In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

Psychrophilic anaerobic digestion emerges as an appealing integrated solution for the management of agricultural waste, particularly for farmers in regions where the average temperature does not exceed 26°C, as seen in coffee cultivation. Therefore, this study seeks to assess the biomethane potential of thermochemical treated coffee husk through psychrophilic anaerobic digestion (C3-20°C-w/pretreatment). To examine its viability, outcomes were compared with reactors operating at both mesophilic (C1-35°C) and psychrophilic (C2-20°C) conditions, albeit without the use of pretreated coffee husk. The C3-20°C-w/pretreatment test demonstrated a 36.89% increase (150.47 mL CH4/g VS; 161.04 mL CH4/g COD), while the C1-35°C test exhibited a 24.03% increase (124.99 mL CH4/g VS; 133.77 mL CH4/g COD), both in comparison to the C2-20°C test (94.96 mL CH4/g VS; 101.63 mL CH4/g COD). Notably, the C3-20°C-w/pretreatment trial yielded superior outcomes, accompanied by an associated energy output of 4262.2 KWh/year, sufficient to meet the annual energy demands of 588 residences. This marks an increase of 100 and 217 residences compared to mesophilic and psychrophilic AD of CH without pretreatment, respectively.

To study the influence of electrothermal effect of ESD protection device on the reliability of inte-grated circuit,a theoretical model of ESD protection device thermal resistance is constructed in the paper based on the Conservation of Energy Principle. By analyzing the temperature relativity between electricity parameter and thermology parameter of ESD protection device characterized by self-heating thermal resistance, the quantitative relationship expression of physical parameter and geometric parameter is obtained, which can be used to improve ESD protection device elec-tro-thermal effect. The simulation testing and the real tape-out testing of ESD protection device self-heating thermal resistance based on ggNMOS are shown and compared. It is concluded that self-heating thermal resistance is an effective index for the protection device electrothermal char-acterization. Based on the established model, the self-heating thermal resistance could be decreased to improve the homogeneity of the heat transference. The uneven distribution of device temperature can be reduced, and the overall reliability of chips can be improved.

Most methods for measuring environmental lead (Pb) content are time consuming, expensive, hazardous, and restricted to specific analytical systems. To provide a facile, safe tool to detect Pb, we created pMet-lead, a portable fluorescence resonance energy transfer (FRET)-based Pb biosensor. pMet-lead comprises a 3D-printed frame housing a 405-nm laser diode — an excitation source for fluorescence emission images (YFP and CFP) — accompanied by optical filters, a customized sample holder with a Met-lead 1.44 M1 (the most recent version)-embedded biochip, and an optical lens aligned for smartphone compatibility. Measuring the emission ratios (Y/C) of the FRET component enables Pb detection with a dynamic range of nearly 2 (1.96), pMet-lead/Pb dissociation constant (K_d) 45.62 nM, and limit of detection 40 nM (0.832 μg/dL, 8.32 ppb). To mitigate earlier problems with lack of selectivity for Pb vs. zinc, we preincubated samples with tricine, a low-affinity zinc chelator. We validated pMet-lead measurements of characterized laboratory samples and unknown samples from six regions in Taiwan by inductively coupled plasma mass spectrometry (ICP-MS). Notably, two unknowns had Y/C ratios significantly higher than that of the control (3.48 ± 0.08 and 3.74 ± 0.12 vs. 2.79 ± 0.02), along with Pb concentrations (10.6 ppb and 15.24 ppb) above the WHO-permitted level of 10 ppb in tap water, while the rest four unknowns showing no detectable Pb upon ICP-MS. These results demonstrate that pMet-lead provides a rapid, sensitive means for on-site Pb detection in water from the environment and in living/drinking supply systems to prevent potential Pb poisoning.

Pollution spillover is an important issue to improve the water environment of transboundary rivers, which has been aggravated by the decentralization of China's pollution control and promotion system.This paper analyzes the evolution of the pollution reduction mandates and the possible change of water environment in China which are tested with the water quality data of state key monitoring sections in 2004-2014 .In terms of research methods, this paper mainly uses Propensity Score Matching reference with group difference test and OLS. Empirical findings support the association between decentralization and pollution levels. The pollution levels of the monitoring points located at the boundary are significantly higher than that of interior counties. The pollution of tributary is more serious than trunk stream，which quickly reversed after the system changed. Water pollution levels rapidly changes when we compare the monitoring site in front of jurisdictional boundaries with that after the jurisdictional boundaries. We draw the following conclusions that local goverments may manipulate pollution within their jurisdictions and total pollutant control system will exacerbate border pollution, while water quality inspection can reduce marginal pollution.

Green products have excellent potential for discovering and producing new medicinal products. In recent years, there is a growing interest towards the green synthesis of metal nanoparticles, particularly from plants sources. This eco-friendly approach was used in the recent research to biosynthesize zinc oxide nanoparticles (ZnO NPs) from Cissus antractica plant. The present study aims to examine the cell cytotoxicity, cell death, and pathways of apoptosis in lung cancer cells (A549) and inflammation properties treated with biosynthesized ZnO NPs. Additionally, synthe-sized ZnO nanoparticles were characterized using UV–vis spectroscopy, X-ray diffraction (XRD), Transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), and Energy-dispersive X-ray spectroscopy (EDS). The XRD and TEM analyses exhibited 31 nm as the size of synthesized nanoparticles, the pure crystal form of ZnO NPs, and the shape of the NPs as nearly spherical and hexagonal quartzite. The MTT assay used for cytotoxicity evaluation de-picted the significant toxic effect of CA-ZnONPs against the A549 lung cancer cells at concentra-tions up to 20 µg/mL than non-cancerous cells. Furthermore, we observed an elevated ROS level in cancer cells. These NPs also demonstrated dose-dependent ability to suppress colony for-mation and cell migration, which increases their potential as a lung cancer treatment. Further-more, The NPs formulation's ability to induce apoptosis in cancer cells was further supported by Hoechst and propidium iodide dye. Further, CA ZnO NPs increased the gene expression of BAX, Cyto-c, Caspase 3 and 9, but decreased the expression of Bcl-2, indicating that the nanoformulation triggered mitochondrial-mediated apoptosis. Besides, this study showed that NPs suppressed in-flammatory responses by subsequently decreasing reactive oxygen species (ROS) generation, downregulating the expression of the pro-inflammatory cytokines TNF-α, iNOS, COX-2, IL-6, IL-8, and NO. Moreover, the study's findings imply that Cissus antractica are a potential source with biosynthesize nanoparticles that can might be further investigated for the progress of an-ti-inflammatory and anti-cancer drugs with further study.

The aim of this systematic review is to describe and to identify prospect of β-Tricalcium Phosphate (β-TCP) as Alveolar Bone Grafting (ABG) in Cleft Lip/Palate (CL/P) or alveolar bone cleft defect. A systematic review protocol based on Preferred Reporting Items for Systematic Reviews and Meta Analyses 2020 (PRISMA 2020) was drafted. The literature search was conducted with MEDLINE/PubMed, Web of Science/ ISI-Web of Knowledge, Scopus and Cochrane Library with English language and no publication year limits. The keywords yielded a total of 5824 publications. There are 3196 suitable articles to evaluate after removing duplicates and languages. Afterward, 1315 studies left after doing title and abstract reading. In addition, 85 full-articles were assessed for eligibility. The read the complete texts of those papers and eventually chose 20 that matched the inclusion requirements. Thirteen of the twenty studies included in this systematic review had a low risk of bias, one had a high risk of bias, and six had moderate bias which not report the randomization. β-TCP as ABG is biocompatible, visible and practical, offers a less invasive procedure, and does not interfere with orthodontic treatment. β-TCP as synthetic ABG can be the alternative to autologous bone grafts with several terms and conditions. Improvement of β-TCP efficacy for ABG in CL/P or alveolar bone cleft defects can be done by a tissue engineering approach combining β-TCP with growth factor, mesenchymal stem cells, or other graft materials and modification of β-TCP physical properties.

Spermatogonial stem cells (SSCs) are the only primitive spermatogonial cells in males that can naturally transmit genetic information to their offspring and replicate throughout their lives. Phospholipase D family member 6 (PLD6) has recently been found to be a surface marker for SSCs in mice and boars; however, it has not been validated in cattle. The results of RT-PCR and qRT-PCR found that the relative expression of PLD6 gene in the testicular tissues of 2-year-old Simmental calves was significantly higher than that of 6-month-old calves. Immunofluorescent staining further verified the expression of PLD6 protein in bovine spermatogenic cells like germ cell marker VASA. Based on multiple bioinformatic databases, PLD6 is a conservative protein which has high homology with mouse Q5SWZ9 protein. It is closely involved in the normal functioning of the reproductive system. Molecular dynamics simulation analyzed the binding of PLD6 as a phospholipase to cardiolipin (CL), and PLD6-CL complex showed high stability. The protein interaction network analysis showed that there is a significant relationship between PLD6 and piRNA binding protein. PLD6 acts as an endonuclease and participates in piRNA production. In addition, PLD6 in bovine and mouse testes has a similar expression pattern with the spermatogonium-related genes VASA and PIWIL2. In conclusion, these analyses imply that PLD6 has a relatively high expression in bovine testes and could be used as a biomarker for spermatogenic cells including SSCs.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and patho-logical detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse at-tention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edge features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy re-duces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation tech-niques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare profes-sionals.

