Stressors in the multicultural construction working environment

Due to the dynamic and complex nature of the construction industry, construction workers are often exposed to a range of stressors which are causative factors for mental health problems. Simultaneously, intercultural contact between workers in a multicultural working environment may aggravate mental health issues. A better understanding of stressors can contribute to the development of targeted measures for mental health prevention and promotion. Therefore, this study aims to investigate the correlation between stressors and mental health for construction workers in a culturally diverse working environment. Data were collected using questionnaires from 252 construction workers in Australia. The Pearson correlation analysis was used to analyse the collected data. The results revealed the significant correlations between stressors and mental health outcomes and indicated the most significant stressors from work, personal and cultural domains. The findings provide valuable insights for practitioners and policymakers on the development of mental health interventions for construction workforce in a multicultural context. Researchers could also benefit from an in-depth comprehension on the causative factors of psychological issues in the construction industry

A Framework for Evaluating Security in the Presence of Signal Injection Attacks

Sensors are embedded in security-critical applications from medical devices to nuclear power plants, but their outputs can be spoofed through electromagnetic and other types of signals transmitted by attackers at a distance. To address the lack of a unifying framework for evaluating the effects of such transmissions, we introduce a system and threat model for signal injection attacks. We further define the concepts of existential, selective, and universal security, which address attacker goals from mere disruptions of the sensor readings to precise waveform injections. Moreover, we introduce an algorithm which allows circuit designers to concretely calculate the security level of real systems. Finally, we apply our definitions and algorithm in practice using measurements of injections against a smartphone microphone, and analyze the demodulation characteristics of commercial Analog-to-Digital Converters (ADCs). Overall, our work highlights the importance of evaluating the susceptibility of systems against signal injection attacks, and introduces both the terminology and the methodology to do so.Comment: This article is the extended technical report version of the paper presented at ESORICS 2019, 24th European Symposium on Research in Computer Security (ESORICS), Luxembourg, Luxembourg, September 201

A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor

Combined X-ray diffraction and absorption tomography using a conical shell beam

We combine diffraction and absorption tomography by raster scanning samples through a hollow cone of pseudo monochromatic X-rays with a mean energy of 58.4 keV. A single image intensifier takes 90x90 (x,y) snapshots during the scan. We demonstrate a proof-of-principle of our technique using a heterogeneous three-dimensional (x,y,z) phantom (90x90x170 mm3) comprised of different material phases, i.e., copper and sodium chlorate. Each snapshot enables the simultaneous measurement of absorption contrast and diffracted flux. The axial resolution was ~1 mm along the (x,y) orthogonal scan directions and ~7 mm along the z-axis. The tomosynthesis of diffracted flux measurements enable the calculation of d-spacing values with ~0.1 Å full width at half maximum (FWHM) at ~2 Å. Thus the identified materials may be color-coded in the absorption optical sections. Characterization of specific material phases is of particular interest in security screening for the identification of narcotics and a wide range of homemade explosives concealed within complex "everyday objects." Other potential application areas include process control and biological imaging

Solar radiation forecasting by Pearson correlation using LSTM neural network and ANFIS method: application in the west-central Jordan

none6siSolar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.Topical Collection "Computer Vision, Deep Learning and Machine Learning with Applications"openHossam Fraihat, Amneh A. Almbaideen, Abdullah Al-Odienat, Bassam Al-Naami, Roberto De Fazio, Paolo ViscontiFraihat, Hossam; Almbaideen, Amneh A.; Al-Odienat, Abdullah; Al-Naami, Bassam; DE FAZIO, Roberto; Visconti, Paol

An Evaluation Method for Unsupervised Anomaly Detection Algorithms

In data mining, anomaly detection aims at identifying the observations which do not conform to an expected behavior. To date, a large number of techniques for anomaly detection have been proposed and developed. These techniques have been successfully applied to many real world applications such as fraud detection for credit cards and intrusion detection in network security. However, there are very little research relating to the method for evaluating the goodness of unsupervised anomaly detection techniques. In this paper, the authors introduce a method for evaluating the performance of unsupervised anomaly detection techniques. The method is based on the application of internal validation metrics in clustering algorithms to anomaly detection. The experiments were conducted on a number of benchmarking datasets. The results are compared with the result of a recent proposed approach that shows that some proposed metrics are very consistent when being used to evaluate the performance of unsupervised anomaly detection algorithms