This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

The World Cancer Research Fund and the American Institute for Cancer Research recommend a plant-based diet to cancer survivors, which may reduce chronic inflammation and excess adiposity associated with worse survival. We investigated associations of plant-based dietary patterns with inflammation biomarkers and body composition in the Pathways Study, in which 3659 women with breast cancer provided validated food frequency questionnaires approximately 2 months after diagnosis. We derived three plant-based diet indices: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI). We assayed circulating inflammation biomarkers related to systemic inflammation (high-sensitivity C-reactive protein [hsCRP]), pro-inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) and anti-inflammatory cytokines (IL-4, IL-10, IL-13). We estimated areas (cm2) of muscle and visceral and subcutaneous adipose tissue (VAT and SAT) from computed tomography scans. Using multivariable linear regression, we calculated the differences in inflammation biomarkers and body composition for each index. Per 10-point increase for each index: hsCRP was significantly lower by 6·9 % (95 % CI 1·6%, 11·8%) for PDI and 9·0 % (95 % CI 4·9%, 12·8%) for hPDI but significantly higher by 5·4 % (95 % CI 0·5%, 10·5%) for uPDI, and VAT was significantly lower by 7·8 cm2 (95 % CI 2·0 cm2, 13·6 cm2) for PDI and 8·6 cm2 (95 % CI 4·1 cm2, 13·2 cm2) for hPDI but significantly higher by 6·2 cm2 (95 % CI 1·3 cm2, 11·1 cm2) for uPDI. No significant associations were observed for other inflammation biomarkers, muscle, or SAT. A plant-based diet, especially a healthful plant-based diet, may be associated with reduced inflammation and visceral adiposity among breast cancer survivors.

Carbon storage in saline aquifers is a prominent geological method for reducing CO2 emissions. However, salt precipitation within these aquifers can significantly impede CO2 injection efficiency. This study examines the mechanisms of salt precipitation during CO2 injection into fractured matrices using pore-scale numerical simulations informed by microfluidic experiments. The analysis of varying initial salt concentrations and injection rates revealed three distinct precipitation patterns, namely displacement, breakthrough and sealing, which were systematically mapped onto regime diagrams. These patterns arise from the interplay between dewetting and precipitation rates. An increase in reservoir porosity caused a shift in the precipitation pattern from sealing to displacement. By incorporating pore structure geometry parameters, the regime diagrams were adapted to account for varying reservoir porosities. In hydrophobic reservoirs, the precipitation pattern tended to favour displacement, as salt accumulation occurred more in larger pores than in pore throats, thereby reducing the risk of clogging. The numerical results demonstrated that increasing the gas injection rate or reducing the initial salt concentration significantly enhanced CO2 injection performance. Furthermore, identifying reservoirs with high hydrophobicity or large porosity is essential for optimising CO2 injection processes.

Exposure to adversity during the perinatal period has been associated with cognitive difficulties in children. Given the role of the nucleus accumbens (NAcc) in attention and impulsivity, we examined whether NAcc volume at age six mediates the relations between pre- and postnatal adversity and subsequent attention problems in offspring. 306 pregnant women were recruited as part of the Growing Up in Singapore Towards Healthy Outcomes Study. Psychosocial stress was assessed during pregnancy and across the first 5 years postpartum. At six years of age, children underwent structural MRI and, at age seven years, mothers reported on their children’s attention problems. Separate factor analyses conducted on measures of pre- and postnatal adversity each yielded two latent factors: maternal mental health and socioeconomic status. Both pre- and postnatal maternal mental health predicted children’s attention difficulties. Further, NAcc volume mediated the relation between prenatal, but not postnatal, maternal mental health and children’s attention problems. These findings suggest that the NAcc is particularly vulnerable to prenatal maternal mental health challenges and contributes to offspring attention problems. Characterizing the temporal sensitivity of neurobiological structures to adversity will help to elucidate mechanisms linking environmental exposures and behavior, facilitating the development of neuroscience-informed interventions for childhood difficulties.