SARS-CoV-2 causes severe pneumonia epidemics and probably originated in horseshoe bats, but the intermediate host is unknown. The interaction of SARS-CoV-2 spike protein and its acceptor protein ACE2 is an important issue in determining viral host range and cross-species infection, while the binding capacity of Spike protein to ACE2 of different species is unknown. Here, we used the atomic structure model of SARS-CoV-2 and human ACE2 to assess the receptor utilization capacity of ACE2s from different species including cats, chimpanzees, dogs, cattles. Our results show, domestic cats (Felis catusc) and dogs (Canis lupus familiaris) are more susceptible to infection by SARS-CoV-2 and that they can efficiently transmit the virus to previously uninfected animals that are housed with them. Especially, cats could be a choice of animal model for screening antiviral drugs or vaccine candidates against SARS-CoV-2.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

Understanding the spatiotemporal characteristics of merging behavior is crucial for the ad-vancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merg-ing patterns, and investigate how various factors, such as merging scenarios and vehicle types, in-fluence driving behavior. Initially, a framework based on High-Definition (HD) Map is devel-oped to extract trajectory information in a meticulous manner. Subsequently, eight distinct merg-ing patterns (Pattern A to H) are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane's spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifying high-risk situations. Our novel findings reveal that Patterns C and F, with Time-to-Collision (TTC) values less than 2.5 seconds, pose a significantly higher risk than other patterns. In traffic simulations, Patterns B and E can be integrated, as they show negligible differences in speed, distance traveled, and duration. Additionally, the practice of truck platooning has a signif-icant impact on vehicle merging behavior. This study enhances the understanding of merging be-havior, facilitating autonomous vehicles' ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic manage-ment, and enabling the effective integration of autonomous driving systems with human drivers.

Haze always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for satellite image dehazing. Specifically, a hybrid CNN-transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage, so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrate that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

The accumulation of waste plastics has a severe impact on the environment, and therefore, the development of efficient chemical recycling methods has become an extremely important task. In this regard, a new strategy of degradation product-promoted depolymerization process was proposed. Using N,N'-dimethyl-ethylenediamine (DMEDA) as depolymerization reagent, an efficient chemical recycling of poly(bisphenol A carbonate) (BPA-PC) material was achieved under mild conditions. The degradation product 1,3-dimethyl-2-imidazolidinone (DMI) was proven to be the critical factor in facilitating the depolymerization process. This strategy does not require catalysts or auxiliary solvents, which is truly green process. This method improves the recycling efficiency of BPA-PC and promotes the development of plastic reutilization.

The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒18.29 m, GP: 0.12‒0.15, and HC: 18.64‒26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22‒2.54, BS: 68.47‒82.34, and GP: 0.1‒0.14.

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

2-Pyridone ligand-facilitated palladium-catalyzed direct C–H bond functionalization via the transient directing group strategy has become an attractive topic. Here we report a Pd-catalyzed direct β-C(sp3) –H arylation reaction of tertiary aliphatic aldehydes by using an α-amino acid as a transient directing group in combination with a 2-pyridone ligand.

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. While a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous-driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming inputs characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming inputs characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming inputs features, we introduce a backbone switcher based on the identified streaming inputs characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming inputs features.

A novel concrete beam-column connection utilizing L-shaped steel bars is proposed to address the growing demand for prefabricated buildings and to ensure good seismic performance in such beam-column structures. After positioning two prefabricated beams with L-shaped tendons into the designated connection points at the top and bottom of the columns, concrete is poured into the post-cast section of the joint and the composite beam area, realizing a connection between the beams and columns. Quasi-static tests were performed on four combined nodes and one cast-in-place node to investigate their failure modes and stress mechanisms. Seismic performance indices, including hysteretic curves, skeleton curves, ductility performance, and energy dissipation capacity, were analyzed. Concurrently, the finite element method (FEM) was employed for parameter analysis. By integrating the test and FEM results, an equation for calculating the shear capacity of the connection was derived. The findings demonstrate that the hysteresis curve of the newly developed prefabricated joints is relatively full, with their overall performance index comparable to that of cast-in-place joints. Additionally, enhancing the post-casting area of concrete, the length of the L-shaped bars, the concrete strength in the composite beam region, the axial compression ratio, or the steel tube dimensions can effectively improve overall performance. The derived equation for the shear-bearing capacity of the connection satisfies design and application requirements.

Keywords: Fatigue crack propagation; Welding residual stress; Plastic re-distribution; Stress ratio effect

An novel indoor positioning method of pigsty based on Ultra Wideband (UWB) technology is proposed via reasonably arranging the base stations (BTS) and positioning tags, which can provide a strong support for the accurate positioning of specific pig pens in the pig house. The distribution of Dilution of Precision (DOP) values are analysed in different deployment schemes which includes the amount, the height, the deployment shape and distance of BTS, though positioning simulation. Moreover, the positioning effect of dynamic following positioning (DFP), the root-mean-square error (RMSE) and variance of static positioning (SP) are measured. It is noteworthy that Kalman filtering algorithm is adopted to smooth the DFP experimental data for reducing the positioning error. The simulated results show that the DOP values decrease with the increasing of the height and the distance, and these results and laws are consistent with the experimental results. However, the DOP values are reached best when the height is from 1.75 m to 2.75 m, and the distance is over 3.00 m. Furthermore, the DOP values of quadrilateral deployment is smaller than the one of triangle. In addition, variance of SP and DFP are decreased by utilizing Kalman filtering algorithm. Therefore, the excellent positioning system and its solutions in pigsty can be provided by our proposed indoor positioning method.

In the context of evaluating physical function in individuals with stroke, the 3-meter backward walk test (3MBWT) emerges as a potential tool of interest. The purpose of this study was to assess the test-retest reliability and construct validity of the 3MBWT and its correlation with falling incidents. Conducted in a neurological rehabilitation center, 35 ambulatory individuals with stroke were enrolled within a month post-stroke onset. These participants, with a Functional Ambulation Category score of ≥4, underwent the 3MBWT, Functional Gait Assessment (FGA), 10-Meter Walk Test (10MWT), and 6-Minute Walk Test (6MWT) three times by different physiotherapists. Results indicated the 3MBWT displayed high reliability (r=0.97-0.98) and showed significant construct validity with other established walking tests like the 6MWT (r=-0.78) and 10MWT (r=0.71), with a moderate correlation to the FGA (r=-0.54). No marked differences in test outcomes were observed between participants based on their fall history. Conclusively, the 3MBWT proves to be highly reliable and validates well with existing walking function assessments for stroke patients, suggesting its potential as a time-efficient alternative.

To obtain a better estimation frequency band for source bearing estimation, source bearing estimation experiments using an ice-mounted geophone were performed at three sites on the Arctic ice cap. By transforming to the frequency domain, errors of the bearing estimation in a number of frequency bands within 1000 Hz and the horizontal peak displacements received by the geophone in the corresponding frequency bands were obtained. After comparing the variation of errors to the peak displacements in the same frequency band, it is found that the errors are related to the peak displacements, and higher peak displacement values mean smaller errors. When the incident angle of the acoustic source and the ice thickness meet certain conditions, some modes of the Lamb waves of the ice plate are excited, the peak displacements at the same frequency band are increased, the errors of bearing estimation are reduced, and the effective frequency band for bearing estimation is broadened. These results provide some new ideas for the bearing estimation of acoustic sources beneath Arctic ice.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

Purpose: This study builds upon an established effective training method to investigate the advantages of high variability phonetic identification training for enhancing lexical tone perception and production in Mandarin-speaking pediatric cochlear implant (CI) recipients, who typically face ongoing challenges in these areas. Method: Thirty two Mandarin-speaking children with CIs were quasi-randomly assigned into the training group (TG) and the control group (CG). The sixteen TG participants received five sessions of high variability phonetic training (HVPT) within a period of three weeks. The CG participants did not receive the training. Perception and production of Mandarin tones were administered before (pretest) and immediately after (posttest) the completion of HVPT via lexical tone recognition task and picture naming task. Both groups participated in the identical pretest and posttest with the same time frame between the two test sessions. Results: TG showed significant improvement from pretest to posttest in identifying Mandarin tones for both trained and untrained speech stimuli. Moreover, perceptual learning of HVPT facilitated trainees’ lexical tone production as rated by a cohort of 10 Mandarin-speaking adults with normal hearing. In contrast, CG did not exhibit significant changes in either perception or production. Conclusion: The results represent initial evidence of HVPT-induced transfer of perceptual learning to lexical tone production in CI users, which supports the application of this speech training protocol to aural rehabilitation practice.

UAV (Unmanned aerial vehicle) communication offers the possibility to establish the new net-works. To overcome the PE (pointing error) and beam misalignment of millimeter-wave massive MIMO (Multiple-in multiple-out)/FSO (Free Space Optical) caused by UAV jitter, a Tensor-train decomposition based hybrid beamforming for millimeter-wave massive MIMO/FSO in UAV with RIS (Reconfigurable Intelligence Surface) networks is investigated to improve the system spectral efficiency. Firstly, the high-dimensional channels of the RIS-assisted millimeter-wave massive MIMO/FSO in UAV are represented as the low-dimensional channels by Tensor-train decomposition. Secondly, the FSO PE caused by UAV jitter can be effectively solved by BIGRU (Bidirectional Gated Recurrent Unit)-attention neural network model. The fast-fading channels and Doppler shifts are estimated by the FCTPM (Fast Circulant Tensor Power Method) based on the Tensor-train decomposition. Finally, the RIS phase shift matrix is optimized by the SVD (Singular Value Decomposition). The Hybrid beamforming and RIS phase shift matrix are esti-mated by the low-complexity PE-AltMin (Phase Extraction Alternating Minimization) method to solve the beam misalignment. Simulation experiments demonstrate that the proposed method has higher spectrum utilization than other methods.

The determination of critical ventilation flow rate is significant for the risk control and the standard development during accidental hydrogen leakage in a confined space with hydrogen-related equipment. In this paper, an analytical model for calculating critical ventilation flow rate was proposed through the quantification and constraint solution of ventilation effect and ventilation cost. The experimental method was used to investigate the effects of nozzle diameter and stagnation pressure on diffusion and ventilation for horizontal hydrogen leakage in a cuboid chamber. Ventilations from 30m3/h to 180m3/h were carried out through the rectangular vent. It was shown that the peak concentration of the measuring point is positively correlated with the stagnation pressure and the nozzle diameter. The experimental data are used to verify the analytical model by calculating the effective ventilation time. The study demonstrates that the critical ventilation flow rate can be increased significantly at higher stagnation pressures and larger nozzle diameters. And the discrepancy of critical ventilation flow rates under different nozzle diameters will be enhanced with the increase of stagnation pressure. For the stagnation pressure of 0.4MPa, the critical ventilation flow rate under 4mm nozzle even increases by 52% relative to the 2mm nozzle.

