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Nowadays, unmanned aerial vehicles (UAVs) have achieved rapid development due to their flexible flight modes and broad application prospects, which have played an important role in scenarios such as aerial photography, unmanned mapping, agricultural plant protection, and power inspection. At the same time, as the frequency of global forest fires is increasing year by year, traditional monitoring and search and rescue methods have little effect. People have begun to consider the introduction of UAVs for forest monitoring and disaster relief. However, in practical applications, when a single UAV performs tasks, there are problems of limited energy, poor robustness, and easy failures that affect the execution efficiency of global tasks. Therefore, it is necessary to obtain the deployment plan of the UAVs offline in advance, and then fine-tune the position of the UAVs online. This paper focuses on the solution of UAV deployment model offline. In complex forest scenarios, due to terrain and signal coverage issues, we need to optimize this when deploying UAVs. At the same time, our goal is to find the location of deployable UAVs in the forest area to maximize the global coverage area of UAVs. Therefore, we divide the forest areas where UAVs need to be deployed and perform a grid modeling on them. On the basis of grid modeling, an Adaptive Multiple Pruning Search Method (AMPSM) is proposed to solve the global UAV deployment and maximum coverage area. In our experiments, we conducted a comparative analysis with the geometric solution to solve the maximum coverage area, which proved the feasibility of the model and the method. The results show that in the complex forest environment, our research can meet the requirements to a great extent, which can be extended to more application scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The optimal choice of distal locking modes remains a subject due to limited available data, and therefore, this study aims to investigate the relationship between distal locking mode and postoperative mechanical complications in an intertrochanteric fracture (ITF) population who underwent closed reduction and intramedullary fixation with a PFNA-II. Methods: Patients aged 65 years or older who underwent surgery with PFNA-II fixation in a university teaching hospital between January 2020 and December 2021 were potentially eligible. Based on the distal locking mode, patients were classified into static, dynamic, and limited dynamic groups, among which the differences were tested using univariate analysis. Multivariate logistic regression was used to examine whether the distal locking mode was independently associated with the risk of postoperative one year mechanical complications, adjusting for covariates and potential confounders. Subgroup analyses were performed to evaluate the robustness of the findings. Result: Among 507 eligible patients, 33 (6.5%) developed postoperative mechanical complications. In the univariate analysis, sex (P = 0.007), fracture type (P = 0.020), LAT Parker ratio (P = 0.023), and lateral femoral (P = 0.003) wall showed that the differences were significant. Compared to the static group, the limited dynamic group and the dynamic group showed higher odds of postoperative mechanical complications (OR = 3.314, 95% CI: 1.215–9.041; and OR = 3.652, 95% CI: 1.451–9.191, respectively). These associations were robust across a series of analyses, including adjusting for confounders and subgroup analyses. Conclusion: Using a distal non-static locking mode significantly increases the risk of postoperative mechanical complications, and static locking could be a preferable option when treating an intertrochanteric fracture. [ABSTRACT FROM AUTHOR]

The rapid progress of the internet of things, cloud computing, and artificial intelligence has increased demand for high-performance computing. This demand has led to a focused exploration of novel nonvolatile memory (NVM) and brain-inspired neuromorphic-computing electronics, with research efforts directed at identifying materials compatible with complementary metal-oxide-semiconductor technology. Exploring fluorite-structured hafnium-zirconium oxide (HZO) mixed oxides has revealed promising ferroelectric (FE) and memristor characteristics, suggesting potential applications in emerging technologies. However, certain intrinsic properties of HZO-based FEs, such as high coercive fields (Ec) and polarization metastability, may pose challenges for commercial viability. Recent investigations of fluorite-structured HZO-based antiferroelectrics (AFEs) have highlighted their advantages, including lower energetic barriers, higher switching speeds, and a uniform phase distribution. These inherent benefits position fluorite-structured HZO-based AFEs as potential candidates within the NVM landscape. Furthermore, the accumulated polarization and spontaneous depolarization characteristics of fluorite-structured HZO-based AFEs make them worthy of potential integration into neuromorphic-computing because they resemble certain aspects of neuron behavior. Despite these positive aspects, a more thorough exploration and consideration are needed to address existing challenges. This review aims to present fluorite-structured HZO-based AFE materials and highlight the current challenges, possible applications, and future opportunities and can act as an update for recent developments in these intriguing materials and provide guidance for future researchers in the optimization and design of HZO-based AFE materials and devices for emerging NVM and neuromorphic-computing applications. [ABSTRACT FROM AUTHOR]

Pyrite is an important proxy used to reflect the redox state of a sedimentary environment. Currently available studies on pyrite focus on the process of sulfur cycles between an ocean and sediment. However, our understanding of the biogeochemical cycle of sulfur in terrestrial lake basins remains unclear, and the growth patterns of different types of pyrite are poorly understood. In this paper, we used samples from the 34 section of the Qianjiang depression in the Jianghan Basin as direct research objects by combining pyrite and sulfur isotope determination. The one-dimensional diffusion–advection–reaction simulation (1D-DAR) model was applied to simulate the changes in the pyrite content and sulfur isotope values in the sediment. The results show that the sediments in the saline lake basin environment contain a high organic matter content, a high sedimentation rate, and a high H2S diffusion oxidation rate, reflecting the strong reducing background and high productivity of this ancient lake. Sensitivity tests revealed that the organic matter content and H2S diffusion oxidation rate at the sediment–water interface are sensitive to the pyrite content. The sedimentation rate, organic matter content, and sulfate concentration are sensitive to the pyrite's sulfur isotope values. However, the variation in the active iron content had little effect on the pyrite content or sulfur isotope value. [ABSTRACT FROM AUTHOR]

Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of marine biogenic emissions on MBL formaldehyde (HCHO). The study reveals that HCHO columnar concentrations peaked in summer with 8.25 × 1015 molec/cm2, but the sea–air exchange processes controlled under the wind and sea surface temperature (SST) made marine biogenic emissions represented by isoprene reach their highest levels in winter with 95.93 nmol/m2/day. Analysis was conducted separately for factors influencing marine biogenic emissions and affecting MBL HCHO. It was found that phytoplankton functional types (PFTs) and biological degradation had a significant impact on marine biogenic emissions, with ratio range of 0.07~15.87 and 1.02~5.42 respectively. Machine learning methods were employed to simulate the conversion process of marine biogenic emissions to HCHO in MBL. Based on the SHAP values of the learning model, the importance results indicate that the factors influencing MBL HCHO mainly included NO2, as well as temperature (T) and relative humidity (RH). Specifically, the influence of NO2 on atmospheric HCHO was 1.3 times that of T and 1.6 times that of RH. Wind speed affected HCHO by influencing both marine biogenic emission and the atmospheric physical conditions. Increased marine biogenic emissions in air masses heavily influenced by human activities can reduce HCHO levels to some extent. However, in areas less affected by human activities, marine biogenic emissions can lead to higher levels of HCHO pollution. This research explores the impact of marine biogenic emissions on the HCHO status of the MBL under different atmospheric chemical conditions, offering significant insights into understanding chemical processes in marine atmospheres. [ABSTRACT FROM AUTHOR]

