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Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind's physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data. [ABSTRACT FROM AUTHOR]

In recent years, the detection of offshore pile foundations has received wide attention in engineering. Compared with traditional methods, the O-cell test has unique advantages in offshore pile foundation detection. To study the load transfer characteristics of the O-cell method for pile testing in coastal soft soil foundation, this paper established the pile–soil numerical model to simulate the O-cell and traditional testing processes. The finite element method and equal displacement method are combined to calculate the conversion coefficient and ultimate bearing capacity, and the distribution forms of axial force, side friction resistance, and tip resistance are discussed. The research results show that the O-cell test method and the traditional method have different load transfer forms. By introducing the equal displacement method into the O-cell pile–soil model, the error between the equivalent conversion ultimate bearing capacity and the calculation result of the surcharge method is less than 0.5%, and the O-cell conversion coefficient can be accurately calculated. [ABSTRACT FROM AUTHOR]

A meta-surface-based arbitrary bandwidth filter realization method for terahertz (THz) future communications is presented. The approach involves integrating a meta-surface-based bandstop filter into an ultra-wideband (UWB) bandpass filter and adjusting the operating frequency range of the meta-surface bandstop filter to realize the design of arbitrary bandwidth filters. It effectively addresses the complexity of designing traditional arbitrary bandwidth filters and the challenges in achieving impedance matching. To underscore its practicality, the paper employs silicon substrate integrated gap waveguide (SSIGW) and this method to craft a THz filter. To begin, design equations for electromagnetic band gap (EBG) structures were developed in accordance with the requirements of through-silicon via (TSV) and applied to the design of the SSIGW. Subsequently, this article employs equivalent transmission line models and equivalent circuits to conduct theoretical analyses for both the UWB passband and the meta-surface stopband portions. The proposed THz filter boasts a center frequency of 0.151 THz, a relative bandwidth of 6.9%, insertion loss below 0.68 dB, and stopbands exceeding 20 GHz in both upper and lower ranges. The in-band group delay is 0.119 ± 0.048 ns. Compared to reported THz filters, the SSIGW filter boasts advantages such as low loss and minimal delay, making it even more suitable for future wireless communication. [ABSTRACT FROM AUTHOR]

Aiming at the problem of researching the reliability of dynamic-pressure mechanical seals, this paper proposes a reliability evaluation method for dynamic-pressure mechanical seals based on the Monte Carlo method. Based on the influence of the mass transfer coefficient on vaporization phase transition, a liquid film vaporization model of a hydrodynamic mechanical seal's end face is established, and the working condition parameters and groove structure parameters are designed using the experimental design method. The vaporization characteristics of the liquid film under various parameters are analyzed, and the functional functions of the vaporization characteristics are obtained by fitting. Combined with the maximum vapor phase volume fraction when the dynamic-pressure mechanical seal changes from the liquid miscible phase to the vapor miscible phase, the limit state equation of the vapor phase volume fraction is obtained. Finally, based on the Monte Carlo simulation method, the sealing reliability under specific groove structure parameters is calculated. Our research shows that this method has practicability and effectiveness for the reliability evaluation of mechanical seals with different working conditions and different groove structures. [ABSTRACT FROM AUTHOR]

Yb:CALYGLO crystals with a dopant concentration of 5 at.% were successfully grown using the Czochralski method. The crystal samples were extensively studied to analyze their structure, room temperature and low temperature spectra, and laser properties. The highest absorption cross-section at 977 nm was calculated to be 1.83 × 10−20 cm2 for σ polarization and 5.32 × 10−20 cm2 for π polarization. Similarly, the emission cross-section was determined to be 1.38 × 10−20 cm2 at 980 nm for σ polarization and 2.28 × 10−20 cm2 at 981 nm for π polarization, with a full width at half maximum (FWHM) of 50.3 nm and 89.5 nm, respectively. The fluorescence lifetime of the 5 at.% Yb:CALYGLO crystal at 2F5/2 was measured to be 1.10 ms. Additionally, gain cross-sections were calculated for different β values. In the continuous laser experiment, the crystal demonstrated a laser output of 20.15 W at 1057 nm, with a slope efficiency of 53.3%. These experimental findings indicate that the lattice of Y3+ in the crystal is partially replaced by Lu3+ and Gd3+, resulting in a broader spectrum. Consequently, this crystal shows promising potential as a gain medium for ultrashort pulse laser crystals. [ABSTRACT FROM AUTHOR]