As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.

In order to assess the degradation state of rolling bearings and accurately grasp the remaining life information of rolling bearings, a bearing remaining life prediction method based on the grey wolf optimization algorithm to improve the BP neural network model is proposed. The method consists of two steps, firstly, using the craggy value and root mean square value to determine the first failure time of rolling bearings so as to approximate the input features, and secondly, using the Grey Wolf optimization algorithm to optimize the BP neural network to construct the degradation model of the bearings through machine learning. The method reduces the prediction error compared with conventional techniques, indicating that the method can effectively simulate the bearing degradation process and predict the remaining useful life (RUL) of the bearing.

These years, disease-causing and disabling diseases have caused great concern. Neurological, musculoskeletal disorders are diverse and affect people of a wide range of ages. And the lack of comprehensive diagnostic methods places a huge burden on healthcare systems and social economies. In this paper, the current status of clinical research on neuromuscular diseases is introduced, and the advantages of magnetic field measurement compared with clinical diagnostic methods are illustrated. The paper makes a comprehensive description about the related technology of superconducting quantum interference devices (SQUIDs), magnetic field detection noise suppression scheme, the development trend of sensor detection system, and the application and model establishment of neuromuscular magnetic field. The current researches and developments in the worldwide are compared simultaneously in this paper and finally the conclusions and outlook are put forward. This review will be very interesting and helpful to the scientific community.

The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.

A variable speed anti-tangling reel mechanism was designed to address the problems of tangling, hanging and rewinding of the tassel wheel and the high paddling loss in the current Chinese rape combine harvester cutting table. The kinematic equations of the reel mechanism were developed, and the analysis showed that the speed in the entry stage is low and the speed in the exit stage is high, and the attitude of the reel teeth change with the rape plant movement during the whole paddling process. A single-factor test and a multi-factor regression orthogonal combination test were carried out using the cutting table loss rate of rape harvesting as the evaluation index. The main and secondary factors affecting the cutting table loss of the variable speed reel were obtained as the reel speed, the machine travel speed and the ground angle of the frame. A mathematical model of regression of cutter loss and the main influencing factors was established, with the coefficient of determination R2 = 0.9918. The optimal combination of parameters for the variable speed reel was obtained with the lowest cutting table loss rate in rape harvesting as the optimization target: reel speed 25r/min, frame parallel to the ground and implement travel speed 0.7m/s. Field validation tests and comparative tests of cutting table loss of the two reels were carried out. The test results show that the regression mathematical model of the cutting table loss is more accurate, and the cutting table loss is reduced by 13.9% with the variable speed reel compared to the cam-action reel, and there are no problems with the paddle teeth hanging and tangling during the paddling process.

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

In the increasingly complex environment of downhole working conditions such as frequent opening and closing of oil and gas wells, acidification/multi-stage fracturing, steam huff and puff, and strong injection and production, the traditional casing string design method standards based on strength design have gradually highlighted their limitations. It is necessary to formulate string design method standards that consider the full life cycle of wells such as drilling and completion, fracturing, and production operations. Starting from the analysis of the advantages and disadvantages of traditional casing string design methods, this paper presents the characteristics of the strain design methods and seal design methods that have been formulated for the life cycle of the string. The strain design method breaks through the traditional design concept and allows the design concept of controllable deformation of the pipe string. The sealing design method is currently the only standard method for the design of tubing strings. At the same time, it further proposes to consider the trend of pipe strength deterioration and establish a time dimension-based life cycle pipe string design method standard, which effectively solves the safety problem of pipe string design in production and operation.

This paper presents a new broadband ocean bottom seismograph (OBS) developed by the SUSTech OBS lab for passive-source seafloor seismic observations. This instrument, called Pankun, has several key features that set it apart from traditional OBS instruments. Besides the seismometer-separated scheme, these features include a unique shielding structure to minimize current-induced noise, a compact gimbal for accurate leveling, and low power consumption for extended operation on the seafloor. The design and testing of Pankun's primary components are thoroughly described in the paper. The instrument has been successfully tested in the South China Sea, demonstrating its ability to record high-quality seismic data. The anti-current shielding structure of Pankun OBS has the potential to improve low-frequency signals, particularly on the horizontal components, in seafloor seismic data.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.

Here, a process for leaching vanadium from calcified roasting pellets (CPVC) of vanadium-titanium iron concentrate by a two-stage sulfuric acid cycle was proposed. When the silicon removal acid concentration of the pellet in the first stage was 1.5 mol/L, the solid-liquid ratio was 6: 1, the silicon removal acid concentration of the leaching solution was 3.0 mol/L, and the standing time was 48 h, the silica gel formation time was 23 h, the filtration time was 70 s, and the loss rates of vanadium and iron were 1.52% and 0.17%, respectively. When the acid concentration was 2 mol/L, at room temperature, using a leaching time of 28 days, and a solid-liquid ratio of 5: 1 in the second stage, the total leaching rates of vanadium and iron were 75.52% and 0.71%, respectively. The concentration of vanadium in the leaching solution reached 6.80 g/L, and vanadium was directly precipitated without extraction. After secondary roasting, the crushing strength of the pellets reached 2250 N, which met the requirement for blast furnace iron making. The Eh-pH diagrams of the V-Fe-H2O system at different temperatures were plotted. Thermodynamically, it was difficult to selectively leach vanadium and iron by changing the conventional acid leaching conditions. In addition, the pellets before and after leaching were analyzed. The grade of iron in the pellets increased slightly after leaching, and the main phases in the pellets remained as Fe2O3 and Fe9TiO15. The S in the sulfuric acid solution entered the leached pellets during the acid leaching reaction and was removed by the secondary roasting of the leached pellets.

Fringe projection profilometry(FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring some objects with high reflective surfaces or high dynamic range surfaces remains challenging for FPP. Some multiple exposure image fusion methods have been proposed and successfully improved the measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by artificial experience or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two main aspects of improvement on adaptively optimizing the initial exposure and the exposure sequence. By introducing the theory of information entropy, combined with the analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is first proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the base of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are given. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method by measuring the objects with different surface reflectivity and in different ambient lighting conditions.

This paper proposes a new structure of the dual-rotor hybrid-excited axial-flux permanent magnet vernier machine (DR-HEAFPMVM) with the modular stator and the consequent-pole PM (CPM) rotor for low-speed, high torque density applications such as in-wheel electric vehicles. The tooth-wound non-overlapping armature windings and direct current (DC) excitation windings are respectively arranged in stator main-teeth and split-teeth to obtain the modulated and adjustable air-gap flux densities, which result in the high torque density and outstanding flux-weakening capability. First, the design consideration, operation principle, and the air-gap flux density distributions of the proposed machine are elaborated based on the air-gap permeance function. Then, the influence of the pole ratios (PRs) and the DC excitation currents on the main electromagnetic performances of the DR-HEAFPMVM, such as the flux-weakening capability, back-electromotive force (back-EMF), on-load electromagnetic torque, loss distribution, and efficiencies, is investigated using the 3-D finite-element method (FEM). Results verify the feasibility of the flux adjustment of the DC excitation windings equipped in the split-tooth, and the design with a pole ratio of 8/1 tends to have higher torque density, higher machine efficiency, and considerable flux-weakening capability compared with the other two PRs.

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Because of the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics, including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. Since PM in swine barns is of primarily biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

In this study, a new method for synthesizing Au-NaYF4:Yb3+/Er3+-DSPE-PEG2K nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Au nanoparticles were doped into upconversion nanomaterials and modified with DSPE-PEG2k up-conversion nanomaterials. This material is known as Ag-UCNPs-DSPE-PEG2k, it improves both the luminous intensity because of the doped Au nanoparticles and has low cytotoxicity because of the DSPE-PEG2k modified. Exciting UCNPs with a wavelength of 980nm near-infrared light will emit light with a wavelength of 520nm to further excite gold nanoparticles to convert light energy into heat. Successful synthesized gold nanoparticles was confirmed using transmission electron microscopy (TEM). The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Au nanoparticles. Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Au nanoparticles. The cytotoxicity of Au-UCNPs-DSPE-PEG2K was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) method.

To overcome the two-phase flow instability of traditional boiling heat dissipation technologies, a porous wick was used for liquid-vapor isolation, thus realizing efficient and stable boiling heat dissipation. A pump-assisted capillary phase-change loop with methanol as working medium was established to study the effect of liquid-vapor pressure difference and heating power on its start-up and steady-state characteristics. The results indicated that the evaporator undergoes four heat transfer modes including flooded, partial flooded, thin film evaporation and overheating. The thin film evaporation mode was the most efficient one with the shortest start-up period. Besides, the heat transfer modes were determined by liquid-vapor pressure difference and power. The heat transfer coefficient could be significantly improved and the thermal resistance could be reduced by increasing liquid-vapor pressure difference as long as it did not exceed 8 kPa. However, when the liquid-vapor pressure difference exceeded 8kPa, its influence on the heat transfer coefficient weakened. In addition, a two-dimensional heat transfer mode distribution diagram considering both liquid-vapor pressure difference and power was drawn through a great number of experiments. During engineering application, the liquid-vapor pressure difference can be controlled to maintain efficient thin film evaporation in order to achieve the optimum heat dissipation effect.

Copper-catalyzed and organocopper-involved reactions are of great significance in organic synthesis. To have a deep understanding of the reaction mechanisms, the structural characterizations of organocopper intermediates become indispensable. Meanwhile, the structure-function relationship of organocopper compounds would advance rational design and development of new Cu-based reactions and organocopper reagents. Compared to the mono-carbonic ligand, the C,N- and C,C-bidentate ligands better stabilize the unstable organocopper compounds. The bidentate ligands can chelate to the same copper atom via ?2-mode, forming a mono-cupra-cyclic compounds with at least one acute C-Cu-C angle. When the bidentate ligands bind to two copper atoms via ?1-mode at each coordinating site, the bimetallic macrocyclic compounds will form nearly linear C-Cu-C angles. The anionic coordinating sites of the bidentate ligand can also bridge two metals via ?2-mode, forming organocopper aggregates with Cu-Cu interactions and organocuprates with contact ion pair structures. The reaction chemistry of some selected organocopper compounds is highlighted, showing their unique structure-reactivity relationships.