Heterojunction photocatalysts possess high efficiency in photogenerated electron-hole separation, which give significant advantages in the field of solar hydrogen production. Herein, a novel ZnIn 2 S 4 @S doped g-C 3 N 4 (ZIS@SCN) heterojunction with mesoporous structure was prepared for photocatalytic water splitting under visible light. Characterization showed that the element of S was successfully introduced into g-C 3 N 4 (CN) by replacing N to form C–S–C bonds and significantly enhanced the visible light absorption ability. The constructed heterojunction exhibited excellent photocatalytic hydrogen evolution under visible light. The best performance belonged to the sample of ZIS@100SCN. The corresponding H 2 production rate reached 9.3 mmol h−1 g−1, which was 1.87-fold higher than that of the undoped one. More importantly, the heterojunction realized overall water splitting of H 2 and O 2 evolution simultaneously with good stability. The mechanism analysis indicted that introducing the element of S could regulate the band structure of CN and induce the charge transfer switching of a ZnIn 2 S 4 @g-C 3 N 4 (ZIS@CN) photocatalyst from Ⅰ-type to S-scheme, which promoted the efficient spatial charge separation and maintained the high redox ability of the ZIS and SCN. This work provides an insight in construction high performance S-scheme heterojunction for water splitting with a doping method. [Display omitted] • ZIS@SCN heterojunction possesses mesoporous structure and strong interfacial interaction. • The heterojunction exhibits excellent photocatalytic water splitting under visible light irradiation. • S doping induces the charge transfer of ZIS@CN from Ⅰ-type to S-scheme. • The enhanced performance comes from the efficient spatial charge separation. [ABSTRACT FROM AUTHOR]

The optimization method provides an effective approach to enhance the low-speed lift of the vortex lift waveriders by deforming the aerodynamic shape refinedly. However, the vortex lift enhancement mechanism of the optimized configuration is unclear. In this study, the flow evolutions of the original and the optimized configurations are studied by employing the delayed detached-eddy simulation. Results indicate that the convex deformation of the leeward surface plays a dominant role in enhancing the vortex lift by enhancing the low-pressure suction at the upstream breakdown location and delaying the vortex breakdown. For the enhancement of the low-pressure suction, the convex deformation intensifies the streamwise vorticity below the axis of the primary vortex of the leading-edge vortex, in turn enhancing the downwash effect and causing the primary vortex to move downward. This reduces the pressure coefficient induced by the primary vortex on the leeward surface, thus enhancing the vortex lift. In terms of the delay of the vortex breakdown, the convex deformation compresses and accelerates the flow between the spanwise convex and the leading edge. These intensities enhance the washing effect along the spanwise direction on the outward wing and cause the primary vortex to deflect toward the outboard wing. Subsequently, the primary vortex and the shedding vortices generated by the shear layer instability merge, which increase the primary vortex intensity, and enhance the streamwise velocity in the vortex axis. Correspondingly, the primary vortex breakdown is delayed. Ultimately, the increased low-pressure region caused by the delay of the vortex breakdown enhances the vortex lift. [ABSTRACT FROM AUTHOR]

The investigation of magnesium (Mg) isotopes in dolomite has mainly focused on marine dolomite environments, leaving a significant gap in the understanding of their dynamics within lacustrine settings, especially in saline lake basins. In this study, a total of 16 sediment core samples from Well BX-7 in the Qianjiang Depression were sequentially selected for scanning electron microscope observation, whole-rock analysis for major and minor elements, and isotopic measurements including δ18Ocarb, δ13Ccarb, δ26Mgdol, and δ26MgSi. In addition, two intact cores were subjected to detailed analysis on the centimeter scale. Sedimentation models were established to elucidate dolomite formation under contrasting climatic conditions, specifically humid climates with a significant riverine Mg input versus relatively dry conditions with a lower Mg input. Furthermore, a quantitative model was developed to assess the magnesium flux and isotopic mass balance within lacustrine systems, simulating the magnesium isotope variations in lake water under different climatic scenarios. The dolomite sample data at a smaller scale (sampling interval ≈ 3~5 mm) demonstrate a consistent trend with the established model, providing additional confirmation of its reliability. Dolomite precipitated under humid climatic conditions exhibits a lower and relatively stable δ26Mgdol, lower δ18O, and higher CIA, indicating higher river inputs and relatively stable Mg isotope values of lake water controlled by river input. Nevertheless, dolomite formed under relatively dry climatic conditions shows a relatively high δ26Mgdol, higher δ18O, and lower CIA, suggesting reduced river inputs and weathering intensity, as well as relatively high magnesium isotope values of the lake water controlled by dolomite precipitation. This study contributes to the understanding of magnesium isotopes in lacustrine dolomite systems. [ABSTRACT FROM AUTHOR]

Radial borehole fracturing is a production method that combines ultrashort radius radial boreholes with hydraulic fracturing, which is expected to effectively improve the drainage and transformation effect of tight oil and gas reservoirs. In view of the problem that radial boreholes cannot be fully fractured when high-pressure liquids are injected into them simultaneously, this paper proposes a new method of fracturing the radial boreholes step by step, and this method is verified by the laboratory fracturing experiments. It designed and processed wellbores with both internal and external pipes, conducted 24 sets of radial boreholes step-by-step experiments of double-layer radial boreholes, analyzed the reproducibility of the experimental results, and compared the results of fracturing radial boreholes simultaneously and fracturing radial boreholes step-by-step experiments. The results show that this method can fracture two sets of radial boreholes distributed in different vertical planes. The results demonstrate that this method is feasible and can solve the problem that not all the radial boreholes are fractured when fracturing fluids are simultaneously injected into them. Besides, the azimuth is the key factor affecting fracture geometries. When the azimuths are 0° and 30°, the fractures can cut through radial boreholes in different layers within the rock samples. If the radial boreholes with larger azimuths are fractured, it will generate higher breakdown pressures. Fracturing radial boreholes with smaller azimuths first and then radial boreholes with larger azimuths should be selected. This study provides a theoretical basis for the field application of radial borehole fracturing technology. Highlights: A new method of fracturing the radial boreholes step by step is proposed. The azimuth is the key factor affecting fracture geometries. If the radial boreholes with larger azimuths are fractured, it will generate higher breakdown pressures. [ABSTRACT FROM AUTHOR]

Objective: This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). Methods: A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. Results: The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. Conclusion: The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD. [ABSTRACT FROM AUTHOR]

Cracking frequently encountered in railway bridges can lead to significant changes in bridge dynamic characteristics and further dynamic performance of train–bridge interaction (TBI) system. It is desirable to develop an effective method to understand the dynamic behavior of coupled train and cracked bridge systems. This paper proposes a framework for coupled vibration analysis of train and cracked bridge systems using multiscale finite element modeling. A multiscale bridge model adopting frictional surface–surface contact to characterize nonlinear breathing phenomenon of the crack is incorporated into the three-dimensional coupled TBI model. Subsequently, a multiloop numerical solution strategy is formulated to solve the established dynamic equation of coupled train and cracked bridge systems. The proposed framework is used to analyze the cracked beams presented in the literature, and the obtained and literature data are compared. Moreover, an actual heavy-haul railway bridge is taken as an illustrative example, in which vehicle dynamic response, bridge vibration, and train running safety index are calculated considering various crack types, train speeds, and track conditions. The results show that apart from the bridge displacement and acceleration, the presence of crack can have a large influence on the vehicle acceleration response, derailment factor, and offload factor. Differences larger than ±10% in the maximum dynamic indices associated with the intact and cracked bridges are observed, indicating the necessity of considering the crack damage in train–bridge coupled dynamic analysis. [ABSTRACT FROM AUTHOR]

