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The rapid development of subway and prefabricated building industry, along with the adoption of the "dual carbon" strategy, have put forward higher demands on early strength, environmental protection and high efficiency for precast concrete products and concrete segments. In this study, a ultra-early strength type polycarboxylate superplasticizer (ES) was synthesized successfully by a design molecule structure such as introducting ultra long polyoxyethlene side chains, functional monomer (2-acrylamide-2-methylpropanesulfonic acid), and cationic monomer (Methacrylatoethyl trimethyl ammonium chloride) composition. Discovered through performance testing, the dispersion capacity of ES firstly are increased and then decreased with increasing the acid-ether ratio, and the same trend as the compressive strength harden concrete with ES at the age of 1, 3, and 7 days. The sulfonate groups and cationic monomers into molecular structure, they have little effect on the dispersion capacity, but the compressive strength of concrete at the age of 1, 3, and 7 days get improved. Through camparative study in the setting time of concrete and compressive strength of concrete between ES and an engineering application product SX. The results show that addition of ES does not retard cement hydration, and accelerates the coagulation of cement. Moreover, the compressive strength of concrete is better than SX at all ages, its further verified with X-ray diffraction and Scanning electron microscopy. This study indicates that rational design of molecular structure through the introduction of ultra-long side chains and functional monomers can maximize the early strength performance of polycarboxylate superplasticizer without affecting later strength development. [ABSTRACT FROM AUTHOR]

In this paper, a novel fractal model for the contact resistance based on axisymmetric sinusoidal asperity is proposed, which focuses on the resistance characteristics of the rough interface at a microscopic scale. By introducing the unique geometric shape of axisymmetric sinusoidal asperity, and combining it with a three-dimensional fractal theory, the micro-morphology characteristics of the rough interface can be characterized more precisely. Subsequently, by conducting a theoretical analysis and numerically solving the deformation mechanisms of asperities on the rough interface, a refined model for contact resistance is constructed. This research comprehensively employs theoretical analysis, numerical simulation, and experimental testing methods to deeply explore the current transmission mechanisms during the contact process of the rough interface. The findings suggest that the proposed model is capable of precisely capturing the intricate interplay of various factors, including contact area, contact load, and material properties, with the contact resistance. Compared to the existing models, the presented model demonstrates significant advantages in terms of prediction accuracy and practicality. This research provides an important theoretical basis and design guidance for optimizing the electrical performance of the rough interface, which has great significance for engineering applications. [ABSTRACT FROM AUTHOR]

Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively. [ABSTRACT FROM AUTHOR]

Highlights: A systematic review of latest developments of untethered micro/nanorobots-based remote sensing systems with an emphasis on designing new coordinated control and sensing approaches. The propulsion/motion control, functionalization of micro/nanorobots, sensing mechanisms, and applications are reviewed based on the up-to-date works. The design and application of micro/nanorobot-based sensing platforms are discussed with the goal of building intelligent remote sensing systems. Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities. Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems, enabling in situ detection of substances that traditional sensing methods struggle to achieve. Over the past decade of development, significant research progress has been made in designing sensing strategies based on micro/nanorobots, employing various coordinated control and sensing approaches. This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots, robot behavior, microrobotic manipulation, and robot-environment interactions. Providing recent studies and relevant applications in remote sensing, we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments, translating lab research achievements into widespread real applications. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

Different from the conventional logistics service network design problem, we design a fast logistics service network based on high-speed railway. An integrative optimization model which is applicable for solving practical problems is established. This paper simultaneously considers three subproblems: Train timetabling, freight flow assignment and electrical multiple units (EMU) routing plan, in which the objectives are simultaneous to minimize the total train travel time, the operation cost and transportation cost of freight transport, the number of freight EMU and the number of maintenance tasks. The constraints imposed in the model include space-time path resource assignment restriction, node operation capability, train safety interval time, train connection time restriction, freight service time window, train loading capacity restriction and EMU routing restriction. Based on the thoughts of divide and conquer, the original problem is decomposed by using the decomposition mechanism of the Lagrange relaxation algorithm to solve the integrated optimization model. To verify the feasibility and effectiveness of the model and algorithm proposed in this paper, a case study is conducted based on Harbin Dalian high-speed railway. [ABSTRACT FROM AUTHOR]

Squeeze film effects between two concentric cylinders are common and important in many mechanical systems. The models for cylindrical squeeze film force and vibrating system with squeeze film effects have not been well understood. In this paper, we model a cylindrical squeeze film force starting from the simplification of the full Navier–Stokes equations and then get an expression for the squeeze film force. We study the influence of a harmonic exciting force on a spring damped vibrator with squeeze film effect by establishing a non-linear mathematical model, and we investigate the behavior of the system numerically. As a forced vibrating system, the fundamental frequency of the response is consistent with the exciting frequency; the response of the resultant non-linear system shows obvious superharmonic phenomenon; the number of the superharmonic peaks increase with Reynolds number, and the superharmonic components mainly come from the non-linearity of the flow convection. [ABSTRACT FROM AUTHOR]

