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In light of the challenges posed by the low density, diminished energy levels, and shrinkage holes in the loading arise of the current cast-cured explosive liquid vehicle, TNT, the investigation of low-melting eutectic materials as potential substitutes for TNT has emerged as a focal point of research. By employing the solvent-nonsolvent method, the lowest eutectic mixture of TNBA(2,4,6-trinitro-3-bromoanisole)/MTNP(1-methyl-3,4,5-trinitropyrazole) was synthesized, distinguished by a moderate melting point and markedly elevated energy levels as compared to TNT. Comprehensive SEM, EDS, IR, XRD, and XPS analyses were conducted on the lowest eutectic mixture, substantiating the absence of any chemical interaction between TNBA and MTNP and affirming their exceptional compatibility. Moreover, an exploration into the thermal decomposition behavior and mechanism of the lowest eutectic mixture was undertaken, its mechanical sensitivity was tested, and its detonation performance was evaluated by EXPLO-5 software. The thermal decomposition parameters of the lowest eutectic mixture closely paralleled those of the precursor materials, with the thermal decomposition kinetics model function being identified as the Jander function. The impact sensitivity of the lowest eutectic was 48.1 cm and the friction sensitivity was 12%. Furthermore, the density of the lowest eutectic mixture surpassed 1.8 g/cm3, and its detonation velocity approached 8000 m/s, markedly surpassing the explosive performance of TNT.

The unit density value is a key quality index for pellet feed production. This study presents an experimental evaluation of the unit density for the pelletizing of caragana and corn straw, under different levels of technological parameters, including moisture content, weight ratio of caragana, and particle size. Results showed that these three parameters of raw materials affected unit density. Through orthogonal test and extreme variance analysis, it was shown that the various moisture content and weight ratio of caragana had a significant effect on the density of pellets, and the influencing factors were ranked as moisture content > weight ratio of caragana > particle size of the materials. In compliance with industry standards, optimizations of the parameters resulted in a granulation density of 1.15 g/cm3 with particle size of 5 mm, moisture content of 13.4% and weight ratio of caragana of 24.8%.

In this work, we carried out a comparative study of optical property on unintentionally doped and Sn-doped β-Ga2O3 crystals by edge-defined film-fed growth (EFG) method with a cylindrical iridium (Ir) die. The non-polarized Raman spectra showed the anisotropy in Raman peak position and intensity for three different planes (i.e., (100), (010) and (001)) of β-Ga2O3 crystals, independent of the Sn dopant. A difference in the Raman spectra between the unintentionally doped and Sn-doped β-Ga2O3 crystals was observed in the (001) plane. The strong in-plane anisotropy of the monoclinic β-Ga2O3 crystal related to the crystallographic orientation was demonstrated by the angle-resolved polarized Raman spectroscopy. The Sn dopant cannot change the optical transmittance and bandgap values of the unintentionally doped and Sn-doped β-Ga2O3 crystals with the (100), (010) and (001) orientations. However, the Sn-doped β-Ga2O3 crystal with the (010) orientation showed the higher valence band maximum and lower surface barrier height values. The X-ray photoelectron spectroscopy of the unintentionally doped and Sn-doped β-Ga2O3 crystals with the (010) orientation was also measured, which showed no obvious difference. The different luminescence centers and level of electron ionization of the unintentionally doped and Sn-doped β-Ga2O3 crystals were observed by the cathodoluminescence spectroscopy and electron paramagnetic resonance, respectively. Based on the comprehensive comparison, it can be concluded that the anisotropy in optical property of the unintentionally doped and Sn-doped β-Ga2O3 crystals with (100), (010) and (001) orientations apparently exist.

Regarding the issue of low granulation qualification rates during the granulation coating of red clover seeds, this study theoretically analyzed the force conditions of seeds and powder particles under the action of liquid to obtain the main factors affecting seed coating quality. During the seed granulation coating process, an intermittent powder supply method combined with continuous liquid supply was utilized to control the ratio of powder to liquid. Using the granulation qualification rate as the evaluation index, single-factor experiments were conducted to investigate the effects of coating pan fill ratio, single powder supply amount, powder supply interval, and liquid supply amount on the quality of red clover seed granulation coating. Based on the results of the single-factor experiments, orthogonal experiments were conducted, revealing that the interaction of factors would influence the experimental results. To further optimize the quality of seed granulation coating, the mechanisms of powder and liquid in the adhesion process on granulation coating were explored. Orthogonal experiments were conducted on the process parameters of the granulation coating machine, and the GA-BP model was employed for optimization and solution. The optimal process parameter combination obtained was a coating pan fill ratio of 33.78 %, a single powder supply amount of 5.17 g, a powder supply interval of 7.7 s, and a liquid supply amount of 0.42 mL/s. Under this optimal parameter combination, granulation coating experiments with red clover seeds were performed, and the seed granulation coating quality was relatively high, with a granulation qualification rate of 97.7 %. The research results can provide a reference for optimization experiments on coating irregular seeds.

