Open Architecture Research Articles

Fault tolerance evaluation study of a RISC-V microprocessor for HEP applications

The utilization of a radiation-hard microprocessor or a System-on-Chip (SoC) design methodology significantly benefits the future design of ASICs for HEP experiments. To evaluate the fault tolerance of a radiation-hard design, it is important to obtain detailed information on the soft error rate and contributing factors. This article presents a simulation-based approach to investigate the effects of faults induced by single event transients in a microprocessor based on the open RISC-V instruction set architecture.

Minimizing Escherichia coli O157:H7 contamination in indoor farming: effects of cultivar type and ultra-violet light quality.

Bacterial contamination of produce is a concern in indoor farming due to close plant spacing, recycling irrigation, warm temperatures, and high relative humidity during production. Cultivars that inherently resist contamination and photo-sanitization using ultraviolet (UV) radiation during the production phase can reduce bacterial contamination. However, there is limited information to support their use in indoor farming. Lettuce (Lactuca sativa) cultivars with varying plant architectures grown in a custom-built indoor farm exhibited differences in E. coli O157:H7 survival after inoculation. The survival of E. coli O157:H7 was lowest in the leaf cultivar (open architecture) and highest in the romaine and oakleaf cultivars (compact architecture). Of the different UV wavelengths that were tested (UV-A, UV-A + B, UV-A + C), UV A + C at an intensity of 54.5 μmol m-2 s-1 (with 3.5 μmol m-2 s-1 of UV-C), provided for 15 min every day, was found to be most efficacious in reducing the E. coli O157:H7 survival on romaine lettuce with no negative effects on plant growth and quality. Contamination of E. coli O157:H7 on lettuce plants can be reduced and the food safety levels in indoor farms can be increased by selecting cultivars with an open leaf architecture coupled with photo-sanitization using low and frequent exposure to UV A + C radiation. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Isotropic cellular structure design strategies based on triply periodic minimal surfaces

Isotropy is a desired characteristic in cellular structures for load bearing and energy absorption applications that must respond uniformly under external loads in all orientations. Triply periodic minimal surface (TPMS) cellular structures are attracting much attention for such applications due to their demonstrated high performance, tailorable properties, and open cell architecture. However, TPMS structures usually display stiffness anisotropy. In this work, new design strategies are presented for isotropic TPMS-based cellular structures, revealing a large available design space in terms of relative density and relative stiffness. The first design strategy arranges TPMS-based cells in a Simple-Cubic/Face-Centred Cubic inspired pattern, resulting in reduced elastic anisotropy. Two parametric optimisation approaches involving level-set mid-surface offsetting and the functional grading of relative density are then applied in a second step to eliminate any residual elastic anisotropy. Anisotropy is characterised through finite element analysis using the Zener ratio. Four families of cells are optimised, each based on a different TPMS unit cell, and then additively manufactured using the material extrusion process with polylactic acid. Finally, experimental quasi-static compressive tests are conducted to characterise stiffness, strength, and energy absorption properties. Optimised designs are tested in three crystal orientations ([001], [101] and [111]) and manufactured in three orthogonal print orientations. The Primitive TPMS-based design is the stiffest of the four designs reaching 64.4% of the Hashin-Shtrikman upper bound of bulk modulus at 20% relative density. Experimental results validate all four optimised cell designs for elastic isotropy and indicate that all the cell designs are also isotropic in terms of crushing strength and energy absorption.

A generative adversarial networks based methodology for imbalanced multidimensional time-series augmentation of complex electromechanical systems

Multidimensional monitoring time-series of complex electromechanical systems (CESs) plays a foundational role in data-based state management, maintenance, and performance adjustment. However, it is still a challenging work to extract valuable and complete information due to the imbalanced data. To address this issue, a methodology called GAN4MTS (Generative Adversarial Networks for Multidimensional Time-Series) that generates synthetic data closely mimicking the characteristics of real data was proposed, thus directly tackling the problem of data imbalance. First, the uniqueness of multidimensional time series of CESs was analyzed to identify the requirements for data augmentation and to define the problem formulations. Second, the architecture and loss functions of GAN4MTS model were designed based on generative adversarial networks and three specific constraints. Finally, the effectiveness of the proposed work was validated through comparative analysis. Furthermore, the intrinsic mechanisms of data augmentation in enhancing the model capabilities were discussed. The proposed methodology serves as a comprehensive technical solution for data augmentation, enabling the generation of high-quality synthetic data that adheres to the constraints of multidimensional time series in CESs. Additionally, as an open architecture model, this work provides novel methods for time-series data augmentation, addressing the issues of low accuracy and limited model generalization in state classification, identification, and prediction tasks for CESs caused by the presence of highly imbalanced data.