Automatically translating chromaticity-free thermal infrared (TIR) images into realistic color visible (CV) images is of great significance for autonomous vehicles, emergency rescue, robot navigation, nighttime video surveillance, and many other fields. Most recent designs use end-to-end neural networks to translate TIR directly to CV; however, compared to these networks, TIR has low contrast and an unclear texture for CV translation. Thus, directly translating the TIR temperature value of only one channel to the RGB color value of three channels without adding additional constraints or semantic information does not handle the one-to-three mapping problem between different domains in a good way, causing the translated CV images not only to have blurred edges but also color confusion. As for the methodology of the work, considering that in the translation from TIR to CV the most important process is to map information from the temperature domain into the color domain, an improved CycleGAN (GMA-CycleGAN) is proposed in this work in order to translate TIR images to grayscale visible (GV) images. Although the two domains have different properties, the numerical mapping is one-to-one, which reduces the color confusion caused by one-to-three mapping when translating TIR to CV. Then, a GV-CV translation network is applied to obtain CV images. Since the process of decomposing GV images into CV images is carried out in the same domain, edge blurring can be avoided. To enhance the boundary gradient between the object (pedestrian and vehicle) and the background, a mask attention module based on the TIR temperature mask and the CV semantic mask is designed without increasing the network parameters, and it is added to the feature encoding and decoding convolution layers of the CycleGAN generator. Moreover, a perceptual loss term is applied to the original CycleGAN loss function to bring the translated images closer to the real images regarding the space feature. In order to verify the effectiveness of the proposed method, the FLIR dataset is used for experiments, and the obtained results show that, compared to the state-of-the-art model, the subjective quality of the translated CV images obtained by the proposed method is better, as the objective evaluation metric FID (Fréchet inception distance) is reduced by 2.42 and the PSNR (peak signal-to-noise ratio) is improved by 1.43. [ABSTRACT FROM AUTHOR]

• A mathematical model of tube bundles subjected to two-phase cross-flow is developed. • The nonlinear dynamics of tubes considering the effects of two-phase cross-flow were investigated. • The relationships between fatigue damage and collision behavior are obtained. In this study, a mathematical model was developed to analyze the vibration of a simply supported tube with clearance restriction in an otherwise rigid rotated triangular tube array subjected to two-phase flow. The study evaluated the fatigue damage and collision behavior of the tube bundles system. The numerical results showed that the reduced velocity significantly affects the dominant frequency, collision frequency, and stable equilibrium position of the system. This suggests that the nonlinear dynamic characteristics of the simply supported tube with clearance restriction play a crucial role in determining fatigue damage. [ABSTRACT FROM AUTHOR]

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as "loss of taste", we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast. [ABSTRACT FROM AUTHOR]

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as "loss of taste", we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast. [ABSTRACT FROM AUTHOR]

• A heat flux partitioning model from temporal and spatial dimensions was developed. • A Monte Carlo method-verified approach determines the overlap area of bubble influence. • An analysis of boiling curves and wall heat flux partitioning was conducted. • The proportion of evaporation at CHF was compared. CFD methodology for subcooled flow boiling is significantly affected by the heat flux partitioning model, which plays a crucial role in predicting the mass source term generated by wall boiling of the liquid phase. Although numerous models have been proposed, most research efforts have focused on the spatial dimensions of boiling heat transfer mechanisms, neglecting the temporal aspect. This study presents a heat flux partitioning model for subcooled flow boiling that considers both temporal and spatial dimensions. The model accounts for the contribution of heat flux from the superheated liquid layer during the bubble growth stage, liquid convection during the bubble wait stage within the bubble influence area in the temporal dimension, and heat flux from the overlapping area of bubble influence in the spatial dimension. An approach was developed to determine the bubble influence area by considering the bubble interaction based on the stochastic nature of nucleation sites on the heated wall, verified by the Monte Carlo method. The bubble dynamics parameters were experimentally derived to reduce the uncertainty associated with the boiling sub-models on the calculation. A comparative analysis of boiling curves and wall heat flux partitioning was conducted between the present model and the RPI model under various flow conditions. The results indicate that both models could reasonably predict the boiling curve. However, the RPI model tends to over-predict the proportion of evaporative heat flux, which is considered a weakness. Based on physical principles, the present model accurately captures the fundamental trend of wall heat flux partitioning. This research enhances the understanding of subcooled flow boiling and increases confidence in multiphase CFD methodology predictions. [Display omitted] [ABSTRACT FROM AUTHOR]

• Field tests were carried out under inner-arch contact explosion with the outer arch side facing water. • The propagation of stress waves in arch slabs was then studied. • The dynamic responses of arch slabs with different working conditions and spans were studied, and the damage mechanisms of arch slabs were also analyzed. The anti-explosion performance of the arch concrete slabs under different working conditions is an important subject that has been widely studied in recent years. In this study, five arch concrete slabs with 60 mm thickness and different spans (500, 550, and 600 mm) with the outer arch side facing water were tested under inner-arch contact explosion. At the same time, the controlled explosion test of the arch slab with the outer arch side facing air was also carried out. The experimental results showed that the damage mode of the arch slabs with the outer arch side facing water was mainly long axial cracks on the mid-span, blasting pit on the inner arch side and radial cracks on the outer arch side. The crack development of the control group specimen was more complete, and there was greater and deeper spalling damage on the outer arch side. Moreover, a fully coupled model of contact explosion was established based on the Lagrange–Euler coupling algorithm, and the reliability and accuracy of the model were verified by comparing with the experimental results. The propagation laws of stress waves in arch slab was then studied. The dynamic responses of arch slabs with different working conditions and spans were studied, and the damage mechanisms of arch slabs were also analyzed. The results showed that, under the same explosion load, the arch slab with the outer arch side facing water exhibited more uniform stress than the arch slab with the outer arch side facing air. And the acceleration response showed that, the arch slab with the outer arch side facing air mainly deformed along the explosion direction, but the arch slab with the outer arch side facing water not only deformed along the explosion direction but also had a rebound trend. Moreover, because of the existence of the water interface, the transmission of compression stress wave in the arch slab increased and the reflection decreased when it reached the outer arch surface. [ABSTRACT FROM AUTHOR]

