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Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain.

Developing high-performance Pt-based catalysts with low Pt loading is crucial but challenging for CO oxidation at temperatures below 100 °C. Herein, we report a Pt-based catalyst with only a 0.15 wt% Pt loading, which consists of Pt–Ti intermetallic single-atom alloy (ISAA) and Pt nanoparticles (NP) co-supported on a defective TiO 2 support, achieving a record high turnover frequency of 11.59 s –1 at 80 °C and complete conversion of CO at 120 °C. This is because the coexistence of Pt–Ti ISAA and Pt NP significantly alleviates the competitive adsorption of CO and O 2 , enhancing the activation of O 2 . Furthermore, Pt single atom sites are stabilized by Pt–Ti ISAA, resulting in distortion of the TiO 2 lattice within Pt–Ti ISAA. This distortion activates the neighboring surface lattice oxygen, allowing for the simultaneous occurrence of the Mars-van Krevelen and Langmuir–Hinshelwood reaction paths at low temperatures. Developing high-performance Pt-based catalysts with low Pt loading is crucial but challenging for CO oxidation. Here, the authors report a novel Pt/TiO 2 catalyst consisting of Pt–Ti intermetallic single-atom alloy and Pt nanoparticles to efficiently catalyze CO oxidation.

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.

The purpose of the present work was to gain a fundamental understanding of how the composition and physico-chemical properties affect the rheology, morphology, miscibility, and thermal stability of poly(lactic acid) (PLA)—poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) biopolymer blends obtained by melt mixing. First, restricted processing conditions were chosen, due to the inherent thermal degradation of PHBV, as proven by rheological dynamic time sweep (DTS) measurements and size-exclusion chromatography (SEC). Based on this, the composition dependence of the blends was investigated using small-amplitude oscillatory shear rheology (SAOS), and the results were confirmed by scanning electron microscopy (SEM) analysis. Subsequently, the changes in glass transition temperatures (Tgs) from the molten to the solid state, as observed by DMA and DSC, were verified by coupling SAOS to dielectric relaxation spectroscopy (DRS). Herein, the thermo-rheological complexity of PLA/PHBV blends in the melt was revealed, especially for PLA-rich blends. Irregularly structured morphologies, caused by highly mismatched viscoelastic properties, illustrated the degree of partial miscibility. Moreover, the thermo-rheological complexity appeared in the molten state of the asymmetric PLA-rich phases could be correlated to the crystal-amorphous interfacial MWS polarization, because of the locally-induced phase separation and heterogeneity, and owing to the differences in their crystallization properties during cooling. The miscibility also suffered from the lower thermal stability of PLA and the even more unstable PHBV. Nevertheless, the melt-induced degradation process of the PLA/PHBV blends seemed to be responsible for some of the in situ self-compatibilization and plasticization mechanisms. As a result, the miscibility and thermo-rheological simplicity were improved for the intermediate and PHBV-rich compositions at low temperatures, since their properties were, to a large extent, governed by the significant degradation of PHBV. The present findings should increase the understanding of morphological changes in PLA/PHBV blends and help control their micro/nanostructure.

Background To investigate the association between clinical periodontal parameters of periodontitis, serum lipid metabolism markers and adipokines’ levels in patients with obesity and periodontitis. Methods A total of 112 patients admitted to Hospital of Xi’an Jiaotong University were included in this study. They were divided into normal body weight group (18.5 < body mass index, BMI < 25, n = 36), overweight group (25 ≤ BMI < 30, n = 38), and obesity group (BMI ≥ 30, n = 38) accordingly. The diagnosis of periodontitis was based on the newest international classification of periodontitis. Full-mouth clinical periodontal measurements included: plaque index, periodontal pocket depth, clinical attachment level, and bleeding on probing. Gingival crevicular fluid samples were analyzed for: Interleukin-1β, tumor necrosis factor-α, Interleukin-6 and C-reactive protein. Serum triglycerides, total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol and glycosylated hemoglobin levels were measured. Visfatin, leptin, resistin, and adiponectin levels in serum were also measured. Results The ratio of participants without periodontitis was significantly highest in normal weight group, and the proportion of severe periodontitis (stage III and IV) was highest in obesity group. The periodontal pocket depth, clinical attachment level, and the inflammatory cytokines in gingival crevicular fluid in obesity group and overweight group were higher than those in normal body weight group. The BMI and waist-to-hip ratio (WHR) were significantly positive correlated with periodontal pocket depth and clinical attachment level. Using a Multivariate logistic regression model, periodontitis correlates to BMI, WHR, serum levels of triglyceride, total cholesterol, low density lipoprotein, and adipokines such as visfatin, leptin, and resistin. Conclusions Obesity is positively correlated with the aggravation of periodontitis. Obesity may aggravate the damage to periodontal tissue by regulating the secretion level of adipokines.