High-resolution infrared remote sensing imaging is critical in planetary exploration, especially under demanding engineering conditions. However, due to diffraction, the spatial resolution of conventional methods is relatively low, and the spatial bandwidth product limits imaging systems' design. Extensive research has been conducted with the aim of enhancing spatial resolution in remote sensing using a multi-aperture structure, but obtaining high-precision co-phase results using a sub-aperture remains challenging. A new high-resolution imaging method utilizing multi-aperture joint-encoding Fourier ptychography (JEFP) is proposed as a practical means to achieve super-resolution infrared imaging using distributed platforms. We demonstrated that the JEFP approach achieves pixel super-resolution with high efficiency, without requiring subsystems to perform mechanical scanning in space or to have high position accuracy. Our JEFP approach extends the application scope of Fourier ptychographic imaging, especially in distributed platforms for planetary exploration applications. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of achieving foam with a high expansion ratio and poor mechanical properties, caused by the low melt viscosity of semi-crystalline polypropylene (PP). We systematically employ a modification approach involving blending PP with polyolefin elastomers (POE), irradiation crosslinking, and fiber reinforcement to prepare fiber-reinforced crosslinked PP/POE composite foam. Through optimization and characterization of material composition and processing conditions, the obtained fiber-reinforced crosslinked PP/POE composite foam exhibits both low density and high performance. Specifically, at a crosslinking degree of 12%, the expansion ratio reaches 16 times its original value, and a foam density of 0.057 g/cm3 is reduced by 36% compared to the non-crosslinked PP/POE system with a density of 0.089 g/cm3. The density of the short-carbon-fiber-reinforced crosslinked sCF/PP/POE composite foam is comparable to that of the crosslinked PP/POE system, but the tensile strength reaches 0.69 MPa, representing a 200% increase over the crosslinked PP/POE system and a 41% increase over the non-crosslinked PP/POE system. Simultaneously, it exhibits excellent impact strength, tear resistance, and low heat shrinkage. Irradiation crosslinking is beneficial for enhancing the melt strength and resistance to high temperature thermal shrinkage of PP/POE foam, while fiber reinforcement contributes significantly to improving mechanical properties. These achieve a good complementary effect in low-density and high-performance PP foam modification. [ABSTRACT FROM AUTHOR]

Background: No large cohort study has evaluated the surgical outcomes of THA between different stages of ONFH patients. This study aimed to compare the surgical outcomes of ONFH patients who underwent THA in ARCO stage III versus IV, in terms of operative parameters, one-year hip function assessments and postoperative at least five-year complications, to inform optimized management of ONFH. Method: From our prospectively collected database, 876 patients undergoing THA between October 2014 and April 2017 were analyzed and divided into ARCO stage III group (n = 383) and ARCO stage IV group(n = 493). Details of demographics, medical record information, adverse events and clinical scores of both groups were collected and compared. Proper univariate analysis was used for the analysis. Result: There were no statistically significant differences in baseline characteristics between the two groups. Compared to ARCO stage IV patients, ARCO stage III patients showed a shorter operative time (p < 0.01), less bleeding (p < 0.01), fewer one-year readmissions (p = 0.026) and complications (p = 0.040), and significantly higher HHS (p < 0.01) one year after THA. In addition, ARCO stage IV patients seem more likely to suffer prosthesis dislocation (p = 0.031). Conclusion: Although ARCO stage IV patients in the study cohorts appeared to suffer more one-year complications, no significant difference was observed at long-term follow-up. Enhanced clinical guidance on preventing early prosthesis dislocation may help improve the prognosis of final-stage ONFH patients. [ABSTRACT FROM AUTHOR]

Due to the proliferation of high-quality copying devices and the significant profits of counterfeit products, it is critical to establish an effective scheme for detecting and preventing the counterfeiting of goods. At present, most anti-faking schemes leave much to be desired in terms of cost, convenience, and ability to facilitate pre-sale authentication. The paper designs a unique textured pattern and proposes a triple anti-counterfeiting authentication (TACA). First, the textured pattern consists of triple encryptions (the first is that the key area of the QR code is covered, the second includes scale and Arnold transformation, and the third involves replacing the black areas of the pattern with random multi-level grayscales), the abundant details in the texture not only effectively conceal information, but also that their distortion will increase. Second, TACA comprises interpretability analysis (IA), spectral feature analysis (SFA), and spot matching analysis (SMA) in a cascaded way. In further detail, IA mainly exploits the positional transformation of individual pixels and the block features of local regions to restore interpretability. SFA uses the low-frequency subgraph of discrete wavelet transform (DWT) at a specified scale to capture macroscopic structural information. SMA is able to capture the detailed information of the pattern by utilizing SURF to detect the peak region rate positions and employing BRISK to accurately describe them before. Finally, this paper investigates the robustness of the proposed anti-counterfeiting scheme under a variety of copying methods (replicating, scanning-printing), capturing devices (smartphones), and attack scenarios (no attack, cropping, noise, blur). [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that there exists a significant correlation between oral microbiome and the occurrence of malignancies. However, the prognostic significance of oral microbiome for cancer patients remains unclear. The purpose of this meta-analysis is to evaluate the impact of oral microbiome on the survival of patients with malignant neoplasms. Methods: We conducted a thorough literature search of PubMed, Embase, and Cochrane Library databases until September 2022. The hazard ratio (HR) with a corresponding 95% confidence interval (CI) was analyzed using Review Manager 5.4 software for survival outcomes, including overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and disease-free survival (DFS). Results: A total of 15 studies, covering 5191 samples with various types of cancers, were selected based on specified inclusion and exclusion criteria. In both univariate and multivariate analysis, patients with low diversity of the oral microbiome, or those with Fusobacterium-high/positive, or P. gingivalis positive in cancer tissue displayed poorer OS (univariate HR = 1.74; 95% CI 1.15–2.62; P = 0.009; multivariate HR = 1.56; 95% CI 1.07–2.27; P = 0.02), DSS (univariate HR = 2.06; 95% CI 1.50–2.84; P < 0.00001; multivariate HR = 1.80; 95% CI 1.48–2.20; P < 0.00001), and PFS/DFS (univariate HR = 2.00; 95% CI 1.12–3.58; P = 0.002; multivariate HR = 1.78; 95% CI 1.05–3.02; P = 0.003). Subgroup analysis revealed that Fusobacterium positive or high abundance in cancer tissues was associated with poor OS in multivariate analysis but had no statistical differences in PFS or DFS in univariate and multivariate analysis. Additionally, P. gingivalis positive in cancer tissue was also associated with worse OS. Conclusions: Our meta-analysis suggests that the composition of the oral microbiome may play a significant role in predicting survival outcomes for cancer patients. [ABSTRACT FROM AUTHOR]

Polycomb repressive complexes (PRCs) play critical roles in cell fate decisions during normal development as well as disease progression through mediating histone modifications such as H3K27me3 and H2AK119ub. How exactly PRCs recruited to chromatin remains to be fully illuminated. Here, we report that YTHDF1, the N6‐methyladenine (m6A) RNA reader that was previously known to be mainly cytoplasmic, associates with RNF2, a PRC1 protein that mediates H2AK119ub in human embryonic stem cells (hESCs). A portion of YTHDF1 localizes in the nuclei and associates with RNF2/H2AK119ub on a subset of gene loci related to neural development functions. Knock‐down YTHDF1 attenuates H2AK119ub modification on these genes and promotes neural differentiation in hESCs. Our findings provide a noncanonical mechanism that YTHDF1 participates in PRC1 functions in hESCs. [ABSTRACT FROM AUTHOR]