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects. [ABSTRACT FROM AUTHOR]

The horizontal axis tidal turbine is a typical apparatus to capture the energy in the ocean, but most traditional design methods of a turbine blade have led to the inefficiency of turbines. Therefore, to improve turbine efficiency, a design methodology of a horizontal axis tidal turbine blade based on bionics research is presented in this work. First, the point cloud of fin samples was scanned and processed. The three-dimensional digital models of pectoral and causal fins were extracted by reverse engineering software. Then, the bionic airfoil blade model was obtained through parameter optimization and coordinate transformation and a shark fin airfoil blade based on the bionic principle was designed, which provided a new method for the turbine blade design. Finally, the hydrodynamic performance of the bionic airfoil turbines at different pitch angles was studied through numerical simulations and underwater experiments, and the results showed that the performance of bionic airfoil turbines designed in this paper was better than that of the traditional blade turbines. [ABSTRACT FROM AUTHOR]

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases. [ABSTRACT FROM AUTHOR]

Introduction: Patients with pre-existing autoimmune diseases have been excluded from clinical trials of immune checkpoint inhibitors (ICIs) for cancer. Real-world evidence is necessary to understand ICI safety in this population. Methods: Patients treated with ICIs from 2011 to 2017 were identified using data from a large health insurer. Outcomes included time to (1) any hospitalization; (2) any hospitalization with an irAE diagnosis; and (3) outpatient corticosteroid treatment. The key exposure was pre-existing autoimmune disease, ascertained within 12 months before starting ICI treatment, and defined either by strict criteria (one inpatient or two outpatient claims at least 30 days apart) or relaxed criteria only (any claim, without meeting strict criteria). Results: Of 4438 ICI-treated patients, pre-existing autoimmune disease was present among 179 (4%) by strict criteria, and another 283 (6%) by relaxed criteria only. In multivariable models, pre-existing autoimmune disease by strict criteria was not associated with all-cause hospitalization (HR 1.27, 95% CI 0.998–1.62), but it was associated with hospitalization with an irAE diagnosis (HR 1.81, 95% CI 1.21–2.71) and with corticosteroid treatment (HR 1.93, 95% CI 1.35–2.76). Similarly, pre-existing autoimmune disease by relaxed criteria only was not associated with all-cause hospitalization (HR 1.11, 95% CI 0.91–1.34), but was associated with hospitalization with an irAE diagnosis (HR 1.46, 95% CI 1.06–2.01) and corticosteroid treatment (HR 1.46, 95% CI 1.13–1.88). Conclusion: Pre-existing autoimmune disease was not associated with time to any hospitalization after initiating ICI therapy, but it was associated with a modest increase in hospitalizations with irAE diagnoses and with corticosteroid treatment. [ABSTRACT FROM AUTHOR]

This report presents a novel approach to estimate the total number of COVID-19 cases in the United States, including undocumented infections, by combining the Centers for Disease Control and Prevention's influenza-like illness surveillance data with aggregated prescription data. We estimated that the cumulative number of COVID-19 cases in the United States by 4 April 2020 was > 2.5 million. [ABSTRACT FROM AUTHOR]

A splashing crown is commonly observed when a high-speed drop impacts a liquid film. The influence of the liquid viscosity on the crown's evolution is not yet clear. We review several existing theories of this problem, and carry out a series of numerical simulations. We find that a three-segment model can describe the crown's motion. In the very early stage when the crown is barely visible, the influence of viscosity is small. Later, a shallow water approach used in most existing models is applicable as long as the initial conditions are formulated properly. They depend on viscous dissipation in the intermediate period. Preliminary estimation based on a dissipation function is proposed to characterize the influence of viscosity in this problem. [ABSTRACT FROM AUTHOR]

Dengue is a mosquito-borne disease that threatens over half of the world’s population. Despite being endemic to more than 100 countries, government-led efforts and tools for timely identification and tracking of new infections are still lacking in many affected areas. Multiple methodologies that leverage the use of Internet-based data sources have been proposed as a way to complement dengue surveillance efforts. Among these, dengue-related Google search trends have been shown to correlate with dengue activity. We extend a methodological framework, initially proposed and validated for flu surveillance, to produce near real-time estimates of dengue cases in five countries/states: Mexico, Brazil, Thailand, Singapore and Taiwan. Our result shows that our modeling framework can be used to improve the tracking of dengue activity in multiple locations around the world. [ABSTRACT FROM AUTHOR]

• A mathematical model of tube bundles subjected to two-phase cross-flow and internal flow is developed. • The eigenvalue problem of tube bundles considering the effects of two-phase cross-flow and internal flow were investigated. • The effect of internal flow on mechanism of fluidelastic instability of tube bundles was discussed. This paper investigates the fluidelastic instability of a flexible tube in a rotated triangular tube array subjected to two-phase cross-flow and internal flow. We first established a mathematical model of a flexible tube in the tube bundles conveying flow considering the effect of two-phase cross-flow. Then, the eigenvalue analysis was performed to study the influences of the internal flow on the threshold and mechanism of the fluidelastic instability of the tube system in two-phase cross-flow. The results indicate that the internal flow has a significant effect on the threshold of the fluidelastic instability of the tube system in two-phase cross-flow, and may cause different kinds of instabilities. And, the mechanism of the fluidelastic instability of the tube system subjected to two-phase cross-flow and internal flow is notably affected by the tube boundary condition. [ABSTRACT FROM AUTHOR]

