OVAR-BPnet: A General Pulse Wave Deep Learning Approach for Cuffless Blood Pressure Measurement.

Pulse wave analysis, a non-invasive and cuffless approach, holds promise for blood pressure (BP) measurement in precision medicine. In recent years, pulse wave learning for BP estimation has undergone extensive scrutiny. However, prevailing methods still encounter challenges in grasping comprehensive features from pulse waves and generalizing these insights for precise BP estimation. In this study, we propose a general pulse wave deep learning (PWDL) approach for BP estimation, introducing the OVAR-BPnet model to powerfully capture intricate pulse wave features and showcasing its effectiveness on multiple types of pulse waves. The approach involves constructing population pulse waves and employing a model comprising an omni-scale convolution subnet, a Vision Transformer subnet, and a multilayer perceptron subnet. This design enables the learning of both single-period and multi-period waveform features from multiple subjects. Additionally, the approach employs a data augmentation strategy to enhance the morphological features of pulse waves and devise a label sequence regularization strategy to strengthen the intrinsic relationship of the subnets' output. Notably, this is the first study to validate the performance of the deep learning approach of BP estimation on three types of pulse waves: photoplethysmography, forehead imaging photoplethysmography, and radial artery pulse pressure waveform. Experiments show that the OVAR-BPnet model has achieved advanced levels in both evaluation indicators and international evaluation criteria, demonstrating its excellent competitiveness and generalizability. The PWDL approach has the potential for widespread application in convenient and continuous BP monitoring systems.

Superhydrophobic Waterborne Epoxy Composite Coating with Good Mechanical Properties, Icing Resistance, Self-Cleaning Properties, and Corrosion Resistance.

Epoxy resins with higher corrosion resistance typically employ environmentally harmful organic solvents such as xylene, while the corrosion resistance of waterborne epoxy resins, which are relatively environmentally friendly, is not as good as that of oil-based epoxy resins. In this study, by coating the surface of waterborne epoxy resin with a superhydrophobic zinc oxide coating, the corrosion resistance of waterborne epoxy resin was enhanced. During the initial immersion, its impedance value can reach 1010 Ω·cm2. The superhydrophobic nature of the coating itself also ensures the surface's resistance to immersion, delaying the intrusion of corrosive media into the coating. Furthermore, this coating exhibits good mechanical properties, self-cleaning performance, anti-icing performance, and so on. The introduction of superhydrophobic surfaces greatly optimizes the performance of traditional waterborne epoxy resin coatings, opening up directions for the metal anticorrosion field of coatings. Meanwhile, during the EIS testing of the superhydrophobic coating, we observed the occurrence of negative impedance in the results. After studying, we speculated that this phenomenon might be related to the degree of wetting of the superhydrophobic coating. Based on this conjecture, we can evaluate the basic properties of the superhydrophobic coating through this phenomenon.

Transformation of Natural Resin Resina Draconis to 3D Functionalized Fibrous Scaffolds for Efficient Chronic Wound Healing.

Chronic wound healing is a major challenge in clinical practice. Secondary dressing damage and antibiotic resistance are the main obstacles for traditional wound dressings. Resina draconis (RD), a natural resin traditionally used in powder form for wound care, is now considered unsuitable due to the lack of gas permeability and moist environment required for wound healing. Here, RD is incorporated in situ by constructing a 3D coiled fibrous scaffold with polycaprolactone/polyethylene oxide. Due to the high porosity of 3D scaffold, the RD-3D dressings have a favorable swelling capacity, providing permeability and moisture for wound repair. Meanwhile, the transformation of RD powder into 3D dressings fully demonstrates capabilities of RD in rapid hemostasis, bactericidal, and inflammation-regulating activities. In vivo evaluations using pressure ulcer and infected wound models confirm the high efficacy of RD-3D dressing in early wound healing, particularly beneficial in the infected wound model compared to recombinant bovine FGF-basic. Further biological analysis shows that resveratrol, loureirin A, and loureirin B, as potentially bioactive components of RD, individually contribute to different aspects of wound healing. Collectively, RD-3D integrated dressings represent a simple, cost-effective, and safe approach to wound healing, providing an alternative therapy for translating medical dressings from bench to bedside.