The wide range of applications and continuous demand for plastics is causing serious global environmental problems. Massive discharges of expanded polystyrene (EPS) are thought to be primarily responsible for the increased white pollution. Waste EPS has received wide attention in the development of innovative products. White light-emitting diodes pumped by a near-UV chip (n-UV WLEDs) are regarded as a very promising solid-state lighting. The performance of the n-UV WLED is largely determined by the properties of the tricolor luminescence materials. In this work, a blue-emitting polymer film for n-UV WLED applications was developed from waste EPS. First, using waste EPS as a raw material, benzimidazole groups were bonded to PS benzene rings by chemical reactions to obtain modified PS (PS-PBI). Then, a film based on PS-PBI was prepared by a simple solution drop-casting method. The PS-PBI film can emit intense blue light when irradiated with 365 nm light. An n-UV WLED pumped by a 365 nm UV chip was fabricated using PS-PBI film as the blue-emitting layer. The fabricated n-UV WLED shows excellent luminescence properties, such as a bright white light with color coordinates of (0.337, 0.331), a relatively low color temperature (CCT, 5270 K), and an especially high color rendering index (CRI, 93.6). The results prove that the blue-emitting PS-PBI film prepared from waste EPS is a very promising candidate for n-UV WLED applications. The strategy of converting waste EPS into a high-value-added blue-emitting film in this work provides a convenient and feasible approach for upcycling waste EPS, achieving significant environmental and economic benefits. [ABSTRACT FROM AUTHOR]

The implementation of joint source-channel coding (JSCC) schemes using deep learning has accelerated the development of semantic communication research. Existing JSCC schemes based on deep learning (DL) are trained on a fixed signal-to-noise ratio (SNR); however, these trained models are not designed for scenarios in which the SNR is dynamic. Therefore, a novel semantic adaptive model for semantic communication—called joint source-channel coding with adaptive models (AMJSCC)—that has a semantic adaptive model selection (SAMS) module is proposed. The joint source-channel encoding (JSCE) model and the joint source-channel decoding (JSCD) model adapt according to both real-time channel conditions and system available computational power resources. Furthermore, residual networks with different layers are investigated to further improve the accuracy of information recovery. Simulation results demonstrate that our model can achieve higher recovery similarity and is more robust and adaptive to the SNR and communication resources. Meanwhile, compared to the state-of-the-art deep JSCC methods, it reduces storage space and communication resource consumption. [ABSTRACT FROM AUTHOR]

In order to derive sodium abundances and investigate the effects of non-local thermodynamic equilibrium (NLTE) on the formation of H-band Na I lines, we update the sodium atomic model by incorporating collision rates with hydrogen from new quantum-mechanical calculations. The differential Na abundances for 13 sample stars are obtained by analyzing high-resolution H-band spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and optical spectra under both local thermodynamic equilibrium (LTE) and NLTE conditions. Consistent abundances from both bands suggest that our updated atomic model is valid for studying the formation of H-band Na I lines. Our calculations show that, in our stellar parameter space, NLTE effects are negative and can result in corrections larger than −0.4 dex on optical lines. The corrections on H-band Na I lines are typically small, within about 0.05 dex, but not negligible if accurate sodium abundance is desired. We note that the [Na/Fe] ratios favor the theoretical galactic chemical model. [ABSTRACT FROM AUTHOR]