Despite progress in land protection, agricultural land in China faces threats from urban expansion and unsustainable practices, necessitating urgent efforts to protect and enhance cultivated land quality. This study quantifies cultivated land quality as a composite indicator through an evaluation model, which reduces the heterogeneity and diversity of cultivated land quality among fields to some extent. This study introduced the concept of geodiversity and constructed a new framework for measuring cultivated land quality type (CLQT) and explored the CLQT in different geomorphic regions of Jiangxi, China. Using landscape metrics, spatial autocorrelation and structural equation modeling, this study analyzed the spatial clustering types and drivers of CLQT diversity. The results showed that 1) the framework of CLQT composed of nine indicators arranged by topographic conditions, soil conditions, ecological conditions, and anthropogenic management conditions yielded 2,565 CLQTs in the research area. 2) The landscape metrics showed a high degree of overlap, with the main overlapping areas distributed in the plains, hills, and mountainous regions in the central and southern parts of the country. SHDI, SHEI, PR, and PD showed low-low cluster in the central plains, while SHDI, SHEI, PR, LSI, PAFRAC, and DIVISION showed high-high cluster in the southern hills.3) The main influencing factors in the plains were natural factors (mean annual temperature and mean annual precipitation) and in the hills were a combination of natural factors (MAT and MAP) and socio-economic factors (Ninput and GDP). The highest diversity of CLQT was observed in the mountainous regions, where the influencing factors include both natural (MAT) and socio-economic factors (POP and GDP). Therefore, it is recommended that in the plains, large-scale operators should adopt diversified planting strategies to enhance CLQT diversity and the stability of cropland ecosystems. In hills and mountainous regions with high CLQT diversity, the government should formulate policies to stimulate farmers’ planting motivation and increase inputs to support agricultural production. This study provides a new perspective on the study of cultivated land quality and provides a reference for improving agroecosystem stability and food production in regions with similar landscapes around the world.

Real time recognition and detection of tomato fruit maturity is a key function of tomato picking robots. Existing recognition and detection algorithms have slow speed and low recognition accuracy for small tomatoes. Here, a tomato fruit maturity detection model YOLOv5s3 based on improved YOLOv5s was proposed and its accuracy was verified through comparative experiments. On the basis of YOLOv5s, an SC module was proposed based on channel shuffle packet convolution. Then, A C3S module is constructed, which replaced the original C3 module with this C3S module to reduce the number of parameters while maintaining the feature expression ability of the original network. And a 3-feature fusion FF module was put forward, which accepted inputs from three feature layers. The FF module fused two feature maps from the backbone network. The C2 layer of the backbone was integrated, and the large target detection head was removed to use dual head detection to enhance the detection ability of small targets. The experimental results showed that the improved model has a detection accuracy of 94.8%, a recall rate of 96%, a parameter quantity of 3.02M, and an average accuracy (mAP0.5) of 93.3% for an intersection over union (IoU) of 0.5. The detection speed reaches 9.4ms. It can quickly and accurately identify the maturity of tomato fruits, and the detection speed is 22.95%, 33.33%, 48.91%, 68.35%, 15%, and 25.98% higher than the original YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv5n, and YOLOv4, respectively. The real-time testing visualization results of different models indicated that the improved model can effectively improve detection speed and solve the problem of low recognition rate for small tomatoes, which can provide reference for the development of picking robots.

BackgroundDespite the potential to significantly reduce complications, many patients do not consistently receive diabetes preventive care. Our research team recently applied user-centered design sprint methodology to develop a patient portal intervention empowering patients to address selected diabetes care gaps (eg, no diabetes eye examination in last 12 months). ObjectiveThis study aims to evaluate the effect of our novel diabetes care gap intervention on completion of selected evidence-based diabetes preventive care services and secondary outcomes. MethodsWe are conducting a pragmatic randomized controlled trial of the effect of the intervention on diabetes care gaps. Adult patients with diabetes mellitus (DM) are recruited from primary care clinics affiliated with Vanderbilt University Medical Center. Participants are eligible if they have type 1 or 2 DM, can read in English, are aged 18-75 years, have a current patient portal account, and have reliable access to a mobile device with internet access. We exclude patients with medical conditions that prevent them from using a mobile device, severe difficulty seeing, pregnant women or women who plan to become pregnant during the study period, and patients on dialysis. Participants will be randomly assigned to the intervention or usual care. The primary outcome measure will be the number of diabetes care gaps among 4 DM preventive care services (diabetes eye examination, pneumococcal vaccination, hemoglobin A1c, and urine microalbumin) at 12 months after randomization. Secondary outcomes will include diabetes self-efficacy, confidence managing diabetes in general, understanding of diabetes preventive care, diabetes distress, patient portal satisfaction, and patient-initiated orders at baseline, 3 months, 6 months, and 12 months after randomization. An ordinal logistic regression model will be used to quantify the effect of the intervention on the number of diabetes care gaps at the 12-month follow-up. For dichotomous secondary outcomes, a logistic regression model will be used with random effects for the clinic and provider variables as needed. For continuous secondary outcomes, a regression model will be used. ResultsThis study is ongoing. Recruitment was closed in February 2022; a total of 433 patients were randomized. Of those randomized, most (n=288, 66.5%) were non-Hispanic White, 33.5% (n=145) were racial or ethnic minorities, 33.9% (n=147) were aged 65 years or older, and 30.7% (n=133) indicated limited health literacy. ConclusionsThe study directly tests the hypothesis that a patient portal intervention—alerting patients about selected diabetes care gaps, fostering understanding of their significance, and allowing patients to initiate care—will reduce diabetes care gaps compared with usual care. The insights gained from this study may have broad implications for developing future interventions to address various care gaps, such as gaps in cancer screening, and contribute to the development of effective, scalable, and sustainable approaches to engage patients in chronic disease management and prevention. Trial RegistrationClinicalTrials.gov NCT04894903; https://classic.clinicaltrials.gov/ct2/show/NCT04894903 International Registered Report Identifier (IRRID)DERR1-10.2196/56123