Optimizing Transportation Services: Using TOGAF for Efficiency and Quality

In the rapidly expanding transportation industry, it is crucial to make focused and coordinated efforts to improve services with maximum efficiency. This paper seeks to explore the optimization of the Enterprise Architecture approach to effectively attain the primary objectives of the transportation industry, specifically the enhancement of service quality. The main emphasis is on implementing the enterprise architecture methodology of the open group architecture framework on a strategic basis. This paper examines how Enterprise Architecture can offer systematic and quantifiable solutions by identifying problems in infrastructure and operational processes. The research aims to provide comprehensive insights into how the Enterprise Architecture concept can optimize operational efficiency and streamline processes in the provision of transportation services. By implementing TOGAF, it is expected that the integration of systems will be seamless, technology usage will be optimized, and customer experiences will be improved. To summarize, this paper demonstrates the desire to improve transportation services. It explains how Enterprise Architecture methods, specifically within the TOGAF framework, can directly lead to advantages such as increased operational efficiency and improved service quality. This paper aims to be easily understood by a wide range of readers, including management, Information Technology professionals, and other stakeholders in the transportation industry. It avoids using overly technical language to ensure accessibility and comprehensibility.

Development of Diagnosis System for Rolling Bearings Faults on Real Time Based on FPGA

The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

Engineered oxidation states in NiCo2O4@CeO2 nanourchin architectures with abundant oxygen vacancies for enhanced oxygen evolution reaction performance

Sluggish oxygen evolution reaction (OER) as the half reaction of water electrolysis hindered the production of clean hydrogen, necessitates a cost-effective electrocatalyst. Spinel nickel cobaltite (NiCo2O4), characterized by its lower cost, flexibility in oxidation states of cations, and stable spinel structure, holds promise as an OER electrocatalyst. However, there is still room for improvement in terms of its intrinsic electrochemical activity, conductivity, and active surface area. Inspired by the flexible valence states of Ni and Co, in this study, CeO2 was introduced into NiCo2O4 for further electronic structure adjustment. The result was the synthesis of an urchin-like NiCo2O4@CeO2 (NCOC), and the elucidation of its formation mechanisms, expressed conclusively through reaction formulas. NCOC, characterized by an open architecture and a rough surface, ensures rapid mass transportation and charge diffusion. Within NCOC, numerous oxophilic Ce3+ sites with oxygen vacancies construct electron transport pathways, accelerating charge-transfer. This simultaneously induces an increase in the oxidation states and proportions of Ni3+ and Co3+ in NCOC, reinforcing the covalence of Ni–O and Co–O bonds, expediting the ad/desorption of intermediates during OER. The electronic structure variations of NCOC were further verified through density functional theory (DFT) analyses, revealing alterations in adsorption configurations of intermediates and a decrease in adsorption free energies for intermediates on NCOC during OER. In practical terms, NCOC demonstrated a low overpotential of 228 mV at a current density of 10 mA cm−2 under 1 M KOH, maintaining long-term electrochemical stability for 100 h. Overall, this study provides a universal approach for designing highly cost-efficient electrocatalysts for OER.

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

Enhanced redox kinetics of Prussian blue analogues for superior electrochemical deionization performance.

Prussian blue analogues (PBAs), representing the typical faradaic electrode materials for efficient capacitive deionization (CDI) due to their open architecture and high capacity, have been plagued by kinetics issues, leading to insufficient utilization of active sites and poor structure stability. Herein, to address the conflict issue between desalination capacity and stability due to mismatched ionic and electronic kinetics for the PBA-based electrodes, a rational design, including Mn substitution and polypyrrole (ppy) connection, has been proposed for the nickel hexacyanoferrate (Mn-NiHCF/ppy), serving as a model case. Particularly, the theoretical calculation manifests the reduced bandgap and energy barrier for ionic diffusion after Mn substitution, combined with the increased electronic conductivity and integrity through ppy connecting, resulting in enhanced redox kinetics and boosted desalination performance. Specifically, the optimized Mn-NiHCF/ppy demonstrates a remarkable desalination capacity of 51.8 mg g-1 at 1.2 V, accompanied by a high charge efficiency of 81%, and excellent cycling stability without obvious degradation up to 50 cycles, outperforming other related materials. Overall, our concept shown herein provides insights into the design of advanced faradaic electrode materials for high-performance CDI.

Kolmogorov-Smirnov based method for detecting black hole attack in vehicular ad-hoc networks

The security of ad hoc networks continues to pose a major challenge in today's digital age. Threatened by unscrupulous users, especially in decentralized and open architectures, ad-hoc vehicular networks make protection against malicious attacks a difficult task. Vehicular networks, a specific subset of ad hoc networks, inherit security vulnerabilities from their parent network. One of the best-documented attacks is the black hole, in which a malicious user attempts to manipulate the routing mechanism to gain unauthorized access to the route, then intercepts and discards data packets. The result is an interruption of service to the intended recipient. In our research, we propose a black hole attack detection system based on the Kolmogorov-Smirnov statistical method. This system is designed to identify communication disruptions caused by such attacks without requiring structural modifications to the routing protocol. The results show that the proposed method can detect the presence of the attack by monitoring network activity. The results are tested and validated using SUMO (Simulation of Urban MObility) to generate microscopic road traffic, and NS-2 (Network Simulator) to generate network communication.

