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Abstract Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers’ access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs’ navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration.

The current study was designed to investigate the consequences of rice cooking and soaking of cooked rice (CR) with or without arsenic (As) contaminated water on As and Fe (iron) transfer to the human body along with associated health risk assessment using additive main-effects and multiplicative interaction (AMMI) and Monte Carlo Simulation model. In comparison to raw rice, As content in cooked rice (CR) and soaked cooked rice (SCR) enhanced significantly (at p

Abstract Low-cost sensors (LCS) provide opportunities for neighborhood-level air pollution data collection, yet significant knowledge gaps remain regarding the accurate application and interpretation of LCS. In this study, we present an in-field calibration of a network of 20 low-cost ambient particulate matter sensors (LCS) in greater Kolkata, India, operating between October 2018–April 2019. In order to understand LCS performance in relation to local reference-grade PM2.5 monitors (RGMs), three of these LCS were co-located with RGMs operated by the West Bengal Pollution Control Board at Rabindra Bharati University (RBU), Victoria Memorial (VICTORIA), and Padmapukur (Howrah, PDM). Data from the co-locations were used to calibrate the LCS network using random forest regression and multiple linear regression approaches. Measured relative humidity and temperature were significant model features. Agreement between the LCS and RGM for 24-h averaged PM2.5 measurements was strongest at RBU, with an uncalibrated root mean squared error (RMSE) of 27.1 µg m−3, followed by PDM (32.6 µg m−3) and VICTORIA (50.7 µg m−3). Multiple linear regression was used to derive calibration models. Cross-calibration between co-located LCS-RGM pairs was tested. The LCS data after cross-calibration correctly identified days as being in or out of attainment with the 24h National Ambient Air Quality Standard of 60 µg m−3 91% of the time. The corrected data accurately identifies days with an India scale Air Quality Index of “poor” or worse 94% of the time. This suggests that LCS can be a useful supplement to RGM networks for air quality management. Diurnal trends and a high level of correlation across the hybrid LCS-RGM network suggest regional and secondary sources of PM2.5 are important in Kolkata.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

Wearable sensors for psychophysiological monitoring are becoming increasingly mainstream in safety critical contexts. They offer a novel solution to capturing sub-optimal states and can help identify when workers in safety critical environments are suffering from states such as fatigue and stress. However, sensors can differ widely in their application, design, usability, and measurement and there is a lack of guidance on what should be prioritized or considered when selecting a sensor. The paper aims to highlight which concepts are important when creating or selecting a device regarding the optimization of both measurement and usability. Additionally, the paper discusses how design choices can enhance both the usability and measurement capabilities of wearable sensors. The hopes are that this paper will provide researchers and practitioners in human factors and related fields with a framework to help guide them in building and selecting wearable sensors that are well suited for deployment in safety critical contexts.

Agricultural productivity is constantly being forced to maintain yield stability to feed the enormously growing world population. However, shrinking arable and nutrient-deprived soil and abiotic and biotic stressor (s) in different magnitudes put additional challenges to achieving global food security. Though well-defined, the concept of macro, micronutrients, and beneficial elements is from a plant nutritional perspective. Among various micronutrients, selenium (Se) is essential in small amounts for the life cycle of organisms, including crops. Selenium has the potential to improve soil health, leading to the improvement of productivity and crop quality. However, Se possesses an immense encouraging phenomenon when supplied within the threshold limit, also having wide variations. The supplementation of Se has exhibited promising outcomes in lessening biotic and abiotic stress in various crops. Besides, bulk form, nano-Se, and biogenic-Se also revealed some merits and limitations. Literature suggests that the possibilities of biogenic-Se in stress alleviation and fortifying foods are encouraging. In this article, apart from adopting a combination of a conventional extensive review of the literature and bibliometric analysis, the authors have assessed the journey of Se in the “soil to spoon” perspective in a diverse agroecosystem to highlight the research gap area. There is no doubt that the time has come to seriously consider the tag of beneficial elements associated with Se, especially in the drastic global climate change era.