Objectives/Goals: Mathematical models of airborne virus transmission lack supporting field and clinical data such as viral aerosol emission rates and airborne infectious doses. Here, we aim to measure inhalation exposure to influenza aerosols in a room shared with persons with community-acquired influenza and estimate the infectious dose via inhalation. Methods/Study Population: We recruited healthy volunteer recipients and influenza donors with polymerase chain reaction (PCR)-confirmed community-acquired infection. On admission to a hotel quarantine, recipients provided sera to determine baseline immunity to influenza virus, and donor infections were confirmed by quantitative real-time polymerase chain reaction. Donors and recipients were housed in separate rooms and interacted in an “event room” with controlled ventilation (0.2 – 0.5 air changes/hour) and relative humidity (20–40%). We collected ambient bioaerosol exposure samples using NIOSH BC-251 samplers. Donors provided exhaled breath samples collected by a Gesundheit-II (G-II). We analyzed aerosol samples using dPCR and fluorescent focus assays for influenza A and sera by hemagglutinin inhibition assay (HAI) against donor viruses and vaccine strains. Results/Anticipated Results: Among two cohorts (24b and 24c), we exposed 11 recipients (mean age: 36; 55% female) to 5 donors (mean age: 21; 80% female) infected with influenza A H1N1 or H3N2. Eight G-II and two NIOSH bioaerosol samples (1–4 µm and ≥4 µm) were PCR positive. We cultured virus from one G-II sample. Based on previous literature, we hypothesized that ~50% of immunologically naïve people (HAI Discussion/Significance of Impact: We demonstrated that it is feasible to recruit donors with community-acquired influenza and expose recipients to measurable virus quantities under controlled conditions. However, baseline immunity was high among volunteers. Our work sets the stage for designing studies with increased sample sizes comprising immunologically naïve volunteers.

By the reason that mathematical analysis is not feasible for practical control of buildings, decentralized control (DC) and fuzzy control (FC) technologies were introduced to optimize the control problem of high-rise building (HRB) structures. For the control problem of HRB structures, magnetorheological fluid dampers (MRFDs) were introduced to optimize the lateral stress problem of each floor, and the influence of different output variables on FC was compared. In the analysis of fuzzy DC experiments, there were significant differences in the impact of different structural controls (SCs) on building acceleration. In the comparison of the interstory displacement (ISD) time history of the lower concrete structure, the maximum ISD value without control was -12 cm in the nineth second, −7 cm in the nineth second of LQR (linear quadratic regularization) control, and -6 cm in the FC. The proposed biomedical evolutionary technology had better SC effects in practical scenarios, with better safety and stability. The research was mainly based on FC controller technology, and in the future, updated IT2FL (interval type2 fuzzy logic) control technology can be adopted. At the same time, machine learning models are used to optimize parameter problems and improve the control effect of concrete structures. Therefore, fluid dampers help reduce vibrations caused by external earthquakes and other dynamic loads. By dampening devices, fluid dampers enhance the overall stability of the building by improving comfort levels. By allowing for lighter structural designs, fluid dampers can reduce the amount of material needed for construction, leading to cost savings. With reduced vibrations and stresses, there may be fewer maintenance issues over time. Fluid dampers can be designed for various types of structures and can be used in conjunction with other damping systems, making them flexible solutions for different engineering challenges. The future study can be effectively combined with base isolation systems to further improve a building’s resilience against seismic forces.

The viruses associated with bats have generated significant concern; however, there is limited knowledge regarding the endoparasites that affect these mammals. This study involved the collection of seven nematode specimens (three males and four females) from the intestines of Hipposideros armiger in Shaoguan City, Guangdong, China. Next-generation sequencing was employed to obtain the mitochondrial DNA (mtDNA) genome, which was determined to be 14,130 base pairs in length. The mitochondrial genome comprised 12 protein-coding genes, 21 tRNA genes, 2 rRNA genes, and an AT-rich non-coding region. Phylogenetic analyses based on mtDNA sequences indicated that the nematode forms a sister clade to Nematodirus, exhibiting only 74% nucleotide identity. In contrast, the nuclear ITS1 gene demonstrated a high degree of nucleotide identity (98.6%–98.8%) with Durettenema guangdongense. Consequently, the parasitic nematode identified from H. armiger is likely to belong to the genus Durettenema and has been designated as Durettenema sp. 888. Furthermore, an epidemiological investigation revealed the presence of the parasitic nematode infections in H. armiger collected from Guangdong, Guangxi, and Guizhou Provinces. Given the widespread distribution of H. armiger and their tendency to inhabit areas in close proximity to human dwellings, the influence of parasite prevalence on bat population numbers and potential for human and domestic animal transmission of this pathogen warrants further investigation.