The contamination of dental unit waterlines (DUWLs) poses a significant risk of cross-infection in dentistry. Although chemical disinfectants have been effective in reducing number of bacteria, they do have limitations. This study aimed to investigate the potential of chlorogenic acid, a natural substance with broadspectrum antibacterial properties, for treating DUWLs. Over a period of three months, we analyzed the microbial communities in 149 DUWLs samples collected from 5 dental units using high-throughput pyrophosphate sequencing. The results revealed that chlorogenic acid treatment had a significant impact on the microbial community profile in the DUWLs, with the most significant changes occurring within the first 15 days and stabilization observed in the last 30 days. The predominant genera detected in the samples were Bacteroides, Lactobacillus, Streptococcus, Methylobacterium, and Phreatobacter. Additionally, the relative abundance of certain beneficial bacteria, such as Alloprevotella, Roseburia, and Blautia, increased, while the presence of opportunistic pathogens like Mycobacteria significantly decreased. The functional prediction analysis using the KEGG database indicated a decrease in the pathogenicity of the bacterial community in the DUWLs following chlorogenic acid treatment. This study introduces a novel approach for the prevention and treatment of infections associated with dental care.

In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness were conducted. Firstly, an empirical formula for the surface roughness of titanium alloys after abrasive belt grinding was constructed based on the balanced weight analysis of the process parameters for titanium alloy surface roughness. Sensitivity analysis was carried out to identify the process parameters with the greatest effect on surface roughness, and the stable and unstable domains of the process parameters were determined. Combined with range analysis in orthogonal experiments, the influence curves of the process parameters on surface roughness were obtained, and the optimal parameter ranges were selected. The research results showed that surface roughness is the most sensitive to changes in abrasive grain size and the least sensitive to changes in abrasive belt linear speed. The optimal ranges of abrasive grain size, abrasive belt linear speed, and grinding pressure were determined to be 120# to 150#, 15 m/s to 20 m/s, and 10 N to 15 N, respectively. This study provides a theoretical method and experimental basis for the control of surface roughness in abrasive belt grinding of titanium alloys.

Dental impressions are essential for accurately capturing the detailed anatomy of teeth and surrounding oral structures. However, these impressions often become contaminated with saliva and blood, making proper disinfection necessary. The application of chemical disinfectants has been associated with negative side effects, leading to suboptimal disinfection practices in clinical settings. The purpose of this study was to evaluate the effectiveness of chlorogenic acid (CA) as a disinfectant for alginate impression materials, the impact of CA disinfection on the physical properties and dimensional accuracy of alginate impressions was also investigated. The physical properties of alginate impression materials, such as elastic recovery, strain-in-compression, initial setting time, and fluidity, were assessed after mixing the alginate impression materials with three different concentrations of CA solution (10 mg/mL, 15 mg/mL, 20 mg/mL). To evaluate the antimicrobial effect of CA, alginate impressions mixed with a 10 mg/mL CA solution and impressions mixed with distilled water (control group) were contaminated with four types of microorganism: , , , and . Following a five-minute incubation period, a CA solution at a concentration of either 50 mg/mL, 55 mg/mL, or 60 mg/mL was sprayed on the samples for disinfection. Samples were collected at different time intervals (10 min, 20 min, 30 min) and cultured to determine the number of colony-forming units (CFU/mL), providing insight into the antimicrobial efficacy of these CA solutions. The dimensional accuracy of alginate impressions was assessed in three groups: one with alginate impressions mixed with distilled water, another with alginate impressions sterilized with available chlorine (2,000 mg/L) mixed with distilled water, and the last group consisting of alginate impressions mixed with 10 mg/mL CA solution and sprayed with 60 mg/mL CA solution. Both the standard model and the plaster model underwent 3D scanning, and the data were processed and compared by software. The root mean square (RMS) was used as a parameter to evaluate the deviation between models. All alginate impression materials mixed with either 10 mg/mL, 15 mg/mL, or 20 mg/mL concentrations of CA solution met the ISO 21563 standard for elastic recovery, strain-in-compression, and fluidity. However, only the material mixed with a concentration of 10 mg/mL CA had an initial setting time within the range specified by the T-6505 Japanese industrial standard. The application of CA solution by mixing or spraying showed significant antimicrobial effects on and . There was no significant difference in the dimensional accuracy of the alginate impressions between the group of the CA solution applied, the blank group, or the chlorine intervention group.

Patches of ionization are common in the polar ionosphere, where their motion and associated density gradients give variable disturbances to high-frequency (HF) radio communications, over-the-horizon radar location errors, and disruption and errors to satellite navigation and communication. Their formation and evolution are poorly understood, particularly under disturbed space weather conditions. We report direct observations of the full evolution of patches during a geomagnetic storm, including formation, polar cap entry, transpolar evolution, polar cap exit, and sunward return flow. Our observations show that modulation of nightside reconnection in the substorm cycle of the magnetosphere helps form the gaps between patches where steady convection would give a \"tongue\" of ionization (TOI).

The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.