The total radiated power (TRP) has been stipulated as the spurious emission limit for a mmWave terminal. Angular sampling is crucial for ensuring the accuracy of the measurement of the TRP of spurious emissions while improving measurement efficiency. In this study, we investigate the sampling requirements for different TRP measurement methods. A novel algorithm based on the statistical analysis of the measurement uncertainty caused by the sampling step is proposed. Specifically, the HPBW of the entire machine radiation pattern is considered instead of that of a simple antenna array. This study was conducted in strict compliance with regulatory requirements to ensure that the statistical algorithm was more closely aligned with practical testing. Based on this, the maximum sampling steps allowed by different terminal models while maintaining measurement accuracy were analyzed. The simulation results demonstrate that negligible measurement uncertainty was produced when the sampling step of the full spherical grid method was less than 6 ∘ . However, for the two-cut and the three-cut methods, a maximum of 2 dB of measurement uncertainty should be considered. These findings are valuable inputs for the ongoing work on electromagnetic compatibility testing and standardization. [ABSTRACT FROM AUTHOR]

At present, the demand for accurate indoor positioning at a low cost is increasing. Based on the architecture of networking passive radio frequency identification (RFID) systems, research into passive location algorithms is important for finding a location solution with ultra-low cost, easy implementation, and no required maintenance. In this paper, TDACC (time difference of arrival with conic curves) based on signal propagation time is proposed, which breaks down the positioning problem into solving the intersection of an ellipse and a hyperbola. The results indicate that this method has a positioning error of 0 m in the absence of signal interference. When the time delay fluctuates to 1 ns and 2 ns, the average errors of TDACC are 0.19 m and 0.33 m, respectively. Different from other time-based localization methods, the proposed method only requires two distribution nodes without time synchronization, which reduces the system cost. These results will help to promote the deeper semantic communication level fusion of passive RFID. By improving the coordinate positioning in the semantic prior knowledge base, this method will lead to more efficient and accurate industry applications. [ABSTRACT FROM AUTHOR]

In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized. [ABSTRACT FROM AUTHOR]

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies. [ABSTRACT FROM AUTHOR]

The clinical benefits of targeting programmed death-ligand 1 (PD-L1) in various cancers represent a strategy for the treatment of immunosuppressive diseases. Here, it was demonstrated that the expression levels of PD-L1 in cells were greatly upregulated in response to H1N1 influenza A virus (IAV) infection. Overexpression of PD-L1 promoted viral replication and downregulated type-I and type-III interferons and interferon-stimulated genes. Moreover, the association between PD-L1 and Src homology region-2, containing protein tyrosine phosphatase (SHP2), during IAV/H1N1 infection was analyzed by employing the SHP2 inhibitor (SHP099), siSHP2, and pNL-SHP2. The results showed that the expressions of PD-L1 mRNA and protein were decreased under SHP099 or siSHP2 treatment, whereas the cells overexpressing SHP2 exhibited the opposite effects. Additionally, the effects of PD-L1 on the expression of p-ERK and p-SHP2 were investigated in PD-L1-overexpressed cells following WSN or PR8 infection, determining that the PD-L1 overexpression led to the decreased expression of p-SHP2 and p-ERK induced by WSN or PR8 infection. Taken together, these data reveal that PD-L1 could play an important role in immunosuppression during IAV/H1N1 infection; thus, it may serve as a promising therapeutic target for development of novel anti-IAV drugs. [ABSTRACT FROM AUTHOR]

Tree crown diameter (CD) values, relating to the rate of material exchange between the forest and the atmosphere, can be used to evaluate forest biomass and carbon stock. To map tree CD values using meter-level optical remote sensing images, we propose a novel method that interprets the relationships between the spectral reflectance of pixels and the CD. The approach employs the spectral reflectance of pixels in the tree crown to express the diversity of inclination angles of leaves based on the radiative transfer model and the spatial heterogeneity of these pixels. Then, simulated and acquired GF-2 images are applied to verify the relationships between spatial heterogeneity and the tree CD. Meanwhile, filter-based and object-based methods are also employed to extract three types of variables (spectral features, texture features, and spatial heterogeneity). Finally, the tree CD values are mapped by four models (random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM), and multiple linear regression (MLR)), using three single types of variables and combinations of variables with different strategies. The results imply that the spatial heterogeneity of spectral reflectance is significantly positively correlated with tree CD values and is more sensitive to tree CD values than traditional spectral features and textural features. Furthermore, the ability of spatial heterogeneity to map tree CD values is significantly higher than traditional variable sets after obtaining stable features with appropriate filter window sizes. The results also demonstrate that the accuracy of mapped tree CD values is significantly improved using combined variable sets with different feature extraction methods. For example, in our experiments, the R2 and rRMSE values of the optimal results ranged from 0.60 to 0.66, and from 15.76% to 16.68%, respectively. It is confirmed that spatial heterogeneity with high sensitivity can effectively map tree CD values, and the accuracy of mapping tree CD values can be greatly improved using a combination of spectral features extracted by an object-based method and spatial heterogeneity extracted by a filter-based method. [ABSTRACT FROM AUTHOR]