For binary plug nozzle, the plug cone is exposed to high-temperature mainstream flow, making it one of the nozzle’s high-temperature components. This paper uses the Realizable k-ε turbulence model and the reverse Monte Carlo method to numerically investigate the aerodynamic and infrared radiation characteristics of the plug nozzle. Various slot cooling configurations were adopted to study the nozzle’s infrared radiation in detail. Results indicate that compared to the baseline nozzle, the plug nozzle’s performance is slightly reduced due to the decrease in effective area of flow over the plug cone. Introducing slot cooling at the rear edge provides significant infrared suppression benefits at low detection angles and notably reduces infrared radiation discrepancy with baseline nozzle at high detection angles. The cooling air from slots causes the nozzle jet to exhibit a ‘thermal layered’ feature. With the same total coolant mass flow, the ‘leading edge + trailing edge’ cooling configuration can lower the area-averaged wall temperature of the plug cone by 5.5% – 12.3%. However, its infrared radiation intensity at each detection angle on the pitch detection plane is higher than that of the ‘trailing edge’ configuration. The significance of leading-edge cooling is focused more on thermal protection for the plug. Thus, it is essential to balance coolant mass flow distribution between infrared radiation suppression and thermal protection.

Species of the genus Corynosoma (Acanthocephala: Polymorphida) mainly parasitize marine mammals and rarely marine birds, and are of veterinary and medical importance due to causing corynosomiasis in wildlife and humans. However, the current knowledge of the mitochondrial genomes and mitogenomic phylogeny of this group remains very insufficient. In the present study, the complete mitochondrial genomes of C. bullosum (von Linstow, 1892) and C. evae Zdzitowiecki, 1984 were sequenced and annotated for the first time. Both mitogenomes comprise 12 protein-coding genes (missing atp8), 22 tRNA genes, and 2 ribosomal RNAs (rrnS and rrnL), plus 2 non-coding regions (NCR1 and NCR2). Corynosoma bullosum has the largest mitogenome (14,879 bp) of any polymorphid species reported so far, while C. evae has the smallest (13,947 bp), except for Sphaerirostris lanceoides (Petrochenko, 1949). Comparative mitogenomic analysis also revealed the presence of distinct discrepancies in A + T content and gene rearrangement across the families Polymorphidae, Centrorhynchidae, and Plagiorhynchidae. Moreover, phylogenetic analyses based on the concatenated amino acid sequences of 12 protein-coding genes strongly supported the monophyly of the order Polymorphida and a close affinity between the families Polymorphidae and Centrorhynchidae in Polymorphida. The present mitogenomic phylogeny provides additional evidence for a sister relationship between the genera Corynosoma and Bolbosoma and demonstrated that C. evae has a closer relationship with C. villosum than C. bullosum in the genus Corynosoma.

Green water loads on prismatic obstacles (representing topside structures) mounted on the raised deck of a simplified vessel are investigated using computational fluid dynamics simulations and physical model testing with emphasis on examining different structure shapes, orientation angles and relative structure size. For each scenario investigated, several flow features are identified that characterize the green water interaction with the structure and influence loads, namely delayed flow diversion, formation of a vertical jet, scattered wave formation and the development of complex wake patterns. Comparing across structures, these interactions are more pronounced for blunt objects, and the associated force impulse is larger. For example, a cube with flow at normal incidence is found to experience approximately twice the force impulse of a circular cylinder of the same projected area. Equally, rotation of the cube leads to reduced run-up height and streamwise force on the structure. To explain these trends, a theoretical model based on Newtonian flow theory is adopted. This model provides an estimate of the streamwise force exerted on obstacles in high-Froude-number flows and shows good agreement with the numerical results when the flow is supercritical, shallow (small water depth relative to structure width) and the structure is tall (large structure height relative to water depth). Despite some limitations, the model should provide an efficient force prediction tool for practical use in design.