Cell-penetrating peptides (CPPs) have been evaluated as enhancers in drug delivery, their addition in medical formulations favors absorption allowing obtaining the pharmacological effect with lower drug doses. In vaccine formulations their inclusion has been also explored with interesting results. Currently mucosal vaccination constitutes a promising alternative with the main advantage of inducing both systemic and mucosal immune responses, which are crucial for control tumors and infections at mucosal tissues. The known CPP Penetratin was recently evaluated in vaccine formulations designed for nasal administration. The authors demonstrated that this non-covalent linked CPP could improve the antigen-specific systemic and mucosal antibody responses. In the present work we evaluate in Balb/C mice the nasal immune-enhancing effect of four CPPs. Animals were intranasally immunized with CPP and the recombinant hepatitis B surface protein (HBsAg) as model antigen. The IgG antibody response in sera and the mucosal IgA response were measured by ELISA. The IFN-g secretion response at spleen was also evaluated by ELISPOT and ELISA. Among the CPPs studied one novel peptide stand out by its ability to potentiate the humoral and cellular immune response against the co-administered antigen. Considering that the use of mucosal routes is a promising strategy in vaccination against infectious diseases and cancer, which are gaining special relevance nowadays in the development of novel candidates against SARS-CoV-2 and other potential emerging respiratory virus, the searching and development of safe mucosal adjuvants constitute a current need.

Theories of reading comprehension emphasize decoding and listening comprehension as two essential components. The current study aimed to investigate how Chinese character decoding and context-driven auditory semantic integration contribute to reading comprehension in Chinese middle school students. Seventy-five middle school students were tested. Context-driven auditory semantic integration was assessed with speech-in-noise tests in which the fundamental frequency (F0) contours of spoken sentences were either kept natural or acoustically flattened with the latter requiring a higher degree of contextual information. Statistical modelling with hierarchical regression was conducted to examine the contributions of Chinese character decoding and context-driven auditory semantic integration to reading comprehension. Performance on Chinese character decoding and auditory semantic integration scores with the flattened (but not natural) F0 sentences significantly predicted reading comprehension. Furthermore, the contributions of these two factors to reading comprehension were better fitted with an additive model instead of a multiplicative model. These findings indicate that reading comprehension in middle schoolers is associated with not only character decoding but also the listening ability to make better use of the sentential context for semantic integration in a severely degraded speech-in-noise condition. The results add to our better understanding of the multi-faceted reading comprehension in children. Future research could further address the age-dependent development and maturation of reading skills by examining and controlling other important cognitive variables, and apply neuroimaging techniques such as functional magmatic resonance imaging to reveal the neural substrates for the contribution of auditory semantic integration and the observed additive model to reading comprehension.

Long-term dewatering of groundwater is a necessary operation for mining safety in open-pit coal mines, while extensive dewatering might cause ecological problems due to dramatical changes of moisture movement in the soil, especially in ecological-fragile areas. This paper presents a quantitative methodology to evaluate the impact of the coal mining operation on moisture movement in the vadose zone by taking the Baorixile open-pit coal mine as an example. A long-term in-situ experiments（from 2004 to 2018), laboratory analysis and numerical modelling were conducted to analyse the mechanisms and relationship among the dropping groundwater level, the vadose-zone moistures, and the ecological responses in the grassland area. The experiment data and modelling results suggest that groundwater level dropping during open-pit mining operation has limited influence on the vadose zone, exhibiting a variation of capillary water zone within a depth of 3 m while the vadose zone and soil water zone were at least 16 m deep. The critical evaporation depth of ground water is 8 m. The long-term influence radius of groundwater dewatering is about 2.72 km during the Baorixile mining operation, and the groundwater level change mainly influences the lower part of the intermediate vadose zone and the capillary water zone below 16 m, with little influence on the moisture contents in the soil water zone where the roots of shallow vegetation grow. The results from this study provide useful insight for sustainable development of coal mining in ecological-fragile areas.

Cancer is the second biggest cause of death in children. With the development of chemotherapy, there is a substantial increase in overall survival rate over recent 30 years. However, the overall mortality rate in children with cancer still remains 25%, and many survivors experience a decline in overall quality of life and long-term adverse effects caused by treatments. Although cancer cells share common characteristics, pediatric cancers are different from adult cancers in their prevalence, mutation load, and drug response. Therefore, there is an urgent unmet need to develop therapeutic approaches specifically designed for children with cancer. Nanotechnology can potentially overcome the deficiencies of conventional methods of administering chemotherapy and ultimately improve clinical outcomes. The nanoparticle-based drug delivery systems can decrease the toxicity of therapy, provide a sustained or controlled drug release, improve pharmacokinetic properties of loading contents, and achieve a targeted drug delivery with achievable modifications. Further, therapeutic approaches based on combining nanoformulated drugs with novel immunotherapeutic agents are emerging. In this review, we discuss the recently developed nanotechnology-based strategies for treating blood and solid pediatric cancers.

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5~2.5 cm and a large void ratio of 18~25%. As a newly asphalt mixture, the structure characteristics differ from traditional pavement. Therefore, it is necessary to investigated the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compactness of the Marshall sample. With the same temperature and number of blows, the scattering resistance of coarse gradation (PAC-1) is better than that of fine gradation (PAC-2), and the increased asphalt viscosity significantly improves the scattering resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage and skid resistance.

Optical tweezers, as a kind of ultra-sensitive acceleration sensing platform, show a minimum measurable value inversely proportional to the square of the diameter of the levitated spherical particle. However, the coupling of the displacement measurement between axes becomes notable, along with the increasing of the diameter. This paper analyzes the source of coupling in a forward scattering far-field detection regime and proposes a novel method of suppression. We theoretically and experimentally demonstrated that when three variable irises added into detection optics, without changing other parts of optical structures, the decoupling of triaxial displacement signals mixed with each other show significant improvement. The detection coupling ratio reduction of 49.1 dB and 22.9dB has been realized in radial and axial direction respectively, which is principally in accord with simulations. This low cost and robust approach makes it possible to accurately measure three-dimensional mechanical quantities simultaneously and even go further such as active cooling the particle to quantum ground state.

Objective: Application of ERA methods to investigate the atmospheric pollution and built environment factors influencing lung cancer incidence rate in Chinese women. Methods: Lung cancer incidence rate among Chinese women at 339 cancer registries were obtained from the China Cancer Registry Annual Report 2017, air quality and built environment data were obtained from the Greenpeace and China Construction Yearbook. After multiple covariates variables were eliminated, an exploratory regression analysis was performed using the world standardized population incidence rate as the dependent variable. Air quality and built environment factors as the independent variable. Results: Shandong Peninsula, Hebei and Liaoning are high incidence rate areas of female lung cancer in China, with significant regional aggregation. In addition to air quality factors such as industrial smoke emission data, the association between built environmental factors such as urbanization rate, development LUI, population density and greening coverage of built-up areas and female lung cancer incidence rate is statistically significant. Conclusion: In addition to air quality factors, urban spatial factors can also significantly affect respiratory health. The LUI is positively while urbanization rates and population density are negatively correlated with the incidence rate of lung cancer. The role of green space for respiratory health has not been proven. In addition, there is little relationship between income and health, and similar findings are found for indicators such as the public transportation and roads network.

This multi-center, single-blinded, randomized controlled study assessed the efficacy of phased exercise rehabilitation programs in patients who underwent a total knee arthroplasty (TKA) and investigated suitable exercise types, intensities, and frequencies for patients undergoing postoperative rehabilitation. Between January and March 2018, 494 patients who had undergone TKA were treated at two medical centers. Patients were randomized and allocated to the rehabilitation group (n=330) or the control group (n=164; treated with postoperative care). The 100-mm Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), range of motion, and Short-Form 36 (SF-36) scores were assessed and compared between the two groups. The average WOMAC score was 84.40±15.20 in the rehabilitation group and 108.30±3.90 in the control group (p=0.009). The mean VAS score was 2.54±0.16 in the rehabilitation group and 2.87±0.31 in the control group (p=0.024). Furthermore, the range of motion was 125.02±6.20 in the rehabilitation group and 116.40±1.40 in the control group (p=0.017). The mean SF-36 score was 105.40±21.50 in the rehabilitation group and 83.70±5.01 in the control group (p=0.043). This study suggests that the phase rehabilitation program could be more effective at improving pain, knee function, and quality of life than condition postoperative care after TKA.

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Due to the advantages such as low power consumption and higher concealment, deceptive jamming against synthetic aperture radar (SAR) receives extensive attention during the past decades. However, the large scene deception jamming is still a challenge because of the huge computing burden. In this paper, we propose a new large scene deceptive jamming algorithm. First, the time-delay and frequency-shift (TDFS) algorithm is introduced to improve the jamming processing speed. The system function of jammer (JSF) for a fake scatter is simplified to the multiplication of the scattering coefficient, a time-delay term in range dimension and a frequency-shift term in azimuth dimension. Then, in order to solve the problem that the effective region of the TDFS algorithm is limited, the scene deceptive jamming template is divided into several blocks according to the SAR parameters and imaging quality control factor. The JSF of each block is calculated by the TDFS algorithm and added together to achieve the large scene jamming. Finally, the correction algorithm in squint mode is derived. The simplification and parallel block processing could improve the calculation efficiency significantly. The simulation results verified the validity of the algorithm.

Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease damages grain quality and reduces yield. The detection of this disease in a high throughput way is crucial to planters and breeders. Our study focused on developing a method for processing wheat color images and accurately detecting disease areas using deep learning and image processing techniques. The color images of wheat at the milky stage were collected and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model can detect spikes, and the coefficient of determination of the number of spikes between the manual count and the detection was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results of spike detection, a new color feature was applied to obtain the gray image of each spike. Then, a modified region growing algorithm was implemented to segment and detect the diseased areas of each spike. Results show that the region growing algorithm performs better than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates that deep learning techniques enable the accurate detection of FHB in wheat using color images, and the proposed method can effectively detect spikes and diseased areas, thereby improving the efficiency of FHB detection.