The relationship between chronic disease variety and quantity and suicidal ideation: A cross-sectional study of NHANES.

BACKGROUND: This cross-sectional study examines the link between chronic diseases and suicidal thoughts in U.S. adults using 2013-2018 National Health and Nutrition Examination Survey (NHANES) data, aiming to identify potential risk factors for suicidal ideation. METHODS: Using NHANES data, we analyzed the association between various chronic conditions (hypertension, diabetes, asthma, etc.) and suicidal thoughts, employing logistic regression models adjusted for demographics and lifestyle factors. RESULTS: The analysis of 8891 participants revealed a significant association between suicidal thoughts and chronic diseases such as liver disease, diabetes, and asthma. The risk of suicidal ideation is higher with the number of chronic conditions. CONCLUSION: Our findings suggest a strong link between the presence and number of chronic diseases and the risk of suicidal thoughts, emphasizing the importance of integrated care approaches that address both physical and mental health needs.

Coordinating Obstacle Avoidance of a Redundant Dual-Arm Nursing-Care Robot.

Collision safety is an essential issue for dual-arm nursing-care robots. However, for coordinating operations, there is no suitable method to synchronously avoid collisions between two arms (self-collision) and collisions between an arm and the environment (environment-collision). Therefore, based on the self-motion characteristics of the dual-arm robot's redundant arms, an improved motion controlling algorithm is proposed. This study introduces several key improvements to existing methods. Firstly, the volume of the robotic arms was modeled using a capsule-enveloping method to more accurately reflect their actual structure. Secondly, the gradient projection method was applied in the kinematic analysis to calculate the shortest distances between the left arm, right arm, and the environment, ensuring effective avoidance of the self-collision and environment-collision. Additionally, distance thresholds were introduced to evaluate collision risks, and a velocity weight was used to control the smooth coordinating arm motion. After that, experiments of coordinating obstacle avoidance showed that when the redundant dual-arm robot is holding an object, the coordinating operation was completed while avoiding self-collision and environment-collision. The collision-avoidance method could provide potential benefits for various scenarios, such as medical robots and rehabilitating robots.

The relationship between weight-adjusted-waist index and suicidal ideation: evidence from NHANES.

BACKGROUND: Amidst growing evidence of the intricate link between physical and mental health, this study aims to dissect the relationship between the waist-to-weight index (WWI) and suicidal ideation within a representative sample of the US population, proposing WWI as a novel metric for suicide risk assessment. METHODS: The study engaged a sample of 9500 participants in a cross-sectional design. It employed multivariate logistic and linear regression analyses to probe the association between WWI and suicidal ideation. It further examined potential nonlinear dynamics using a weighted generalized additive model alongside stratified analyses to test the relationship's consistency across diverse demographic and health variables. RESULTS: Our analysis revealed a significant positive correlation between increased WWI and heightened suicidal ideation, characterized by a nonlinear relationship that persisted in the adjusted model. Subgroup analysis sustained the association's uniformity across varied population segments. CONCLUSIONS: The study elucidates WWI's effectiveness as a predictive tool for suicidal ideation, underscoring its relevance in mental health evaluations. By highlighting the predictive value of WWI, our findings advocate for the integration of body composition considerations into mental health risk assessments, thereby broadening the scope of suicide prevention strategies.

Phyllosphere bacterial community dynamics in response to bacterial wildfire disease: succession and interaction patterns.