• Density and viscosity for the mixtures (1,3,5-trimethyladamantane + 1,2,3,4-Tetrahydronaphthalene + n -octanol) were investigated. • Excess properties and viscosity deviations for the mixtures were calculated and analyzed. • The nonideal behaviors of the V m E and Δ η can be attributed to the change of the structural effect and hydrogen bond force between molecules. Densities and viscosities for the ternary mixture, 1,3,5-trimethyladamantane (1,3,5-TMA) + 1,2,3,4-Tetrahydronaphthalene + n -octanol, and their corresponding binary mixtures (1,3,5-TMA + 1,2,3,4-tetrahydronaphthalene, 1,3,5-TMA + 1-octanol, and 1,2,3,4-tetrahydronaphthalene + 1-octanol) have been determined over the whole composition range at different temperatures ranging from 293.15 K to 343.15 K. The excess molar volume (V m E) and the viscosity deviation (Δ η) for these binary and ternary systems were calculated and then fitted to the Redlich–Kister equation. The V m E values are negative for the nonpolar system of 1,3,5-TMA and 1,2,3,4-tetrahydronaphthalene. For the systems containing 1-octanol, the V m E values become positive with increasing temperature. The V m E values for the ternary system are partially negative, and they increase against the temperature. All the Δ η values show negative deviations for the measured binary and ternary mixtures and increase obviously with enhancement in temperature. In addition, the nonideal behaviors of excess properties and viscosity deviations for the mixtures were analyzed. The measured and correlation results could supplement basic data for the design and modelling of high-energy-density hydrocarbon fuels. [ABSTRACT FROM AUTHOR]

• A mathematical model of tube bundles with clearance restriction was developed. • Flow-induced vibration fatigue damages of the tubes were estimated based on S - N curves. • Effects of flow pitch velocity and void fraction on fatigue damage were discussed. Flow-induced vibration of the tube bundles in two-phase flow involves complicated interactions between the fluids and structures. Although a substantial number of studies have been devoted to investigating the flow-induced vibration and fluidelastic instability of the tube bundles subjected to two-phase cross-flow, the fatigue damage features of the tubes are not fully understood. In this paper, considering the effects of the turbulence random forces and the fluidelastic forces, a mathematical model of the tube bundles subjected to two-phase flow and the clearance restriction was developed. The vibration responses of the tube bundles within five void fraction conditions were calculated. The flow-induced vibration fatigue damages of the tubes were estimated based on S - N curves. The effects of the clearance restriction, the flow pitch velocity, and the void fraction of the two-phase flow on the fatigue damage were discussed. The numerical results demonstrate that the void fraction and flow pitch velocity of the two-phase flow have a significant influence on the fatigue damage of the tube bundles. And, the clearance restriction can change the location of the maximum fatigue damage. [ABSTRACT FROM AUTHOR]

Flow-induced vibration of the tube bundles is an important issue in the security of a steam generator. Several investigations were performed to study the fluidelastic instability of the tube subjected to cross-flow. It should be noted that turbulence-induced vibration of the tube bundles subjected to cross-flow and loose support may cause a potential tube fretting wear and fretting fatigue when the flow pitch velocity is lower than the threshold of the fluidelastic instability. However, there is seldom theoretical analysis to study the vibration characteristics of the tube bundles considering the effect of the loose support. Therefore, a mathematical model of a single flexible tube in tube bundles subjected to turbulence random force and loose support was presented in this paper. An experiment was carried out to obtain the turbulence random forces acting on the tube bundle both in the entrance and intermediate regions. The influences of the turbulence force on the vibration characteristics of the tube were investigated. • Turbulence-induced vibration of tube bundles subjected to cross-flow was studied. • An envelope spectrum of turbulence excitation force was proposed. • The effects of loose support on the vibration characteristic of the tube were discussed. [ABSTRACT FROM AUTHOR]

• A mathematical model of tube system with clearance restriction is developed. • Considering the effect of internal flow, the vibration responses of tube system were calculated. • Nonlinear dynamics of tube system within two-phase cross-flow and internal flow were studied. This paper investigates the nonlinear dynamics of a flexible tube in a rotated triangular tube array subjected to two-phase cross-flow, internal flow, and clearance restriction. Considering the effects of internal fluid force and fluidelastic force, we first established a mathematical model of the tube bundles system. Then, based on this model, the vibration responses and bifurcation diagrams for different internal fluid velocity conditions within 20%, 40%, 60%, and 80% void fractions of the two-phase cross-flow were calculated, respectively. The numerical solutions demonstrate that the internal flow has a considerable impact on the bifurcation behavior and nonlinear vibration characteristics of the tube bundles system subjected to two-phase cross-flow and clearance restriction. [ABSTRACT FROM AUTHOR]

This paper investigated the two-phase cross-flow-induced vibration of the tube bundles considering the effects of the tube support structure. A mathematical model of the tube bundles subjected to two-phase flow and the clearance restriction was developed. Based on this model, the vibration responses and bifurcation diagrams of the tube bundles in 20%, 40%, 60%, and 80% void fractions were calculated. The effects of the symmetric and asymmetric clearance restrictions on the nonlinear dynamics of the tube bundles were discussed. The results show that the two-phase flow void fraction has a great effect on the nonlinear dynamic characteristics of the tube bundles with clearance restriction. With increasing of the flow pitch velocity, the original equilibrium position could become unstable, and a new stable equilibrium will be generated within the four void fraction conditions. At the new stable equilibrium position, the nonlinear dynamics characteristics of the tube bundles system are much more complicated. • A mathematical model of tube bundles subjected to two-phase flow and the clearance restriction was developed. • The effect of symmetric clearance restriction on nonlinear dynamics of the tubes was investigated. • The effect of asymmetric clearance restriction on nonlinear dynamics of the tubes was discussed. [ABSTRACT FROM AUTHOR]