To improve the efficiency of polycrystalline silicon solar cells, process optimization is a key technology in the photovoltaic industry. Despite the efficiency of this technique to be reproducible, economic, and simple, it presents a major inconvenience to have a heavily doped region near the surface which induces a high minority carrier recombination. To limit this effect, an optimization of diffused phosphorous profiles is required. A "low-high-low" temperature step of the POCl3 diffusion process was developed to improve the efficiency of industrial-type polycrystalline silicon solar cells. The low surface concentration of phosphorus doping of 4.54 × 1020 atoms/cm3 and junction depth of 0.31 μm at a dopant concentration of N = 1017 atoms/cm3 were obtained. The open-circuit voltage and fill factor of solar cells increased up to 1 mV and 0.30%, compared with the online low-temperature diffusion process, respectively. The efficiency of solar cells and the power of PV cells were increased by 0.1% and 1 W, respectively. This POCl3 diffusion process effectively improved the overall efficiency of industrial-type polycrystalline silicon solar cells in this solar field. [ABSTRACT FROM AUTHOR]

Simple Summary: Lymphatic vessels are low-pressure, blind-ended tubular structures essential in maintaining tissue fluid homeostasis, immune cell transport, and lipid transport. More and more evidence showed that lymphangiogenesis might be closely related to the development of many diseases, and the intervention of lymphangiogenesis may be a new direction of disease treatment. This review aims to discuss the molecular mechanisms of lymphangiogenesis, the effect of lymphangiogenesis on tumor immune tolerance, the emerging role of meningeal lymphatics and cardiac lymphatics, and the promising applications of lymphangiogenesis in immunotherapy and bioengineering materials. The lymphatic system is a channel for fluid transport and cell migration, but it has always been controversial in promoting and suppressing cancer. VEGFC/VEGFR3 signaling has long been recognized as a major molecular driver of lymphangiogenesis. However, many studies have shown that the neural network of lymphatic signaling is complex. Lymphatic vessels have been found to play an essential role in the immune regulation of tumor metastasis and cardiac repair. This review describes the effects of lipid metabolism, extracellular vesicles, and flow shear forces on lymphangiogenesis. Moreover, the pro-tumor immune tolerance function of lymphatic vessels is discussed, and the tasks of meningeal lymphatic vessels and cardiac lymphatic vessels in diseases are further discussed. Finally, the value of conversion therapy targeting the lymphatic system is introduced from the perspective of immunotherapy and pro-lymphatic biomaterials for lymphangiogenesis. [ABSTRACT FROM AUTHOR]

Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest. [ABSTRACT FROM AUTHOR]

As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models. [ABSTRACT FROM AUTHOR]

A plasma jet-triggered gas switch (PJT-GS) has been developed as an important piece of equipment to operate in an ± 800 kV ultra-high voltage direct current transmission system (UHV DC) to achieve grid system protection and control. The crucial factors that would affect its operational performance, such as the current level the PJT-GS could withstand and the gas gap distance between the two rotating electrodes, are comparatively studied in the present work by analysing the arc dynamic characteristics. The rotating electrode used in the PJT-GS is designed with a helical-slotted structure, and the arc can be rotated circularly driven by the produced transverse magnetic field (TMF) along the electrode edge. The objective of such research is to provide a thorough study of the arc dynamic behaviour during the current flowing process of the PJT-GS and also to characterise the physical mechanism that affects the arc rotation and the PJT-GS operation performance. The magnetohydrodynamic-based (MHD) approach is applied by establishing a 3D arc model. Following such a study, the variation of arc characteristics under different operation conditions could be thoroughly determined and it also could provide the guidance for the PJT-GS optimum design reasonably to support its corresponding engineering applications. [ABSTRACT FROM AUTHOR]

