Publications
177 results found
Magnusdottir EH, Johannsdottir KR, Majumdar A, et al., 2022, Assessing cognitive workload using cardiovascular measures and voice, Sensors, Vol: 22, Pages: 1-17, ISSN: 1424-8220
Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.
Long F, Carter H, Majumdar A, 2022, Casualty behaviours during incidents involving hazardous materials, Safety Science, Vol: 152, Pages: 105758-105758, ISSN: 0925-7535
PurposeHuman Behaviour during emergency situations is a crucial component of any response. The ability of responders to effectively engage with casualties is critical to ensuring that any instructions given are followed and in doing assist rather than hinder the response. In order to improve the likelihood of this occurring it is essential to understand what drives decision making during emergencies in order to be able to effectively influence these. This paper will seek to establish what these behaviours are likely to be and what is likely to influence these in order to inform responder tactics and training.Design/methodology/approachThis paper seeks to develop a psychological model of casualty behaviour during a hazardous materials evacuation. The study utilises a survey of members of the public evacuated from their homes or places of work due to a fire impacting an ammonia tank in February 2019 at the Ocado distribution warehouse in Andover. The results of this survey were used to validate a hypothesised psychological model utilising Path Analysis.FindingsThe research identifies the importance of recognising the ability of casualties involved in emergency situations to remain rational and utilise information and instructions given to them. The paper highlights the importance which trust plays in engaging with casualties in order to provide effective information and instructions and how trust is constructed of both legitimacy and competency and influenced by the communications of responders. Most crucially the paper identified how trust during an emergency situation is the key driver of whether casualties are likely to co-operate with instructions and emergency responders.Originality/valueThe research utilised real world data to validate findings demonstrating the need for emergency responders to effectively engage with casualties and has implications for both guidance and training of emergency responders in managing casualties.
Rahman AU, Saeed M, Mohammed MA, et al., 2022, Supplier selection through multicriteria decision-making algorithmic approach based on rough approximation of fuzzy hypersoft sets for construction project, Buildings, Vol: 12, Pages: 1-20, ISSN: 2075-5309
The suppliers play a significant role in supply chain management. In supplier selection, factors like market-based exposure, community-based reputation, trust-based status, etc., must be considered, along with the opinions of hired experts. These factors are usually termed as rough information. Most of the literature has disregarded such factors, which may lead to a biased selection. In this study, linguistic variables in terms of triangular fuzzy numbers (TrFn) are used to manage such kind of rough information, then the rough approximations of the fuzzy hypersoft set (FHS-set) are characterized which are capable of handling such informational uncertainties. The FHS-set is more flexible as well as consistent as it tackles the limitation of fuzzy soft sets regarding categorizing parameters into their related sub-classes having their sub-parametric values. Based on these rough approximations, an algorithm is proposed for the optimal selection of suppliers by managing experts’ opinions and rough information collectively in the form of TrFn-based linguistic variables. To have a discrete decision, a signed distance method is employed to transform the TrFn-based opinions of experts into fuzzy grades. The proposed algorithm is corroborated with the help of a multi-criteria decision-making application to choose the best supplier for real estate builders. The beneficial facets of the put forward study are appraised through its structural comparison with few existing related approaches. The presented approach is consistent as it is capable to manage rough information and expert’s opinions about suppliers collectively by using rough approximations of FHS-set.
Abbas T, Ali SF, Mohammed MA, et al., 2022, Deep learning approach based on residual neural network and SVM classifier for driver’s distraction detection, Applied Sciences, Vol: 12, Pages: 6626-6626, ISSN: 2076-3417
In the last decade, distraction detection of a driver gained a lot of significance due to increases in the number of accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed to solve this problem. With the advent of high processing power at cheaper costs, deep learning-based driver distraction detection techniques have shown promising results. The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver. ReSVM is compared with six state-of-the-art approaches on four datasets, namely: State Farm Distracted Driver Detection, Boston University, DrivFace, and FT-UMT. Experiments demonstrate that ReSVM outperforms the existing approaches and achieves a classification accuracy as high as 95.5%. The study also compares ReSVM with its variants on the aforementioned datasets
Lakhan A, Morten Groenli T, Majumdar A, et al., 2022, Potent blockchain-rnabled socket RPC Internet of Healthcare Things (IoHT) framework for medical enterprises, Sensors, Vol: 22, Pages: 4346-4346, ISSN: 1424-8220
Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.