Lipid droplets (LDs) are important organelles conserved across eukaryotes with a fascinating biogenesis and consumption cycle. Recent intensive research has focused on uncovering the cellular biology of LDs, with emphasis on their degradation. Briefly, two major pathways for LD degradation have been recognized: (1) lipolysis, in which lipid degradation is catalyzed by lipases on the LD surface, and (2) lipophagy, in which LDs are degraded by autophagy. Both of these pathways require the collective actions of several lipolytic and proteolytic enzymes, some of which have been purified and analyzed for their in vitro activities. Furthermore, several genes encoding these proteins have been cloned and characterized. In seed plants, seed germination is initiated by the hydrolysis of stored lipids in LDs to provide energy and carbon equivalents for the germinating seedling. However, little is known about the mechanism regulating the LD mobilization. In this review, we focus on recent progress toward understanding how lipids are degraded and the specific pathways that coordinate LD mobilization in plants, aiming to provide an accurate and detailed outline of the process. This will set the stage for future studies of LD dynamics and help to utilize LDs to their full potential. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify independent risk factors for immediate postoperative deep vein thrombosis (DVT) in patients with open wedge high tibial osteotomy (OWHTO) and to develop and validate a predictive nomogram. Methods: Patients who underwent OWHTO for knee osteoarthritis (KOA) from June 2017 to December 2021 were retrospectively analyzed. Baseline data and laboratory test results were collected, and the occurrence of DVT in the immediate postoperative period was regarded as the study outcome event. Multivariable logistic regression identified independent risk factors associated with a higher incidence of immediate postoperative DVT. The predictive nomogram was constructed based on the analysis results. The stability of the model was further assessed in this study using patients from January to September 2022 as an external validation set. Results: 741 patients were enrolled in the study, of which 547 were used in the training cohort and the other 194 for the validation cohort. Multivariate analysis revealed a higher Kellgren–Lawrence (K–L) grade (III vs. I–II OR 3.09, 95% CI 0.93–10.23. IV vs. I–II OR 5.23, 95% CI 1.27–21.48.), platelet to hemoglobin ratio (PHR) > 2.25 (OR 6.10, 95% CI 2.43–15.33), Low levels of albumin (ALB) (OR 0.79, 95% CI 0.70–0.90), LDL-C > 3.40 (OR 3.06, 95% CI 1.22–7.65), d-dimer > 1.26 (OR 2.83, 95% CI 1.16–6.87) and BMI ≥ 28 (OR 2.57, 95% CI 1.02–6.50) were the independent risk factors of immediate postoperative DVT. The concordance index (C-index) and Brier score of the nomogram were 0.832 and 0.036 in the training set, and the corrected values after internal validation were 0.795 and 0.038, respectively. The receiver-operating characteristic (ROC) curve, the calibration curve, the Hosmer–Lemeshow test, and the decision curve analysis (DCA) performed well in both the training and validation cohorts. Conclusion: This study developed a personalized predictive nomogram with six predictors, which allows surgeons to stratify risk and recommended immediate ultrasound scans for patients with any of these factors. Level of evidence: III. [ABSTRACT FROM AUTHOR]

Background: The primary objectives of this study were to focus on one - year unplanned readmissions after THA in ONFH patients and to investigate rates, causes, and independent risk factors. Methods: Between October 2014 and April 2019, eligible patients undergoing THA were enrolled and divided into unplanned readmission within one year and no readmission in this study. All unplanned readmissions within 1 year of discharge were reviewed for causes and the rate of unplanned readmissions was calculated. Demographic information, ONFH characteristics, and treatment-related variables of both groups were compared and analysed. Results: Finally, 41 out of 876 patients experienced unplanned readmission. The readmission rate was 1.83% in 30 days 2.63% in 90 days, and 4.68% in 1 year. Prosthesis dislocation was always the most common cause at all time points studied within a year. The final logistic regression model revealed that higher risks of unplanned readmission were associated with age > 60 years (P = 0.001), urban residence (P = 0.001), ARCO stage IV (P = 0.025), and smoking (P = 0.033). Conclusions: We recommend the introduction of a strict smoking cessation program prior to surgery and the development of comprehensive management strategies, especially for the elderly and end-stage ONFH patients, and pay more attention to preventing prosthesis dislocation in the early days after surgery. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we investigated the response mechanism of type I interferon receptor in the Japanese eel (Anguilla japonica) after an immune challenge, and we also explored the key role of type I interferon receptor in its immune response mechanism, which will provide new evidence for the study of type I interferon receptor in vertebrates, especially in teleost. IFNAR1, one of the type I IFN receptors, is crucial to mammalian host defense against viral invasion. However, largely unknown is the immunological role of the fish teleost protein IFNAR1, also known as CRFB5. We have successfully cloned the whole cDNA of the Japanese eel's (Anguilla japonica) CRFB5a homolog, AjCRFB5a. The two fibronectin-3 domains and the transmembrane region (238–260 aa) of AjCRFB5a are normally present, and it shares a three-dimensional structure with zebrafish, Asian arowana, and humans. According to expression analyses, AjCRFB5a is highly expressed in all tissues found, particularly the liver and intestine. In vivo, Aeromonas hydrophila, LPS, and the viral mimic poly I:C all dramatically increased AjCRFB5a expression in the liver. Japanese eel liver cells were reported to express AjCRFB5a more strongly in vitro after being exposed to Aeromonas hydrophila or being stimulated with poly I: C. The membranes of Japanese eel liver cells contained EGFP-AjCRFB5a proteins, some of which were condensed, according to the results of fluorescence microscopy. Luciferase reporter assays showed that AjCRFB5a overexpression strongly increased the expression of immune-related genes in Japanese eel liver cells, such as IFN1, IFN2, IFN3, IFN4, IRF3, IRF5, and IRF7 of the type I IFN signaling pathway, as well as one of the essential antimicrobial peptides LEAP2, in addition to significantly inducing human IFN-promoter activities in HEK293 cells. Additionally, RNA interference (RNAi) data demonstrated that knocking down AjCRFB5a caused all eight of those genes to drastically lower their expression in Japanese eel liver cells, as well as to variable degrees in the kidney, spleen, liver, and intestine. Our findings together showed that AjCRFB5a participates in the host immune response to bacterial infection by inducing antimicrobial peptides mediated by LEAP2 and favorably modulates host antiviral immune responses by activating IRF3 and IRF7-driven type I IFN signaling pathways. [ABSTRACT FROM AUTHOR]

The tobacco drying process in the cigarette production has an important effect on the final product quality. Therefore, the intelligent control methods have been widely investigated to ensure the stability of tobacco's outlet moisture content. The existing work mostly uses a relatively fixed set point of the outlet moisture content for different tobacco batches, which can lead to unforeseen product quality after several processes following the drying process and inaccessible amount of dehydration for the rotary dryer. Some tobacco moisture prediction methods have been studied recently while the relationship with the intelligent control methods remain largely unexplored. To deal with these issues, a novel method is proposed in this paper to identify the optimal set point of the drying outlet moisture content for each tobacco batch. An encoder-decoder model is first developed to forecast the post-drying moisture trajectory. Then, an adaptive filter with specially designed mechanisms and a confidence interval of the dehydration level are constructed to obtain the design constraints. Based on all above, a constrained optimization problem is formulated and solved by the genetic algorithm. Extensive experiments on 895 tobacco batches from a large cigarette factory are carried out, which involves both algorithmic evaluation and field test. It turns out that the proposed method achieves the superior performance and leads to an improvement of the product quality in real production. [ABSTRACT FROM AUTHOR]

We study stochastic zeroth-order gradient and Hessian estimators for real-valued functions in |$\mathbb{R}^n$|⁠. We show that, via taking finite difference along random orthogonal directions, the variance of the stochastic finite difference estimators can be significantly reduced. In particular, we design estimators for smooth functions such that, if one uses |$ \varTheta \left (k \right) $| random directions sampled from the Stiefel manifold |$ \text{St} (n,k) $| and finite-difference granularity |$\delta $|⁠ , the variance of the gradient estimator is bounded by |$ \mathscr{O} \left (\left (\frac{n}{k} - 1 \right) + \left (\frac{n^2}{k} - n \right) \delta ^2 + \frac{ n^2 \delta ^4} { k } \right) $|⁠ , and the variance of the Hessian estimator is bounded by |$\mathscr{O} \left (\left (\frac{n^2}{k^2} - 1 \right) + \left (\frac{n^4}{k^2} - n^2 \right) \delta ^2 + \frac{n^4 \delta ^4 }{k^2} \right) $|⁠. When |$k = n$|⁠ , the variances become negligibly small. In addition, we provide improved bias bounds for the estimators. The bias of both gradient and Hessian estimators for smooth function |$f$| is of order |$\mathscr{O} \big(\delta ^2 \varGamma \big)$|⁠ , where |$\delta $| is the finite-difference granularity, and |$ \varGamma $| depends on high-order derivatives of |$f$|⁠. Our results are evidenced by empirical observations. [ABSTRACT FROM AUTHOR]