Sepsis-induced muscle weakness is a debilitating consequence of prolonged critical illness, often associated with a poor prognosis. While recent research has shown that STAT6 functions as an inhibitor of myogenesis, its role in sepsis-induced muscle weakness remains unclear. In this study, we hypothesized that inhibiting STAT6 could attenuate sepsis-induced muscle atrophy and weakness, and we explored the underlying mechanisms. Leveraging a microarray dataset from sepsis patients, we identified significant enrichment of genes related to muscle function, ferroptosis, and the p53 signalling pathway in muscle tissue from sepsis patients. Using a murine sepsis model induced by cecum ligation and puncture (CLP), we explore the multifaceted role of STAT6 inhibition. Our findings demonstrate that STAT6 inhibition effectively attenuates muscle atrophy, enhances grip strength, preserves mitochondrial integrity, and modulates ferroptosis in septic mice. Additionally, we identify elevated levels of CHI3L1 in septic muscle tissue, which are significantly reduced by STAT6 inhibition. In-depth analysis of primary muscle satellite cells reveals that CHI3L1 overexpression is associated with increased expression of key regulators of satellite cell myogenicity, while negatively impacting cell viability. Silencing CHI3L1 expression mitigates satellite cell injury and loss, highlighting its pivotal role in sepsis-induced muscle damage. In summary, this study unveils the potential of STAT6 as a therapeutic target for mitigating sepsis-induced muscle atrophy and weakness. Our findings underscore the regulation of mitochondrial dysfunction, ferroptosis, and CHI3L1-mediated satellite cell damage by STAT6, offering promising avenues for therapeutic intervention in the management of sepsis-induced muscle weakness.

To simulate the creep characteristics of corn straw particles under uniaxial compression, a 6-level 2D model was created using PFC 2D software according to the actual uniaxial creep test, the grain size, and shape of corn straw particles. Five groups of controlled tests were designed by the control variable method to study the influence of Maxwell body parameters Em, ηm; Kelvin body parameters Ek, ηk; and friction coefficient f on the creep curve of Burgers model. Results revealed that the creep characteristics of corn straw are affected by these 5 parameters. After multiple debugging by trial-and-error method, simulation parameters suitable for describing the creep characteristics of corn straw particles were obtained. The creep curves obtained by simulation under these parameters are consistent with those obtained by physical tests. It was shown that PFC software can not only study the creep characteristics of geotechnical materials, but also study the creep characteristics of agricultural fiber materials, which provides a reference for the subsequent rheological characteristics of biomass materials.

Abstract Objectives Lung adenocarcinoma (LUAD) exhibits a higher fatality rate among all cancer types worldwide, yet the precise mechanisms underlying its initiation and progression remain unknown. Mounting evidence suggests that long non-coding RNAs (lncRNAs) exert significant regulatory roles in cancer development and progression. Nevertheless, the precise involvement of lncRNA CYP4A22-AS1 in LUAD remains incompletely comprehended. Methods Bioinformatics analyses evaluated the expression level of CYP4A22-AS1 in lung adenocarcinoma and paracancer. The LUAD cell line with a high expression of CYP4A22-AS1 was constructed to evaluate the role of CYP4A22-AS1 in the proliferation and metastasis of LUAD by CCK8, scratch healing, transwell assays, and animal experiments. We applied transcriptome and microRNA sequencing to examine the mechanism of CYP4A22-AS1 enhancing the proliferation and metastasis of LUAD. Luciferase reporter gene analyses, west-blotting, and qRT-PCR were carried out to reveal the interaction between CYP4A22-AS1, miR-205-5p/EREG, and miR-34c-5p/BCL-2 axes. Results CYP4A22-AS1 expression was significantly higher in LUAD tissues than in the adjacent tissues. Furthermore, we constructed a LUAD cell line with a high expression of CYP4A22-AS1 and noted that the high expression of CYP4A22-AS1 significantly enhanced the proliferation and metastasis of LUAD. We applied transcriptome and microRNA sequencing to examine the mechanism of CYP4A22-AS1 enhancing the proliferation and metastasis of LUAD. CYP4A22-AS1 increased the expression of EREG and BCL-2 by reducing the expression of miR-205-5p and miR-34-5p and activating the downstream signaling pathway of EGFR and the anti-apoptotic signaling pathway of BCL-2, thereby triggering the proliferation and metastasis of LUAD. The transfection of miR-205-5p and miR-34-5p mimics inhibited the role of CYP4A22-AS1 in enhancing tumor progression. Conclusion This study elucidates the molecular mechanism whereby CYP4A22-AS1 overexpression promotes LUAD progression through the miR-205-5p/EREG and miR-34c-5p/BCL-2 axes.