The hybrid augmented intelligence open innovation platform's architecture and scheme, which combine interconnected virtual and actual systems

The hybrid augmented intelligence open innovation platform's architecture and scheme, which combine interconnected virtual and actual systems

Design of RISCV processor using verilog

The main goal of this paper is to develop a 32-bit pipelined processor with several clock domains based on the RISCV (open source RV32I Version 2.0) ISA. To minimize the complexity of the instruction set and speed up the execution time per instruction, a RISC (Reduced Instruction Set Computer) processor that uses less hardware than a CISC (Complex Instruction Set Computer) is used. Furthermore, this paper constructed this processor with five levels of pipelining with the aid of necessary block diagrams, and all of the processes are well described. In this paper, a RISCV processor is designed and simulated using Verilog. The design of the RISCV processor provides an alternative for software and hardware design to the computer designers as it provides free and open instruction set architecture (ISA). Besides, the designed RISCV processor will be using 5-stage pipeline techniques to improve the overall performance of the processor. This system is started by implementing several main modules, such as alu, aludec, maindec, imem, dmem, regfile, pc_mux, result_mux, pipeline register (IF/ID, ID/IEx, IEx/IMem, and IMem/IW), forwardMuxA, and forwardMuxB. Besides, a hazard unit is implemented into the design to mitigate hazard conditions. The functionality of these modules was simulated and verified by using Xilinx Vivado software.

Embedded open platform communications unified architecture server-based online error motion measurement system

Embedded open platform communications unified architecture server-based online error motion measurement system

GET WITH THE PROGRAM: PROGRAMMATIC ADVERTISING AND THE DATAFICATION OF PODCAST AUDIENCES

The podcasting landscape has been reshaped in the past several years by acquisitions and mergers among players in the industry. Major platform services like Spotify, SiriusXM, iHeartMedia, Google, and Apple have all attempted to more closely bind consumers to their proprietary services, threatening the open architecture of distribution via RSS. While control and monetization of intellectual property is one key driver of platformization in podcasting, another key institutional shift is being accelerated these changes: the datafication of the audience. In short, datafication involves the quantification of human activity to enable surveillance, prediction, and mass customization of advertising. In this paper, I explore one significant impact of widescale platformization within podcasting: the emergence of programmatic advertising markets. By essentially “listening in” to these industry discourses about podcast advertising (in podcasts and in the Podcast Upfront presentations from Spring 2022), this essay outlines the importance of platform-to-platform data transactions and highlights the resulting shifts in the podcasting ecosystem: away from the intimate, relationship-driven ethos of the medium and toward a quantitative, surveillance-driven ecosystem.

ADVANCED AUTOMATIC SYSTEM FOR REMOTE CONTROL IN THE OIL AND GAS INDUSTRY

The purpose of this work is to design and implement a cascade control system of the level with the flow rate, with remote supervision, in an industrial type installation, located in the Laboratory of Measurement Techniques, Department of Automation, Computers and Electronics, within the Faculty of Mechanical and Electrical Engineering. The operating parameters of the installation are as follows: suction pressure: 0 – 2 bar; discharge pressure: 0 -10 bar; water flow: 0-30 m3/h; transported liquid: water. The automation system is structured on three hierarchical levels: level 1: the instrumentation mounted on the installation (transducers, signaling devices, etc.); level 2: includes the TGA System; level 3: constitutes the upper hierarchical level and includes the HMI (Human Machine Interface). The TGA system is a computerized system that ensures the monitoring, command and control functions of all the stand's component elements. It contains a programmable logic controller PLC 1214 – Siemens, which communicates via an Ethernet IP network with an operator interface and with the network of the Faculty of Mechanical and Electrical Engineering, which allows remote control of the installation. It also includes the protection system in case of failure, which involves stopping the rotating equipment, isolating the pipes by closing the control valve and alarming the operators. The HMI (Human Machine Interface) interface presents 3 options on the start page, from where the operator can select which application he wants to test: flow control loop application; level adjustment loop application; cascade adjustment loop application. The HMI equipment integrates SCADA functions, database update, evolution curves of analog quantities, sound and monitor alarm system, keyboard command, periodic records, event records, alarm records and technological schemes at the human-machine interface. The entire software is made in an open architecture, recommended by ISO norms. The PLC communicates in the network by adopting the protocol TCP/IP. An executable program used for the real-time application program was developed.