Mastoi Q-U-A, Wah TY, Mohammed MA, et al., 2022, Novel DERMA fusion technique for ECG heartbeat classification, Life, Vol: 12, Pages: 842-842, ISSN: 2075-1729
An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.
Khan MA, Muhammad K, Wang S-H, et al., 2022, Gastrointestinal Diseases Recognition: A Framework of Deep Neural Network and Improved Moth-Crow Optimization with DCCA Fusion, HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, Vol: 12
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- Citations: 8
Faheem Saleem M, Muhammad Adnan Shah S, Nazir T, et al., 2022, Signet ring cell detection from histological images using deep learning, Computers, Materials & Continua, Vol: 72, Pages: 5985-5997, ISSN: 1546-2226
Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area.
Tsai C-Y, Hsu W-H, Lin Y-T, et al., 2022, Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population., J Clin Sleep Med, Vol: 18, Pages: 1003-1012
STUDY OBJECTIVES: Dementia is associated with sleep disorders. However, the relationship between dementia and sleep arousal remains unclear. This study explored the associations among sleep parameters, arousal responses, and risk of mild cognitive impairment (MCI). METHODS: Participants with the chief complaints of memory problems and sleep disorders, from the sleep center database of Taipei Medical University Shuang-Ho Hospital, were screened, and the parameters related to the Cognitive Abilities Screening Instrument, Clinical Dementia Rating, and polysomnography were determined. All examinations were conducted within 6 months and without a particular order. The participants were divided into those without cognitive impairment (Clinical Dementia Rating = 0) and those with MCI (Clinical Dementia Rating = 0.5). Mean comparison, linear regression models, and logistic regression models were employed to investigate the associations among obtained variables. RESULTS: This study included 31 participants without MCI and 37 with MCI (17 with amnestic MCI, 20 with multidomain MCI). Patients with MCI had significantly higher mean values of the spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage than those without MCI. An increased risk of MCI was significantly associated with increased spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage with various adjustments. Significant associations between the Cognitive Abilities Screening Instrument scores and the oximetry parameters and sleep disorder indexes were observed. CONCLUSIONS: Repetitive respiratory events with hypoxia were associated with cognitive dysfunction. Spontaneous arousal, especially in non-rapid eye movement sleep, was related to the risk of MCI. However, additional longitudinal studies are required to confirm their causality. CITATION: Tsai C-Y, Hsu W-H, Lin Y-T, et al. Associations among sleep-disordered breathing, arousal respon
Tsai C-Y, Liu Y-S, Majumdar A, et al., 2022, Association between cyclic variation in the heart rate index and biomarkers of neurodegenerative diseases in obstructive sleep apnea syndrome: A pilot study., J Clin Neurosci, Vol: 98, Pages: 37-44
PURPOSE: Obstructive sleep apnea syndrome (OSAS) has mostly been examined using in-laboratory polysomnography (Lab-PSG), which may overestimate severity. This study compared sleep parameters in different environments and investigated the association between the plasma levels of neurochemical biomarkers and sleep parameters. METHODS: Thirty Taiwanese participants underwent Lab-PSG while wearing a single-lead electrocardiogram patch. Participants' blood samples were obtained in the morning immediately after the recording. Participants wore the patch for the subsequent three nights at home. Sleep disorder indices were calculated, including the apnea-hypopnea index (AHI), chest effort index, and cyclic variation of heart rate index (CVHRI). The 23 eligible participants' derived data were divided into the normal-to-moderate (N-M) group and the severe group according to American Association of Sleep Medicine (AASM) guidelines (Lab-PSG) and the recommendations of a previous study (Rooti Rx). Spearman's correlation was used to examine the correlations between sleep parameters and neurochemical biomarker levels. RESULTS: The mean T-Tau protein level was positively correlated with the home-based CVHRI (r = 0.53, p < 0.05), whereas no significant correlation was noted between hospital-based CVHRI and the mean T-tau protein level (r = 0.25, p = 0.25). The home-based data revealed that the mean T-Tau protein level in the severe group was significantly higher than that in the N-M group (severe group: 24.75 ± 6.16 pg/mL, N-M group: 19.65 ± 3.90 pg/mL; p < 0.05). Furthermore, the mean in-hospital CVHRI was higher than the mean at-home values (12.16 ± 13.66 events/h). CONCLUSION: Severe OSAS patients classified by home-based CVHRI demonstrated the higher T-Tau protein level, and CVHRI varied in different sleep environments.