Laboratory experiments were performed to measure the supercritical CO2 (scCO2) storage ratio (%) of the conglomerate and sandstone in Janggi Basin, which are classified as rock in Korea available for CO2 storage. The scCO2 storage capacity was evaluated by direct measurement of the scCO2 amount replacing pore water in a reservoir rock core. The scCO2 sealing capacity of the cap rock (i.e., tuff and mudstone), was also compared by measuring the initial scCO2 seepage pressure (Δp) into the rock core. The measured average scCO2 storage ratio of the conglomerate and the sandstone in Janggi Basin was 30.7 % and 13.1 %, respectively, suggesting that the scCO2 storage capacity is greater than 360,000 metric tons in the Janggi Basin. The initial scCO2 seepage pressure of the tuff in the Janggi Basin was 15 bar and continuous scCO2 injection into the tuff core occurred at Δp higher than 20 bar. For the mudstone, the initial scCO2 seepage pressure was higher than 150 bar (10 times higher than that of the tuff), demonstrating that the mudstone is more suitable than the tuff to shield scCO2 leakage from the reservoir rock in the Janggi Basin.

Background: Dendrobium officinale is not only an ornamental plant, but also a valuable medicinal herb that is both effective and widely used in traditional Chinese medicine. However, distinguishing D. officinale from other Dendrobium species is usually a difficult task that need much time and complex technologies due to their very similar external morphologies. The aim of this study is to develop a fast, even on-spot approach to identify D. officinale. Methods: We used DNA barcode-based loop-mediated isothermal amplification (LAMP) method with species-specific LAMP primers targeting the internal transcribed spacer (ITS) region of the rDNA of D. officinale. LAMP reaction time and temperature were optimized and the specificity and sensitivity of LAMP species-specific primers were assessed. Results: This technique showed a high specificity and sensitivity to amplify the genomic DNA of D. officinale and allowed for rapid amplification (within 40 min) of the ITS region under a constant and mild temperature range of 65 °C without using thermocyclers. Besides, by using SYBR® Green I dye as the color developing agent, the color change was easily observed with naked eye. Reaction mixture containing DNA of D. officinale changed from orange to green, while the other Dendrobium species and the negative control retained original orange color. The specificity of this LAMP-based method was confirmed by testing 17 samples of D. officinale and 32 adulterant samples from other Dendrobium species. Conclusions: This LAMP-based rapid identification method does not require expensive equipment or specialized techniques and can be used in field surveys for accurate and fast on site identification.

Discovery of inhibitors for endothelial-related transcription factors can contribute to the development of angiogenic therapies that treat diseases ranging from cardiovascular to cancer. The role of transcription factor Vezf1 in vascular development and regulation of angiogenesis has been defined by several earlier studies. Through construction of a computational model for Vezf1, work here has identified a novel small molecule drug capable of inhibiting Vezf1 from binding to its cognate DNA binding site. Using structure-based design and virtual screening of the NCI Diversity Compound Library, 12 shortlisted compounds were tested for their ability to interfere with the binding of Vezf1 to DNA using electrophoretic gel mobility shift assays. We identified one compound, T4, which has an IC50 of 20uM. Using murine endothelial cells, MSS31, we tested the effect of T4 on endothelial cell viability and angiogenesis by using tube formation assay. Our data show that addition of T4 in cell culture medium does not affect cell viability at concentrations lower or equal to its IC 50 but strongly inhibits the network formation by MSS31 in the tube formation assays. Given its potential efficacy, this inhibitor has significant therapeutic potential in several human diseases raging from wound healing to cancer.

Band-gap alignment engineering has now been extensively studied due to its high potential application. Here we demonstrate a simple route to synthesize two metal oxide layers and align them together according to their bandgaps on surface of crystalline silicon (c-Si) solar cells. The metal oxide layers can not only extend absorption spectrum to generate extra carriers but also serve to separate electron-hole pairs more efficiently. As a consequence, the photovoltaic performance of SnO₂/CdO /Si double-layer solar cell (DLSC) is highly improved compared to CdO/Si and SnO₂/Si single-layer solar cells(SLSCs) and SnO₂/CdO/Si double-layer solar cell (DLSC). By the alignment engineering, the SnO₂/CdO/Si DLSC produces a short circuit photocurrent (J_sc) of 38.20 mA/cm², an open circuit photovoltage (V_oc) of 0.575 V and a fill factor (FF) of 68.7%, corresponding to a light to electric power conversion efficiency (η) of 15.09% under AM1.5 illumination. These results suggest that with the use of metal oxide layers by band-gap alignment engineering, new avenues have been opened for developing high-efficiency and cost-effective c-Si solar cells.

Cogging is a key part in the production process of superalloy, but inclusions affect the performance of superalloy. It is therefore particularly important to study the effects of cogging on the inclusions in superalloy. In our study, the superalloy GH4738 was made by double vacuum smelting. Cogging was performed by unidirectional drawing, upsetting and drawing, and upsetting/drawing with radial forging. The types and distributions of the inclusions after the three cogging processes were analyzed using scanning electron microscopy, energy dispersive spectroscopy, and software such as Image Pro Plus. The results showed that double vacuum smelting essentially determined the types of the inclusions in the GH4738 superalloy. Four types of inclusions were found in the experiments: TiC-TiN-Mo-S composite, TiC-TiN composite, Ce-Mo-S composite, and SiC inclusions. In the case of cogging by unidirectional drawing, the average size of the inclusions first decreased and then increased, from the center to the edge. In the case of upsetting and drawing, and upsetting/drawing with radial forging, the average size of inclusions decreased from the center to the edge.

This paper proposes a crack recognition method based on high-resolution line array cameras and adaptive lifting algorithm. By defining the crack rate, this algorithm calculates the ratio of the crack area to the area of the entire collected image to characterize the damage extent of the current section. The algorithm first uses image preprocessing to reduce the image noise, then uses histogram equalization to enhance the feature of the crack region, divides the whole image into multiple sub-blocks, and extracts region features in the sub-block. At the same time, this algorithm defines related feature descriptors, and constructs weak classifiers according to each feature descriptor, and converts the weak classifiers into strong classifiers by using an adaptive lifting algorithm. Finally, this algorithm realizes the division of the crack regions. Experimental results show that the proposed algorithm can meet the actual needs and is better than other classical algorithms.

The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable had a larger impact on the downscaling results and (2) what was the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the Chinese mainland. To solve this queries, 41 3-month long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements; nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. Moreover, the optimum nudging scheme varied with simulated regions and layers. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable.

Surgical smoke is produced by energy-based surgical instruments. The airborne volatile organic compounds (VOCs) from surgical smoke may have potential health risk. This study aimed to evaluate the evidence for the harmful effects on the operating theater staff. An internationally recognized evaluation model of health risk assessment (HRA) was adapted to preliminarily assess the health risks caused by VOCs in surgical smoke. Results of HRA indicated that non-carcinogenic risk indexes of VOCs did not exceed one, indicating that these pollutants didn't cause significant non-carcinogenic harm to the health of the operating theater staff. But the cancer risk indexes exposed to formaldehyde and benzene all exceed 10-5, which was higher than the suggested value of USEPA (10-6) and might cause potential harm to the health of the operating theater staff. Long-term exposure of such surgical smoke will be harmful to the health of the operating theater staff and have a great risk of cancer.

Recently, a high internal surface of the titanium alloy pipe is required due to the increment of various applications of the pipe such as aerospace. However, the traditional machining process is unable to effectively polish the internal surface of the slender pipe. Thus, a vertical magnetorheological polishing (VMRP) apparatus is carried out in this work to implement high polishing performance on the internal surface of the titanium alloy pipe. The polishing process is realized by the combination of the rotating motion of the pipe and the reciprocating linear motion of the polishing head. Moreover, a series of comparative experiments are conducted to investigate the polishing mechanism of magnetorheological (MR) fluid and enhance the polishing performance. It is shown from the experimental results that proposed VMRP approach exhibits the better polishing performance than the conventional horizontal MR polishing method under same testing conditions. This is because the proposed VMRP method well eliminates the traces of polishing particles owing to the combination of polishing particles with different sizes. In addition, it is identified from the experimental test that the polishing time of the VMRP required to achieve the same polishing effect as the conventional method is shortened under the combination of different rotation speeds. It is identified from the above benefits of the VMRP that the final surface roughness after polishing is below 0.05 μm.

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of quadruped robots with different degrees of spine flexibility, providing a parameterized description of the flight phase motion for both rigid trunks and flexible spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.

Matrix asphalt material has poor road performance, which is improved by adding polymer modified agents. The original asphalt material experiment research has a certain limitation in that it cannot truly expose the mechanism and influence of modifiers on asphalt materials. To explore the gain effect of the modified agent on the asphalt material's road performance, the molecular dynamic simulation technology is used to explain its mechanism in the micro dimension. The three parameters of the free volume score, radial distribution function and average directional shift diffusion coefficient are analysed to improve the performance of the modifier on asphalt material, an exploration of the microdimension shows that the amount of SBS modifiers is 6% and the amount of rubber modified asphalt material is 22%. The improvement of materials is better than that of rubber modifiers.

Infective endocarditis (IE) is a rare but severe disease with high mortality and healthcare burden. Cardiac surgery plays a major role in the contemporary clinical management of IE patients. During cardiac surgery, cardiopulmonary bypass significantly contributes to an increased risk for organ dysfunction and mortality by inducing an acute inflammatory response, vascular endothelial cell injury, impairment of the coagulation cascade, and ischemia‒reperfusion injury. During the past decade, the use of extracorporeal hemoadsorption therapy with the CytoSorb® hemoadsorber has been proposed as an adjuvant therapy to mediate inflammatory responses in IE patients receiving cardiac surgery with cardiopulmonary bypass. However, there is currently a lack of systemic evaluation of the effect of CytoSorb® hemoadsorption on clinical outcomes such as hemodynamics, organ dysfunction, and mortality in patients with IE. Therefore, in this review, we exclusively discuss contemporary findings concerning the rationale, clinical evidence and future perspectives for CytoSorb® hemoadsorption therapy in IE patients.