The perception of the /da/–/ga/ series, distinguished primarily by the third formant (F3) transition, is affected by many nonspeech and speech sounds. Previous studies mainly investigated the influences of context stimuli with frequency bands located in the F3 region and proposed the account of spectral contrast effects. This study examined the effects of context stimuli with bands not in the F3 region. The results revealed that these non-F3-region stimuli (whether with bands higher or lower than the F3 region) mainly facilitated the identification of /ga/; for example, the stimuli (including frequency-modulated glides, sine-wave tones, filtered sentences, and natural vowels) in the low-frequency band (500–1500 Hz) led to more /ga/ responses than those in the low-F3 region (1500–2500 Hz). It is suggested that in the F3 region, context stimuli may act through spectral contrast effects, while in non-F3 regions, context stimuli might activate the acoustic cues of /g/ and further facilitate the identification of /ga/. The combination of contrast and acoustic cue effects can explain more results concerning the forward context influences on the perception of the /da/–/ga/ series, including the effects of non-F3-region stimuli and the imbalanced influences of context stimuli on /da/ and /ga/ perception. [ABSTRACT FROM AUTHOR]

Capacitive pressure sensors which convert pressure into electric signals are a kind of highly efficient and reliable sensor and have wide applications. The flexible structure of capacitive pressure sensors may undergo large deformation, and the sensitivity and linearity with respect to the deformation are two major performance indices of capacitive pressure sensors. Thus, a simple and accurate analytical model to predict the deformation and electromechanical coupling under external pressure is essential for the quantitative design of the sensors. In this paper, we propose an electromechanical analytical model which couples the capacitance change under large deformation of the flexible curved electrodes. The theory is used to predict the deformation and capacitance of a flexible rhomboidal capacitive sensor, and is verified by both numerical simulations and experiments. The electromechanical analytical model can be applied to other cantilever-like capacitors with symmetric flexible/curved electrodes under symmetric loading. The theory can degenerate into the existing analyses in the literature for the case of small deformation. The accurate analytical model can facilitate design and analyses of capacitive pressure sensors and other similar structures. [ABSTRACT FROM AUTHOR]

Contact planning is crucial to the locomotion performance of robots: to properly self-propel forward, it is not only important to determine the sequence of internal shape changes (e.g., body bending and limb shoulder joint oscillation) but also the sequence by which contact is made and broken between the mechanism and its environment. Prior work observed that properly coupling contact patterns and shape changes allows for computationally tractable gait design and efficient gait performance. The state of the art, however, made assumptions, albeit motivated by biological observation, as to how contact and shape changes can be coupled. In this paper, we extend the geometric mechanics (GM) framework to design contact patterns. Specifically, we introduce the concept of "contact space" to the GM framework. By establishing the connection between velocities in shape and position spaces, we can estimate the benefits of each contact pattern change and therefore optimize the sequence of contact patterns. In doing so, we can also analyze how a contact pattern sequence will respond to perturbations. We apply our framework to sidewinding robots and enable (1) effective locomotion direction control and (2) robust locomotion performance as the spatial resolution decreases. We also apply our framework to a hexapod robot with two back-bending joints and show that we can simplify existing hexapod gaits by properly reducing the number of contact state switches (during a gait cycle) without significant loss of locomotion speed. We test our designed gaits with robophysical experiments, and we obtain good agreement between theory and experiments. [ABSTRACT FROM AUTHOR]

Background: Dual-energy CT (DECT) shows good performance in differentiating hemorrhage from contrast staining (CS). However, no guidelines have standardized the post-endovascular thrombectomy (EVT) examination time. We evaluated the value of performing DECT immediately and 24 h post-EVT in the diagnosis and prediction of hemorrhagic transformation (HT). Methods: Two readers evaluated simulated conventional CT (sCCT) images compared with a second reading with DECT, establishing the diagnosis of HT immediately and 24 h post-EVT. Another reader's diagnosis 2–7 days post-EVT using non-contrast CT was identified as the final diagnostic criteria. Results: DECT performed immediately and 24 h post-EVT changed 22.4% (52/232) and 12.5% (29/232) of sCCT-based HT diagnoses, respectively (χ2 = 10.7, p < 0.05). The sensitivity, negative predictive value (NPV), and accuracy of DECT performed immediately post-EVT for predicting the final diagnosis of HT were 33.6%, 58.9%, and 65.9%, respectively, whereas those for DECT performed 24 h post-EVT were 82.4%, 84.3%, and 90.9%, respectively (χ2 = 58.0, χ2 = 42.9, χ2 = 13.6; p < 0.05). The specificity and positive predictive value were both 100.0%. Delayed HT occurred in 50.0% (78/156) and 42.2% (19/45) of patients with CS diagnosed immediately and 24 h post-EVT, respectively. Conclusions: DECT performed immediately post-EVT changed a greater proportion of real-time HT diagnoses, whereas that performed 24 h post-EVT had higher sensitivity, NPV, and accuracy in predicting the final diagnosis of HT. A substantial proportion of patients with CS diagnosed at these two post-EVT timepoints subsequently developed delayed HT. [ABSTRACT FROM AUTHOR]

Due to unavoidable uncertainties, it is critical to perform probabilistic analysis when examining the safety of coupled train-bridge system based on the dynamic interaction analysis. This paper proposes a novel approach for probabilistic safety analysis of coupled train-bridge system using deep learning based surrogate model. Deep neural network embedded with convolutional neural network is designed and developed to construct the surrogate model substituting the 3D train-bridge interaction system model for reducing the computational efforts and efficiently predicting a series of dynamic indices of the coupled system. Due to the lack of explicit expression of the performance function, automatic differentiation is exploited to derive the surrogate model and further to realise the reliability and sensitivity analysis with the first-order method. The obtained reliability and sensitivity indices are compared to that resulting from the Monte Carlo simulation method with importance sampling and tail modelling. The proposed approach is applied on a high-speed train and bridge system, treating multiple main system parameters as random variables. Effects of the railway operational speed on the system reliability are investigated. The results show that the proposed approach can provide an efficient solution to the probabilistic safety analysis of coupled train-bridge system especially with small failure probability. [ABSTRACT FROM AUTHOR]

We study Hessian estimators for functions defined over an |$n$| -dimensional complete analytic Riemannian manifold. We introduce new stochastic zeroth-order Hessian estimators using |$O (1)$| function evaluations. We show that, for an analytic real-valued function |$f$|⁠ , our estimator achieves a bias bound of order |$ O (\gamma \delta ^2) $|⁠ , where |$ \gamma $| depends on both the Levi–Civita connection and function |$f$|⁠ , and |$\delta $| is the finite difference step size. To the best of our knowledge, our results provide the first bias bound for Hessian estimators that explicitly depends on the geometry of the underlying Riemannian manifold. We also study downstream computations based on our Hessian estimators. The supremacy of our method is evidenced by empirical evaluations. [ABSTRACT FROM AUTHOR]

The practical use of polyethylene oxide polymer electrolyte in the solid‐state sodium metallic batteries (SSMBs) suffers from the retard Na+ diffusion at the room temperature, mechanical fragility as well as the oxidation tendency at high voltages. Herein, a hetero‐layered composite polymeric electrolyte (CPE) is proposed to enable the simultaneous interfacial stability with the high voltage cathodes (till 4.2 V) and Na metallic anode. Being incorporated within the polymer matrix, the sand‐milled Na3Zr2Si2PO12 nanofillers and nanocellulose scaffold collectively endow the thin‐layer (25 µm), ultralightweight (1.65 mg cm−2) CPE formation with an order of magnitude enhancement of the mechanical strength (13.84 MPa) and ionic conductivity (1.62 × 10−4 S cm−1) as compared to the pristine polymer electrolyte, more importantly, the improved dimension stability up to 180 °C. Upon the integration of the hetero‐layered CPE with the iron hexacyanoferrate FeHCF cathode (1 mAh cm−2) and the Na foil, the cell model can achieve the room‐temperature cycling stability (93.73% capacity retention for 200 cycles) as well as the high temperature tolerance till 80 °C, which inspires a quantum leap toward the surface‐wetting‐agent‐free, energy‐dense, wide‐temperature‐range SSMB prototyping. [ABSTRACT FROM AUTHOR]