BackgroundIn 2021, Mozambique initiated community-based oral HIV self-testing (HIVST) to increase testing access and uptake among priority groups, including adult males, adolescents, and young adults. Within an HIVST pilot project, we conducted a performance evaluation assessing participants' ability to successfully conduct HIVST procedures and interpret results.MethodsA cross-sectional study was performed between February-March 2021 among employees, students (18-24 years of age), and community members, using convenience sampling, in two rural districts of Zambézia Province, Mozambique. We quantified how well untrained users performed procedures for the oral HIVST (Oraquick®) through direct observation using a structured checklist, from which we calculated an HIVST usability index (scores ranging 0-100%). Additionally, participants interpreted three previously processed anonymous HIVST results. False reactive and false non-reactive interpretation results were presented as proportions. Bivariate analysis was conducted using Chi-square and Fisher exact tests.ResultsA total of 312 persons participated (131[42%] community members, 71[23%] students, 110[35%] employees); 239 (77%) were male; the mean age was 28 years (standard deviation 10). Average usability index scores were 80% among employees, 86% among students, and 77% among community members. Main procedural errors observed included "incorrect tube positioning" (49%), "incorrect specimen collection" (43%), and "improper waiting time for result interpretation" (42%). From the presented anonymous HIVST results, 75% (n = 234) correctly interpreted all three results, while 9 (3%) of study participants failed to correctly interpret any results. Overall, 36 (12%) gave a false non-reactive result interpretation, 21 (7%) a false reactive result interpretation, and 14 (4%) gave both false non-reactive and false reactive result interpretations. Community members generally had lower performance.ConclusionsDespite some observed testing procedural errors, most users could successfully perform an HIVST. Educational sessions at strategic places (e.g., schools, workplaces), and support via social media and hotlines, may improve HIVST performance quality, reducing the risk of incorrect interpretation.

Rapidly determining the density of effective gerbil holes is ecologically important but technically challenging. However, unmanned aerial vehicles (UAVs) offer new methods to identify effective gerbil holes and understand the spatial relationships between gerbils and grass coverage. In this study, we focused on Meriones unguiculatus, which live in desert grasslands of Ordos, and gathered UAV imagery to explore the performance of rule-based classification in object-based image analysis (OBIA) for identifying effective gerbil holes. To improve the classification accuracy and reduce the time cost of repeated ‘trial and error’, we propose a novel method to build rule sets. We adopted the estimation of scale parameter_2 (ESP2) method during the segmentation stage to enable fast and objective segmentation parameterization. For the classification stage, a correlation-based feature selection (CFS) algorithm was applied for feature selection and threshold prediction. Our analysis determined the following universally recommended rule sets for identifying effective gerbil holes using OBIA: scale parameter of 107, shape factor of 0.2, compactness value of 0.3, excess green value less than −5, brightness feature value in the 143–149 range and roundness feature less than 0.9. With these rule sets, OBIA exhibited higher classification accuracy than maximum likelihood (ML) classification, with an overall classification accuracy of 88.25%. We also found that the relationship between the area of effective gerbil holes (y) and the grass coverage (x) satisfied a quadratic function: This study provides practical guidance for grassland management and rodent infestation control.

A series of advanced equipment exposed to sub-/supercritical water environments at high temperatures, high pressures, and extreme water chemistry with high salt and dissolved oxygen content faces serious corrosion problems. Obtaining on-site corrosion data for typical materials in harsh environments is crucial for operating and maintaining related equipment and optimizing various corrosion prediction models. First, this article introduces the advantages and disadvantages, usage scenarios, and future development potential of several in situ monitoring technologies, including ultrasonic thickness measurement, the infrared thermography method, microwave imaging, eddy current detection, and acoustic emission. Considering the importance of electrochemical corrosion data in revealing microscale and nanoscale corrosion mechanisms, in situ testing techniques such as electrical resistance probes, electrochemical corrosion potential, electrochemical impedance spectroscopy, and electrochemical noise that can be applied to sub-/supercritical water systems were systematically discussed. The testing platform and typical data obtained were discussed with thick and heavy colors to establish a mechanical prediction model for corrosion behavior. It is of great significance to promote the development of corrosion monitoring techniques, such as breaking through testing temperature limitations and broadening the industrial application scenarios and maturity.

This investigation was devoted to exploring the thermal decomposition and sensitivity characteristics of solid propellants using nano-AN and nano-AP as oxidizers. The morphology, surface elements, and crystal structure of nano-AP and nano-AN were investigated by SEM, BET, EDS, and XRD. DSC and TG-MS were used to investigate the thermal decomposition mechanism of nano-AP and nano-AN solid propellants. The mechanical sensitivity of the different solid propellants containing nano-AN and nano-AP was tested and their energy performance was also evaluated. The results show that the microscopic morphology of nano-AN and nano-AP is network-like with the one-dimensional scale less than 100 nm, and the crystal phases of the two are consistent with the raw AN and raw AP, respectively. The thermal decomposition activation energy of AN/CMDB-4 propellant containing nano-AN is 717.46 kJ/mol, while that of AP/HTPB-6 propellant containing nano-AP is 422.33 kJ/mol. The main decomposition products of AN/CMDB-4 propellant are CO2, N2O, NO, CH2O, CO, N2, H2O, CH4 and H2. The products of CO2, N2O, NO, CH2O, CO, N2, H2O, CH4 and H2 are also generated during the decomposition of AP/HTPB-6 propellant. With the increase of nano-AN and nano-AP in propellants, the mechanical sensitivity of AN/CMDB solid propellant decreases, while that of AP/HTPB solid propellant increases. The theoretical standard specific impulse Isp of AN/CMDB and AP/HTPB propellants are 2439.5 N·s/kg and 2449.3 N·s/kg, respectively. The above conclusions show that nano-AP and nano-AN can improve the thermal decomposition performance and the safety of solid propellants, which are expected to be widely used in solid propellants.