Solid tumors universally possess the ability to evade contact inhibition of proliferation (CIP), a mechanism halting cell proliferation when cell-cell contacts occur. Merlin, an ERM-like protein, crucially regulates CIP and is frequently deactivated in various cancers, indicating its signifi-cance as a tumor suppressor in cancer biology. Despite extensive investigations into Merlin's role in cancer, its lack of intrinsic catalytic activity and frequent conformation changes has made it notoriously challenging to study. To address this challenge, we harnessed innovative luciferase technologies to create and validate a split-luciferase biosensor system. This system enables precise quantification of Merlin's conformation and activity both in vitro and within living cells. This biosensor significantly enhances the study of Merlin's molecular functions, serving as a potent tool to explore its contributions to CIP and tumorigenesis.

Antiphospholipid syndrome (APS) is a group of autoimmune diseases caused by antiphospholipid antibodies (aPLs) characterized by arteriovenous thrombosis and a morbid pregnancy. We deliberated a case report of seven cases to investigate whether inhibitors of tissue necrosis factor-α could reduce pregnancy dangers caused by APS. The seven patients are with increased tumor necrosis factor, poor improvement after receiving traditional inhibitory drugs. To address these situations, the tumor necrosis factor inhibitors were prescribed after getting the patient's informed consent, and the state of illness was carefully watched for. After treatment, symptoms of contractions and abdominal pain disappeared and the B ultrasound examination showed embryo survived with good obstetric outcomes. Subsequently, maternal and neonatal health is compromised. An intervention by the administration of TNF-α inhibitors was posited to be an effective way of reducing dangerous pregnancies like preterm pregnancies, premature deliveries, abortions and miscarriages. We reviewed outcomes from seven cases in an attempt to establish how TNF- α Inhibitors could save pregnancies. We focused on key pregnancy outcomes like birth weight, gestational weeks and preterm miscarriage to tell the outcomes of TNF- α Inhibitors in pregnancy. After careful evaluation of the results surrounding these aspects, we found that TNF- α Inhibitors could prolong gestational period. Other positive outcomes include the attainment of birth weights approach the threshold, zero incidence of preterm miscarriage.

In recent years, magnetic fields have emerged as a non-thermophysical treatment with a signifi-cant impact on microbial fermentation processes. Brassica trispora is a microorganism known for its indus-trial-scale production of lycopene and high yield of single cells. This study aimed to in-vestigate the impact of low frequency magnetic fields on lycopene synthesis by Brassica trispora and elucidate the underlying mechanism for enhancing lycopene yield. The results indicated that both the intensity and duration of the magnetic field treatment influenced the cell. Exposing the cell to a 0.5 mT magnetic field for 48 hours on the second day of fermentation resulted in a lyco-pene yield of 1473.16 mg/L, representing a re-markable increase of 216.1% compared to the con-trol group. Transcriptome analysis revealed that al-ternating magnetic field significantly up-regulated genes related to ROS and cell membrane structure, leading to a substantial increase in lycopene production. Scanning electron microscopy revealed that the magnetic field treatment resulted in rough, loose, wrinkled surface morphology of the mycelium, along with a few mi-cropores, thereby altering the cell membrane permeability to some extent. Moreover, there was a significant increase in intracellular ROS content, cell membrane permeability, key enzyme activi-ty involved in lycopene metabolism, and ROS-related enzyme activity. In conclusion, the alter-nating fre-quency magnetic field can activate a self-protective mechanism that enhances lyco-pene synthesis by modulating intracellular ROS content and cell membrane structure. These find-ings not only deepen our understanding of the impact of magnetic fields on microbial growth and metabolism but also provide valuable insights for developing innovative approaches to en-hance carotenoid fermentation.

In order to improve the wear resistance of copper contacts in high-voltage switches and improve the abnormal discharge phenomenon caused by wear gaps, laser remelting technology was used to strengthen the surface of copper contacts. The surface morphology, microhardness, and wear resistance of the remelted samples were tested and characterized using scanning electron microscopy (SEM), microhardness tester, and friction and wear tester. The test results indicate that laser frequency, pulse width, and current parameters can directly affect the surface morphology and wear resistance of the sample, but their influencing processes vary. The laser frequency is achieved by the variation of the superposition relationship between the impact points, while the pulse width and current are achieved by the variation of the laser intensity at the impact points. When the pulse frequency is 10Hz, the pulse width is 10ms, and the current is 100A, the sample exhibits a more balanced surface morphology, microhardness, and wear resistance.

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismic estimation. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five earthquake categories were defined contingent on locale (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postsismic remission signatures. Casework found 10GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36GHz horizontal-polarized performed best for arid, vegetated landmasses. This preliminary investigation establishes an analytical framework, albeit reliant on qualitative appraisals. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.

With the rapid development of Earth observation and information technology, people are increasingly able to access geospatial models. Geospatial models, based on principles of geography, utilize mathematical, statistical, as well as computer science methods to interpret and predict geographic phenomena. These models can be applied in the fields such as urban planning, environmental protection, traffic management to help decision-makers solve geography-related problems. However, integrating different geospatial models to collaboratively solve complex geographic problems still faces significant obstacles due to heterogeneity in model structure, dependencies, and running modes. In this study, we propose a containerized service-based integration framework for heterogeneous geospatial models (GeoCSIF). GeoCSIF consists of three main components: (1) Model encapsulation. It breaks down complicated geospatial models into independently manageable model units, and builds as unified service packages with a templated constraint method. (2) Model orchestration. It achieves an optimal combination of large-scale models with complex dependencies using a prioritization-based orchestration method. (3) Model publication. It incorporates heuristics into the model scheduling process, which can provide adaptive deployment for different model runs. Finally, a prototype system was developed to validate the effectiveness and progressiveness of GeoCSIF by the integrating process of heterogeneous flood disaster models.

Grouting materials with good insulation and reinforcement performance are key factors in solving the temperature control problem of high temperature tunnels using curtain grouting, while existing grouting materials cannot balance their working performance and insulation performance in high-temperature environments. In response to the above issues, this article uses red mud to prepare high ground temperature red mud based grouting materials (RMGS), and conducts tests on the working performance (viscosity, setting time, compressive strength) and insulation performance (thermal conductivity, specific heat capacity) of grouting materials at different temperatures (20, 40, 60, and 80 ℃), and analyzes the variation patterns and micro mechanisms of various characteristics with temperature. The results indicate that an increase in temperature will accelerate the viscosity development and condensation of the grouting material, and lead to a decrease in thermal conductivity and specific heat capacity. In addition, an appropriate temperature can also improve the compressive strength of the material. The increase in temperature will accelerate the hydration reaction rate of the grouting material and the development of internal pores, which is the reason why temperature affects the performance of the grouting material. In engineering applications, cement slurry is suitable for static water grouting, cement-sodium silicate slurry is suitable for dynamic water grouting, and RMGS is suitable for high-temperature tunnel grouting.

Wetlands, which is composed of soil, vegetation and water, has sufficient water supply and is sensitive to climate change. This study analyzed the coupling degree between wetlands and atmosphere, and discussed the influence of environmental factors (solar radiation and water vapor pressure deficit) on latent heat flux by using the experimental data from the Maduo Observatory of Climate and Environment of the Northwest Institute of Eco-Environment and Resource, CAS and WRF model. The results showed, during the vegetation growthing season, the average value of Ω (decoupling factor) is 0.38 in alpine wetlands, indicating the coupling between wetlands and atmosphere is poor; Solar radiation is the main factor influencing the latent heat flux in the results of both observation data analysis and model simulation, and solar radiation and water vapor pressure deficit still have an opposite reaction to latent heat flux; when solar radiation and water vapor pressure deficit increase by 30%, the average daily amount of latent heat flux increases from 5.57 MJ·m-2 to 7.50 MJ·m-2 and decreases to 5.17 MJ·m-2, respectively. This study provides a new research approach for the study of the parameterization of latent heat flux and evapotranspiration under the context of global climate change.

The prefabricated ECC/RC combined shear wall structure is an innovative prefabricated composite structure with better seismic performance by using ECC materials with better ductility in the main force and energy consumption regions of the prefabricated reinforced concrete (RC) shear wall structure. The key factors controlling the seismic energy dissipation capacity of this kind of structure are the use regions of ECC in composite coupling beams, use regions of ECC in shear walls, strength of ECC, stirrup ratio of coupling beams, strength of longitudinal reinforcement and so on. The parameters affecting seismic energy consumption are quantified by the finite element analysis method. By comparing and analyzing the load-displacement curves, hysteresis curves, energy dissipation capacity and stiffness degradation of the prefabricated ECC/RC combined shear wall structure specimen under these parameters, it is proposed that the best use regions of ECC is the cast-in-situ zone of the coupling beams with the scope of no more than400 mm at the shear walls’ bottom. The strength grade, the strength grade of longitudinal reinforcement, and the stirrup ratio of coupling beams are suggested design as E40, HRB400 and 0.5%, respectively.