Reliable joining of SiC particle–reinforced aluminium metal matrix composites (SiCp/Al MMCs) at a lower temperature is an important requirement in the application of advanced electronic packaging. Therefore, ultrasonic-assisted soldering of SiCp/Al MMCs at 250 ℃ was required and investigated. The joint forming process and mechanical properties were the focus of this paper. A liquid solder of Sn-9Zn alloy was forced to flow into the gap of the joints at a speed of 50~60 mm/s due to the effect of ultrasound during the soldering process. Acoustic flow arising in the liquid solder was beneficial in transferring the bubbles out of the joint, and the porosity in the joint decreased from 12% to 0 within 5 s. The periodic change of sound pressure resulted in the flowing quickly of liquid solder into the gap. During the soldering, the oxide film on the surface of the Al matrix was broken gradually, exhibiting obvious asynchronism. The oxide film of the Al matrix was completely removed within 5 s, and SiC particles began to appear in the bond. The Al matrix dissolved by the solder precipitated in the bond to form α-Al phases at 10 s. The typical microstructure of the soldered joint was Al MMCs/β-Sn + Sn–Zn eutectic structure + SiC particles + η-Zn + α-Al/Al MMCs. The maximum shear strength of the joints reached 70 MPa, which was approximately 2.5 times the reported shear strength of soldered SiCp/Al MMCs with a Ni coating. This lays a foundation to the application of SiCp/Al MMCs in the field of electronic packaging. [ABSTRACT FROM AUTHOR]

The small size and wireless actuation of microrobots make them potential candidates for minimally invasive medicine. To advance microrobots to future clinical application, microrobotics researchers have investigated a number of key issues, in which swarm control is a primary challenge and is attracting increasing attention. As a single microrobot has limited volume and surface area, clinically relevant tasks, including in-vivo tracking, usually require simultaneous control of a large swarm of microrobots. Unlike macroscale robots, implementing on-board actuators and sensors for microrobots is challenging, which differentiates swarm microrobotics from other swarm robotics approaches. This article systematically summarizes the state of the art for this emerging field, including actuation systems with different power sources, swarm behaviors modeling and simulation, swarm control strategies, and targeted biomedical applications. Actuation principles of microrobot swarms are categorized in detail, and critical comparisons are made to provide guidance and insight for future swarm microrobotics researchers. Considering the unique features of swarm microrobotics compared to traditional swarm robotics, this article also emphasizes the modeling, simulation, and control of microrobot swarms. Furthermore, recent biomedical applications of microrobot swarms are summarized to illustrate specific application scenarios. Finally, we provide an assessment of the future directions of swarm microrobotics. [ABSTRACT FROM AUTHOR]

Background: Liver lipid dysregulation is one of the major factors in the decline of production performance in late-stage laying hens. Silymarin (SIL), a natural flavonolignan extracted from milk thistle, is known for its hepatoprotective and lipid-lowering properties in humans. This study evaluates whether SIL can provide similar benefits to late-stage laying hens. A total of 480 68-week-old Lohmann Pink laying hens were randomly assigned into 5 groups, each group consisting of 6 replicates with 16 hens each. The birds received a basal diet either without silymarin (control) or supplemented with silymarin at concentrations of 250, 500, 750, or 1,000 mg/kg (SIL250, SIL500, SIL750, SIL1000) over a 12-week period. Results: The CON group exhibited a significant decline in laying rates from weeks 9 to 12 compared to the initial 4 weeks (P = 0.042), while SIL supplementation maintained consistent laying rates throughout the study (P > 0.05). Notably, the SIL500 and SIL750 groups showed higher average egg weight than the CON group during weeks 5 to 8 (P = 0.049). The SIL750 group had a significantly higher average daily feed intake across the study period (P < 0.05), and the SIL500 group saw a marked decrease in the feed-to-egg ratio from weeks 5 to 8 (P = 0.003). Furthermore, the SIL500 group demonstrated significant reductions in serum ALT and AST levels (P < 0.05) and a significant decrease in serum triglycerides and total cholesterol at week 12 with increasing doses of SIL (P < 0.05). SIL also positively influenced liver enzyme expression (FASN, ACC, Apo-VLDL II, FXR, and CYP7A1; P < 0.05) and altered the cecal microbiota composition, enhancing species linked to secondary bile acid synthesis. Targeted metabolomics identified 9 metabolites predominantly involved in thiamin metabolism that were significantly different in the SIL groups (P < 0.05). Conclusions: Our study demonstrated that dietary SIL supplementation could ameliorate egg production rate in late stage laying hens, mechanistically, this effect was via improving hepatic lipid metabolism and cecal microbiota function to achieve. Revealed the potentially of SIL as a feed supplementation to regulate hepatic lipid metabolism dysregulation. Overall, dietary 500 mg/kg SIL had the best effects. [ABSTRACT FROM AUTHOR]

Accurate, high-resolution tracking of influenza epidemics at the regional level helps public health agencies make informed and proactive decisions, especially in the face of outbreaks. Internet users' online searches offer great potential for the regional tracking of influenza. However, due to the complex data structure and reduced quality of Internet data at the regional level, few established methods provide satisfactory performance. In this article, we propose a novel method named ARGO2 (2-step Augmented Regression with GOogle data) that efficiently combines publicly available Google search data at different resolutions (national and regional) with traditional influenza surveillance data from the Centers for Disease Control and Prevention (CDC) for accurate, real-time regional tracking of influenza. ARGO2 gives very competitive performance across all US regions compared with available Internet-data-based regional influenza tracking methods, and it has achieved 30% error reduction over the best alternative method that we numerically tested for the period of March 2009 to March 2018. ARGO2 is reliable and robust, with the flexibility to incorporate additional information from other sources and resolutions, making it a powerful tool for regional influenza tracking, and potentially for tracking other social, economic, or public health events at the regional or local level. [ABSTRACT FROM AUTHOR]