Background Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations.Methods In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices.Results First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea–Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI.Conclusion Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters—namely heart rate variability, oxygen saturation, and body profiles—to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

BackgroundExposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations.ObjectivesThis study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution.MethodsThis retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined.ResultsSignificant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA.ConclusionBecause exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

Qualitative research using interviews is a crucial and established inquiry method in social sciences to ensure that the study outputs represent the researched people and area rather than those who are researching. However, first hand primary data collection is not always possible, often due to external circumstances. Additionally, the use of secondary data, particularly open data, is progressively preferred to increase efficiency and gain geographical breadth. Therefore, this paper proposes a new step-by-step secondary qualitative inquiry methodology for online, publicly available interview data. Such procedural approach can help increase rigor, explicitly consider and mitigate potential pitfalls, and expand the research community’s datasets. The 7-step methodology is based on a hybrid approach with elements of pragmatic qualitative approach, discursive grounded theory, and narrative approach and refers to research ethical principles of autonomy, equity, and diversity. One of the proposed steps is data quality assessment, which consists of quality assessment of data context and data content with a total of 16 quality dimensions to filter the collected data. The data analysis method comprises of content analysis for dataset categorization and thematic discourse analysis to answer the pre-set research questions. Additionally, the methodology covers the ethical and legal considerations relating to secondary online data and reporting the research findings of such data. By providing an example in the field of forced migration, we demonstrate how the methodology provides structure when conducting secondary qualitative research.

Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

BackgroundChronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations.MethodsThis retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals’ data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables.ResultsThe low-DSP group (

ObjectivesObstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD.MethodsPatients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared.ResultsWe included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea–hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product.ConclusionsRecurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

Industrialisation in its true sense began in India only after her independence in 1947. It has been a long journey since then. Initially, for about four and half decades since 1947, based on the Import-substitution strategy, Indian industry functioned in a highly regulated and protected environment dominated by the public sector. However, a paradigm shift occurred in India’s industrialisation strategy in the early 1990s as a new policy regime aimed at opening up and deregulation of Indian industry was adopted to facilitate the growth of competition. While the Indian industry has been functioning according to this policy regime for the last three decades, new issues have started to emerge. The paper attempts to unravel the cause and consequence of the process of industrialisation that has unfolded in India over the last seven decades and tries to analyse the challenges confronting the Indian industry at present and suggest the policy initiatives that could tackle them.

Transport properties of melts in the deep Earth have dictated the evolution of the early Earth’s magma oceans and also govern many modern dynamic processes, such as plate tectonics. Here, the authors find there is a reversal in the trends of transport properties of basaltic melts at pressures near 50 GPa, with implications for the timescales of early Earth’s magma oceans.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E–I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E–I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aβ42), and the product of the two (Aβ42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAβ42: 0.58 pg/mL; ΔAβ42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aβ42 decreased considerably in the CPAP group (ΔT-Tau: −2.22 pg/mL; ΔAβ42 × T-Tau: −44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea–hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

In order to achieve holistic urban plans incorporating transport infrastructure, public space and the behavior of people in these spaces, integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decision-makers. This paper describes a systematic literature review following a four-part framework. Firstly, to understand the relationship of elements of transport, spaces, and humans, we review policy and urban design strategies for promoting positive interactions. Secondly, we present an overview of the integration methods and strategies used in urban design and policy discourses. Afterward, metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed. Finally, this paper gives a review of state-of-the-art tools with a focus on seven computer simulation paradigms. This article explores mechanisms underlying the complex system of transport, spaces, and humans from a multidisciplinary perspective to provide an integrated toolkit for designers, planners, modelers and decision-makers with the current methods and their challenges. Keywords: Transport infrastructure, Public space, Human behavior, Integrated design, Decision support, Computer simulation

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

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.

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.

Diamond anvil cell techniques have been improved to allow access to the multimegabar ultrahigh-pressure region for exploring novel phenomena in condensed matter. However, the only way to determine crystal structures of materials above 100 GPa, namely, X-ray diffraction (XRD), especially for low Z materials, remains nontrivial in the ultrahigh-pressure region, even with the availability of brilliant synchrotron X-ray sources. In this work, we perform a systematic study, choosing hydrogen (the lowest X-ray scatterer) as the subject, to understand how to better perform XRD measurements of low Z materials at multimegabar pressures. The techniques that we have developed have been proved to be effective in measuring the crystal structure of solid hydrogen up to 254 GPa at room temperature [C. Ji et al., Nature 573, 558–562 (2019)]. We present our discoveries and experiences with regard to several aspects of this work, namely, diamond anvil selection, sample configuration for ultrahigh-pressure XRD studies, XRD diagnostics for low Z materials, and related issues in data interpretation and pressure calibration. We believe that these methods can be readily extended to other low Z materials and can pave the way for studying the crystal structure of hydrogen at higher pressures, eventually testing structural models of metallic hydrogen.

