Architectural Framework Research Articles

A Framework for Innovating the Technological Startup in the Cordillera Region through an E-commerce Mobile Platform

The technological startup community is beginning to be recognized in solving various needs in the Cordillera region. However, these technological startups have limited growth because they encounter challenges such as financial concerns, limited market reach, lack of awareness of intellectual property rights, and inadequate digital infrastructure. Thus, the researcher proposed the development of an e-commerce mobile application that addresses the needs of startups. Through the use of data gathering techniques, the researcher was able to determine the design considerations in terms of the architecture framework and features of the e-commerce mobile application that addressed the problems encountered by startups in the Cordillera region. The use of the 4+1 view model illustrated the architecture framework of the e-commerce mobile platform for intended stakeholders by providing a logical view, process view, physical view, development view, and scenario view. The architecture framework was used to determine the features to be integrated into the proposed e-commerce mobile application to address the challenges of the technological startups of the Cordillera region.

Study on intelligent assembly process planning and execution system based on digital twin

PurposeThe purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.Design/methodology/approachBased on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.FindingsDigital twin technology enhances the quality and efficiency of assembly process planning and execution system.Originality/valueIt provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

An intelligent mechanism for dynamic spectrum sharing in 5G IoT networks

Amid the rapid advancement of wireless network technologies, the forthcoming ubiquity of the Internet of Things (IoT) necessitates the development of sophisticated architectural frameworks and algorithms capable of handling the associated spectrum management complexities. This study proposes an innovative Mobile Broadband Internet of Things (MBIN) architecture, designed to facilitate efficient progression of the impending IoT revolution. The centerpiece of this architecture is a ground-breaking Advanced Cloud-Enabled Double Auction (ACEDA) algorithm. Deployed within the MBIN framework, this mechanism optimizes the sharing of spectrum resources, thereby enhancing the performance of the IoT structure. Theoretical grounding, algorithmic implementation, simulation results, and user satisfaction analysis are discussed in-depth to validate the efficacy of this approach.

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT).

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Data-Driven Enterprise Architecture for Pharmaceutical R&D

This paper addresses the research gap in the realm of data-driven transformation by leveraging the Resource-Based View (RBV) theory and the dynamic capabilities concept to the contours of a data-driven enterprise. It confronts the limitations of conventional digital and data transformation programs, which often prioritize technological enhancements over crucial organizational and cultural shifts. Proposing a more holistic perspective, the Data-Driven Enterprise Architecture Framework (DDA) is introduced, emphasizing the domain decomposition and productization of an architecture, distributed ownership, and federated governance, while ensuring the continuous harmonization of data, application, and business architecture. A case study featuring a leading pharmaceutical company illustrates the practical implementation of the DDA framework as a pillar of their Digital Transformation Strategy. By integrating scalable and distributed data architecture into the overarching Enterprise Architecture landscape, the company has initiated their data-driven transformation journey, showcased through their initial and very early results. This research not only offers valuable insights for pharmaceutical organizations navigating the complexities of data-driven transformations, but also addresses a research gap in the field.

A Knowledg Graph Model for e-Government

Many governments around the world have invested huge amount of resource to build their e- Government capabilities, to meet government objectives of effective public service delivery and citizens engagement. The increase in size of an e-Government landscape has led to the increase in complexity of the infrastructure. This increasing complex infrastructure presents a challenge for governments to continue to meet its objectives. Knowledge Graph (KG), a constituent AI technology, has shown a lot of promise in helping governments meet its objectives in the midst of the complexity. A major aspect of this complexity is the need to maintain a single view of the world, in the form of a unified meaning of data, within a given e-Government instance, given the heterogeneity in data models used in the different departments within an e-Government instance. In this paper, we present a unique perspective in addressing the problem of deriving semantic meaning from disparate data in an e-Government context, using KG. Our aim is to advance the objectives of effective service delivery and citizens engagement in a complex e- Government instance. We focus on creating a data- centric architectural model that is single, simple and extensible, based on KG. We create a functional model based on architectural view and viewpoints from standards such as The Open Group Architectural Framework (TOGAF). The functional model highlights the various components that underpin the functions. We have developed our model within the context of a Design Science Research (DSR) approach, and we provide evaluation of same model within that context. An e- Government KG model guides the development of KG solutions in e-Government, in order to achieve the e- Government enterprise goals of effective service delivery and citizens engagement.

Digital Twin Models for Personalised and Predictive Medicine in Ophthalmology

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.

Human-Centric Lighting Design: A Framework for Supporting Healthy Circadian Rhythm Grounded in Established Knowledge in Interior Spaces

Over the past 300 years, scientific observations have revealed the significant influence of circadian rhythms on various human functions, including sleep, digestion, and immune system regulation. Access to natural daylight is crucial for maintaining these rhythms, but modern lifestyles often limit its availability. Despite its importance, there is a lack of a comprehensive design framework to assist designers. This study proposes an architectural design framework based on the review of literature, lighting-related codes and standards, and available design and analysis tools that guides the creation of lighting systems supporting healthy circadian rhythms. The framework outlines key decision-making stages, incorporates relevant knowledge, and promotes the integration of dynamic lighting techniques into building design. The proposed framework was presented to a group of design professionals as a focus group and their feedback on the relevance and usability of the tool was obtained through a survey (n = 10). By empowering designers with practical tools and processes, this research bridges the gap between scientific understanding and design implementation, ensuring informed decisions that positively impact human health. This research contributes to the ongoing pursuit of creating lighting environments that support healthy circadian rhythms and promote human well-being.

Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

A new early warning system based on metaheuristics in CPS architecture

ABSTRACT Cyber security is the most studied in recent years by the association of metaheuristic techniques thanks to their ability to solve optimization problems in a limited time and NP-hard problems. The Early Warning System (EWS) can identify malicious exercises and anomalies in the CPS and provide robust network systems protection. In addition, grouping attacks in EWS is important for defining defense policies. Information security in the CPS architecture is about securing information during data aggregation, large-scale processing and sharing in the network environment, especially low-linkage open networks. Motivated by these considerations, we propose a new security approach based on the Artificial Bee Colony (ABC) metaheuristic algorithm to strengthen the security framework of the CPS architecture against the system and within CPS attacks. In this work, we propose a new Early Warning System approach based on metaheuristics. Based on two fundamental concepts of clustering, coherence and separation, an integrated index was proposed based on Davies-Bouldin (DB) and Silhouette Index (SI). An improved ABC algorithm was proposed based on these indices. After having identified two different types of an objective function, based on the above concepts, and after having tested the algorithm on DBSI, the results of the experiments found are auspicious.

Anti-Impact Performance Enhancement of Two-Way Spanning Slab through the Implementation of Steel Trussed Bars

Reinforced concrete (RC) slabs represent integral structural components extensively employed in architectural and infrastructural frameworks owing to their inherent robustness and longevity. In contemporary times, there has been a pronounced surge in endeavors aimed at comprehensively elucidating the anti-impact properties inherent in RC slabs. This surge is propelled by a compelling necessity to fortify these structures against the deleterious effects of low-velocity impacts, thereby ensuring their steadfastness and resilience. Consider the thorough investigation into the anti-impact characteristics of RC slabs, which has been rigorously pursued through both experimental and computational methodologies. A plethora of scholarly discourse on this topic is readily available, providing invaluable insights into the structural dynamics governing slabs subjected to low-velocity impacts. However, there is a noticeable gap in research concerning the strengthening of slabs through shear reinforcement, particularly through economical, easily fabricated, and efficient systems such as fabricated trussed bars. The primary objective of this study is to explore the structural behavior of RC slabs fortified with custom-designed trussed bars under the influence of low-velocity impacts. To accomplish this, the Abaqus software platform is explicitly employed for analysis. The slab without any shear reinforcement is experimentally tested and serves as a reference model for numerical verification. Its anti-impact performance is compared with numerical findings. Following validation, simulations are conducted for square slabs strengthened by fabricated trussed bars in orthogonal and diagonal layouts. The results demonstrate that employing fabricated truss bars shear reinforcement with a 3 mm diameter in orthogonal and diagonal layouts enhances the resistance of slabs to damage, resulting in a 28.41% and 47.06% decrease in damage, respectively. The utilization of engineered truss bars as shear reinforcement yields significant improvements in strength, rigidity, and ductility when compared to control samples lacking such reinforcement. This enhancement is particularly evident when the engineered truss bars are arranged in orthogonal and diagonal configurations.

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach.

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

Designing Integrated IT Architecture for Health Monitoring Internet of Things: Findings Exploratory Study

IT integration with healthcare, mainly through Internet of Things-based health monitoring systems, is crucial to improving healthcare management in the digital age. However, challenges remain in the design of an integrated IT architecture that can support the sustainability and effectiveness of IoT health monitoring systems, which still need to be addressed. The shortcomings in the literature related to the application of a holistic IT architecture framework to address these challenges indicate a knowledge gap that needs to be filled. Through the application of the TOGAF methodology, this research seeks to design and analyze an integrated IT architecture for IoT-based health monitoring systems in Indonesia, taking a qualitative approach through case studies, in-depth interviews, and document analysis. The main findings show that the application of the TOGAF framework successfully addresses the challenges of interoperability, data security, and system scalability by effectively integrating IoT technologies in the healthcare environment and considering the local social and infrastructural context. The implementation of the IT architecture developed based on the TOGAF methodology demonstrated improved coordination between IoT devices and backend systems, facilitated secure and real-time data flow, and accommodated the scalability and sustainability needs of the system. The findings have significant implications in supporting the development of more efficient and effective health monitoring systems, offering strategic guidance for system developers, policymakers, and IT practitioners within the healthcare sector.

Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration

This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.

Energy-Efficient Framework for Green Cloud Computing in Dynamic Resource Allocation

Cloud Computing has been a trending technology for a few years supporting computational services over internet. But ever since its adoption, cloud’s consistent challenge is in its dynamic resource allocation. The existing cloud model details the online and offline algorithms used to decide the dynamic resource allocation. The goal is to have a dynamic resource allocation framework that aligns to cloud data management’s objective of maximizing revenue with minimum cost. This encourages both consumers and cloud providers not only with energy-efficient power usage but also high CPU utilization. This article discusses the impediments of migrating to Public Cloud, what is dynamic resource allocation, HPC workloads with complex communication path on cloud platform, and the benefits of bare metal platform for latency-sensitive applications. We shed light on trade- offs (compute balance) between Private and Public Cloud, how existing resources can be leveraged, Random forest (RF) solutions including a study on hybrid cloud computing capacity optimization framework. Understanding RL architecture, problem solving approach, learning structure and Hybrid Cloud Management Architecture framework are also explored. Also given are a few RL implemented gaming examples on how it makes an impact. Lastly, we shall do the comparisons of RL with other Machine Learning (ML) approaches.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.