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A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks
Get PDFData Availability Statement: In this research, experimental data were not recorded.Copyright © 2024 by the authors. Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.This research received no external funding
Towards Optimized Multi-Channel Modulo-Adcs: Moduli Selection Strategies And Bit Depth Analysis
Get PDFThis paper presents a theoretical analysis of multi-channel modulo analog-to-digital converters (ADCs) for high-dynamic range sampling under bounded noise. In particular, we derive the maximum error tolerance in terms of ADC dynamic range, signal dynamic range, and channel number. Additionally, we present closed-form expressions for ADC thresholds, ensuring near-optimal error resilience, and analyzing the minimal bit-depth needed for stable recovery. Compared to single-channel modulo ADCs, our approach achieves superior error tolerance with reduced sampling rates. Moreover, it demands a minor bit rate increase compared to conventional ADCs but operates with a significantly smaller ADC dynamic range
Seismic Resilience of Interdependent Built Environment for Integrating Structural Health Monitoring and Emerging Technologies in Decision-Making
No full textThis article results from the joint work of the members of IABSE TG.8 “Design Requirements for Infrastructure Resilience” of Working Commission C1.Data Availability Statement:
The data that support the findings of this study are available upon reasonable request. Contact the corresponding author for assistance.The functionality of interdependent infrastructure and resilience to seismic hazards has become a topic of importance across the world. The ability to optimize an engineered solution and support informed decision-making is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this article, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-the-science algorithms within the interdependent networked community resilience modeling environment (IN-CORE). The methodology includes a six-step chained/linked process consists of: (a) community data and information, (b) spatial seismic hazard analysis using next-generation attenuation, (c) interdependent community model development, (d) physical damage and functionality analysis, (e) socio-economic impact analysis and (f) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the discussion of results, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting an holistic framework on resilience that includes SHM and ET will allow reducing uncertainties that are still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics
Social Enterprise Growth by Design: Using design to incubate and accelerate social enterprises
Get PDFPurpose:
The paper aims to explore the roles and impact of design in incubating and accelerating social enterprises. It aims to understand design’s influence on social enterprise ecosystems and in improving outcomes for social enterprises.
Design/methodology/approach:
The study used an exploratory, qualitative approach, using case studies and interviews. The comparative case-study methodology was applied to evaluate the influence of design on the development of social enterprises in the UK and South Korea and identify critical issues in their utilisation of design. Empirical data included: in-depth case studies of design utilisation practices (UK = 6; South Korea = 15) and design applications (UK = 2; South Korea = 2) for the growth of social enterprise and its ecosystem; 27 social enterprise/design experts (UK = 17; South Korea = 10); and 22 social enterprises (UK = 12; South Korea = 10). Content and thematic analysis were used to synthesise the findings.
Findings:
Findings demonstrate the differing influences of design on social enterprise, from improving products/services and business models to enhancing social enterprise ecosystem support and networks. Future directions are suggested for applying design for social enterprise growth, business stage development and systematising interactions between the social enterprise and design sectors.
Research limitations/implications:
The research is based on case studies from only two countries. Further, the adoption of working definitions of social enterprise in the countries may result in the research underestimating the heterogeneity of social enterprise.
Practical implications:
The findings contribute to optimising efficient ecosystem development to improve social enterprise competitiveness and innovation.
Originality/value:
This paper establishes a research foundation on design for social enterprise, offering theoretical and practical insights into its impact on growth
Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network
Get PDFJEL Classification: C45; D53; E42; G10Availability of data and materials:
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.Brunel Research Initiative and Enterprise Fund to FD
Ban mining, ban dining? Re(examining) the policy and practice of ‘militarised conservationism’ on ASM operations
Get PDFThe artisanal and small-scale mining (ASM) frontier continues to advance in most mineral-endowed countries due to rising unemployment and general economic decline particularly in rural communities. The sector, however, is often viewed in a negative light because it is highly environmentally destructive. In seeking to address the environmental challenges, many governments have, on occasion, actioned military strategies aimed at presenting facets of ‘sanitisation’ to a highly informal industry that has historically been tagged as an enemy of the environment. This study examines such ‘mining vs. environment’ discourses that have resulted in military crackdowns on ASM operations in parts of sub-Saharan Africa. Overall, the findings bust the ‘myth’ of the appropriateness of military interventions regarding ASM operations. Offering insights into the livelihood dimensions of ASM operations, we submit that our understanding of mining-ban failures can be assisted by an understanding of the broader geographical, socio-economic, technological, and institutional antecedents that combine to allow illegal mining operations to proliferate
Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior–Posterior Radiograph
Get PDFData Availability Statement:
Dataset available on request from the authors.Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior–posterior (AP) radiography image. We introduce an encoder–decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks.This research received no external funding
Application-Layer Anomaly Detection Leveraging Time-Series Physical Semantics in CAN-FD Vehicle Networks
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The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the need for confidentiality of application layer protocols for car companies.The Controller Area Network with Flexible Data-Rate (CAN-FD) bus is the predominant in-vehicle network protocol, responsible for transmitting crucial application semantic signals. Due to the absence of security measures, CAN-FD is vulnerable to numerous cyber threats, particularly those altering its authentic physical values. This paper introduces Physical Semantics-Enhanced Anomaly Detection (PSEAD) for CAN-FD networks. Our framework effectively extracts and standardizes the genuine physical meaning features present in the message data fields. The implementation involves a Long Short-Term Memory (LSTM) network augmented with a self-attention mechanism, thereby enabling the unsupervised capture of temporal information within high-dimensional data. Consequently, this approach fully exploits contextual information within the physical meaning features. In contrast to the non-physical semantics-aware whole frame combination detection method, our approach is more adept at harnessing the physical significance inherent in each segment of the message. This enhancement results in improved accuracy and interpretability of anomaly detection. Experimental results demonstrate that our method achieves a mere 0.64% misclassification rate for challenging-to-detect replay attacks and zero misclassifications for DoS, fuzzing, and spoofing attacks. The accuracy has been enhanced by over 4% in comparison to existing methods that rely on byte-level data field characterization at the data link layer.National Natural Science Foundation of China under Grants 52202494 and 52202495
Techno-economic feasibility study of coupling low-temperature evaporation desalination plant with advanced pressurized water reactor
No full textData availability:
Data will be made available on request.The increasing demand for freshwater necessitates sustainable desalination solutions, and nuclear power plants offer a promising avenue by utilizing their low-grade waste heat. This study assesses a techno-economic feasibility of coupling a 5 MWth low-temperature evaporation plant with a UAE-based Advanced Pressurized Water Reactor (APR1400). The system addresses freshwater demand, aligning with sustainability goals through low-grade heat utilization. The investigation explores three extraction points for low-grade heat steam, with temperatures ranging from 80 °C to 130 °C. Thermodynamic evaluations using DE-TOP illustrate power requirements and losses, considering variations in maximum brine temperature from 50 °C to 65 °C. Economic analysis using DEEP estimates water production costs. Findings reveal negligible variances in power plant parameters and a minimal reduction in overall efficiency (<0.5 %). The power loss ratio increases proportionally (10 % to 18.6 %) with higher-temperature heat extraction, while the total power requirements for the desalination plant rises by around 30 %, with a negligible power output reduction ratios (0.03 % to 0.07 %). A consistent linear correlation between water production rate and maximum brine temperature doubles water production (∼900 to 1800 m3/day). Applying multiple extraction points across low-grade heat sources demonstrates scalability, reaching three times that of single-point extraction, with marginal increases in power requirements and losses, while maintaining the power reduction ratio below 0.15 %. Economic feasibility indicates competitive water production costs, ranging from 1.53 to 0.87 $/m3 for desalination capacities between 900 and 5400 m3/day. This study provides valuable insights into sustainable water production at the nexus of nuclear energy and desalination, with implications for necessary policy intervention.The Research Institute of Science and Engineering (RISE) at the University of Sharjah, Nuclear Energy System Simulation and Safety (NE3S) research group supported and funded this research
Exploring the contribution of lifestyle to the impact of education on the risk of cancer through Mendelian randomization analysis
Get PDFData availability: UK Biobank individual level data used in this work can be accessed after applying for access at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Genetic association data are publicly available in the original studies.Supplementary Information is available online at: https://link.springer.com/article/10.1038/s41598-024-54259-7#Sec17 .Educational attainment (EA) has been linked to the risk of several types of cancer, despite having no expected direct biological connection. In this paper, we investigate the mediating role of alcohol consumption, smoking, vegetable consumption, fruit consumption and body mass index (BMI) in explaining the effect of EA on 7 cancer groupings. Large-scale genome wide association study (GWAS) results were used to construct the genetic instrument for EA and the lifestyle factors. We conducted GWAS in the UK Biobank sample in up to 335,024 individuals to obtain genetic association data for the cancer outcomes. Univariable and multivariable two-sample Mendelian randomization (MR) analyses and mediation analyses were then conducted to explore the causal effect and mediating proportions of these relations. MR mediation analysis revealed that reduced lifetime smoking index accounted for 81.7% (49.1% to 100%) of the protective effect of higher EA on lower respiratory cancer. Moreover, the effect of higher EA on lower respiratory cancer was mediated through vegetable consumption by 10.2% (4.4% to 15.9%). We found genetic evidence that the effect of EA on groups of cancer is due to behavioural changes in avoiding well established risk factors such as smoking and vegetable consuming.Brunel Research Initiative and Enterprise Fund to FD. This research has been conducted using the UK Biobank Resource under project 44566 (https://www.ukbiobank.ac.uk/2018/12/genetic-and-non-genetic-factors-able-to-predict-and-modify-the-risk-of-different-types-of-cancer/)
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