Crowd sit-to-stand (STS) loading are always featured with pulse-like action, high crowd density and high synchronization rate among individuals, which may cause severe vibration comfort or even safety issues of supporting structures. However, related work in structural dynamics remains limited. As an early attempt, this study focuses on the individual STS loading and tries to develop an efficient tool to evaluate the potential dynamic response of the supporting structure when subjected to an individual STS action. Firstly, a comprehensive test campaign involving twenty participants was conducted by simultaneously footfall force measurement and 3D motion tracking of the human body. Secondly, the mathematical model of the individual STS loading was developed. Then a response spectrum framework that can give quick estimation of structural dynamic response induced by individual STS loading was proposed. The design response spectrum was formulated based on the measured force data. The effect of seat height, with or without armrests and the types of vibration assessment index on response spectrum was further discussed. The effectiveness of the method was finally verified on four full-scale structures having fundamental frequency from 2 Hz to 10 Hz subjected to individual STS actions. [ABSTRACT FROM AUTHOR]

Emotions play an important role in human–computer interaction. Multimodal emotion recognition combines feature information from different modalities to recognize emotional states. However, in real application scenarios, data from all modalities may not always be available. Thus, in multimodal emotion recognition a big challenge is how to utilize the semantic information from available modalities to predict missing modality data. To address this issue, this study proposes a Semantic-Wise Guidance for Missing Modality Imagination Network (SWG-MMIN) consisting of three main modules, that is, the Comprehensive Modality Feature Enrichment (CMFE) module, the Semantic-Wise Fusion (SWF) module, and the Semantic-Wise Feature Guided Imagination (SWGI) module. The CMFE module addresses the issue of semantic loss in the process of integrating multimodal features by enhancing the semantic information. The SWF module performs an adaptive fusion of invariant and specific features of multimodal data. The SWGI module facilitates the missing modality data generation and enhances the robustness of joint multimodal representation. Extensive experiments are conducted on two benchmark datasets, IEMOCAP and MSP-IMPROV. The experimental results demonstrate that the SWG-MMIN model surpasses all baseline models under full modalities and uncertain missing modalities, significantly improving emotion recognition performance. [ABSTRACT FROM AUTHOR]

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infections is an important public health problem worldwide and closely affect extrahepatic cancer. Several recent studies have investigated the relationship between HBV infection and head and neck cancer (HNC), but their findings were inconsistent.In order to address the limitations of small sample sizes, we conducted a meta-analysis to assess the association between HBV and HNC. Methods: We systematically searched PubMed, Web of Science, Embase, Scopus, Cochrane Library, and China National Knowledge Infrastructure from inception to August 2023. Original articles published as a case-control or cohort study were included. HBV infection was identified by HBsAg, HBV DNA or ICD codes. Review articles, meeting abstracts, case reports, communications, editorials and letters were excluded, as were studies in a language other than English or Chinese. According to the MOOSE guidelines, frequencies reported for all dichotomous variables were extracted by two reviewers independently. Similarly, the outcomes of OR, RR or HR, and 95% CIs after adjusting for age and gender were collected. Results: Thirteen relevant studies and 58,006 patients with HNC were included. Our analysis revealed a positive correlation between HBV and HNC (OR = 1.50; 95% CI: 1.28–1.77). After adjusting for age and gender, the similar result (OR = 1.30; 95% CI: 1.10–1.54) was obtained. Subgroup analysis further demonstrated a significant association between HBV infection and oral cancer (OR = 1.24; 95% CI: 1.05–1.47), as well as nasopharyngeal carcinoma (OR = 1.41; 95% CI: 1.26–1.58). However, due to the limited number of studies included, the statistical significance was not reached for cancer of the oropharynx (OR = 1.82; 95% CI: 0.66–5.05), hypopharynx (OR = 1.33; 95% CI: 0.88-2.00), and larynx (OR = 1.25; 95% CI: 0.69–2.24) after adjusting for age and gender. When excluding the interference of HIV/HCV, smoking and alcohol use, the final outcome (OR = 1.17; 95% CI: 1.01–1.35) got the same conclusion. Conclusions: Our study confirmed a positive relationship between HNC, specifically oral cancer and nasopharyngeal carcinoma, and HBV infection. However, further investigation is required at the molecular level to gather additional evidence in HNC. [ABSTRACT FROM AUTHOR]

There are inevitably all kinds of uncertainties in the gear transmission system. These uncertainties will have an important impact on the vibration response of the system. In this paper, the vibration response characteristics of herringbone gear transmission system with uncertain parameters are investigated. A six-degree-of-freedom dynamic model of single-stage herringbone gear-shaft-bearing system is established based on the finite element method. The stochastic agent model of the system is constructed by polynomial chaotic expansion (PCE), and the effects of gear meshing stiffness, bearing stiffness and damping parameters uncertainty on the vibration response of the system are analyzed. The results show that the uncertainty of gear meshing stiffness leads to the offset of the boundary value of the vibration amplitude of the system, and the uncertainty of bearing damping mainly affects the vibration amplitude of the main resonance region of the system. The uncertainty of bearing stiffness has little effect on the vibration amplitude of the system. And the hybrid uncertainty has comprehensive effect on the vibration amplitude of the system. [ABSTRACT FROM AUTHOR]