Plants interact with complex microbial communities in which microorganisms play different roles in plant development and health. While certain microorganisms may cause disease, others promote nutrient uptake and resistance to stresses through a variety of mechanisms. Developing plant protection measures requires a deeper comprehension of the factors that influence multitrophic interactions and the organization of phyllospheric communities. High-throughput sequencing was used in this work to investigate the effects of climate variables and bacterial wildfire disease on the bacterial community's composition and assembly in the phyllosphere of tobacco (Nicotiana tabacum L.). The samples from June (M1), July (M2), August (M3), and September (M4) formed statistically separate clusters. The assembly of the whole bacterial population was mostly influenced by stochastic processes. PICRUSt2 predictions revealed genes enriched in the M3, a period when the plant wildfire disease index reached climax, were associated with the development of the wildfire disease (secretion of virulence factor), the enhanced metabolic capacity and environmental adaption. The M3 and M4 microbial communities have more intricate molecular ecological networks (MENs), bursting with interconnections within a densely networked bacterial population. The relative abundances of plant-beneficial and antagonistic microbes Clostridiales, Bacillales, Lactobacillales, and Sphingobacteriales, showed significant decrease in severally diseased sample (M3) compared to the pre-diseased samples (M1/M2). Following the results of MENs, we further test if the correlating bacterial pairs within the MEN have the possibility to share functional genes and we have unraveled 139 entries of such horizontal gene transfer (HGT) events, highlighting the significance of HGT in shaping the adaptive traits of plant-associated bacteria across the MENs, particularly in relation to host colonization and pathogenicity.

Analytical solution of a pollutant transport model for unsaturated soil considering the effects of consolidation and temperature.

The migration behavior of pollutants is affected by consolidation and temperature when using thermal desorption technology to clean contaminated sites. Based on a one-dimensional consolidation model for unsaturated soil and the traditional heat conduction equation, a pollutant transport model accounting for the combined effects of consolidation and temperature was established in this paper. An analytical solution was obtained by using the separation of variables method and the integral transformation method. In addition, the correctness of the proposed model was verified via a comparison between the existing analytical solution and the theoretical model. Finally, adopting benzene as the research object, the influence of different factors on pollutant migration was studied. It was found that the growth rate of the pollutant concentration increased with increasing consolidation pressure, and the final pollutant concentration decreased with increasing consolidation pressure. The pollutant concentration increment due to temperature first increased and then decreased with increasing migration distance. The higher the Soret coefficient and volumetric moisture content are, the higher the pollutant concentration.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units.

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

[Modeling and comfort analysis of arrayed air cushion mattress for pressure ulcer prevention and assisted repositioning].

Assisting immobile individuals with regular repositioning to adjust pressure distribution on key prominences such as the back and buttocks is the most effective measure for preventing pressure ulcers. However, compared to active self-repositioning, passive assisted repositioning results in distinct variations in force distribution on different body parts. This incongruity can affect the comfort of repositioning and potentially lead to a risk of secondary injury, for certain trauma or critically ill patients. Therefore, it is of considerable practical importance to study the passive turning comfort and the optimal turning strategy. Initially, in this study, the load-bearing characteristics of various joints during passive repositioning were examined, and a wedge-shaped airbag configuration was proposed. The airbags coupled layout on the mattress was equivalently represented as a spring-damping system, with essential model parameters determined using experimental techniques. Subsequently, different assisted repositioning strategies were devised by adjusting force application positions and sequences. A human-mattress force-coupled simulation model was developed based on rigid human body structure and equivalent flexible springs. This model provided the force distribution across the primary pressure points on the human body. Finally, assisted repositioning experiments were conducted with 15 participants. The passive repositioning effectiveness and pressure redistribution was validated based on the simulation results, experimental data, and questionnaire responses. Furthermore, the mechanical factors influencing comfort during passive assisted repositioning were elucidated, providing a theoretical foundation for subsequent mattress design and optimization of repositioning strategies.

Ionic Polyimine-Based Composite Membrane with Inductive and Complexation Synergistic Effects for Sensitive and On-Site Fluorescent Detection of Volatile Iodine.