Lakhan A, Sodhro AH, Majumdar A, et al., 2022, A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks, Sensors (Basel, Switzerland), Vol: 22, ISSN: 1424-8220
Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.
Aqeel A, Hassan A, Khan MA, et al., 2022, A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease., Sensors (Basel), Vol: 22
The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.
Kumar A, Arora HC, Kapoor NR, et al., 2022, Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models, SUSTAINABILITY, Vol: 14
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- Citations: 26
Majumdar A, Manole I, Nalty R, 2022, Analysis of Port Accidents and Calibration of Heinrich’s Pyramid, Transportation Research Record: Journal of the Transportation Research Board, Vol: 2676, Pages: 476-489, ISSN: 0361-1981
<jats:p> Academics and the maritime industry have used the Heinrich Pyramid for decades to justify overall safety theory, risk assessments, and accident prevention strategies. Most use Heinrich’s original severity ratios (1:29:300) for accident causation development in a factory setting. However, to use the Pyramid effectively and mitigate risks/hazards, it must be calibrated to represent specific industry reality. This paper, for the first time, focuses on calibration of Heinrich’s Pyramid to maritime accident data, using databases from the Marine Accident Investigation Branch of the Department for Transport. This research clusters five years (2013–2017) of accident data, using K-Means clustering on categorical variables and severity levels of accidents, similar logic to Heinrich’s analysis. This approach and descriptive statistics provide new ratios between accident severity classifications for casualties with a ship (CS) and occupational accidents (OAs) separately. Results show that the data do not appear to fall into Heinrich’s Pyramid shape and yield a vastly different and lower ratio to that of Heinrich’s. Especially concerning was that Very Serious and Serious accidents occurred at a 1:5 ratio for CS and 4:1 for OA, very different from Heinrich’s 1:29. Although these results calculated a new ratio, it may not represent reality owing to accident reporting requirements under UK law, a lack of an agreed taxonomy of risk and hazard definitions, and likely underreporting of less severe accidents. This is proven because, in 2017, CS data became pyramid shaped, after a decrease in the number of accidents and a 17% increase in near-misses. </jats:p>
Kumar A, Arora HC, Kumar K, et al., 2022, Prediction of FRCM-Concrete Bond Strength with Machine Learning Approach, SUSTAINABILITY, Vol: 14
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- Citations: 12
Tsai C-Y, Kuan Y-C, Hsu W-H, et al., 2021, Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features., Diagnostics (Basel), Vol: 12, ISSN: 2075-4418
Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.
Liu W-T, Lin S-Y, Tsai C-Y, et al., 2021, Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch., Sensors (Basel), Vol: 21
Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices-namely, the apnoea-hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson's correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients' OSA severity. The high supine percentage of sleep may cause OSA indices' overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.
Cheong H-I, Wu Z, Majumdar A, et al., 2021, One-way coupling of fire and egress modeling for realistic evaluation of evacuation process, Transportation Research Record, Vol: 2675, Pages: 1244-1259, ISSN: 0361-1981
In the discipline of fire engineering, computational simulation tools are used to evaluate the available safe egress time (ASET) and required safe egress time (RSET) of a building fire. ASET and RSET are often analyzed separately, using computational fluid dynamics (CFD) and crowd dynamics, respectively. Although there are advantages to coupling the ASET and RSET analysis to quantify tenability conditions and reevaluate evacuation time within a building, the coupling process is computationally complex, requiring multiple steps. The coupling setup can be time-consuming, particularly when the results are limited to the modeled scenario. In addition, the procedure is not uniform throughout the industry. This paper presents the successful one-way coupling of CFD and crowd dynamics modeling through a new simplified methodology that captures the impact of fractional effective dose (FED) and reduced visibility from smoke on the individual evacuee’s movement and the human interaction. The simulation tools used were Fire Dynamics Simulator (FDS) and Oasys MassMotion for crowd dynamics. The coupling was carried out with the help of the software development kit of Oasys MassMotion in two different example geometries: an open-plan room and a floor with six rooms and a corridor. The results presented in this paper show that, when comparing an uncoupled and a coupled simulation, the effects of the smoke lead to different crowd density profiles, particularly closer to the exit, which elongates the overall evacuation time. This coupling method can be applied to any geometry because of its flexible and modular framework.