Abstract Remdesivir is the first US Food and Drug Administration (FDA)‐approved drug for the treatment of coronavirus disease 2019 (COVID‐19). We conducted a retrospective pharmacogenetic study to examine remdesivir‐associated liver enzyme elevation among Million Veteran Program participants hospitalized with COVID‐19 between March 15, 2020, and June 30, 2021. Pharmacogene phenotypes were assigned using Stargazer. Linear regression was performed on peak log‐transformed enzyme values, stratified by population, adjusted for age, sex, baseline liver enzymes, comorbidities, and 10 population‐specific principal components. Patients on remdesivir had higher peak alanine aminotransferase (ALT) values following treatment initiation compared with patients not receiving remdesivir. Remdesivir administration was associated with a 33% and 24% higher peak ALT in non‐Hispanic White (NHW) and non‐Hispanic Black (NHB) participants (p

Barium sulfate has been widely used in different fields due to its excellent physical and chemical properties, but its application in solid propellants has not yet been explored. In this work, novel microreactor was used to prepare the barium sulfate nanoparticles. Then the barium sulfate nanoparticles were characterized by Scanning electron microscopy (SEM), Transmission electron microscope (TEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Brunauer-Emmett-Teller (BET). The results show that the barium sulfate nanoparticles are almost spherical with a diameter of 54.34 nm. More, it also has a larger specific surface area. Then 3% barium sulfate nanoparticles were mixed with AP, AN and CL-20 respectively. The results shows that the decomposition peaks of AP and AN are significantly advanced, indicating that nano-barium sulfate has a significant catalytic effect on AP and AN. Nano barium sulfate also has the characteristics of eliminating secondary flames and absorbing harmful radiation, so it is expected to be widely used in solid propellants.

There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network’s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios.

Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5–2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.

Learning from limited labelled examples is key a research hotspot with excellent scenarios and potential applications. Currently, most of metric learning-based few-shot models still have the problem of low recognition accuracy. This is mainly because that they only use the top-layer abstract feature with semantic information, which ignores the low-layer features that are also critical for the few-shot recognition. Therefore, the extracted features do not have abundant representation ability, and it is difficult to recognize easily confusing objects. Moreover, they usually adopt a fixed distance function or train a comparable network to measure features. These methods lack adaptability, cannot sufficiently fuse features, which leads to weaken the fitting ability of the metric function. And the same or different classes of images are treated equally, which makes the metric function have no emphasis point during training. To address these issues, we propose an end-to-end, metric learning-based model in this paper, called multi-scale decision network with feature fusion and weighting for few-shot learning (MSDN). Considering the importance of the low-layer features, we exploit a convolutional network to extract each layer feature. Then, we exploit a relation network to learn a non-linear metric between the support set and the query set features of each layer and classify the test images via a voting decision. During feature concatenation, we design a non-linear feature fusion item to improve the way of concatenation, so that the relation network can have a stronger function fitting ability to learn the relation score. Meanwhile, we introduce the attention mechanism by calculating the cosine similarity between the support set and the query set features as their weight, which makes the relation network pay more attention to the same class of images. Our model achieves the state-of-the-art accuracy result on Omniglot and miniImageNet datasets compared with popular few-shot recognition models.

Object extraction refers to the operation of obtaining an object area from an image based on a small amount of mark information given by users, which is a key step in image processing. In order to obtain a complete object profile, current methods usually require a large number of manual annotations, especially for objects with irregular contours. Since traditional algorithms rely on low-level pixel features without semantic information, and are based on obvious mathematical assumptions (ie, strong inductive bias), it is difficult to completely identify objects. At present, in order to improve the integrity of object extraction, semantic segmentation-based methods increase the complexity and latancy by adding more pre-processing and post-processing steps. In this paper, we propose a novel model named IOEBSS, which includes a fast binary plane pre-processing, an improved Deeplab v3+ semantic segmentation model, and an auxiliary loss function named Label Loss. Through the fast binary plane pre-processing, the model can accelerate the transformation of interactive inputs. The improved semantic segmentation model makes the extracted results more semantically complete, and Label Loss is more conducive to gradient flow and accelerates training convergence. For the above reasons, IOEBSS can accurately and quickly identify objects with complex contours and colors. On Pascal VOC and COCO datasets, compared to current methods, IOEBSS has a significant improvement in accuracy, inference speed, and convergence speed.

During the crop harvesting process, it is important to obtain the crop yield quickly, accurately and in real time to accelerate the development of smart agriculture. This paper investigated a denoising method applicable to the impact-type sunflower yield sensor signal under the influence of complex noise background in the pneumatic seed delivery structure for a sunflower combine harvester. A signal processing method combining complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising (WTD) based on an adaptive decomposition capability was proposed by analyzing the non-smoothness of the signal with the impact-type sunflower yield sensor signal in sunflower fields. CEEMD was used to decompose the sunflower seed impact analog signal and field impact-type sunflower yield sensor signal adaptively, and the high frequency components were processed by WTD. Finally the de-noised signal was obtained by reconstruction. An evaluation objective function of the denoising ability of the algorithm based on signal-noise ratio, root mean square error, smoothness and waveform similarity indexes with different weights was also constructed. The results showed that the evaluation objective functions of the simulated and measured signals after denoising by the CEEMD-WTD method are 1.9719 and 4.5318, respectively, which are better than the single denoising methods of EMD (1.5096 and 4.0012), EEMD (1.8248 and 4.0724), CEEMD (1.9516 and 4.3384), and WTD (1.8737 and 4.5294). This method provides a new idea for signal denoising of the impact-type sunflower yield sensor installed in the pneumatic seed delivery structure, and further provides theoretical support and technical references for the development of sunflower high-precision yield measurements in smart agriculture.