The development of a cooperative pursuit strategy for capturing escaping criminals or dangerous animals in urban public safety emergencies is becoming increasingly in demand. An ideal strategy should consider both the encirclement needed to prevent criminals from evading and the distance that pursuers need to move. This article proposes a fine-grained navigation network-based cooperative hunting (FINNCH) method. A fine-grained navigation network is created to provide detailed information about the traversability in urban areas. Three interaction rules inspired by biological behaviors in nature are introduced to achieve dynamic cooperation between pursuers. An heuristic search strategy is used to guide the pursuers toward potentially good directions, which consequently reduces the search effort. Two spatial constraints, namely, the direction centrality constraint (DCC) and encirclement distance constraint (EDC), are then constructed to evenly distribute the pursuers around the evader. Several experiments are conducted to evaluate the effectiveness and efficiency of the proposed method. The results show that FINNCH can provide navigation for multiple pursuers in complex urban environments comprised of roads, meadows, trees, water, and buildings. These findings point to the promising future of FINNCH for practical applications. [ABSTRACT FROM AUTHOR]

Field monitoring data provide evidence of excessive vibration on the wind turbines founded in warm permafrost (referring to soils frozen for more than two consecutive years with a temperature close to its melting point), which was attributed to resonance between the rotor and the tower-foundation system. This study presents a finite element (FE) model of the turbine-tower-foundation-soil system for analyzing causes of resonance and assessing effectiveness of mitigation measures. This model decouples the aerodynamic effects of the rotor from the turbine system. Aerodynamic simulation of the rotor was performed to provide wind thrust time series on the tower top. The lateral interaction between pile foundation and warm permafrost was modeled by distributed p-y springs and details on how to evaluate the permafrost p-y curves were provided. The FE model was used to investigate the sensitivity of wind turbine structural fundamental frequency to the permafrost temperature. It is found that, in addition to 9% seasonal change, the turbine-tower-foundation-soil fundamental frequency can decrease by 7% when the permafrost temperature increases from −2 °C to 0 °C. Analyses by using this model show that resonance can occur when the rotor speed is in the vicinity of the structural fundamental frequency, and increase the fatigue load on both the tower and foundation. The resonance is likely caused by the seasonal and long-term change in fundamental frequency of the turbine-tower-foundation-soil system and the inability of the controller to adjust its control parameters with time. Results show one tuned-mass-damper of 3% mass ratio is capable of reducing the tower peak acceleration and displacement during resonance by 50%, and reducing the peak shear and bending moment in the foundation by 40%. Adopting a controller with parameter updating capability together with using passive structural control techniques such as tuned-mass-dampers may be an effective control design strategy for wind turbines founded in warm permafrost. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG). • A novel explainable deep learning framework for EEG/MEG source imaging (XDL-ESI) is proposed. • XDL-ESI is based on the Unrolled Optimization Neural Networks (UONN). • XDL-ESI establishes a data-driven way to learn the spatial-temporal regularizations. • XDL-ESI has the advantages of interpretability and great approximation capability. • XDL-ESI demonstrates good performance validated with simultaneously recorded EEG and iEEG. [ABSTRACT FROM AUTHOR]

• A mathematical model of tube bundles subjected to cross-flow was presented; • An experimental study was performed to study the streamwise fluidelastic instability of the tube bundles; • The mechanism of streamwise fluidelastic instability of the tube bundle in cross-flow was revealed; • Effects of boundary constraint condition and void fraction of two-phase flow on streamwise fluidelastic instability were discussed. Fluidelastic instability is an important security issue for the steam generator tube bundles subjected to cross-flow. Lots of studies have been conducted on the fluidelastic instability of the tube bundles in the transverse direction. Recently, the streamwise fluidelastic instability of the tube bundles in cross-flow has been observed in some experimental studies. However, the mechanism of the fluidelastic instability in the parallel is not clear. Thus, in this paper, experimental and theoretical studies were performed to investigate the fluidelastic instability of a rotated triangular tube array subjected to cross-flow in the parallel direction. The influences of the boundary constraint condition and the void fraction of two-phase flow on the threshold of the streamwise fluidelastic instability of the tube bundles were discussed. [ABSTRACT FROM AUTHOR]

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

• A mathematical model of cracked tube subjected to cross-flow and loose support is developed. • The effect of crack damage on dynamic characteristics of cracked tube was investigated. • The dynamic responses of cracked tube for three flow pitch velocity were calculated. Flow-induced vibration of the tube bundles subjected to cross-flow and loose support is an important issue in the security of a steam generator. In the operation of nuclear power station, the flow-induced vibration of the tube bundles may lead to crack initiation and propagation, which can affect the natural frequency and the dynamic response of the tube. However, there is seldom theoretical analysis to study the dynamic characteristics of cracked tube bundles subjected to cross-flow. Therefore, a mathematical model of a single cracked tube in a rotated triangular tube array subjected to cross-flow and loose support was presented in this paper. The model takes into account the variations of the tube natural frequency and mode of vibration owing to the crack damage. The effect of the crack damage on the dynamic characteristics of a flexible tube in tube bundles subject to cross-flow and loose support was investigated, and the dynamic responses of the cracked tube for three flow pitch velocity conditions were calculated. [ABSTRACT FROM AUTHOR]