The metallization of polyetherimide (PEI) is widely considered to enhance its surface properties and enhance its application in engineering fields; however, adhesion is a key factor in determining the reliability of PEI metallization. A Ni/Cu/Ni multilayer coating was successfully manufactured on a batch of PEI resin reinforced with glass fibers by a two-step metallization process, including sandblasting and activation/acceleration. The microstructure and morphology of the top-surface and cross-section of the coatings were observed by scanning electron microscopy. The chemical state and composition of the deposits were characterized by both X-ray photoelectron and energy-dispersive spectroscopy. The adhesion state was qualitatively evaluated by cross-cut tests with 3M tape. The surface roughness of the substrate significantly increased after the sandblasting process, which could improve the adhesion between the multilayer coating and the PEI substrate. After the standard activation process, the acceleration made an effect on the deposition of the initial Ni layer for electroless plating. The influence of acceleration on the appearance quality of metallization on the PEI substrate was studied and, at the same time, the mechanism of acceleration was investigated and addressed. [ABSTRACT FROM AUTHOR]

Herpes simplex virus type I (HSV-1) infection is a potential risk factor involved in the Amyloid β (Aβ) associated neuropathology. However, further understanding of the neuropathological effects of the HSV-1 infection is hampered by the limitations of existing infection models due to the distinct differences between human brains and other mammalians' brains. Here we generated cerebral organoid models derived from pluripotent stem cells to investigate the HSV-induced Aβ associated neuropathology and the role of antiviral drugs in the phenotypic rescue. Our results identified that the HSV-1-infected cerebral organoids recapitulated Aβ associated neuropathology including the multicellular Aβ deposition, dysregulated endogenous AD mediators, reactive gliosis, neuroinflammation, and neural loss, indicating that cerebral organoids offer an opportunity for modeling the interaction of HSV-1 with the complex phenotypes across the genetic, cellular, and tissue levels of the human Alzheimer's disease (AD). Furthermore, we identified that two antiviral drugs, namely Ribavirin (RBV) and Valacyclovir (VCV), inhibited HSV-1 replication and rescued the neuropathological phenotypes associated with AD in the HSV-1-infected cerebral organoids, implying their therapeutic potential to slow down the progression of AD. Our study provides a high-fidelity human-relevant in-vitro HSV-1 infection model to reconstitute the multiscale neuropathological features associated with AD and discover therapeutic drug candidates relevant to the AD viral hypothesis. [ABSTRACT FROM AUTHOR]

This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation results of the extended Kalman filter (EKF) are compared with the proposed method. It improves convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Temporal connectives play a crucial role in marking the sequence of events during language comprehension. Although existing studies have shown that sentence comprehension can be modulated by temporal connectives, they have mainly focused on languages with grammatical tense such as English. It thus remains unclear how temporal information is processed in tenseless languages. The present study used event-related potentials (ERPs) to examine how world knowledge is retrieved and integrated in sentences linked by zhiqian (before) and zhihou (after) in Mandarin Chinese (e.g., After/Before going to the countryside, Grandpa went to the city because the air there was fresh and pure). The critical words (e.g., fresh) were either congruent or incongruent with world knowledge. Relative to the after-congruent sentences, the after-incongruent sentences evoked a P600 on critical words and a negativity on sentence-final words, whereas relative to before-congruent sentences, before-incongruent sentences showed no significant difference on critical words but a sustained negativity on sentence-final words. Additionally, before-congruent sentences elicited a larger sustained positivity (P600) than after-congruent sentences. The results suggest that before is more difficult to process than after in Mandarin Chinese, supporting the iconicity account of temporal relations. [ABSTRACT FROM AUTHOR]

