Understanding the End-Users and Technical Requirements for Real-Time Streaming Data Analytics and Visualisation

Today's digital world deals with tremendous amounts of data with high complexity and velocity. The data being generated continuously from a variety of sources at very high speeds is considered as streaming data. The number of technologies to ingest, explore, analyze, and visualize the streaming data is growing rapidly across many industries. However, the use of real-time technologies in the Architecture, Engineering Construction (AEC) sector is still at its early stages of development and further research is needed to make the tools truly commercial. The AEC is a data-intensive, project-based industry that relies on real-time information to help projects stay on schedule and enable companies to cut costs. The technologies supporting real-time data capability in the AEC sector must satisfy several key functional and nonfunctional requirements. This paper aims to identify and prioritize both end-users and technical requirements for AEC businesses when adopting a real-time streaming data analytics and visualization technology in their production control rooms. To this aim, an expert questionnaire survey is conducted to investigate the requirements in four distinct areas, namely: (i) data collection and ingestion, (ii) stream processing, (iii) real-time analytics, and (iv) presentation and visualization. A five-level Likert scale is used in the questionnaire to allow respondents to indicate their perceptions on the importance of each of the identified requirements. The survey is distributed using Qualtrics online tool to over twenty organizations all over the world and the responses are analyzed using SPSS statistical software. The findings of the study reveal that 'interactivity' and 'user assistance' are the most important requirements from the end-users' perspective, whereas 'fast data processing', 'real-time reporting', 'scalable data processing', and 'dashboard connectivity' are the most important requirements from the technology developers' perspective. The findings of this research can help stakeholders and researchers get a better picture of the current challenges facing the AEC sector in terms of streaming data analytics and provide insight into the possible directions of future technological changes

A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines

Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA

Current practice and challenges towards handling uncertainty for effective outcomes in maintenance

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study. The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle

Prevalence of Metabolic Syndrome in an Adult Urban Population of the West of Iran

Objectives. We determine the prevalence of the metabolic syndrome in an urban population of Zanjan, a province located to the west of Tehran. Methods. Randomly selected adults >20 years were studied using stratified sampling. Target study sample was 2941 (1396 males and 1545 females). Metabolic syndrome was diagnosed using Adult Treatment Panel-III (ATP-III) guidelines when any three of the following were present: central obesity, raised triglycerides ≥150 mg/dl, low high-density lipoprotein (HDL) cholesterol, blood pressure ≥ 130/ ≥ 85 mm Hg, and diabetes or fasting plasma glucose (FPG) ≥ 100 mg/dl. Results. Metabolic syndrome was present in 697 (23.7%) subjects (CI 95%:22%–25%, P = .001), prevalence was 23.1% in men and 24.4% in women (P : .4). The prevalence increased from 7.5% in the population younger than 30 y to 45.6% in ages more than 50 years. Low HDL was the most common metabolic abnormality in both sexes. Most of those with metabolic syndrome had three components of the syndrome (75.6%), 170 subjects (24.4%) had four and none had five components simultaneously. The prevalence of obesity (BMI ≥ 30 kg/m2), hypercholesterolemia (≥200 mg/dl) and high LDL cholesterol (≥130 mg/dl) was greater in the metabolic syndrome group than normal subjects (P = .00). Conclusions. There is a high prevalence of metabolic syndrome in this urban population of the northern west of Iran. Focus of cardiovascular prevention should be undertaken in this area

The anticancer agent prodigiosin is not a multidrug resistance protein substrate

The brilliant red pigments prodiginines are natural secondary metabolites that are produced by select species of Gram-negative and Gram-positive bacteria. These molecules have received significant attention due to their reported antibacterial, antifungal, immunosuppressive, and anticancer activities. In this study, a Serratia marcescens SER1 strain was isolated and verified using 16s rDNA. The prodigiosin was purified using silica chromatography and was analyzed by 1H-NMR spectroscopy. The cell cytotoxic effects of

Sustainable Edge Node Computing Deployments in Distributed Manufacturing Systems

The advancement of mobile internet technology has created opportunities for integrating the Industrial Internet of Things (IIoT) and edge computing in smart manufacturing. These sustainable technologies enable intelligent devices to achieve high-performance computing with minimal latency. This paper introduces a novel approach to deploy edge computing nodes in smart manufacturing environments at a low cost. However, the intricate interactions among network sensors, equipment, service levels, and network topologies in smart manufacturing systems pose challenges to node deployment. To address this, the proposed sustainable game theory method identifies the optimal edge computing node for deployment to attain the desired outcome. Additionally, the standard design of Software Defined Network (SDN) in conjunction with edge computing serves as forwarding switches to enhance overall computing services. Simulations demonstrate the effectiveness of this approach in reducing network delay and deployment costs associated with computing resources. Given the significance of sustainability, cost efficiency plays a critical role in establishing resilient edge networks. Our numerical and simulation results validate that the proposed scheme surpasses existing techniques like shortest estimated latency first (SELF), shortest estimated buffer first (SEBF), and random deployment (RD) in minimizing the total cost of deploying edge nodes, network delay, packet loss, and energy consumption

Failure mode, effects and criticality analysis of railway rolling stock assets

International audienceIn recent years, a great deal of attention has been paid to the analysis of failure modes occurring in railway infrastructure assets, e.g. bridges, rail tracks, sleepers, etc. However, there have been few attempts by researchers to develop failure criticality assessment models for railway rolling stock. A rolling stock failure may be very costly in terms of monetary loss and/or passenger inconvenience. It may also cause delays to train services or even result in catastrophic derailment accidents. In this paper, a failure mode, effects and criticality analysis approach is presented to identify, analyse and evaluate the causes and consequences of rolling stock failures. The most critical failure modes with respect to both reliability and economics are identified and potential protective measures are then proposed to prevent their recurrence. For the purpose of illustrating the proposed approach, the model is applied to a rolling stock passenger door system. The data required for the study are collected from both the literature and the maintenance information system available in a Scottish train operating company. The results of this study can be used to plan a cost-effective preventive maintenance programme for different components of rolling stock