Accurate prediction of the degree of airport delays under the influence of convective weather is crucial for collaborative traffic management implementation and improving the efficiency of airport operations. However, existing studies usually only consider numerical-type quantitative features of weather-affected traffic in their models, and lack the introduction of spatial information to comprehensively portray the traffic operation scenarios under the influence of weather. In order to overcome this problem, this paper firstly designs a new image representation of weather-affected air traffic, and constructs a multi-channel traffic and weather scene image (MTWSI) by populating the airspace two-dimensional grid with traffic and meteorological information to represent the overall traffic operation situation in the terminal area under the influence of convective weather; then, a deep convolutional neural network-based airport delay prediction model ( ADLCNN), which takes MTWSI images as input and uses a specific CNN model to extract the deep features that affect traffic operation in it to input into the subsequent classification algorithm to predict the flight delay level; finally, a series of comparative experiments are carried out on the operational data of Guangzhou Baiyun Airport, and the experimental validation shows that, compared with the traditional machine learning methods, the proposed CNN-based airport delay prediction The experimental validation shows that the proposed CNN-based airport delay prediction model has satisfactory prediction performance compared with the traditional machine learning methods, which also proves that the proposed MTWSI method can more comprehensively respond to the real traffic conditions.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

How people recognize linguistic and emotional prosody in different listening conditions is essential for understanding the complex interplay between social context, cognition, and communication. The perception of both lexical tones and emotional prosody depends on prosodic features including pitch, intensity, duration, and voice quality. However, it is unclear which aspect of prosody is perceptually more salient and resistant to noise. This study aimed to investigate the relative perceptual robustness of emotional prosody and lexical tone recognition in quiet and in the presence of multi-talker babble noise. Forty young adults with normal hearing listened to monosyllables either with or without background babble noise and completed two identification tasks, one for emotion recognition and the other for lexical tone recognition. Compared with emotional prosody, lexical tones were more perceptually salient in multi-talker babble noise. Native Mandarin Chinese participants identified lexical tones more accurately and quickly than vocal emotions at the same signal-to-noise ratio. Lexical tone perception is also more robust against babble speech noise degradation than emotional prosody perception for native Mandarin Chinese listeners. Acoustic and cognitive dissimilarities between linguistic prosody and emotional prosody may have led to the phenomenon, which calls for further explorations into the underlying psychobiological and neurophysiological mechanisms.

Metal-organic framework (MOF) is a porous hybrid material of metal ions connected by organic bridging ligands. The coordination bonds link the metal ions, metal-ion clusters, and organic ligands to create the MOFs, and the materials are a distinctive class of crystalline frameworks. These porous materials possess relatively large surface area, tunable pore sizes, various functionalities, and high thermal stability. Therefore, diverse area of research including electrochemical sensor development utilizes distinctive and engineered MOFs materials. The review critically analyzes the strategy adopted for synthesizing a variety of MOFs materials. The role of these engineered materials in the fabrication of a miniaturized device demonstrates the detection of various emerging water contaminants in an aqueous medium. The studies demonstrated an understanding of the insights of sensor and device development. Moreover, the challenges encountered utilizing the MOFs in the electrochemical sensor development are precisely included, along with future perspectives of these studies.

This prospective study aimed to evaluate the ability of point-of-care Doppler ultrasound measurements of carotid-corrected flow time and changes in corrected carotid flow time (FTc) induced by volume expansion to predict fluid responsiveness in patients undergoing robot-assisted gynecological surgery in a modified head-down lithotomy position. FTc was measured using Doppler images of the common carotid artery before and after volume expansion. The stroke volume index at each time point was recorded using the MostCare instrument. Fluid responsiveness was defined as a stroke volume index ≥10% increase after volume expansion. Half (50%) of the 52 patients responded positively. The areas under the receiver operating characteristic curves measured to predict fluid responsiveness for the corrected carotid flow time and changes in the FTc were 0.82 [95% confidence interval:0.705–0.937; P < 0.0001] and 0.67 (95% confidence interval:0.520–0.815; P < 0.05), respectively. The optimal cut-off value for the FTc and the change in FTc were 356.5 ms and 19.5 ms, respectively. FTc is a more reliable predictor of fluid responsiveness than FTc induced by volume expansion in gynecological patients undergoing robot-assisted laparoscopic surgery in the modified head-down lithotomy position.

Climate and its fluctuations have wide-ranging impacts on the tourism industry. High temperatures, as a typical meteorological and climatic factor, influence tourists' travel intentions and spatial behavior. This study conducted a descriptive analysis and market trend analysis of Chinese tourism during periods of high temperatures, revealing several key findings. Firstly, tourists seeking respite from summer heat exhibit a preference for short-distance trips and resource-rich destinations. Secondly, heat-escape tourism products undergo changes over time, gradually shifting towards mountainous areas and waterfront locations. Furthermore, taking Shanghai Disneyland as a case study demonstrates that the holiday system holds greater significance than temperature constraints. Despite experiencing a significant decline in the quality of their tourist experience in high-temperature environments, long-distance travelers are not deterred from choosing pre-booked tourism products by sudden bouts of hot weather.

To describe the hysteresis nonlinearities in smart actuators, numerous models have been presented in the literature, among which the Preisach operator would be the most effective one due to its capability in capturing multi-loop or sophisticated hysteresis curves. When such an operator is coupled with uncertain nonlinear dynamics, especially in noncanonical form, it is a challenging problem to develop techniques to cancel out the hysteresis effects, and at the same time achieve asymptotic tracking performance. To resolve this problem, in this paper, we investigate the problem of iterative inverse-based adaptive control for an uncertain noncanonical nonlinear systems with unknown input Preiasch hysteresis, and a new adaptive version of the closest match algorithm is proposed to compensate for the Preisach hysteresis. With our scheme, the stability and convergence of the closed-loop system can be established. The effectiveness of the proposed control scheme is illustrated by simulation and experiment results.

Background and purpose: Dynamic 18F-FDG PET-CT scanning can accurately quantify 18F-FDG uptake and has been successfully applied in diagnosing and evaluating therapeutic effects in various malignant tumors. There is no conclusion as to whether it can accurately distinguish benign and malignant lymph nodes in nasopharyngeal cancer. The main purpose of this study is to reveal the diagnostic value of dynamic PET-CT in cervical lymph node metastasis of nasopharyngeal cancer through analysis. Method: We first searched for cervical lymph nodes interested in static PET-CT, measured their SUV-Max values, and found the corresponding lymph nodes in magnetic resonance images before and after treatment. The valid or invalid groups were included according to the changes in lymph node size before and after treatment. Their Ki values were measured on dynamic PET-CT and compared under different conditions. Then, we conducted a correlation analysis between different factors and Ki values. Finally, diagnostic tests were conducted to compare the sensitivity and specificity of Ki and SUV-Max. Result: We included 67 cervical lymph nodes from different regions of 51 nasopharyngeal cancer patients and divided them into valid and invalid groups based on changes before treatment. The valid group includes 50 lymph nodes, while the invalid group includes 17. Significant differences (P<0.001) between the two groups in SUV-Max, Ki-Mean, and Ki-Max values. When the SUV-Max≤4.5, there was no significant difference in the Ki-Mean and Ki-Max between the two groups (P>0.05). When SUV-Max≤4.5 and pre-treatment lymph nodes<1.0cm, the valid group had significantly higher Ki-Mean (0.00910) and Ki-Maximum (0.01004) values than the invalid group (Ki-Mean=0.00716, Ki -Max=0.00767) (P<0.05). When SUV-Max≤4.5, pre-treatment lymph nodes<1.0cm, and EBV DNA replication was normal, Ki-Mean (0.01060) and Ki-Max (0.01149) in the valid group were still significantly higher than the invalid group (Ki-Mean=0.00670, Ki-Max=0.00719) (P<0.05). The correlation analysis between different factors (SUV-Max, T-stage, normal EB virus DNA replication, age, pre-treatment lymph node<1.0cm) and Ki value showed that SUV-Max and pre-treatment lymph node<1.0cm were related to Ki-Mean and Ki-Max. Diagnostic testing was conducted, the AUC value of the SUV-Max value was 0.8259 (95% confidence interval: 0.7296-0.9222), the AUC value of the Ki-Mean was 0.8759 (95% confidence interval: 0.7950-0.9567), and the AUC value of the Ki-Max was 0.8859 (95% confidence interval: 0.8089-0.9629). It was found that the sensitivity of SUV-Max was 88%, the specificity was 76%, the sensitivity of Ki-Mean was 88%, the specificity was 80%, the sensitivity of Ki-Max was 94%, the specificity was 76%, the specificity of Ki-Mean was better than SUV-Max, the sensitivity of Ki-Max was better than SUV-Max. Conclusion: Dynamic PET-CT has shown significant diagnostic value in diagnosing cervical lymph node metastasis of nasopharyngeal cancer, especially for the small SUV value, and lymph nodes do not meet the metastasis criteria before treatment, and EBV DNA replication is normal. Moreover, its sensitivity and accuracy are superior to static PET-CT.

Keywords: FTY720, self-assembled nanoparticles, intracerebral hemorrhage, neuroprotection

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method to study wildfire contribution to PM2.5 using satellite-derived smoke plume indicators and PM2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy.

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebu-larine (ZEB) inhibit the DNMT family with no specificities and consequently would bring side effects during the treatment. Therefore, it is vital to understand their inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a me-thyl-transferred intermediate (IM3). Such IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

Ultra-high temperature oxidation-resistant alloys (UTORAs) have received a lot of attention due to the increased research demand for deep space exploration around the world. But UTORAs have the disadvantages of easy oxidation and chalking. So, in this study, a UTORA was prepared by hot-press sintering on VZrHfNbTa (HEA) substrate coated with hafnium. The bonding mechanism, resistance to high-temperature oxidation, and hardness of the sample tests were carried out. The results show that zirconium in the matrix will diffuse into the hafnium coating during the high-temperature sintering process and form the HfZr alloy transition layer, the coating thickness of the composite is about 120 μm, and the diffusion distance of zirconium in the hafnium coating is about 60 μm, this transition layer chemically combines the hafnium coating and the HEA substrate into a monolithic alloy composite. The results of high-temperature oxidation experiments show that the oxidation degree of the hafnium-coated VZrHfNbTa composite material was significantly lower than that of the VZrHfNbTa HEA after oxidation in air at 1600 °C for 5 h. The weight gain of the coated sample after oxidation is 56.56 mg/cm2, which was only 57.7 % compared to the weight gain of the uncoated sample (98.09 mg/cm2 for uncoated), and the surface of the uncoated HEA showed obvious dents, oxidation, and pulverization occurred on the surface and interior of the sample. In contrast, the coated composite alloy sample mainly underwent surface oxidation sintering to form a dense HfO2 protective layer, and the internal oxidation of the hafnium-coated VZrHfNbTa composite alloy was significantly lower than that of the uncoated VZrHfNbTa HEA.