Pseudorabies virus (PRV) infection could cause severe histopathological damage via releasing multiple factors, including cytokines, peptides, etc. Here, peptidomic results showed that 129 peptides were identified in PRV-infected mouse lungs and were highly involved in the process of PRV infection. The role of one down-regulated biological peptide (designated as AGDP) during PRV infection was investigated. To verify the expression profiles of AGDP in response to PRV infection, the expression level of the precursor protein of AGDP mRNA was significantly decreased in PRV-infected mouse lungs and cells. The synthesized AGDP-treating cells were less susceptible to PRV challenges than the controls, as demonstrated by the decreased virus production and gE expression. AGDP not only inhibited the expression of TNF-α and IL-8 but also appeared to suppress the extracellular release of high-mobility group box 1 (HMGB1) by inhibiting the output of nuclear HMGB1 in cells. AGDP could also inhibit the degradation of IκBα and the phosphorylation levels of P65 after PRV infection. In total, our results revealed many meaningful peptides involved in PRV infection, thereby enhancing the current understanding of the host response to PRV infection, and how AGDP may serve as a promising candidate for developing novel anti-PRV drugs. [ABSTRACT FROM AUTHOR]

In this study, a spiral groove liquid film vaporization model based on the viscosity–temperature equation, fluid internal friction, saturation temperature, and pressure relationship equation was established. Using a multiphase flow model based on the finite volume method, the influence of the change in the mass transfer coefficient on the vaporization of the liquid film was studied. Moreover, the influence law of structural parameter changes in liquid film vaporization characteristics and sealing performance was analyzed. The results indicate that, with an increase in the mass transfer coefficient, the average vapor phase volume fraction first increases and then gradually stabilizes. When calculating the average vapor phase volume fraction, it is necessary to consider the influence of the mass transfer coefficient, whereas its effect on the opening force and leakage can usually be neglected. Under the optimal mass transfer coefficient conditions, the average vapor phase volume fraction increases with an increase in the helix angle, groove-weir ratio, and groove depth. By comparison, with an increase in the groove-diameter ratio, the average vapor phase volume fraction first increases and then decreases. The opening force decreases with an increase in the helix angle, groove-to-weir ratio, and groove depth. On the other hand, it first decreases and then increases with an increase in the groove-diameter ratio. The leakage rate increases first and then stabilizes with an increase in the helix angle. Moreover, it increases continuously with an increase in the groove-diameter ratio, groove-weir ratio, and groove depth. [ABSTRACT FROM AUTHOR]

High-precision disk resonator gyroscope has a high quality factor in order to improve the performance of the gyroscope, as the high quality factor can lead to a long starting time. In this paper, a control system of the driving loop of the disk MEMS resonant gyroscope with the quick start is designed. The control system has functions of quick frequency locking and fast step response. Coarse-precision mode transition system is designed for quick frequency locking. A large-small mode transition system is designed for fast step response. The correctness of the design is verified by circuit test. The test results show that the start-up time is reduced by over 80% compared with the traditional control loop. [ABSTRACT FROM AUTHOR]

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms. [ABSTRACT FROM AUTHOR]

Flexible resource scheduling and network forecast are crucial functions to enhance mobile vehicular network performances. However, BaseStations (BSs) and their computing unit which undertake the functions cannot meet the delay requirement because of limited computation capability. Offloading the time-sensitive functions to User Equipment (UE) is believed to be an effective method to tackle this challenge. The disadvantage of the method is offloading occupies communication resources, which deteriorate the system capability. To better coordinate offloading and communication, a multi-connectivity enhanced joint scheduling scheme for distributed computation offloading and communication resources allocation in vehicular networks is proposed in this article. Computation tasks are divided into many slices and distributed to UEs to aggregate the computation capability. A communication-incentive mechanism is provided for involving UEs to compensate the loss of UEs, while multi-connectivity is adopted to enhance the system throughput. We also defined offloading failure ratio as a conclusive condition for offloading size by analyzing the movement of UEs. By a two-step optimization, the co-scheduling of offloading size and throughput is solved. The system-level simulation results show that the offloading size and throughput of the proposed scheme are larger than comparisons when the time constraint is tight. [ABSTRACT FROM AUTHOR]