We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit. This topologically assisted optimization contains two steps. First, a global evolutionary optimization is performed for the shape parameters, and then a topological analysis reveals the local and global extrema of the objective function directly from the data. This non-dimensional objective function compares the achieved thrust with the required torque. Rotor blades have a decisive contribution to the performance of quadcopters. A two-blade rotor with pre-defined chord length distribution is chosen as the baseline model. The simulation is performed in a moving reference frame with a k − ω turbulence model for the hovering condition. The rotor shape is parameterized by the twist angle distribution. The optimization of this distribution employs a genetic algorithm. The local maxima are distilled from the data using a novel topological analysis inspired by discrete scalar-field topology. We identify one global maximum to be located in the interior of the data and five further local maxima related to errors from non-converged simulations. The interior location of the global optimum suggests that small improvements can be gained from further optimization. The local maxima have a small persistence, i.e., disappear under a small ε perturbation of the figure of merit values. In other words, the data may be approximated by a smooth mono-modal surrogate model. Thus, the topological data analysis provides valuable insight for optimization and surrogate modeling. [ABSTRACT FROM AUTHOR]

With the rapid development of high-voltage direct current transmission systems and trends of miniaturization and integration of power and electronic equipment, higher requirements have been imposed on the key physical properties of insulating materials, such as their electrical insulation and thermal and mechanical properties. This has made research on the modification of epoxy resin by doping with nanoparticles a timely topic. In this paper, molecular dynamics simulations are used to study changes in various key physical properties of composites after doping epoxy resin with C60, C70, or the C60 derivatives phenyl-C61-butyric acid methyl ester ([60]PCBM) and bis-phenyl-C61-butyric acid methyl ester (Bis[60]PCBM). All doping treatments effectively reduced the thermal expansion coefficient, the free volume ratio, and relative permittivity; inhibited molecular chain segment motion; and increased thermal conductivity as well as Young's modulus, bulk modulus, and shear modulus. The effects on glass transition temperature were not significant. Furthermore, the analysis indicated that Bis[60]PCBM had the most prominent effects. Therefore, doping epoxy resin with Bis[60]PCBM may be an effective material modification solution for improving dielectric properties in the future. [ABSTRACT FROM AUTHOR]

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case–control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs. [ABSTRACT FROM AUTHOR]

Key Message: Two major and stably expressed QTL for traits related to mature wheat embryo independent of kernel size were identified and validated in a natural population that contained 171 Sichuan wheat accessions and 49 Sichuan wheat landraces. As the juvenile of a highly differentiated plant, mature wheat (Triticum aestivum L.) embryos are highly significant to agricultural production. To understand the genetic basis of traits related to wheat embryo size, the embryo of mature kernels in a recombination inbred line that contained 126 lines from four environments was measured. The genetic loci of embryo size, including embryo length (EL), embryo width (EW), embryo area (EA), embryo length/kernel length (EL/KL), embryo width/kernel width (EW/KW), and EL/EW, were identified based on a genetic linkage map constructed based on PCR markers and the Wheat 55 K single nucleotide polymorphism (SNP) array. A total of 50 quantitative trait loci (QTL) for traits related to wheat embryo size were detected. Among them, QEL.sicau-2SY-4A for EL and QEW.sicau-2SY-7B for EW were major and stably expressed and were genetically independent of KL and KW, respectively. Their effects were further verified in a natural population that contained 171 Sichuan wheat accessions and 49 Sichuan wheat landraces. Further analysis showed that TraesCS4A02G343300 and TraesCS7B02G006800 could be candidate genes for QEL.sicau-2SY-4A and QEW.sicau-2SY-7B, respectively. In addition, significant positive correlations between EL and kernel-related traits and the 1,000-grain weight were detected. Collectively, this study broadens our understanding of the genetic basis of wheat embryo size and will be helpful for the further fine-mapping of interesting loci in the future. [ABSTRACT FROM AUTHOR]

The surface texture of urbanized regions is altered by the replacement of natural vegetated surfaces with hardened pavement surfaces, which have been described as a heat source for the formation of urban heat islands. Grasslands may store rainfall in their roots and leaves for later cooling, but this has received little attention. This study investigated the radiant flux and temperature of a tropical grassland throughout the summer in order to understand the albedo, long-wave radiation, short-wave radiation, and surface temperature of the grassland over 10 days. The grassland had an albedo of 0.13, which did not fluctuate during the day compared to the albedo of other surfaces in metropolitan areas. Even if the local weather changes considerably, this albedo does not alter significantly. The surface temperature and the air temperature above the grassland increase linearly with the upwelling reflectance, incident solar radiation, and upwelling long-wave radiation. These two temperatures do not correspond with downwelling long-wave radiation, which is influenced by cloud cover in the sky. However, the peaks of these temperatures lag behind the incident shortwave radiation and net radiation that reaches the grassland surface. The finding that the thermal properties of grasslands could be harnessed to reduce the heat absorbed by urban surfaces provides valuable insights into the grasslands' potential to mitigate the impacts of urbanization on temperature. [ABSTRACT FROM AUTHOR]

Though considered a significant barometer of urban sustainable development, urban carrying capacity (UCC) has yet to be clearly defined and the concepts it comprises have not received adequate scholarly attention in relation to their empirical applicability. This article, by introducing the concept of human well-being and redefining it in the context of UCC, presents a novel framework for UCC assessment. The proposed framework discards the pursuit of “limits of growth,” but instead focuses on whether the human well-being has been (or will be) compromised through monitoring the status of the associated UCC indicators. This framework is further applied to China’s four first-tier cities, Beijing, Shanghai, Guangzhou, and Shenzhen, to assess their UCC in empirical terms. The results show that all of them are generally able to meet the survival-related well-being, while the prospect of meeting challenges of varying magnitude in order to realize higher-level goals. We developed this UCC assessment framework to provide a rational way for the implementation of UCC monitoring and forewarning, and in turn, to guide sustainable development of urban areas according their urban carrying status. [ABSTRACT FROM AUTHOR]

Bridge failures inevitably cause severe social consequences in terms of the economic losses or even human fatalities. Scour is one of the leading causes for bridge failures. Due to its complexity and multidisciplinary nature, the mechanism of scour has not been completely understood yet. Inspired by the rapid development of Machine Learning (ML) techniques, this paper aims to construct a novel data-driven extreme gradient boosting (XGBoost) algorithm based model to predict the scour risk around bridge piers. Data used in the present study is provided by the French National Railway Company (SNCF). The performance of XGBoost-based model is compared with three commonly used algorithms: support vector machine, random forest and multilayer perceptron. Results show that the XGBoost classifier achieves high accuracy (0.959/0.938), precision (0.970/0.961), recall (0.974/0.956) and low false positive rate (0.085/0.114) for training and test set respectively. Moreover, the classifier obtains an area under the ROC curve (AUC) score equal to 0.974 for test set (a perfect classifier has an AUC equal to 1). This paper presents a cutting-edge application of XGBoost algorithm in the maintenance of railway bridges. The proposed methodology may allow engineers to determine the scour risk of bridge piers in an accurate and rapid way. [ABSTRACT FROM AUTHOR]