Majumdar A, Houghton R, Lister D, 2021, Examining Cognitive Workload During Covid-19: A Qualitative Study, Human Mental Workload: Models and Applications 5th International Symposium, H-WORKLOAD 2021, Virtual Event, November 24–26, 2021, Proceedings, Publisher: Springer, ISBN: 9783030914073
This book constitutes the refereed proceedings of the 5th International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2021, held virtually in November 2021.The volume presents 9 revised full papers, which were ...
Bateman G, Abdel Haleem H, Majumdar A, 2021, Is user-generated social media content useful for informing planning and management of emergency events? – An investigation of an active shooting event in a U.S. Airport, Case Studies on Transport Policy, Vol: 9, Pages: 1015-1025, ISSN: 2213-624X
Tsai CY, Cheong HI, Houghton R, et al., 2020, Dangerous Driving Prediction Model based on Long Short-term Memory Network with Dynamic Weighted Moving Average of Heart-Rate Variability
Dangerous driving behaviours contribute significantly to road accidents. Researchers have developed numerous models for predicting dangerous behaviours. However, these models have remained at the development stage. This paper proposes using a dynamic weight moving average (DWMA) method for processing heart rate variability (HRV) indices and establishing prediction models using long short-term memory (LSTM) networks. The changes in HRV indices between baseline and pre-event stages were also investigated. Thirty-three Taiwanese commercial drivers, which were 19 urban drives and 14 highway drivers, were recruited (between September 2019 and June 2020). Their driving behaviours and physiological signals during tasks were obtained by navigation software and an HRV watch. The DWMA and exponential moving average were applied to process the physiological signals. The derived data set was split into training and testing sets (ratio: 80% to 20%). To establish the models, the LSTM networks were trained using the training set and K-fold cross-validation (K = 10). Prediction performance was evaluated by sensitivity, specificity, and accuracy. For the urban drivers, the significantly raised values in the normalized low-frequency spectrum and the sympathovagal balance index were found. The significantly elevated values in the standard deviation of NN intervals were observed. For the highway drivers, the significantly increased heart rate and root mean square of successive RR interval differences can be observed. Besides, the LSTM models based on DWMA demonstrated the highest accuracy in urban and highway groups (Urban driving group: 80.31%, 95% confidence interval: 84.65-91.71%; Highway driving group: 80.70%, 95% confidence interval: 72.25-87.49%). The authors recommend using these models to prevent dangerous driving behaviours.
Bateman G, Majumdar A, 2020, Characteristics of emergency evacuations in airport terminal buildings: A new event database, Safety Science, Vol: 130, Pages: 104897-104897, ISSN: 0925-7535
Emergency evacuations are typically employed in airport terminal buildings to protect occupants against harmful events such as fires and security incidents. Despite the importance of ensuring the effectiveness of planned for evacuation procedures, little research has been conducted to obtain a better understanding of these events, largely due to a lack of data. This paper describes how a new database containing details of airport terminal building evacuation events (ATBEE) was created, and utilised to explore the characteristics of these events. Online news articles were used as the main source material to populate the database with events. The completed database was subjected to a data quality assessment procedure, and a new quantitative index was developed to assess the reliability of the news articles utilised as source material. The database currently contains situational and etiological details of 235 evacuation events that occurred during the period January 2016 to December 2017. Analysis of the database contents determined the majority of evacuation events recorded occurred in North America and Europe, and the main cause of these events were security and terrorism related incidents, such as the identification of suspicious items. Statistical testing indicated an association exists between the geographical location of the recorded evacuations and the cause of an evacuation, and also the evacuation’s cause and the total evacuation time. A significant correlation was furthermore found between the number of evacuation events occurring at an airport, and the size of the airport.