Along with the development of nuclear power, concerns about radioactive emissions and the potential for nuclear leakage have been widely raised, particularly of harmful iodine isotopes. However, as a significant component of nuclear air waste, the enrichment and detection of air-dispersed gaseous iodine remain a challenge. In this work, it is focused on developing an attraction-immobilization-detection strategy-based fluorescence method for the on-site detection of volatile iodine, by employing a photoluminescent ionic polyimine network-polyvinylpyrrolidone (IPIN-PVP) composite membrane. This strategy synergizes ion-induced dipole interactions from IPIN and complexation effects from PVP, allowing effective iodine enrichment and immobilization. As a result, the optimized IPIN-PVP membrane exhibits rapid response times of 5 s and a low detection limit of 4.087 × 10-8 m for gaseous iodine. It also introduces a portable handheld detection device that utilizes the composite membrane, offering a practical solution for real-time on-site detection of volatile iodine. This innovation enhances nuclear safety measures and disaster management by providing rapid and reliable iodine detection capabilities.

Modeling and Analysis of Foot Function in Human Gait Using a Two-Degrees-of-Freedom Inverted Pendulum Model with an Arced Foot.

Gait models are important for the design and control of lower limb exoskeletons. The inverted pendulum model has advantages in simplicity and computational efficiency, but it also has the limitations of oversimplification and lack of realism. This paper proposes a two-degrees-of-freedom (DOF) inverted pendulum walking model by considering the knee joints for describing the characteristics of human gait. A new parameter, roll factor, is defined to express foot function in the model, and the relationships between the roll factor and gait parameters are investigated. Experiments were conducted to verify the model by testing seven healthy adults at different walking speeds. The results demonstrate that the roll factor has a strong relationship with other gait kinematics parameters, so it can be used as a simple parameter for expressing gait kinematics. In addition, the roll factor can be used to identify walking styles with high accuracy, including small broken step walking at 99.57%, inefficient walking at 98.14%, and normal walking at 99.43%.

Tactile Features of Human Finger Contact Motor Primitives.

The human hand interacts with the environment via physical contact, and tactile information is closely associated with finger movement patterns. Studying the relationship between motor primitives of the finger and the corresponding tactile feedback provides valuable insight into the nature of touch and informs the simulation of humanoid tactile. This research decomposed finger contact into three fundamental motor primitives: contact-on, stick-to-slip, and full slip, then examined the tactile features associated with each motor primitive, including the center of mass (COM) and the centroid of the contact pressure distribution matrix and the total contact area. The change in fingertip contact area during contact-on was in accordance with a first-order kinetic model. In the stick-to-slip, there was a generalized linear relationship between the fingertip skin stretch and the magnitude of the tangential force. Moreover, the skin stretch of the fingertip mirrored the direction of the motion. During the full slip, the COM's movement effectively represented the direction of the tangential force, with an error margin of no more than five degrees. Experiments showed that certain fingertip motions can be portrayed, transmitted, and replicated using tactile information. This research opens potential avenues for remote immersive physical communication in robotics and other related fields.

Method of depth simulation imaging and depth image super-resolution reconstruction for a 2D/3D compatible CMOS image sensor.

This paper presents a depth simulation imaging and depth image super-resolution (SR) method for two-dimensional/three-dimensional compatible CMOS image sensors. A depth perception model is established to analyze the effects of depth imaging parameters and evaluate the real imaging effects. We verify its validity by analyzing the depth error, imaging simulation, and auxiliary physical verification. By means of the depth simulation images, we then propose a depth SR reconstruction algorithm to recover the low-resolution depth maps to the high-resolution depth maps in two types of datasets. With the best situation in depth accuracy kept, the root mean square error (RMSE) of Middlebury dataset images are 0.0156, 0.0179, and 0.0183 m. The RMSE of RGB-D dataset images are 0.0223 and 0.0229 m. Compared with other listed conventional algorithms, our algorithm reduces the RMSE by more than 16.35%, 17.19%, and 23.90% in the Middlebury dataset images. Besides, our algorithm reduces the RMSE by more than 9.71% and 8.76% in the RGB-D dataset images. The recovery effects achieve optimized results.