The dynamic behaviour of helicopter during water impact, considering variations in initial downward velocity and pitching angle, have been investigated numerically and theoretically in the present study. The air-water two-phase flows are simulated by solving unsteady Reynolds-averaged Navier-Stokes equations enclosed by standard $k - \omega $ turbulence model. A treatment for computational domain in combination with a global dynamic mesh technique is applied to deal with the relative motion between the helicopter and water. Results indicate that the initial downward velocity of helicopter exhibits behaviour similar to that of a V-shaped body impacting on water, as does the initial pitching angle. To extend the theoretical approach for predicting the kinematic parameters during helicopter ditching, a shape factor capturing the combined effect of various attributes and an average deadrise angle for asymmetric wedges are also introduced.

Understanding beliefs, values, and preferences of patients is a tenet of contemporary health sciences. This application was motivated by the analysis of multiple partially ordered set (poset) responses from an inventory on layman beliefs about diabetes. The partially ordered set arises because of two features in the data—first, the response options contain a Don’t Know (DK) option, and second, there were two consecutive occasions of measurement. As predicted by the common sense model of illness, beliefs about diabetes were not necessarily stable across the two measurement occasions. Instead of analyzing the two occasions separately, we studied the joint responses across the occasions as a poset response. Few analytic methods exist for data structures other than ordered or nominal categories. Poset responses are routinely collapsed and then analyzed as either rank ordered or nominal data, leading to the loss of nuanced information that might be present within poset categories. In this paper we developed a general class of item response models for analyzing the poset data collected from the Common Sense Model of Diabetes Inventory. The inferential object of interest is the latent trait that indicates congruence of belief with the biomedical model. To apply an item response model to the poset diabetes inventory, we proved that a simple coding algorithm circumvents the requirement of writing new codes such that standard IRT software could be directly used for the purpose of item estimation and individual scoring. Simulation experiments were used to examine parameter recovery for the proposed poset model.

Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. However, ordinal variables are very popular in many areas of psychological science, and the population often consists of several different groups based on the heterogeneity in ordinal data. Driven by these needs, we introduce the finite mixture of ordinal graphical models to effectively study the heterogeneous conditional dependence relationships of ordinal data. We develop a penalized likelihood approach for model estimation, and design a generalized expectation-maximization (EM) algorithm to solve the significant computational challenges. We examine the performance of the proposed method and algorithm in simulation studies. Moreover, we demonstrate the potential usefulness of the proposed method in psychological science through a real application concerning the interests and attitudes related to fan avidity for students in a large public university in the United States.

We propose a two-way Bayesian vector spatial procedure incorporating dimension reparameterization with a variable selection option to determine the dimensionality and simultaneously identify the significant covariates that help interpret the derived dimensions in the joint space map. We discuss how we solve identifiability problems in a Bayesian context that are associated with the two-way vector spatial model, and demonstrate through a simulation study how our proposed model outperforms a popular benchmark model. In addition, an empirical application dealing with consumers’ ratings of large sport utility vehicles is presented to illustrate the proposed methodology. We are able to obtain interpretable and managerially insightful results from our proposed model with variable selection in comparison with the benchmark model.

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.

This paper proposes a cooperative midcourse guidance law with target changing and topology switching for multiple interceptors intercepting targets in the case of target loss and communication topology switching. Firstly, a three-dimensional guidance model is established and a cooperative trajectory shaping guidance law is given. Secondly, the average position consistency protocol of virtual interception points is designed for communication topology switching, and the convergence of the average position of virtual interception points under communication topology switching is proved by Lyapunov stability theory. Then, in the case of the target changing, the target handover law and the handover phase guidance law are designed to ensure the acceleration smoothing, at last, the whole cooperative midcourse guidance law is given based on the combination of the above guidance laws. Finally, numerical simulation results show the effectiveness and the superiority of the proposed cooperative midcourse guidance law.