Aiming at the lack of a precise discrete element model in the simulation analysis of corn straw crushing, this paper established bond models with different structure and particle models with different properties of corn straw based on Hertz-Mindlin with bonding, and verified the discrete element model by combining physical test and simulation optimization design methods. Firstly, this study generates the bonded particle models based on Hertz-Mindlin with bonding for epidermis-epidermis, inner flesh-internal flesh, and epidermis-internal flesh, respectively. The bonding parameters of the model are calibrated with the help of a bending damage test, bending damage simulation test, and actual test with 2 mm/min speed. It shows that the maximum destructive force errors of the epidermis and inner flesh are 2.4% and 1.6%, respectively. On this basis, a discrete element model of corn straw is established by combining the calibrated parameters of the bonding parameters of epidermis—epidermis, inner flesh—inner flesh, and epidermis—inner flesh bond. The bending failure test shows that the mechanical properties of corn straw are similar between the simulated test and the actual test, and the maximum destructive power is 288 N and 292 N, respectively. The relative error is 1.36%. The feasibility of the discrete element model for simulation analysis is verified, which shows that the established discrete element model can be applied to the simulation analysis of corn straw cutting and crushing process.

In order to perform quantitative analysis and predict the wear of the forming channel, this study makes biomass pellets as experimental samples. The surface morphology was scanned by a scanning electron microscope (SEM), and the surface morphology data and images were collected by a roughness instrument. Then, we collected the data of arithmetical average deviation Ra, density ρ, as well as hardness HD, and further calculated the fractal dimension D, which help to study the influencing factors of fractal dimension on the circular surface morphology of biomass pellet. The results show that, the density ρ and hardness HD of biomass pellets both decreased and with the increase in diameter d, the arithmetical average deviation Ra increased with the diameter d, the quality of the pellet is reduced, meanwhile, the fractal dimension D also shows a downward trend. Using the value, trend of fractal dimension D to analyze the quality of biomass pellet can predict the wear status and life of forming channel in biomass briquette machine. It is concluded that, the fractal expression of surface morphology in biomass pellet relates to inner surface morphology of forming channel in biomass pelleting machine. Additionally, fractal dimension of the surface morphology of biomass pellet can be the basis of preliminary research for friction and wear detection and prediction of forming channel.

Operation state calculation (OSC) provides safe operating boundaries for power systems. The operators rely on the software-aid OSC results to dispatch the generators for grid control. Currently, the OSC workload has increased dramatically, as the power grid structure expands rapidly to mitigate renewable source integration. However, the OSC is processed with a lot of manual interventions in most dispatching centers, which makes the OSC error-prone and personnel-experience oriented. Therefore, it is crucial to upgrade the current OSC in an automatic mode for efficiency and quality improvements. An essential process in the OSC is the tie-line power (TP) adjustment. In this paper, a new TP adjustment method is proposed using an adaptive mapping strategy and a Markov Decision Process (MDP) formulation. Then, a model-free deep reinforcement learning (DRL) algorithm is proposed to solve the formulated MDP and learn an optimal adjustment strategy. The improvement techniques of “stepwise training” and “prioritized target replay” are included to decompose the large-scale complex problems and improve the training efficiency. Finally, five experiments are conducted on the IEEE 39-bus system and an actual 2725-bus power grid of China for the effectiveness demonstration.

Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. These approaches can generate images with general shape and color but often produce distorted global structures with unnatural local semantic details. It is due to ineffectiveness of convolutional neural networks in capturing the high-level semantic information for pixel-level image synthesis. In this paper, we propose a Dual Attentional Generative Adversarial Network (DualAttn-GAN) in which the dual attention modules are introduced to enhance local details and global structures by attending to related features from relevant words and different visual regions. As one of the dual modules, the textual attention module is designed to explore the fine-grained interaction between vision and language. On the other hand, visual attention module models internal representations of vision from channel and spatial axes, which can better capture the global structures. Meanwhile, we apply an attention embedding module to merge multi-path features. Furthermore, we present an inverted residual structure to boost representation power of CNNs and apply spectral normalization to stabilize GAN training. With extensive experimental validation on two benchmark datasets, our method significantly improves state-of-the-art models over the evaluation metrics of inception score and Fréchet inception distance.

Mindfulness has been found to have many positive effects on life outcomes, including mental health and educational achievement. However, less is known about the antecedents of mindfulness, particularly in Chinese college students. This study examines the effect of adverse childhood experiences (ACEs) on mindfulness among Chinese college students in September 2020, during the COVID-19 pandemic. We hypothesized that ACEs negatively affected students' mindfulness. The data were collected from 1,871 college students from 12 colleges across China. The results aligned with our hypothesis that ACEs was negatively associated with mindfulness. In particular, emotional abuse and neglect in childhood appear to have the most negative effects on mindfulness compared to other dimensions of ACEs such as physical abuse and household challenges.

