerations in shaping its evolution.

Strategic Planning of Work Training Center Information System Using TOGAF ADM and ITIL

This study aims to develop an implementation model of TO-GAF and ITIL frameworks in enterprise architecture (EA) for the Technical Implementation Unit (UPTD) of the Tegal Re-gency Vocational Training Center (BLK) and determine the improvement of services provided by BLK after the develop-ment of enterprise architecture. This type of research is a case study research where data collection is done by direct observation or observation, interviews and surveys using questionnaires. The population in this study were employees and students of BLK Tegal Regency, totaling 100 people. While the sample was 15 people. TOGAF ADM is used to un-derstand the strategic planning process systematically, from problem identification to the development of measurable solutions. Analysis and design of enterprise architecture is prelimenary phase, requirement management, architecture vision, business architecture, information systems architecture and opportunities and solutions. While the ITIL approach in addition to providing practical guidance in managing BLK IT services, ITIL is also used to test the level of maturity of the system against the blueprint produced so that a tested sys-tem design is obtained. The results of the maturity level of the service operation domain system are at level 4 with a value of 3.94 and have not yet reached level 5 which means that some activities have not been fully carried out to the maximum. To achieve the expected maturity process, management must always supervise every decision making in accordance with existing procedures.

Governance in Samarinda city using TOGAF (The open group architecture framework): Literature review

The governance in Samarinda City has faced various challenges related to efficiency, transparency, and citizen engagement. To address these issues, this study proposes the application of The Open Group Architecture Framework (TOGAF) for enhancing administrative and operational processes. TOGAF provides a structured approach to enterprise architecture, ensuring alignment between IT and business objectives, which is crucial for public sector entities aiming to improve service delivery and operational efficiency. This literature review explores the existing governance practices in Samarinda City, identifies key areas for improvement, and demonstrates how TOGAF can be effectively implemented to address these challenges. By integrating TOGAF principles, the study aims to create a comprehensive framework that can streamline processes, enhance data management, and foster better communication between government departments and the public. The review highlights successful case studies of TOGAF implementation in similar contexts, offering valuable insights and practical recommendations for Samarinda City. Through this approach, the study seeks to contribute to the body of knowledge on public sector governance and provide a viable roadmap for the effective adoption of TOGAF in local government settings.

Omnichannel Strategy in Cloud-Based Contact Centers: A Blueprint for Success

This comprehensive technical article examines the evolution and implementation of omnichannel contact center solutions, focusing on the transition from traditional siloed systems to integrated cloud platforms. The article investigates the architectural framework, implementation challenges, and strategic advantages of modern contact center solutions, with particular emphasis on platforms like Genesys Cloud. Through detailed case studies and performance metrics, the research demonstrates how organizations across various sectors have achieved significant improvements in customer satisfaction, operational efficiency, and cost reduction through systematic implementation of omnichannel strategies. The article provides insights into emerging technologies, scalability considerations, and best practices for successful contact center transformation, while highlighting the critical role of AI-driven solutions and microservices architecture in enhancing customer experience and operational excellence.

Multiple allelic configurations govern long-range Shh enhancer-promoter communication in the embryonic forebrain.

Developmental gene regulation requires input from enhancers spread over large genomic distances. Our understanding of long-range enhancer-promoter (E-P) communication, characterized as loops, remains incomplete without addressing the role of intervening chromatin. Here, we examine the topology of the entire Sonic hedgehog (Shh) regulatory domain in individual alleles from the mouse embryonic forebrain. Through sequential Oligopaint labeling and super-resolution microscopy, we find that the Shh locus maintains a compact structure that adopts several diverse configurations independent of Shh expression. The most frequent configuration contained distal E-P contacts at the expense of those more proximal to Shh, consistent with an interconnected loop. Genetic perturbations demonstrate that this long-range E-P communication operates by Shh-expression-independent and dependent mechanisms, involving CTCF binding sites and active enhancers, respectively. We propose a model whereby gene regulatory elements secure long-range E-P interactions amid an inherent architectural framework to coordinate spatiotemporal patterns of gene expression.

Architectural Framework For Enhancing The Artificial Brain -Memory Segmentation Mechanism Using K- Mapping Algorithm

Architectural Framework For Enhancing The Artificial Brain -Memory Segmentation Mechanism Using K- Mapping Algorithm

Perencanaan Enterprise Arsitektur Menggunakan Framework Togaf pada Aplikasi Srikandi

Information technology (IT) has developed rapidly and is known to be the most important part of all aspects of life, such as education, business, and also government. Information technology allows organizations to increase efficiency and create innovative solutions and has an important role in improving the accountability of public services, especially in the aspect of government. The development of technology in the government aspect has an important role. One of the innovations in Indonesia is the SRIKANDI (Integrated Dynamic Archives Information System) application. SRIKANDI is an application developed to digitize archive and document management in government. The development of enterprise architecture in government institutions is crucial in ensuring that information systems can support processes effectively and efficiently. This research aims to develop enterprise architecture using the TOGAF (The Open Group Architecture Framework) method on the SRIKANDI application at the Baturraden District Office. The SRIKANDI application, which is an Integrated Dynamic Archive Information System, plays an important role in digitizing government archive management. The TOGAF method was chosen for its comprehensive approach in designing, implementing, and managing information system architecture.

Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities

Extended Reality (XR) technology is proposed as a key enabler for the development of immersive applications, thus transforming the way users interact within digital environments. In communication networks, XR will be able to introduce immersive network functions with the potential to enhance the network experience. This paper proposes a comprehensive architectural framework designed to support the deployment of XR Functions (XRFs) within a Digital Twin Network (DTN) environment in order to provide immersive functions of any type, including control and management functions, that are not available in the original network. The proposal can be deployed in relevant fields where digital twins are promising, such as medical applications, surveillance systems, autonomous driving and space communications. The proposed architecture must consider the need for minimal hardware and software resources, along with a network topological configuration with the objective of providing a robust yet lightweight framework, allowing devices with limited computing power, such as smartphones and laptops, to benefit from the enhanced network functionalities provided in the Digital Twin Network, without affecting the original network (ON).

COMPARATIVE ANALYSIS OF RF, SVR WITH GAUSSIAN KERNEL AND LSTM FOR PREDICTING LOAN DEFAULTS

This investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing the nonlinear and sequential dynamics inherent in financial data, thereby necessitating the examination of more sophisticated machine learning methodologies. This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Regression (SVR)—to assess their efficacy in forecasting loan defaults. The models are evaluated using metrics such as Mean Squared Error (MSE), F1 score, and Accuracy, and their proficiency in addressing imbalanced datasets and elucidating intricate data relationships is highlighted. The results indicate that while the Random Forest model surpasses its counterparts in terms of accuracy and MSE, the LSTM model exhibits considerable potential in managing imbalanced data, as evidenced by its stable F1 score. Although SVR reveals competitive precision, it exhibits deficiencies in addressing class imbalance. The ANOVA analyses substantiate that the disparities in model performance are statistically significant. The research acknowledges that both the LSTM and SVR models remain in the developmental stages, with ongoing initiatives aimed at refining these models through hyperparameter optimization and advanced architectural frameworks to enhance their predictive efficacy in practical applications.

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.

Solvent-induced SC-SC transformation between 3D and 1D Co-MOFs: Fluorescence sensing and magnetic properties

By utilizing an aromatic π-conjugated organic ligand as a linker, we successfully synthesized two new coordination complexes, namely, a three-dimensional framework (3D) [Co(DCPA)(DMA)(H2O)] (Co-MOF-1) and a one-dimensional chain (1D) [Co(DCPA)(H2O)4]·4H2O (Co-MOF-2) (DCPA = 9, 10-di (p-carboxyphenyl) anthracene). Both complexes were determined by single crystal X-ray diffraction analysis, Thermogravimetric analysis, PXRD, XPS, UV–vis and IR spectrum, etc. Interestingly, the reversibly single-crystal-to-single-crystal (SC-SC) structural transformation between the 3D framework and 1D chain structures could be achieved successfully by controlling the stimulation of external temperature and solvent. Besides, a series of fluorescence sensing experiments were carried out for Co-MOF-1, demonstrating excellent selective and sensitive “turn-on” towards Al3+, Cr3+, Amoxicillin (AMX), and 4,5-Imidazoledicarbonitrile (DCI), especially Al3+ and AMX. Moreover, the detection mechanism was also further investigated. In addition, the magnetic studies indicated that Co-MOF-1 and Co-MOF-2 have weak antiferromagnetic interaction. The current research elucidates different structural variations exhibited by cobalt-containing compounds in extended framework architectures and properties.

Impact of fluvial meander-belt sedimentary heterogeneity on the efficiency of low-enthalpy geothermal doublets: heat-transport simulations of forward stratigraphic models

In the present study, different geocellular models of meander-belt stratigraphic architectures were produced that are representative of the sedimentary products of sand-bed meandering rivers and their petrophysical heterogeneity. The static models were created combining a rule-based stratigraphic modelling approach with geostatistical modelling, and were applied in groundwater-flow and heat-transport simulations using MODFLOW-2005 and MT3D-USGS. Overall, histories of injected cold-water plume propagation were examined considering (i) three architectural frameworks as representative of different river morphodynamics, (ii) four scenarios of facies architecture, and (iii) alternative well layouts. The presence, size and spatial distribution of sedimentary heterogeneities related to river hydrodynamics, channel-form abandonment, or to modes of meander-transformation are seen to control the shape of the thermal plume, thereby affecting well-doublet performance. The considered scenarios of facies make-up for point-bar deposits have a modest influence on temperature decline near the abstraction well. The presence of sandstone beds in the lower heterolithic parts of abandoned-channel fill does not facilitate significant thermal-plume expansion beyond mud-plug. The effect of basal lags made of open-framework conglomerates on heat advection depends on their geometry, but is effectively negligible when it makes up less than 1% of deposits. Relatively thin mud drapes lying on point-bar accretion surfaces are seen to act as baffles to flow, but their impact is minimal in view of their small number. The study provides useful and novel insight on the potential impact of sedimentary heterogeneity in fluvial reservoirs, which can be applied to the design of well doublets, and highlights areas deserving further investigations.