Abstract This study is an innovative study on intelligent online language teaching that is based on the development of online Chinese learning resources and learner needs. The recommendation technology of collaborative filtering algorithm is used to find, analyze and recommend online Chinese learning resources that meet the needs of learners and teachers. This study assists learners and teachers in quickly searching for various learning resources under specified conditions, analyzing the resources obtained in detail, and providing results based on machine learning and big data sample analysis. The research is structured as follows to highlight the characteristics of Chinese learning resources and the personalized demand orientation of recommendation results: (1) Based on the personalized demand orientation of learners’ language learning and the goal oriented theoretical standards of Chinese teaching, a recommendation system based on multiple collaborative filtering hybrid algorithms is designed; (2) Evaluate the operation of the recommendation system using teaching practice; (3) This article investigates the influence of online language learning from technical factors and the operation mechanism of Chinese learning resource recommendation. The results of the experiments show that this hybrid approach has some advantages when it comes to recommending Chinese learning resources. HIGHLIGHTS A system for recommendating online Chinese learning resources is constructed. The recommendation results based on learners’ personalized needs and learning goal oriented design have high accuracy. The recommendation system based on hybrid mode has a high recommendation effect. The recommendation system can find and predict learners’ learning needs. Cultural differences, mental states, and learning psychology from evaluators may lead to differences in recommendation results. A system for recommendating online Chinese learning resources is constructed.The recommendation results based on learners’ personalized needs and learning goal oriented design have high accuracy.The recommendation system based on hybrid mode has a high recommendation effect.The recommendation system can find and predict learners’ learning needs.Cultural differences, mental states, and learning psychology from evaluators may lead to differences in recommendation results. [ABSTRACT FROM AUTHOR]

The Kuroshio Extension (KE) system has been observed to experience a decadal cycle between dynamically stable and unstable states. However, divergent conclusions on its interaction with the atmosphere obfuscate the understanding of its oscillatory nature at the preferred decadal time scale. Here, using satellite observations and the ERA-Interim reanalysis in 2002–16, physical process-oriented diagnoses suggest that the wintertime finer-scale thermodynamic response to mesoscale oceanic surface conditions and slow oceanic Rossby wave adjustment frame a coupled ocean–atmosphere delayed oscillator for the decadal KE variability. During the stable state of the KE system, the downstream KE transition region is rich in mesoscale oceanic warming associated with warm eddies, which induces surface wind convergence and upward motion, probably via the enhanced turbulent mixing. Meanwhile, increased finer-scale diabatic heating in the lower troposphere with abundant moisture supply from warmer water likely facilitates the deep-reaching updraft that adiabatically cools the middle troposphere. The background northwesterly wind helps to spread out the cooling, leading to southward deflection of local atmospheric eddy available potential energy (EAPE) production by baroclinic conversion. Consequently, the synoptic eddy activity displaces southward across the basin with additional energy supply from the increased diabatic production of EAPE downstream. Anomalous synoptic eddy thermal and vorticity forcing eventually fosters the basin-scale equivalent-barotropic cyclonic circulation anomaly, which is further maintained by energy conversion from the background state. The resultant wind-driven negative sea surface height anomalies propagate westward into the upstream KE region with a delay of ∼4 years and can trigger the unstable state of the KE system. [ABSTRACT FROM AUTHOR]

Time-history responses of the bridge induced by the moving vehicle provide crucial information for bridge design, operation, maintenance, etc. As inspired by this, this work attempts to provide a new paradigm for vehicle–bridge interaction (VBI) by highlighting the comparison of different deep learning algorithms applied to the prediction of time-history responses of the bridge under vehicular loads. Particularly, three deep learning architectures with few and measurable input features developed by using fully-connected feedforward neural network, long short-term memory (LSTM) network, and convolutional neural network (CNN) are proposed on the basis of the governing equation of bridge vibrations. Three VBI systems with various vehicle models are developed and further validated to produce reliable training data. To examine the accuracy of the predictive models, two advanced metrics are exploited for time-history estimate. Moreover, the proposed deep learning models are comprehensively investigated through a parametric study on the influential factors associated with the VBI system and network architecture. The results show that deep feedforward neural network (DFNN), LSTM network, and CNN can be applied in VBI analysis to estimate the bridge time-history response. The three neural networks have comparable prediction accuracies. When considering the irregularity excitation, CNN is found to be the most efficient predictive model, while DFNN needs the least training time under perfect bridge surface condition. [ABSTRACT FROM AUTHOR]

This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance. [ABSTRACT FROM AUTHOR]

Masters athletes are endowed with high functional capacity, long-term health, and psychological outlook. However, their sexual activity and function are largely unknown. We investigated the influence of sustained moderate to vigorous exercise on sexual life using a model of masters athletes. A 55-item survey adapted from previous American Association of Retired People (AARP) sexuality study were distributed to masters athletes associations. The responses from 166 masters athletes were compared with AARP dataset that represented a general population. Both male and female masters athletes demonstrated more frequent sexual desire and greater involvements in different types of sexual activities (i.e., kissing or hugging, touching or caressing, sexual intercourse, oral sex, and self-stimulation; all p < 0.05). Sexual satisfaction was greater in female masters athletes than in female general population (p = 0.025), but no such group difference was observed in men. Male athletes had a lower degree of erectile dysfunction. No difference was observed in orgasm frequency between the two population groups. Age-associated decline in sexual activity was apparent in general population (p < 0.05) but absent in masters athletes. Females recorded lower engagements in partner-involved sexual behaviors than men in the general population, but no such gender difference was observed in women. The primary factor predicting improvement in sexual satisfaction was "better health for myself" (29.7%) in the general population, whereas master athletes identified "better health for partner" (23.5%). Masters athletes in general and female athletes in particular demonstrate superior sexual function, satisfaction, and behavior presumably due to their better overall health status. [ABSTRACT FROM AUTHOR]

The moisture content of tobacco at the end of a drying process is controlled to guarantee the product quality. However, the unforeseen moisture at dryer inlet usually leads to improper dehydration level that degrades the product quality. To overcome the problem, a suitable control reference of moisture should be set much earlier for the conditioning-casing process to reach. This boils down to the challenging task of modeling intermediate moisture dissipation with long feedback delay and streaming data under concept drift. In this paper, a novel method is proposed based on Recursive Least Squares (RLS) for online moisture modeling and in-advance optimization. For modeling, a multi-step representation is employed on processed mechanism-based features to alleviate the feedback delay. Besides, a variable forgetting factor is designed for RLS to maintain the tracking ability toward unpredictable concept drift. Then the optimization can be fast achieved via a feature forecasting strategy and reverse model inference. Extensive experiments are performed on two years' real production data covering 2280 valid tobacco batches. The algorithmic evaluation shows the proposed method outperforms others on all metrics involved, achieving R 2 of 0.815 and 0.875 for year 2021 and 2020 with least single estimation time of 0.213 ms. The on-site evaluation manifests the improvement of production quality represented by the increased stability of drying dehydration level around the standard 5.9%. [ABSTRACT FROM AUTHOR]