Psyllou E, Majumdar A, 2020, The Analysis of Airspace Infringements Over Complex Airspace in Europe and the United States of America, Journal of Navigation, Vol: 73, Pages: 1036-1051, ISSN: 0373-4633
<jats:p>The increase in the number of commercial flights highlights the need for air traffic to follow air procedures. Unfortunately, general aviation aircraft used for recreational purposes keep entering controlled and restricted airspace without obtaining permission from air traffic services. Given the safety and operational problems this could potentially cause, this paper examines the underlying reasons for these incidents occurring. In particular, it analyses airspace infringements between 2008 and 2017 involving general aviation flights that were recorded in airspace in which a large number of commercial flights also fly in Europe and America. The reports were analysed based on an initial assessment of their quality. Information was latent in the narrative and subsequently both qualitative (content analysis) and quantitative methods (descriptive statistics) of analysis were used. The analysis revealed that airspace infringements were related to the pilot's flight planning, that is, flight-route choice, navigation skills and communication, in addition to requirement to adhere to airspace procedure. The findings could be used by national authorities and flying clubs to promote safe flying in these regions.</jats:p>
Yan F, Zhang S, Majumdar A, et al., 2020, A Failure Mapping and Genealogical Research on Metro Operational Incidents, IEEE Transactions on Intelligent Transportation Systems, Vol: 21, Pages: 3551-3560, ISSN: 1524-9050
Ochieng W, Nascimento F, Majumdar A, 2020, redictive Safety Through Survey Interviewing - Developing a Task-Based Hazard Identification Survey Process in Offshore Helicopter Operations, Advances in Human Aspects of Transportation Proceedings of the AHFE 2020 Virtual Conference on Human Aspects of Transportation, July 16-20, 2020, USA, Editors: Stanton, Publisher: Springer Nature, ISBN: 9783030509439
This book discusses the latest advances in the research and development, design, operation, and analysis of transportation systems and their corresponding infrastructures.
Ochieng W, Nascimento F, Majumdar A, 2020, Predictive Safety Through Survey Interviewing - Developing a Task-Based Hazard Identification Survey Process in Offshore Helicopter Operations, Advances in Human Aspects of Transportation Proceedings of the AHFE 2020 Virtual Conference on Human Aspects of Transportation, July 16-20, 2020, USA, Editors: Stenton, Publisher: Springer Nature, ISBN: 9783030509439
Offshore helicopters play a vital role in energy production worldwide and must be operated safely. Safety is underpinned by hazard identification, which aspires to be predictive and remain operationally relevant. A process to elicit pilots’ operational hazard knowledge in a predictive manner is currently absent. This paper redresses this by developing a Task-Based Hazard Identification Survey Process which, through Talk-Through interviewing, collects data from a statistically representative sample of pilots based in specified regions. A factual and exhaustive hazards’ template is formed, to which various statistical methods are applied. Subjected to multiple validation and reliability checks, the process delivers on the aspiration to be predictive on safety.