Evapotranspiration (ET) is an essential part of energy flow between the surface of the earth and the atmosphere, simultaneously involving the water, carbon, and energy cycles. It is mainly determined by climate change, land use, and land cover changes. Climate change is expected to intensify the hydrological cycle and alter ET. Land use affects ET within regional ecosystems mainly through vegetation changes and agricultural activities such as farmland reclamation, crop cultivation, and agricultural management. However, there is still a need for quantitative characterization of the impacts of climate change and human activities on ET and regional water resource efficiency in arid and semiarid regions. Based on Landsat-8 remote sensing imagery and land use data, the planting structure in the Liangzhou District of the middle reaches of the Shiyang River Basin was identified using a multiband and multitemporal approach in this study. Subsequently, the ET of major cash crops was inverted using the three-temperature model. This research quantitatively describes the responses of wheat and corn to the climate and human activities over a two-year period. Furthermore, the impact of planting structure and climatic factors on ET was elucidated. The results indicate that a combination of multitemporal green and shortwave infrared 1 bands is the optimal spectral combination to extract the planting structure. Compared to 2019, the wheat area decreased by 23.27% in 2020, while the corn area increased by 5.96%. Both crops exhibited significant spatial heterogeneity in ET during the growing season. The typical daily range of ET for wheat was 0.4–7.2 mm/day, and for corn, it was 1.5–4.0 mm/day. Among the climatic factors, temperature showed the highest correlation with ET (R = 0.80, p ≤ 0.05). Our research findings provide valuable insights for the fine identification of planting structures and a better understanding of the response of ET to climatic factors and human activities.

Potato (Solanum tuberosum L) gradually becomes a stable food worldwide since it can be treated as practical nutritional supplement and antioxidant, as well as energy provider for human beings. Financially and nutritionally, cultivation and utility of potatoes is worthy enough attention of the world. To further explore functionality and maximize utilization of component parts, also to develop new products based on potato still be an ongoing issue. To maximize the benefits of potato and induce new high-value products while avoiding unfavorable properties of the crop has been a growing trend in food and medical areas. This review intends to summarize the factors that influence the changes in the key functional components of potatoes and discuss referred research focuses, then help further researchers to make efforts into. Afterwards, it summarizes the application of the latest commercial products and potential value of components existing in potato. Particularly, there are several main tasks for the next potato research: preparing starchy foods for special group of people and developing fiber-rich products to supply the dietary fiber intake as well as manufacturing bio-friendly and specific design films/coating in packaging industry; extracting bioactive proteins and potato protease inhibitors with high biological activity, and continue to build new commercial products based on potato protein and examine their health benefits. Noteworthy, the preservation methods play a key role in phytochemicals contents left in foods and potato performs superior to many common vegetables when meeting demand of mineral daily intake and alleviating mineral deficiencies.

On the basis of the force and safety factor analysis of traditional retaining wall, a new analytical method of force and displacement of slope is suggested, the numerical theoretical solution of the stress distribution of the sliding body can be obtained under the conditions, which the stresses distribution are satisfied with the differential equilibrium equations, the boundary conditions, the compatibility equation and the macroscopic equilibrium equations. The interface stresses between the sliding body and the retaining wall are continuous, the theoretical solution of retaining wall stress distributions can be gotten, while the stress differential equilibrium equation, the compatibility equation, macroscopic force and moment balance equations are satisfied. The strain and displacement solutions can be obtained by using Duncan Chang and Hooke constitutive equations for the slope and the retaining wall respectively. The transfer station of landfill in the Guandukou Town of Badong County is taken as an example, the results of the sliding body and retaining wall analysis show: The stress and strain solutions of a slope and a retaining wall can be obtained by the proposed method, the forces between the retaining wall and the sliding body include shear stress and normal stress, and the stress distributions change with the deformation of the sliding body, the shape and material of retaining wall can be optimized according to the calculation results，it is feasible for the proposed analysis method of slope with retaining wall design to be run many years.

Near space environment is the airspace at an altitude of 20 km-100 km, where complex conditions such as low temperature, low pressure, high wind speed and solar radiation exist. As one of the important meteorological parameters, temperature is crucial for space activities, but the influence of the complex environment makes the error of conventional temperature measurement methods large. Therefore, a new microbridge temperature sensor was designed that can reduce solar radiation and achieve a fast response. And through simulation analysis, the three factors influencing the temperature errors of Joule heat, solar radiation heat and aerodynamic heat were investigated. And the influence of temperature error is reduced by optimizing the installation position of the sensor. Through the temperature error model, the error value in the actual measurement value is removed, to realize the high accuracy detection of near-space temperature.

The concepts of fractional calculus and the related techniques have been gaining momentum in circuit system fields, and an increasing body of evidence suggests that fractional-order characteristics are widely distributed in electronic components. In this paper, we consider the fractional characteristics of non-solid aluminum electrolytic capacitors and focus on the nonlinear dynamic characteristics of peak-current-mode controlled (PCMC) dc-dc boost converters with such components. In the work, we establish a piecewise smooth non-commensurate fractional-order model to describe the converter, in which the fractional-order equivalent impedance model of capacitors is adopted and the detailed control loop is considered. In order to analyze the converter, we use the fractional Adams-Bashforth-Moulton typed method (F-ABM) and stroboscopic map technique to obtain the time-domain solutions and the bifurcation diagram. The results of bifurcation diagrams indicate that, with the change of different parameters, the converter enters chaotic state through period doubling bifurcation. In order to provide empirical evidence for the nonlinear dynamic analysis, we consider both circuit-level simulation and experiments in the work, the results of which further confirm the results of theoretical analysis.

Pregnancy is a dynamic state with multiple metabolic changes occurring including insulin resistance. Gestational diabetes mellitus (GDM), a form of diabetes that appears during pregnancy, develops if metabolic aberrations occur, in particular, in normal pregnancy-induced insulin resistance. Multi-omics is a powerful approach for uncovering the mechanisms driving metabolic change in different physiologic and pathologic states. A recent study demonstrated that the gestational gut microbiome mediates pregnancy metabolic adaptations through effects on gut indoleamine-2,3 dioxygenase 1 activity and the production of kynurenine. Using the dataset generated from this highly controlled study, we performed a comprehensive analysis of the pregnancy-specific physiological and metabolic profiles, 16S rRNA microbiome, and plasma untargeted LC-MS metabolome data. To facilitate the utilization of these analysis results by other researchers, we developed MOMMI-MP, a database that provides an easy-to-use platform to browse and search differential abundant microbial taxa and metabolites, and to examine metabolic pathways. The datasets consist of data collected from 3 genetically diverse strains of mice (C57BL/6J, CD1, and NIH-Swiss) over 6 time points during the gestational (days 0, 10, 15, and 19 during gestation) and postpartum (days 3 and 20 after delivery) states, totaling 180 samples for each strain. The computational results are presented in various tables and plots, and organized in MOMMI-MP to empower exploratory analyses by other researchers. In conclusion, MOMMI-MP is a resource to facilitate the investigation of novel mechanisms governing metabolic changes during pregnancy.

This study aims to develop an in-situ field repair approach, special for aircraft composite structures, to enhance the interlaminar toughness of plain woven composites (PWCs) by adding multi-walled carbon nanotubes (MWCNTs). MWCNTs are dispersed at each interface between prepreg layers by means of solvent spraying with the density is 1.58 g/m2. And then, the layers are stacked with the predefined sequence and cured at 120℃ and 1 bar pressure using the heat repairing instrument. Moreover, double cantilever beam (DCB) standard test is used to investigate the interlaminar toughening effect due to the MWCNTs. For comparison, original samples are also prepared, the results indicate that the introduction of MWCNTs can favorably enhance the interlaminar toughness of PWCs at field repair approach and the Mode I fracture energy release rate GIC increases by 102.92%. Based on finite element method (FEM) of continuum damage mechanics, the original and MWCNTs toughening specimen under DCB Mode I fracture are modeled and analyzed. The simulation and experiment are in good agreement. Finally, the toughening mechanism of MWCNTs is explored by scanning electron microscope (SEM), it is founded that a large amount of Fiber-matrix (F-M) interface debonding and matrix cracking of mountain shape are the major modes of fracture accompanied with few fiber breakage and matrix peeling off for the MWCNTs toughening specimens.

Detailed flow distributions in vascular systems are the key to identifying hemodynamic risk factors for the development and progression of vascular diseases. Although computational fluid dynamics (CFD) has been widely used in bioengineering research on hemodynamics predictions, not only are high-fidelity CFD simulations time-consuming and computing-expensive, but also not friendly to clinical applications due to the difficulty of comprehensive numerical calculations. Machine learning (ML) algorithums to estimate the flow field in vascular systems based on the angiographic images of the blood flow using existed diagnostic tools are emerging as a new pathway to facilitate the mapping of hemodynamics. In present work, the dye injection in a water flow was simulated as an analogy of the contrast perfusion in blood flow using CFD. In the simulation, the light passes through the flow field and generates projective images, as an analogy of X-ray imaging. The simulations provide both the ground truth velocity field and the projective images of the flow with dye patterns. A rough velocity field was estimated using the optical flow method (OFM) based on projective images. ML algorithums are then trained using the ground truth CFD data and the OFM velocity estimation as the input. Finally, the interpretable (logistic regression) and deep (neural networks, convolutional neural networks, long short term memory) machine learning models are validated by using parallel in vitro experiments on the same flow setup. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% in average for the v-velocity estimation.

The eutrophication caused by excessive total nitrogen(TN) and total phosphorus(TP) emissions has been widely concerned by the whole society. Studies have revealed the relationship between land use and TN and TP, but the relationship between land use compound topographic position and TP and TN was seldom studied. Therefore, Spearman correlation and redundancy analyses were used to reveal the relationship between land use compound topographic position and TN and TP based on the monthly data of 28 water quality sampling sites and the land use data of 2013 and 2016 in the lakes of Guizhou Plateau. The results show that the nutritional state of the HBA watershed is medium. The trophic level index (TLI) value and TN concentration were high during flood, while TP concentration was high in dry period. The TN concentration in the tributaries was higher than that in the reservoir area. Construction and valley were the sources of the pollution, whereas forest land and gentle slope were the sink. According to the”source-sink”effect, the optimal zoning of land use was completed, and the urban land pollution govern area should be strengthened firstly. This paper can provide decision support for water environment management and sustainable development decision-making.