BackgroundThere are few studies that characterize sex-related differences in HIV outcomes among adolescents and young adults (AYA) 15-24 years of age.MethodsWe conducted a retrospective cohort study among AYA who enrolled in a comprehensive HIV program in Mozambique between 2012-2016. We assessed patients by sex and pregnancy/lactation status, comparing time to combination antiretroviral therapy (ART) initiation using Cox proportional hazard models. We employed multivariable logistic regression to investigate pre- and post-ART retention. Patients were defined as 'retained pre-ART' if they attended at least 3 of 4 required visits or started ART in the 6 months after enrollment, and 'retained post-ART' if they had any ART pickup or clinical visit during the last 90 days of the one-year follow-up period.ResultsOf 47,702 patients in the cohort, 81% (n = 38,511) were female and 19% (n = 9,191) were male. Of the females, 57% (n = 21,770) were non-pregnant and non-lactating (NPNL) and 43% (n = 16,741) were pregnant or lactating (PL). PL (aHR 2.64, 95%CI:2.47-2.81) and NPNL females (aHR 1.36, 95%CI:1.30-1.42) were more likely to initiate ART than males. PL females had higher odds of pre-ART retention in care (aOR 3.56, 95%CI: 3.30-3.84), as did NPNL females (aOR 1.71, 95%CI: 1.62-1.81), compared to males. This was also true for retention post-ART initiation, with higher odds for both PL (aOR 1.78, 95%CI:1.63-1.94) and NPNL females (aOR 1.50, 95%CI:1.35-1.65) compared to males.ConclusionsPL females were most likely to initiate ART and remain in care post-ART in this AYA cohort, likely reflecting expansion of Option B+. Despite pregnancy and policy driven factors, we observed important sex-related disparities in this cohort. NPNL females were more likely to initiate ART and be retained in care before and after ART initiation than males. These data suggest that young males need targeted interventions to improve these important care continuum outcomes.

Throwing device is an important factor that directly affects the performance of chaff cutter. In this work, the dynamic analysis linked with the problem of low efficiency and residue blockage of disc knife chaff cutter is executed. Based on this perspective, the mathematical model, simulation, and testing of the material movement have been carried out. Simulations are performed in MATLAB/Simulink environment. An anemometer records the airflow velocity, which provides data for simulation analysis. The simulation results showed that during the movement along the blade, the material first performs deceleration and then accelerates; in other stages, only deceleration. And finally calculated the throwing distance. To support the presented simulations, an experimental study is conducted. The experimental results are compared with simulation results, the maximum relative error between the simulated value and the experimental value is 9.42%, which verified the correctness of the model. This research provides a theoretical basis for the structural design, parameter optimization, and matching of the chaff cutter.

We previously reported that the highly reactive cell-free heme (CFH) is increased in the plasma of patients with chronic lung injury and causes pulmonary edema in animal model of acute respiratory distress syndrome (ARDS) post inhalation of halogen gas. However, the mechanisms by which CFH causes pulmonary edema are unclear. Herein we report for the first time that CFH and chlorinated lipids (formed by the interaction of halogen gas, Cl2, with plasmalogens) are increased in the plasma of patients exposed to Cl2 gas. Ex vivo incubation of red blood cells (RBC) with halogenated lipids caused oxidative damage to RBC cytoskeletal protein spectrin, resulting in hemolysis and release of CFH. Patch clamp and short circuit current measurements revealed that CFH inhibited the activity of amiloride-sensitive epithelial Na+ channel (ENaC) and cation sodium (Na+) channels in mouse alveolar cells and trans-epithelial Na+ transport across human airway cells with EC50 of 125 nM and 500 nM, respectively. Molecular modeling identified 22 putative heme-docking sites on ENaC (energy of binding range: 86–1563 kJ/mol) with at least 2 sites within its narrow transmembrane pore, potentially capable of blocking Na+ transport across the channel. A single intramuscular injection of the heme-scavenging protein, hemopexin (4 μg/kg body weight), one hour post halogen gas exposure, decreased plasma CFH and improved lung ENaC activity in mice. In conclusion, results suggested that CFH mediated inhibition of ENaC activity may be responsible for pulmonary edema post inhalation injury.

Dynamic security assessment (DSA) is widely used in dispatching operation systems, and the small signal stability is one of the DSA's most time-consuming calculation methods. In this article, a fast method is proposed aiming to predict the small signal stability metrics of designated oscillation mode, for example frequency or damping ratio. The method is much faster than the simulation and suitable for the online application. First, the t-distributed stochastic neighbour embedding (t-SNE) algorithm is performed which can create a mapping from the power system components to 2D coordinate depending on the electrical distance of each other; then, it will be transformed into a grid structure by meshing operation, on which the convolutional neural network (CNN) model can be run properly. Finally, with a large amount of simulation samples, the CNN model can be well trained using static quantities as its input and small signal stability metrics as its prediction target. While a new operation mode needs to be evaluated, the result will be obtained by CNN directly. The validity of proposed method is verified using online data of State Grid Corp of China. It is proved that the method meets the requirements for speed and accuracy of online analysis system.

Dangguiliuhuang decoction (DGLHD) has been demonstrated to be effective in treating inflammatory, hepatic steatosis, and insulin resistance. In the study, we tried to elucidate the pharmacological efficacy and mechanism of DGLHD against liver fibrosis and predicate potential active ingredients and targets via network analysis and experimental validation. In the formula, we totally discovered 76 potential active ingredients like baicalein, berberine, and wogonin, and 286 corresponding targets including PTGS (prostaglandin-endoperoxide synthase) 2, PPAR (peroxisome proliferator-activated receptors) -γ, and NF-κB (nuclear factor-κB). Pathway and functional enrichment analysis of these putative targets indicated that DGLHD obviously influenced NF-κB and PPAR signaling pathway. Consistently, DGLHD downregulated levels of ALT (alanine transaminase) and AST (aspartate transaminase), reduced production of proinflammatory cytokines-TNF (tumor necrosis factor) -α and IL (Interleukin) -1β in serum and liver from mice with hepatic fibrosis, and inhibited hepatic stellate cell (HSC)-T6 cells proliferation. DGLHD decreased TGF (transforming growth factor) -β1 and α-SMA (smooth muscle actin) expression as well, maintained MMP (matrix metalloprotein) 13-TIMP (tissue inhibitor of metalloproteinases) 1 balance, leading to mitigated ECM (extracellular matrix) deposition in vivo and in vitro. Moreover, our experimental data confirmed that the alleviated inflammation and ECM accumulation were pertinent to NF-κB inhibition and PPAR-γ activation. Overall, our results suggest that DGLHD aims at multiply targets and impedes the progression of hepatic fibrosis by ameliorating abnormal inflammation and ECM deposition, thereby serving as a novel regimen for treating hepatic fibrosis in clinic.