• Baseline-free damage identification method is proposed for tube bundles. • Proposed two damage indexes can identify both damage location and extent. • Flow pitch velocity has little influence on damage location identification. • Number of sensors has little influence when damage extent is larger than 5%. Dynamic characteristics of tube bundles with crack subjected to cross-flow and nonlinear loose support are very complicated, making the damage identification more difficult, and the information of healthy tube bundles could not be used in the damage identification process. In this paper, a baseline-free damage detection method is proposed to detect the crack damage of tube bundles subjected to cross-flow and loose support. Two damage indexes, based on the root mean square value of structural vibration responses and the gapped smoothing method, are proposed to identify the damage location and damage extent. Cases with different damage locations and damage extents are considered. The influences of the flow pitch velocity, loose support and the number of measure points on the effectiveness of the method are also discussed. The results showed that the index based on the normalized values of root mean square displacement and velocity responses can identify the damage location accurately within the flow pitch velocity range considered in this study. An improved index based on the normalized values of root mean square displacement and velocity responses and a parameter reflecting damage extent can identify the damage extent when the flow pitch velocity is small. When the flow pitch velocity is large, the effectiveness of the improved index is affected. Meanwhile the loose support location also has an influence on the proposed method, especially when the flow pitch velocity is large. Number of measure points has little influence when the damage extent is larger than 5%. [ABSTRACT FROM AUTHOR]

• An experimental study was performed to obtain the flow-induced vibration of tube bundles. • A mathematical model was presented to study the flow-induced vibration of the tube. • The CFD calculations were carried out to obtain the periodic fluid force acting on the tube. • The effect of the periodic force on the vibration responses of the tube was discussed. This paper investigated the flow-induced vibration of the tube bundles considering the effect of periodic fluid force in a rotated triangular tube array. We first performed an experiment to obtain the vibration responses of a rotated triangular tube array with the pitch-to-diameter ratio P / D = 1.48. To further investigate the vibration characteristics of the tube bundles, a mathematical model of the tube bundles considering the effect of the periodic fluid force and the clearance restriction was developed. Based on the Computational Fluid Dynamics calculation, the fluid force coefficients and the Strouhal numbers of the periodic fluid force in the tube bundles were determined. Considering the effect of the periodic fluid force, the vibration responses of the tube bundles were calculated. The vibration characteristics of the tube bundles induced by the periodic fluid force were discussed. The results demonstrated that some complex vibrations such as the beat vibrations and resonance may be induced by the periodic fluid force in the rotated triangular tube array, and the frequency of the periodic fluid force has a significant influence on the vibration characteristics of the tube bundles before the fluidelastic instability. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Iron-rich biomass was obtained as sole precursor for magnetic biocarbon synthesis. • Magnetic property of Fe/BC resulted from zero valent iron in-situ reduced by carbon. • Microporous structure enabled Fe/BC a high adsorption capacity for DCF. • Adsorption isotherm, kinetic, thermodynamic and recycle studies were conducted. Abstract Microorganisms in nature have been suggested as effective synthetic platform for functional materials construction. In this study, we cultured a typical white rot fungus of Phanerochaete chrysosporium in iron-containing medium to obtain iron-rich biomass, serving as sole precursor for magnetic biocarbon synthesis. The accumulated iron in biomass reached to 4.6 wt%. After carbonization and activation, microporous magnetic biocarbon (Fe/BC) with high specific surface area of 1986 m2 g−1 was obtained. When applied as adsorbent for a model pharmaceutical (diclofenac sodium, DCF) removal from aqueous solution, a high adsorption capacity of 361.25 mg g−1 was found for the developed Fe/BC. Systematic isotherm, kinetic, thermodynamic and recycle studies were conducted to investigate adsorption behaviors of DCF onto Fe/BC. This work not only provides a novel strategy for magnetic biocarbon construction, but also envisions new perspective on the utilization of a variety of microorganisms in nature for functional materials preparation. [ABSTRACT FROM AUTHOR]

Background: Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports.Methods: We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE).Results: Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons.Conclusions: Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is an orthopedic inflammatory disease which can cause functional disability and chronic pain. MiRNAs are known to play important roles in OA. To identify the targets for the treatment of OA, bioinformatics analysis was performed to explore differentially expressed miRNAs between OA and normal samples. Bioinformatics analysis was conducted to identify differentially expressed miRNAs. To mimic OA in vitro , primary chondrocytes were stimulated with IL-1β. Meanwhile, flow cytometry was performed to detect the cell apoptosis and cycle distribution. In addition, protein and mRNA expressions were detected by Western blot and RT-qPCR, respectively. Finally, in vivo model of OA was constructed to investigate the function of miR-892b in OA. The data indicated that miR-892b was identified to be upregulated in OA samples. Additionally, miR-892b antagomir markedly reversed IL-1β-induced growth decline of chondrocytes via inhibiting the apoptosis. IL-1β notably elevated the expressions of MMP1 and MMP13 and downregulated the level of Aggrecan in chondrocytes, while miR-892b antagomir reversed these phenomena. Meanwhile, cyclin D1 and cyclin D2 were the direct targets of miR-892b. In addition, IL-1β-induced G1 phase arrest in chondrocytes was partially abolished by of miR-892b antagomir. In vivo study indicated miR-892b antagomir could significantly alleviate the symptom of OA in a rat model. MiR-892b antagomir inhibits the progression of OA via targeting Cyclin D1 and Cyclin D2. Thus, our finding might supply a novel target for OA treatment. • miR-892b antagomir reversed IL-1β-induced chondrocyte growth inhibition. • miR-892b directly targeted cyclin D1 and cyclin D2. • miR-892b antagomir inhibited the symptom of osteoarthritis in vivo. [ABSTRACT FROM AUTHOR]

In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication. [ABSTRACT FROM AUTHOR]