Arsenic (As) is a toxic metalloid classified as group 1 carcinogen. The presence of As in high concentrations in paddy soil and irrigation water results into high As accumulation in rice grains posing a threat to the health of millions of people worldwide. The main reason for As contamination is the biogeochemical weathering of rocks and the release of bound As into groundwater. Human interventions through intensive agricultural practices and excessive groundwater consumption have contributed greatly to the prevailing As contamination. The flooded cultivation practice of rice favors the accumulation of As in rice grains. The formation of iron (Fe) plaque on paddy root surfaces, changes in the level of Fe and manganese (Mn) hydro(oxides), soil organic matter, soil pH, soil redox potential, and microbial activities under flooding conditions influence concentrations of various As species in the water–soil–paddy agroecosystem and favor the predominance of highly mobile arsenite [As(III)]. Once inside the rice plant, the concentration of As is regulated by arsenate reduction, arsenite efflux, root-to-shoot translocation, and vacuolar sequestration of As. The detailed understanding gained about the factors affecting As dynamics in soil and transport in rice plants may be helpful in developing feasible methods for sustainable cultivation of rice plants with low grain As. There is also need to ensure high production yields as well as grain quality to achieve the goals of sustainable development. This article discusses the aspects of As in the water–soil–paddy agroecosystem and presents suitable strategies to reduce the As load in rice grains.

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.

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.

The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world’s accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.

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.

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.

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.

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.

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.

Human errors occurring during railway maintenance activities can significantly reduce the availability of equipment. Identification of potential human errors, their causes and prediction of the associated probabilities are important stages in order to manage such errors. This paper investigates the probability of human error during the maintenance of railway bogies. A case study examines technicians performing maintenance on the disc brake assembly unit, wheel set, and bogie frame under various error producing conditions in a railway maintenance workshop in Luleå, Sweden. The Human Error Assessment and Reduction Technique (HEART) is employed to determine the probability of human error occurring during each of the maintenance tasks, while fault tree analysis is used to define the potential errors throughout the maintenance process. The probability of a technician committing an error during the maintenance of the disc brake assembly, wheel set, and bogie frame is found to be 0.20, 0.039 and 0.021 respectively, with the human error probability (HEP) for the entire bogie 0.24. Time pressure, ability to detect and perceive problems, over-riding information, the need to make decisions and mismatches between the operator and designer’s model turn out to be major contributors to human error. These findings can help maintenance management personnel to better understand the error producing conditions that may lead to errors and in turn serve as an input to modify policies and guidelines for railway maintenance tasks.

Pulmonary emphysema is a connective tissue disease characterized by the progressive destruction of alveolar walls leading to airspace enlargement and decreased elastic recoil of the lung. However, the relationship between microscopic tissue structure and decline in stiffness of the lung is not well understood. In this study, we developed a 3D computational model of lung tissue in which a pre-strained cuboidal block of tissue was represented by a tessellation of space filling polyhedra, with each polyhedral unit-cell representing an alveolus. Destruction of alveolar walls was mimicked by eliminating faces that separate two polyhedral either randomly or in a spatially correlated manner, in which the highest force bearing walls were removed at each step. Simulations were carried out to establish a link between the geometries that emerged and the rate of decline in bulk modulus of the tissue block. The spatially correlated process set up by the force-based destruction lead to a significantly faster rate of decline in bulk modulus accompanied by highly heterogeneous structures than the random destruction pattern. Using the Karhunen-Loève transformation, an estimator of the change in bulk modulus from the first four moments of airspace cell volumes was setup. Simulations were then obtained for tissue destruction with different idealized alveolar geometry, levels of pre-strain, linear and nonlinear elasticity assumptions for alveolar walls and also mixed destruction patterns where both random and force-based destruction occurs simultaneously. In all these cases, the change in bulk modulus from cell volumes was accurately estimated. We conclude that microscopic structural changes in emphysema and the associated decline in tissue stiffness are linked by the spatial pattern of the destruction process.