Dangguiliuhuang decoction (DGLHD) is a traditional Chinese medicine (TCM) formula, which mainly consists of angelica, radix rehmanniae, radix rehmanniae praeparata, scutellaria baicalensis, coptis chinensis, astragalus membranaceus, and golden cypress, and used for the treatment of diabetes and some autoimmune diseases. In this study, we explored the potential mechanism of DGLHD against insulin resistance and fatty liver in vivo and in vitro. Our data revealed that DGLHD normalized glucose and insulin level, increased the expression of adiponectin, diminished fat accumulation and lipogenesis, and promoted glucose uptake. Metabolomic analysis also demonstrated that DGLHD decreased isoleucine, adenosine, and cholesterol, increased glutamine levels in liver and visceral adipose tissue (VAT) of ob/ob mice. Importantly, DGLHD promoted the shift of pro-inflammatory to anti-inflammatory cytokines, suppressed T lymphocytes proliferation, and enhanced regulatory T cells (Tregs) differentiation. DGLHD also inhibited dendritic cells (DCs) maturation, attenuated DCs-stimulated T cells proliferation and secretion of IL-12p70 cytokine from DCs, and promoted the interaction of DCs with Tregs. Further studies indicated that the changed PI3K/Akt signaling pathway and elevated PPAR-γ expression were not only observed with the ameliorated glucose and lipid metabolism in adipocytes and hepatocytes, but also exhibited in DCs and T cells by DGLHD. Collectively, our results suggest that DGLHD exerts anti-insulin resistant and antisteatotic effects by improving abnormal immune and metabolic homeostasis. And DGLHD may be a novel approach to the treatment of obesity-related insulin resistance and hepatic steatosis.

Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

To simulate the creep characteristics of corn straw particles under uniaxial compression, a 6-level 2D model was created using PFC 2D software according to the actual uniaxial creep test, the grain size, and shape of corn straw particles. Five groups of controlled tests were designed by the control variable method to study the influence of Maxwell body parameters Em, m; Kelvin body parameters Ek, k; and friction coefficient f on the creep curve of Burgers model. Results revealed that the creep characteristics of corn straw are affected by these 5 parameters. After multiple debugging by trial-and-error method, simulation parameters suitable for describing the creep characteristics of corn straw particles were obtained. The creep curves obtained by simulation under these parameters are consistent with those obtained by physical tests. It was shown that PFC software can not only study the creep characteristics of geotechnical materials, but also study the creep characteristics of agricultural fiber materials, which provides a reference for the subsequent rheological characteristics of biomass materials. [ABSTRACT FROM AUTHOR]

Abstract Background Dynamic spasticity (DS) is a common complication post-spinal cord injury (SCI), marked by intermittent increases in muscle tone during postural transitions or movement. Despite its prevalence, high-quality research on DS incidence, risk factors, and underlying mechanisms in SCI patients remains limited. With the growing application of spinal cord stimulation (SCS) for spasticity control, the role of proprioception in DS development has garnered attention. Additionally, advances in diffusion tensor imaging (DTI) allow for the analysis of the reticulospinal tract’s (RST) role in postural control among SCI patients. However, the impact of microstructural changes in RST and proprioception on DS following SCI remains unclear. Objective To investigate the relationship between microstructural changes in the RST and proprioceptive circuits in SCI patients, exploring their association with DS and potential predictive factors that influence recovery. Methods This is a 12-month prospective cohort study. The neurophysiological and global functional status of the participants will be assessed. Primary outcomes include DTI indices of RST and proprioception, and spasticity assessment. Secondary outcomes include auditory startle reflex (ASR), H-reflex, shear wave velocity (SWV), overall functional status including spinal cord independence measure, gait analysis, modified Barthel Index, and the 36-item short form health survey. Data will be collected at 1, 3, 6, and 12 months after enrolment. Matched healthy controls will be measured during the same period after recruitment. Discussion This study is the first to explore the role of microstructural changes in the RST and proprioception in the assessment and prediction of DS following SCI. The findings aim to enhance theoretical understanding of SCS in spasticity management and to establish pre-treatment criteria for SCS interventions targeting motor function recovery in SCI. Trial registration number ChiCTR2400090724.

Abstract Utilizing ultrafast light-matter interaction to manipulate electronic states of quantum materials is an emerging area of research in condensed matter physics. It has significant implications for the development of future ultrafast electronic devices. However, the ability to induce long-lasting metastable electronic states in a fully reversible manner is a long-standing challenge. Here, by using ultrafast laser excitations, we demonstrate the capability to manipulate the electronic polar states in the charge-density-wave material EuTe4 in a non-volatile manner. The process is completely reversible and is achieved at room temperature with an all-optical approach. Each induced non-volatile state brings about modifications to the electrical resistance and second harmonic generation intensity. The results point to layer-specific phase inversion dynamics by which photoexcitation mediates the stacking polar order of the system. Our findings extend the scope of non-volatile all-optical control of electronic states to ambient conditions, and highlight a distinct role of layer-dependent phase manipulation in quasi-two-dimensional systems with inherent sublayer stacking orders.