Long F, Bateman G, Majumdar A, 2020, The impact of fire and rescue service first responders on participant behaviour during chemical, biological, radiological and nuclear (CBRN)/Hazmat incidents, International Journal of Emergency Services, Vol: 9, Pages: 283-298, ISSN: 2047-0894
<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>Decontamination following chemical, biological, radiological and nuclear (CBRN)/Hazmat incidents is a critical activity carried out in order to mitigate and contain the risk posed by any hazardous materials involved. Human behaviour plays a crucial role in such incidents, as casualties will have little understanding of the situation they find themselves in, leading to uncertainty in what actions to take. This will result in very difficult circumstances within which first responders must operate. However, the importance of human behaviour appears to be a fundamental element being missed in the preparation, training and planning assumptions being made by emergency services and planners in preparation for these events.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>This paper looks to understand the scope of this omission by reviewing relevant literature on the subject and engaging with Fire and Rescue Service personnel and managers in the UK. This study utilised semi-structured interviews with 10 Fire and Rescue Service Mass Decontamination Operatives, four Fire and Rescue Service Hazardous Material Advisers and three Fire and Rescue Service Strategic Officers participating. These interviews were then analysed using a thematic framework to identified key themes from the research which were then validated using two independent researchers to provide an inter-rater reliability measure. Finally, a follow-up validation questionnaire was also developed to test the validity of the themes identified and this was completed by another with 36 Fire and Rescue Service Mass Decontamination Operatives.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><ja
Tsai C-Y, Liu W-T, Lin Y-T, et al., 2020, Risk Screening of Obstructive Sleep Apnea Syndrome by Body Profiles via Random Forests Model
<jats:title>Abstract</jats:title> <jats:p>BackgroundObstructive Sleep Apnea Syndrome (OSAS) is a major global health concern and is typically diagnosed by in-lab polysomnography (PSG). This examination though has high medical manpower costs and alternative portable methods have further limitations. This paper develops a new model for screening the risk of OSAS in different age groups and gender by using body profiles. The effects of body profiles for different subgroups in sleep stage alteration and OSAS severity are also investigated.MethodsThe data is derived from 6614 Han-Taiwanese subjects who have previously undergone PSG in order to assess the severity of OSAS in the sleep center of Taipei Medical University Shuang-Ho Hospital between March 2015 and October 2019. Characteristics of subjects, including age, gender, body mass index (BMI), neck circumference, and waist circumference, were obtained from a questionnaire. Pearson regression was used to evaluate the correlations between body profiles and sleep stages as well as sleep disorder indexes. To develop an age and gender independent model, random forests (RF), which is an ensemble learning method with high explainability, were trained by the four groups by gender and age (older or younger than 50 years old) with ratios of 70% (training dataset) and 30% (testing dataset), respectively. Prediction performance was evaluated by sensitivity, specificity and accuracy. Variable importance was assessed by averaging the impurity decrease to account for the effect of different factors.ResultsResults indicate that high BMI, neck circumference and waist circumference decreased the duration of slow-wave sleep and increased the sleep disorder indices and the percentage of wake and N1. Additionally, screening models for different gender and age utilizing anthropometric features as predictors via RF were established and demonstrated to have high accuracy (75.63% for younger males, 74.72% for elder m
Teoh R, Schumann U, Majumdar A, et al., 2020, Mitigating the climate forcing of aircraft contrails by small-scale diversions and technology adoption, Environmental Science and Technology (Washington), Vol: 54, Pages: 2941-2950, ISSN: 0013-936X
The climate forcing of contrails and induced-cirrus cloudiness is thought to be comparable to the cumulative impacts of aviation CO2 emissions. This paper estimates the impact of aviation contrails on climate forcing for flight track data in Japanese airspace and propagates uncertainties arising from meteorology and aircraft black carbon (BC) particle number emissions. Uncertainties in the contrail age, coverage, optical properties, radiative forcing, and energy forcing (EF) from individual flights can be 2 orders of magnitude larger than the fleet-average values. Only 2.2% [2.0, 2.5%] of flights contribute to 80% of the contrail EF in this region. A small-scale strategy of selectively diverting 1.7% of the fleet could reduce the contrail EF by up to 59.3% [52.4, 65.6%], with only a 0.014% [0.010, 0.017%] increase in total fuel consumption and CO2 emissions. A low-risk strategy of diverting flights only if there is no fuel penalty, thereby avoiding additional long-lived CO2 emissions, would reduce contrail EF by 20.0% [17.4, 23.0%]. In the longer term, widespread use of new engine combustor technology, which reduces BC particle emissions, could achieve a 68.8% [45.2, 82.1%] reduction in the contrail EF. A combination of both interventions could reduce the contrail EF by 91.8% [88.6, 95.8%].
Yang L, van Dam KH, Majumdar A, et al., 2019, Integrated design of transport infrastructure and public spaces considering human behavior: A review of state-of-the-art methods and tools, FRONTIERS OF ARCHITECTURAL RESEARCH, Vol: 8, Pages: 429-453, ISSN: 2095-2635
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