Abstract The study presented in this article is a ground-breaking investigation into the quick advancement of intelligent online language instruction based on wireless communication networks, learner physiological and psychological alterations;to predict and prompt learners’ sleepiness, a hybrid technology of facial feature point distribution model is applied to the process of online learning of Chinese for international students. This technology aids teachers in timely monitoring and discovering learners’ levels of sleepiness in the process of online learning and adjusting it, ensuring effective teacher-student interaction and the accomplishment of learning objectives in the classroom. The following specific designs are carried out in order to increase the contribution of teaching and training samples to the classification effect: (1) To train the neural network classifier and ensure more stable and accurate recognition results, the system employs the particle swarm optimization approach with adaptive parameter adjustment; (2) A simulated annealing technique is suggested to address the premature convergence flaw of the conventional particle swarm optimization algorithm, and an adjustable inertia weight factor is created to enhance the optimization capability of particle swarm optimization; (3) A PSO-SVM based facial expression recognition system based on multi-scale wavelet transform theory and support vector regression machine is created in order to further enhance the network’s generalization capability. The simulation findings demonstrate some improvement in the convergence of the two widely used face recognition techniques. Additionally, this study provides a principal component analysis approach based on DCT coefficient singular decomposition and a texture feature selection method based on LDA model in an effort to apply wavelet analysis theory to face recognition. Highlights A system for predicting and prompting learners’ sleepiness in online foreign language classroom teaching is constructed In the case of being unable to actively integrate into online classroom teaching, learners are prone to lack of concentration, sleepiness, even anxiety, irritability and facial muscle twitching Eye fatigue identified at a distance of 0.3–1.5 m from ordinary cameras is relatively easy to identify The appearance of a large proportion of tired students in the online classroom suggests that teachers should adjust the teaching design scheme The reasons leading to the burnout of online learners are very complex, and it still needs interdisciplinary in-depth research A system for predicting and prompting learners’ sleepiness in online foreign language classroom teaching is constructedIn the case of being unable to actively integrate into online classroom teaching, learners are prone to lack of concentration, sleepiness, even anxiety, irritability and facial muscle twitchingEye fatigue identified at a distance of 0.3–1.5 m from ordinary cameras is relatively easy to identifyThe appearance of a large proportion of tired students in the online classroom suggests that teachers should adjust the teaching design schemeThe reasons leading to the burnout of online learners are very complex, and it still needs interdisciplinary in-depth research [ABSTRACT FROM AUTHOR]

Network function virtualization redefines a network service as a softwarized chain of virtual network functions (VNFs), which decouples the specific network service from dedicated devices and greatly reduces the hardware cost. The VNFs are generally deployed on common off-the-shelf servers and statistically share computational resources. Due to the inherent integer constraints, the corresponding VNF mapping and scheduling issue is a highly challenging task. In this paper, we consider the mapping and scheduling of VNFs for a given network service, for which a flexible job shop scheduling problem is formulated to optimize the max-min fairness while ensuring the delay requirements of different service chains. Specifically, we propose a deep reinforcement learning method based on offline proximal policy optimization, which dynamically determines the mapping and scheduling decision based on the state of unfinished service chains. The proposed algorithm is scalable to the number of service chains and can be enhanced by Monte Carlo tree search. Numerical results show that the proposed algorithm outperforms the traditional random forest and the greedy algorithms in terms of both service fairness and acceptance ratio. [ABSTRACT FROM AUTHOR]

Purpose: The present research investigates the influence of P11 gene DNA methylation combined with life stress on the response to antidepressants in the first two weeks.Methods: A total of 291 Han Chinese patients with major depressive disorder and 100 healthy controls were included. The Life Events Scale and the Childhood Trauma Questionnaire were used to assess stress. The primary endpoint was the Hamilton Depression Rating Scale-17 reduction rate after two weeks of treatment. The Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples.Results: The mean methylation of all P11 CpG sites, as well as the methylation at 4 CpG sites (P11-2-169, P11-2-192, P11-2-202, P11-2-204), were significantly higher in patients with MDD than in healthy controls (FDR-corrected P < 0.05). The response to antidepressants was associated with the following interactions: the CTQ score and P11-3-185 site methylation (OR = 0.297, FDR-corrected P = 0.023), the CTQ physical neglect score and P11-2-117 site methylation (OR = 0.005, FDR-corrected P = 0.033), and the CTQ emotional abuse score and P11-3-185 site methylation (OR = 0.001, FDR-corrected P = 0.023).Conclusions: The methylation of the P11 gene was significantly higher in patients with major depressive disorder. The interaction of P11 DNA methylation and early-life stress may influence the short-term antidepressant treatment response. [ABSTRACT FROM AUTHOR]

With the increasing popularity of online foreign language teaching and learning practice, learners and teachers have developed a high demand for evaluation of teaching effectiveness. When we focus discussion on international Chinese teaching, which has been developed for a relatively short time and not experienced enough, online teaching effectiveness evaluation has become an important obstacle to the development of teaching. This paper introduces a hybrid technique based on fuzzy evaluation method, for determining and suggesting possible types of errors in international Chinese online listening and speaking instruction and giving suggestions for improvement. The system can help learners to identify and determine the types of errors in Chinese listening and speaking learning in a timely manner and make a more objective and comprehensive evaluation of learning performance; at the same time, it helps teachers to trace the effectiveness of teaching design and implementation in a targeted manner and make corresponding scientific decisions. This hybrid technology combines existing language teaching evaluation models, takes advantage of data from online education, and creates corresponding criteria through machine learning fuzzy algorithms and large data sample training, combined with the theory of effective teaching evaluation, which is beneficial for all participants of online Chinese listening and speaking teaching to improve their learning effectiveness. [ABSTRACT FROM AUTHOR]

Building roads in permafrost region is challenged because permafrost is sensitive to temperature increase. As an embankment gains/drains heat mostly at the upper surface, accurately modeling the heat transfer in the upper surface is crucial to understand the thermal stability of the road. Popular methods treat the upper boundary as a temperature-controlled model (TCM), where temperature of the upper surface is set as a sinusoidal function. This simple function, however, fails to identify the influences of solar irradiance, heat convection, and thermal irradiance on the heat transfer on the ground surface. Here we introduce a heat-flux model (HFM) to calculate the heat fluxes at the embankment upper surface and at the adjacent ground surface. HFM-predicted temperature under an embankment is compared against the observed temperature to validate the model, and is compared to the TCM-predicted temperature. While TCM-predicted temperatures and HFM-predicted ones are similar in trend and in pattern, the HFM-predicted temperatures are far more coincident with the observed ones. The pros and cons of both HFM and TCM are discussed. Further studies are expected to use HFM to understand the heat flux components such as solar absorption, heat convection, and thermal irradiance on the temperature of permafrost under embankments. [ABSTRACT FROM AUTHOR]

• The transformation of impurity Al was investigated during co-precipitated regeneration of cathode materials from spent LIBs. • Al-doped regenerated LiNi 0.9 Co 0.05 Mn 0.05 O 2 were successfully prepared based on Al-containing leaching solutions. • Effect mechanism of impurity Al on the macromorphology and microstructure of regenerated LiNi 0.9 Co 0.05 Mn 0.05 O 2 was revealed. • Al-doped LiNi 0.9 Co 0.05 Mn 0.05 O 2 with ultra-high cycling stability was regenerated at an optimal impurity Al concentration. Aluminum (Al) from current collector is a quite important impurity element affecting the regenerated cathode materials from spent lithium-ion batteries (LIBs). Currently, Al in the leaching solution is difficult to be removed in depth without causing loss of nickel or cobalt. Herein, the quantitative relationship between Al in the leaching solution and regenerated precursors and cathode materials is investigated, and the effects of Al impurity on the macromorphology and microstructure of the materials is revealed. It suggests that Al introduction suppresses the ordered growth of precursors and further leads to disordered grain morphology of regenerated cathode materials, twisting Li+ diffusion pathway. Meanwhile, Al doping could widen LiO 6 layer channel, synchronously stabilize layered structure and interfacial properties. Thus, by balancing the effects between morphology and lattice, Al-doped LiNi 0.9 Co 0.05 Mn 0.05 O 2 with superior cycling stability (95.1 % capacity retention efficiency after 200 cycles at 1 C) and rate performance (101.8 mAh g−1 discharge capacity at 10 C) is regenerated when Al concentration in the leaching solution is controlled at 1.08 g/L. This study transforms the harmful Al impurity into a beneficial component, and provides a favorable in-situ Al-doping strategy for regenerating high-performance cathode materials from spent LIBs. [ABSTRACT FROM AUTHOR]