Data driven ecosystems for health and prevention: a shared decision-making model for local data governance.

Summary: As our healthcare system is in transition towards a more personalized preventive healthcare approach, enabled by the digital transformation, we aim to design a harmonized data driven population health approach, shared by local healthcare actors and wherein citizens have trust through a local data governance.
 Background: Our healthcare system is in transition towards a more personalized preventive healthcare approach, which is enabled by the digital transformation. To enable this transition, there is a need for a harmonized data driven population health approach, shared by local healthcare actors. To create this based on citizen-generated data, it is crucial that citizens have trust in sharing their data, which can be enabled by a local data governance. The trade-off or interaction between local governance of these data and cashing in on health gains based on that data should thus be investigated for all stakeholders in the quadruple helix (citizens, healthcare providers, governments, and companies). 
 Objectives: The aim of this research was to consult primary care zones, general practitioners, pharmacists, and local governments in Flanders on their current way of working, more specifically the use of data in their healthcare prevention strategy. We aimed to investigate what data they are already using, the data sources and the reason of data use. We also examined the current gaps in their data strategy and what they wanted to improve. 
 Design: The project has been externally funded by VLOCA and EMPOWERCARE Interreg 2 Seas. Online consultations were conducted with in total 16 participants from a general practitioner center, 3 primary care zones in Flanders, the overarching body of the primary care zones, a mutuality, a pharmacy network and 2 local governments. In addition, we conducted an online focus group with 25 participants within the healthcare sector, with the participants partially overlapping with the participants from the online consultations. 
 Results: The online consultations and focus group with participants from the primary healthcare sector in Flanders give an insight on the current data driven approaches in healthcare prevention. Participants indicate data is valuable in healthcare prevention, such as targeted interventions and population health management. Some participants are already working on a data driven approach in healthcare prevention. The majority, however, indicates that there is a lack on real world data relating to lifestyle.
 Conclusion: The present study gives an insight on the current use of data in healthcare prevention in Flanders. The current research informs us there is a need for a harmonized healthcare data driven approach. Moreover, it provides us insights on the trust-relationship between citizens and data, which is a necessity when data driven approaches are implemented. As next steps, the results of these consultations will be combined in an open data architecture on prevention and lifestyle. Moreover, we will further investigate how this research relates to the international context.

The Engineering Practice of Industrial Heritage Renovation and Renewal from the Perspective of Open Architecture -- A Case Study of a Machine Tool Factory

The Engineering Practice of Industrial Heritage Renovation and Renewal from the Perspective of Open Architecture -- A Case Study of a Machine Tool Factory

PowerPulse: Power energy chat model with LLaMA model fine‐tuned on Chinese and power sector domain knowledge

AbstractRecently, large‐scale language models (LLMs) such as chat generative pre‐trained transformer and generative pre‐trained transformer 4 have demonstrated remarkable performance in the general domain. However, inadaptability in a particular domain has led to hallucination for these LLMs when responding in specific domain contexts. The issue has attracted widespread attention, existing domain‐centered fine‐tuning efforts have predominantly focused on sectors like medical, financial, and legal, leaving critical areas such as power energy relatively unexplored. To bridge this gap, this paper introduces a novel power energy chat model called PowerPulse. Built upon the open and efficient foundation language models (LLaMA) architecture, PowerPulse is fine‐tuned specifically on Chinese Power Sector Domain Knowledge. This work marks the inaugural application of the LLaMA model in the field of power energy. By leveraging pertinent pre‐training data and instruction fine‐tuning datasets tailored for the power energy domain, the PowerPulse model showcases exceptional performance in tasks such as text generation, summary extraction, and topic classification. Experimental results validate the efficacy of the PowerPulse model, making significant contributions to the advancement of specialized language models in specific domains.

Legal Challenges for International Fintech Startups

Financial technology (Fintech) startups looking to scale globally often face complex regulations across jurisdictions inhibiting market access and rapid customer adoption. This research investigates legal obstacles frequently encountering international Fintech platforms through qualitative research methods. It aims to highlight recurring concerns like data sovereignty, interoperability, licensing barriers and talent acquisition issues for innovators attempting multi-country service availability. Though results indicate regulatory principles remain largely unharmonized currently, recommendations urge sectoral cooperation advancing unified standards spanning data management, oversight rules and open banking architectures. Phasing proportionate need-based compliances and controlled sandboxes can enable clearer policy signaling benefiting responsibly expanding startups. Effective policy incentives and dispute assistance schemes may ease market entry and sustain trust lacking today. However, truly unraveling Fintech possibilities requires expediting progressive, agile and internationally integrated legal systems protecting consumers while fostering inclusive innovation ecosystems.

Golden Search Optimization based adaptive and diagonal kernel convolution neural network for disease prediction and securing IoT data in cloud

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency