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Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.

Berries are nutritious and valuable, but their thin skin, soft flesh, and fragility make harvesting and picking challenging. Manual and traditional mechanical harvesting methods are commonly used, but they are costly in labor and can damage the fruit. To overcome these challenges, it may be worth exploring alternative harvesting methods. Using berry fruit-picking robots with perception technology is a viable option to improve the efficiency of berry harvesting. This review presents an overview of the mechanisms of berry fruit-picking robots, encompassing their underlying principles, the mechanics of picking and grasping, and an examination of their structural design. The importance of perception technology during the picking process is highlighted. Then, several perception techniques commonly used by berry fruit-picking robots are described, including visual perception, tactile perception, distance measurement, and switching sensors. The methods of these four perceptual techniques used by berry-picking robots are described, and their advantages and disadvantages are analyzed. In addition, the technical characteristics of perception technologies in practical applications are analyzed and summarized, and several advanced applications of berry fruit-picking robots are presented. Finally, the challenges that perception technologies need to overcome and the prospects for overcoming these challenges are discussed.

Abstracts: Airborne fine particulate matter (PM2.5) can cause pulmonary inflammation and even fibrosis, however, the underlying molecular mechanisms of the pathogenesis of PM2.5 exposure have not been fully appreciated. In the present study, we explored the dynamics of glycolysis and modification of histone lactylation in macrophages induced by PM2.5-exposure in both in vivo and in vitro models. Male C57BL/6 J mice were anesthetized and administrated with PM2.5 by intratracheal instillation once every other day for 4 weeks. Mouse RAW264.7 macrophages and alveolar epithelial MLE-12 cells were treated with PM2.5 for 24 h. We found that PM2.5 significantly increased lactate dehydrogenase (LDH) activities and lactate contents, and up-regulated the mRNA expression of key glycolytic enzymes in the lungs and bronchoalveolar lavage fluids of mice. Moreover, PM2.5 increased the levels of histone lactylation in both PM2.5-exposed lungs and RAW264.7 cells. The pro-fibrotic cytokines secreted from PM2.5-treated RAW264.7 cells triggered epithelial-mesenchymal transition (EMT) in MLE-12 cells through activating transforming growth factor-β (TGF-β)/Smad2/3 and VEGFA/ERK pathways. In contrast, LDHA inhibitor (GNE-140) pretreatment effectively alleviated PM2.5-induced pulmonary inflammation and fibrosis via inhibiting glycolysis and subsequent modification of histone lactylation in mice. Thus, our findings suggest that PM2.5-induced glycolysis and subsequent modification of histone lactylation play critical role in the PM2.5-associated pulmonary fibrosis.

ABSTRACTCarbapenem-resistant Enterobacterales (CRE) are some of the most important pathogens causing infections, which can be challenging to treat. We identified four blaIMP-carrying CRE isolates and collected clinical data. The transferability and stability of the plasmid were verified by conjugation, successive passaging, and plasmid elimination assays. The IncC blaIMP-4-carrying pIMP4-ECL42 plasmid was successfully transferred into the recipient strain, and the high expression of traD may have facilitated the conjugation transfer of the plasmid. Interestingly, the plasmid showed strong stability in clinical isolates. Whole-genome sequencing was performed on all isolates. We assessed the sequence similarity of blaIMP -harboring plasmid from our institution and compared it to plasmids for which sequence data are publicly available. We found that four blaIMP-carrying CRE belonged to four different sequence types. The checkerboard technique and time-kill assays were used to investigate the best antimicrobial therapies for blaIMP-carrying CRE. The time-kill assay showed that the imipenem of 1× minimum inhibitory concentration (MIC) alone had the bactericidal or bacteriostatic effect against IMP-producing strains at 4–12 h in vitro. Moreover, the combination of tigecycline (0.5/1/2 × MIC) and imipenem (0.5/1 × MIC) showed a bactericidal effect against the blaIMP-26-carrying CRECL60 strain.IMPORTANCECarbapenem-resistant Enterobacterales (CRE) are an urgent public health threat, and infections caused by these microorganisms are often associated with high mortality and limited treatment options. This study aimed to determine the clinical features, molecular characteristics, and plasmid transmissible mechanisms of blaIMP carriage as well as to provide a potential treatment option. Here, we demonstrated that conjugated transfer of the IncC blaIMP-4-carrying plasmid promotes plasmid stability, so inhibition of conjugated transfer and enhanced plasmid loss may be potential ways to suppress the persistence of this plasmid. The imipenem alone or tigecycline-imipenem combination showed a good bactericidal effect against IMP-producing strains. In particular, our study revealed that imipenem alone or tigecycline-imipenem combination may be a potential therapeutic option for patients who are infected with IMP-producing strains. Our study supports further trials of appropriate antibiotics to determine optimal treatment and emphasizes the importance of continued monitoring of IMP-producing strains in the future.

Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

Short-chain fatty acids are important nutrients that regulate milk fat synthesis. They regulate milk synthesis via the sterol regulatory element binding protein 1 (SREBP1) pathway; however, the details are still unknown. Here, the regulation and mechanism of sodium acetate (SA) in milk fat synthesis in bovine mammary epithelial cells (BMECs) were assessed. BMECs were treated with SA supplementation (SA+) or without SA supplementation (SA-), and milk fat synthesis and activation of the SREBP1 pathway were increased (P = 0.0045; P = 0.0042) by SA+ and decreased (P = 0.0068; P = 0.0031) by SA-, respectively. Overexpression or inhibition of SREBP1 demonstrated that SA promoted milk fat synthesis (P = 0.0045) via the SREBP1 pathway. Overexpression or inhibition of TATA element modulatory factor 1 (TMF1) demonstrated that TMF1 suppressed activation of the SREBP1 pathway (P = 0.0001) and milk fat synthesis (P = 0.0022) activated by SA+. Overexpression or inhibition of TMF1 and SREBP1 showed that TMF1 suppressed milk fat synthesis (P = 0.0073) through the SREBP1 pathway. Coimmunoprecipitation analysis revealed that TMF1 interacted with SREBP1 in the cytoplasm and suppressed the nuclear localization of SREBP1 (P = 0.0066). The absence or presence of SA demonstrated that SA inhibited the expression of TMF1 (P = 0.0002) and the interaction between TMF1 and SREBP1 (P = 0.0001). Collectively, our research suggested that TMF1 was a new negative regulator of milk fat synthesis. In BMECs, SA promoted the SREBP1 pathway and milk fat synthesis by suppressing TMF1. This study enhances the current understanding of the regulation of milk fat synthesis and provides new scientific data for the regulation of milk fat synthesis.

Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest operations efficiently. Vision systems for automated orchard yield estimation have received growing attention toward fruit identification with different maturity stages. However, due to interfering factors such as varying outdoor illuminations, similar colors with the surrounding canopy, imaging distance, and occlusion in natural environments, it remains a serious challenge to develop reliable visual methods for identifying blueberry fruits with different maturities. This study constructed a YOLO-BLBE (Blueberry) model combined with an innovative I-MSRCR (Improved MSRCR (Multi-Scale Retinex with Color Restoration)) method to accurately identify blueberry fruits with different maturities. The color feature of blueberry fruit in the original image was enhanced by the I-MSRCR algorithm, which was improved based on the traditional MSRCR algorithm by adjusting the proportion of color restoration factors. The GhostNet model embedded by the CA (coordinate attention) mechanism module replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-BLBE model. The BIFPN (Bidirectional Feature Pyramid Network) structure was applied in the neck network of the YOLO-BLBE model, and Alpha-EIOU was used as the loss function of the model to determine and filter candidate boxes. The main contributions of this study are as follows: (1) The I-MSRCR algorithm proposed in this paper can effectively amplify the color differences between blueberry fruits of different maturities. (2) Adding the synthesized blueberry images processed by the I-MSRCR algorithm to the training set for training can improve the model’s recognition accuracy for blueberries of different maturity levels. (3) The YOLO-BLBE model achieved an average identification accuracy of 99.58% for mature blueberry fruits, 96.77% for semi-mature blueberry fruits, and 98.07% for immature blueberry fruits. (4) The YOLO-BLBE model had a size of 12.75 MB and an average detection speed of 0.009 s.

Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal.

Abstract Background To compare the gait patterns between posterior cruciate retention and substitution in total knee arthroplasty (TKA). Methods Electronic databases including the PubMed, Embase, CINAHL, Web of Science, and Cochrane databases were searched to identify clinical trials investigating posterior cruciate retention versus substitution in TKA. The outcome measurements were the kinematic gait parameters (flexion at heel strike, maximum flexion during loading response, flexion range during loading, minimal flexion at terminal stance, maximal flexion at the swing, and total flexion during the gait cycle), Knee Society Score (KSS), knee flexion, knee extension, and walking speed. Statistical software Review Manager 5.4 and Stata 14.0 were used for data analysis. Results There were finally 9 studies included in this meta-analysis. The results did not reveal differences between posterior cruciate retention (CR) and posterior cruciate substitution (PS) groups in TKA, in terms of kinematic gait parameters, knee extension, walking speed, and KSS. However, the PS group had a significantly larger knee flexion angle than that in the CR group [weighted mean difference = − 3.20, 95% CI − 6.13 to − 0.28, P = 0.03]. Conclusion Both the posterior cruciate retention and posterior cruciate substitution lead to obvious improvements in patient function and have their advantages in getting a good cup position. The PS design is significantly better on the knee flexion, while there are no statistical differences in kinematic gait parameters and outcome scores between them. This might indicate that surgeons do not necessarily need a PS design to substitute the posterior cruciate ligament during TKA.

Acquisition of continuous drift ice characteristic parameters such as ice size, shape, concentration, and drift velocity are of great importance for understanding river freezing and thawing processes. This study acquired hourly oblique images captured by a shore-based camera in the winter of 2021–2022 on the Yellow River, China. The pixel point scale method for correcting oblique images is provided. The 61 lines were measured at the calibration site and the absolute error between the measured value and the calculated value was in the range of 0.009–0.850 m, with a mean error of 0.236 m. After the correction of oblique images, the true equivalent diameter of drift ice during the freezing period ranged from 0.52–13.10 m with a mean size of 3.36 m, which was larger than that of 2.30 m during the thawing period which ranged from 0.20–12.54 m. It was found that the size of drift ice increased with time during the freezing period and decreased with time during the thawing period. The fractal dimension and roundness were used to represent drift ice shape. The fractal dimension ranged from 1.0–1.3 and the roundness ranged from 0.1–1.0. A Gaussian distribution was used to estimate drift ice size and shape distributions. There is a nonlinear relationship between ice concentration and drift velocity, which can be well expressed by the logistic function. In the future, drift ice parameters for more years and hydrometeorological data for the same time need to be accumulated, which helps to analyze the freezing and thawing patterns of river ice.

Carbapenem-resistant Enterobacter cloacae complex (CRECC) has increasingly emerged as a major cause of healthcare-associated infections, with colistin being one of the last-resort antibiotics of treatment. Mobile colistin resistance (mcr)-9 is a member of a growing family of mcr genes and has been reported to be an inducible gene encoding an acquired phosphoethanolamine transferase. Here, we collected 24 ECC strains from Chongqing, China from 2018 to 2021. Subsequently, antibiotic resistance genes and the transmission dynamics of the strains were determined by PCR, whole-genome sequencing, and bioinformatic analysis. The mcr-9 was identified in IncHI2/2A or IncHI2/2A + IncN plasmids from six CRECC strains and was co-located with blaNDM-1 or blaIMP-4 in 2/6 plasmids. The genetic environment of mcr-9.1 was composed of IS903B-mcr-9.1-wbuC-IS26 in the five mcr-9.1-harboring-plasmid, but IS1B was located downstream of mcr-9.2 in the pECL414-1 sequence. We also found that the pNDM-068001 plasmid carrying mcr-9.1 could be a hybrid plasmid, formed by a Tn6360-like blaNDM-1 region inserted into an mcr-9.1-positive IncHI2/2A plasmid. A conjugation assay showed that plasmids mediated the co-dissemination of mcr-9 and metallo-β-lactamase (MBL) genes. In addition, we performed induction assays with sub-inhibitory concentrations of colistin and found an increase in the relative expression levels of the mcr-9.2, qseC, and qseB genes, as well as an increase in the minimum inhibitory concentration values of colistin in the CRECC414 strain. These findings provide a basis for studying the regulatory mechanisms of mcr-9 expression and highlight the importance of effective monitoring to assess the prevalence of MBL and mcr-9 co-existing plasmids.

Post-traumatic osteoarthritis is a special type of osteoarthritis and a common disease, with few effective treatments available. α2-Macroglobulin (α2M) is important to chondral protection in post-traumatic osteoarthritis. However, its injection into xenogeneic joint cavities involves safety hazards, limiting clinical applications. Exploring serum α2M-enriching strategies and the therapeutic effect and mechanism of α2M-rich serum (α2MRS) autologous joint injection to treat post-traumatic osteoarthritis has significant value. In the present study, a unique filtration process was used to obtain α2MRS from human and mini pig serum. We evaluated the potential of α2MRS in protecting against post-surgery cartilage degeneration. We identify the potential of α2MRS in reducing the expression of inflammatory cytokines and factors that hasten cartilage degeneration in post-operative conditions leading to post-traumatic osteoarthritis. The potential of α2MRS was analyzed in interleukin-1β induced human chondrocytes and mini pig models. In the chondrocyte model, α2MRS significantly promoted human chondrocyte proliferation and reduced apoptosis and chondrocyte catabolic cytokine gene transcription and secretion. The anterior cruciate ligament autograft reconstruction model of mini pigs was randomized into groups, operated on, and injected with α2MRS or saline. The results showed that α2MRS injection significantly suppressed the levels of inflammatory factors, improved gait, and showed significantly lower cartilage degeneration than the groups that did not receive α2MRS injections. This study highlights the chondroprotective effects of α2MRS, elucidated its potential applications against cartilage degeneration, and could provide a basis for the clinical translation of α2MRS.

Coronavirus disease 2019 (COVID-19) caused by coronavirus-2 (SARS-CoV-2) infection has rapidly spread throughout the world and become a major threat to human beings. Cytokine storm is a major cause of death in severe patients. Abatacept can suppress cytokines used as antirheumatic drugs in clinical applications. This study analyzed the molecular mechanisms of abatacept treatment for COVID-19. Differentially expressed genes (DEGs) were identified by analyzing expression profiling of abatacept treatment for rheumatoid arthritis (RA) patients and SARS-CoV-2 infection patients. We found that 59 DEGs were upregulated in COVID-19 patients and downregulated following abatacept treatment. Gene set enrichment analysis (GSEA) and Gene Ontology (GO) analysis showed that immune and inflammatory responses were potential regulatory mechanisms. Moreover, we verified 8 targeting genes and identified 15 potential drug candidates for the treatment of COVID-19. Our study illustrated that abatacept could be a promising property for preventing severe COVID-19, and we predicted alternative potential drugs for the treatment of SARS-CoV-2 infection.

Background: Previous studies have partly explored the role of B-cell CLL/lymphoma 7 protein family member B (BCL7B) in tumorigenesis and development. However, the prognosis and immunoregulatory value of BCL7B in pan-cancer patients remains unclear.Methods: Through The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, the distinct expression of BCL7B gene in 33 tumors and adjacent normal tissues was analyzed. The Kaplan–Meier method (univariate Cox regression analysis and Kaplan–Meier curve) was used to identify the cancer types whose BCL7B gene expression was related to prognosis. The receiver operating characteristic (ROC) curve was used to elucidate the diagnosis value of BCL7B gene. Spearman’s rank correlation coefficient was used to explore the relationship between BCL7B gene expression and immune cell infiltration, immune checkpoints, DNA methylation, DNA repair genes, immune-activating genes, immune-suppressing genes, immune subtypes, tumor mutation burden (TMB), and microsatellite instability (MSI). The Wilcoxon rank sum test and Kruskal–Wallis test were used to compare the expression of BCL7B gene in tumor tissues with different clinicopathological features. Gene set enrichment analysis (GSEA) was conducted to identify the tumor-related pathways in pan-cancer. The Human Protein Atlas (HPA) database was used to verify the BCL7B gene expression at the protein level.Results: High expression of BCL7B was associated with an inferior prognosis in glioblastoma multiforme (GBM), glioma (GBMLGG), kidney chromophobe (KICH), brain lower grade glioma (LGG), oral squamous cell carcinoma (OSCC), rectum adenocarcinoma (READ), and uveal melanoma (UVM). Low expression of BCL7B was associated with a poor prognosis in kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), skin cutaneous melanoma (SKCM), thyroid carcinoma (THCA), and sarcoma (SARC). The BCL7B gene expression had varying degrees of correlation with 24 immune cell subsets in 37 tumor environments such as adrenocortical carcinoma (ACC) and bladder urothelial carcinoma (BCLA). Spearman’s rank correlation coefficient showed that BCL7B gene expression had different degrees of correlation with 47 immune checkpoints, 46 immune-activating genes, 24 immune-suppressing genes, 5 DNA repair genes, and DNA methylation, TMB, and MSI in 39 tumors. GSEA suggested that BCL7B was notably associated with cancer-related and immune-related pathways.Conclusion: In summary, BCL7B gene has a high diagnostic and prognostic value in pan-cancer and is related to the infiltration of 24 immune cell subsets in pan-cancer.

After the formation of the bend ice cover, the ice thickness of the bend is not uniformly distributed, and an open-water area is usually formed downstream of the bend. The spatial and temporal variation of the ice thickness in seven cross sections was determined via Unmanned Aerial Vehicle Ground Penetrating Radar (UAV-GPR) technology and traditional borehole measurements. The plane morphology change of the open water was observed by Sentinel-2. The results show that the average dielectric permittivity of GPR was 3.231, 3.249, and 3.317 on three surveys (5 January 2022, 16 February 2022, and 25 February 2022) of the Yellow River ice growing period, respectively. The average ice thickness of the three surveys was 0.402 m, 0.509 m, and 0.633 m, respectively. The ice thickness of the concave bank was larger than that of the convex bank. The plane morphology of the open water first shrinks rapidly longitudinally and then shrinks slowly transversely. The vertical boundary of the open water was composed of two arcs, in which the slope of Arc I (close to the water surface) was steeper than that of Arc II, and the hazardous distance of the open-water boundary was 10.3 m. The increased flow mostly affected the slope change of Arc I. Finally, we discuss the variation of hummocky ice and flat ice in GPR images and the physical factors affecting GPR detection accuracy, as well as the ice-thickness variation of concave and convex banks in relation to channel curvature.

The ice thermal parameters are the key to reasonably simulating ice phenology, distribution, and thickness, but they have always been a “vulnerable group” in ice research. Technically, it may seem simple to obtain accurate ice thermal property parameters, but in reality, there are numerous impact factors, requiring a rigorous research process. In the 1980s, the thermal conductivity of ice was explored in the field and laboratory, after which there has been no significant progress in China. In this century, mathematics is introduced, after which the inversion identification and analysis with the time-series data of the vertical temperature profiles of ice layers by in situ testing are carried out. The in situ thermal diffusivities of different natural ices were obtained and cross-validated with the inversion identification results. Both natural freshwater ice and sea ice exhibited differences in the thermal diffusivity of the pure ice chosen for the current simulations due to impurities within the unfrozen water among the ice crystals, but the trends are consistent with the results of a small number of laboratory tests on different types of saltwater frozen ice. In this paper, the inversion identification results of the thermal diffusivity of typical ice were selected, and the factors constraining the thermal diffusivities were analyzed. The importance of parameterizing the thermal diffusivity in the phase transition zone of ice under the trend of global warming was illustrated. Future research ideas on the physical mechanism, application value, and parameterization scheme of the thermal diffusivity of natural ice were envisaged.

Unfrozen free and non-free water between ice crystals in flat and hummock ice in the Yellow River exists as water films with varying contents based on ice temperature. These contents can affect the radar wave velocity of the ice despite its theoretical dependence on the crystal structure and ice body components. The unfrozen water content in ice depends on the ice temperature, which is controlled by the air temperature, solar radiation, and ice thickness. Winter air temperature and radar-detected ice thickness data observed at the Shisifenzi bend in the Yellow River from 2020 to 2021 were analyzed. The unfrozen water content in the ice was the primary factor influencing the accuracy of flat ice thickness detection. The heat flux at the ice–water interface in the Yellow River was determined. The evolution of ice thickness and temperature were simulated using a one-dimensional (1D) ice thermodynamic model forced by the local weather station data (i.e., air temperature, solar radiation, wind speed, and cloud cover). On this basis, the measured ice thickness data of 13 drill holes were combined to calculate 1251 thermodynamically simulated ice thicknesses consistent with the ice thickness detection time of the radar; therefore, statistical relationships regarding the influence of air temperature and the combined action of air temperature and ice thickness on the radar wave velocity in granular and columnar ice during air temperature increases and decreases were determined. Finally, the statistical relationship between the combined influence of air temperature and ice thickness on radar wave velocity was selected as a parameterization scheme to dynamically correct the radar wave velocity of flat ice. To enhance the radar detection accuracy for flat ice thickness, the radar wave velocity of ice was parameterized as a function. Given the presence of unfrozen frazil ice and accumulated broken ice blocks in the Yellow River, radar is suggested to detect the thickness of different types of ice in future research.

Abstract Background Carbapenem-resistant hypervirulent Klebsiella pneumoniae strains have recently come into existence worldwide; however, researchers in northeast China are not aware of their clinical features and molecular characteristics. Methods Here, the molecular and virulent characteristics of 44 carbapenem-resistant K. pneumoniae (CRKP) isolates collected from January 2015 to December 2017 were studied. Multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE) were carried out to define the clonal relatedness among the isolates. PCR and capsular serotyping of the virulence-associated genes, as well as biofilm formation and serum complement-mediated killing assays, were employed to determine the virulent potential. The genomic features and associated mobile genetic elements of JmsCRE57 were detected by whole genome sequencing. Results The only positive isolate was JmsCRE57, which belonged to the ST375 serotype K2 that expressed uge, mrkD, fimH, kpn, aerobactin and rmpA virulence-associated genes and showed strong biofilm formation and serum sensitivity. Sequencing results showed that the JmsCRE57 genome mainly consisted of a circular chromosome, three antimicrobial resistant plasmids and a virulent plasmid. The antimicrobial resistant plasmid expressing bla KPC-2 , bla CTX-M-15 , aph(3″)-Ib, aph(6)-Id, qnrB1, aac(3)-IIa, aac(6′)-Ib-cr, bla OXA-1 , bla TEM-1B , catB4, sul2, dfrA14 and bla SHV-99 . The virulent plasmid belonged to the IncHI1B group, which is mainly composed of mucoid phenotype genes and siderophore-associated genes. The remaining CRKP strains that expressed uge, fimH, mrkD and kpn virulence-associated genes were not successfully typed. Conclusion Our results provide new insights on the epidemiology of carbapenem-resistant K2 hypervirulent K. pneumoniae ST375 and CRKP ST76 strains in northeast China, which may help control their future outbreaks.

Abstract Background Enterobacter cloacae complex (ECC) is one of the most common extended-spectrum β-lactamase and carbapenemase-producing pathogen that threatens millions of the elderly and vulnerable sick persons. The objective of this study was to perform the molecular characteristics of the carbapenem-resistant E. cloacae complex (CREC) emerged in Heilongjiang Province of China. Methods Six CREC strains were isolated from the patients with infectious diseases. The identities of ECC isolates were confirmed by sequencing the polymerase chain reaction (PCR) products of 16S rRNA gene. The characterization of the CREC isolates were analyzed by sequencing PCR products of the carbapenemase, ampC and fluoroquinolone resistance genes and performing multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE) and whole genome sequencing. Results All 6 isolates harbored multiple resistance genes. Of them, 5 carried metallo-β-lactamases and one was bla KPC-2-positive. The levofloxacin and ciprofloxacin-resistant strains had substitutions of gyrA83, gyrA87, and parC80 in the quinolone-resistance determining regions. The MLST analyses revealed that 6 isolates belonged to five sequence types (ST520, ST528, ST1119, ST1120, and ST93) while the PFGE patterns of the isolates fallen into four clusters. The strain ST1120 was found to carry two separated plasmids that encode bla NDM-1 and bla IMP-4. Conclusions Our study, for the first time, identified a CREC strain that co-produces bla NDM-1 and bla IMP-4 in the Northeast China. Our finding emphasizes an urgent need for more intensive surveillance and precaution measures to prevent the CERC spread.

A ring-shaped plasmonic nanostructure is established for realizing the plasmonic field enhancement and focusing through a radial vector beam incidence. The plasmonic light field of the proposed nanostructure is simulated by finite-difference time-domain (FDTD). The results show that the obvious plasmonic field enhancement and focusing spot can be observed when a ring-shaped Au nanostructure is placed on the gold layer. Especially, the maximum of intensity ratio for single ring-shaped nanostructure can reach up to 41, the minimum of the full width at half maximum (FWHM) of focusing spot closes to 60 nm, and spot size beyond the diffraction limit is as small as 0.19λ0. Moreover, the plasmonic field enhancement and focusing can not be realized in the cases of linear polarization, circular polarization, and angular vector polarization incidences. What’s more, the double ring-shaped nanostructure can further develop the intensity ratio to 109 caused by the interference enhancement of surface plasmons. These research results may pave the way for designing the ultra-strong plasmonic field and plasmonic focusing devices.

Viral vectors: Easy gene modification in strawberries Apple latent spherical virus (ALSV) offers a powerful tool to reveal strawberry gene function and induce early flowering. Preferential strawberry traits are highly influenced by their genetics; however, the specific mutations that change these traits are largely unknown. This prompted Nobuyuki Yoshikawa and his team from Iwate University, Japan, to develop ALSV to infect and introduce intended genetic material into strawberries. Infecting in vitro cultured plants at a rate of 58—100%, ALSV-vectored gene introduction proved able to ‘silence’ a natural strawberry gene, constituting a method to identify gene functions. The infection was not pathogenic and did not carry on to the plants’ progeny. The team also used ALSV to introduce a gene that induced flowering 2 months post inoculation, a technique that could be used in future to promote shorter cross-breeding times.

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

Apple russet ring and apple green crinkle are graft-transmitted diseases first reported more than 60 years ago, but at present, no association between a specific virus (variant) and the disease has been clearly demonstrated. In this study, we conducted the following series of experiments to identify the causal viruses (variants) of these apple diseases; (1) comprehensive analysis by next-generation sequencing of all viruses in each apple tree affected with russet ring or green crinkle disease, (2) amplification of full-length genomic cDNA of viruses using primers containing the T3 promoter and the in vitro transcription of infectious viral RNAs, (3) inoculation of viral RNA transcripts to both herbaceous and apple plants, (4) analysis of sequence variants of viruses present in infected plants, (5) back-inoculation of sequence variants of candidate viruses to apple seedlings combined with the virus-induced flowering technology using the apple latent spherical virus vector to reproduce the symptom on the fruit as soon as possible, and (6) reproduction of symptoms on the fruits of apple trees inoculated with sequence variants and the re-isolation of each virus variant from apples showing fruit symptoms. The results showed that one of the sequence variants of the apple chlorotic leaf spot virus causes a characteristic ring-shaped rust on the fruits of infected apple trees and that a sequence variant of the apple stem pitting virus probably causes green crinkle symptoms on an infected apple fruit. Thus, we were able to fulfill Koch’s postulates to prove the viral etiology of both the apple russet ring and green crinkle diseases. We also propose an experimental system that can prove whether a virus found in diseased tissues is the pathogen responsible for the diseases when the etiology is undetermined.

Background: Most prior studies adopted a static residence-based approach in the assessment of noise exposure, which may lead to biased exposure estimates and misleading findings in noise–health relationships. Relatively little is known about personal noise exposure based on individuals’ space-time behavior and its effect on mental health. Objectives: This study aims to analyze and geo-visualize personal exposure to noise in various microenvironments based on individuals’ space-time trajectories at a very fine resolution and to further investigate the relationships between mental health and personal noise exposure at both the activity/travel episode level and the entire day level. Methods: Individual-level real-time data were collected with portable noise sensors and GPS trackers from a sample of 117 residents aged 18–60 years old from December 2017 to February 2018 in Beijing, China. Descriptive statistics and geo-visualization methods were used to examine how personal noise exposure varied across different activity types, travel modes, and among residents living in the same residential neighborhood on workdays and weekends based on individuals’ space-time behaviors. Logistic regression models were applied to examine the relationships between personal noise exposure and self-reported mental health. Results: We observed substantial differences in personal noise exposure across different activity types. The equivalent sound levels (Leq, dB(A)) for sleeping were the lowest, while the average Leq for work-related activities was the highest in indoor environments. The noise exposure levels for activities in outdoor environments were higher than indoor noise levels but differed between workdays and weekends. Variations in noise exposure associated with different travel modes were also evident, with the average Leq for public transport being much higher than that of other travel modes. A-weighted equivalent sound pressure level measured over 24 h for each individual (Leq,24h, dB(A)) varied significantly for residents living in the same residential neighborhood, ranging from 36 to 97 dB(A), with the majority of respondents being exposed to noise levels above 55 dB(A) on both workdays and weekends. Regarding the noise–health relationships, the modeling results showed that individual-level objective noise exposure based on space-time behaviors measured over a 24-h period (Leq,24h) was strongly associated with residents’ self-reported mental health. Higher exposure to noise was significantly associated with worse mental health. However, personal noise exposure at the activity/travel episode level (Leq) was not significantly associated with mental health on weekdays, but this link turned out to be significant in the weekend model. Conclusions: There were large variations in personal noise exposure associated with different activity types and travel modes, and the individual-level noise exposure varied significantly across time of day and between residents living in the same residential neighborhood. Variations in personal exposure strongly depend on different space-time behaviors and individual-specific microenvironments experienced in daily life, and they were significantly correlated with mental health.

We study spectral responses in a bright-dark mode ellipse-shaped resonators coupled waveguide. The results show that the plasmon-induced transparency (PIT) can be observed in the ellipse-shaped resonators coupled waveguide, and the symmetric line shape at the PIT window can be tuned by the radii of the long axis in ellipse-shaped resonators, the coupling distance, and the symmetry broken degree. Interestingly, the radii of the long axis for the bright mode and dark mode have opposite effect on the spectra of PIT. Our research results may pave the way for the understanding and tuning of PIT.

We study dual-Fano resonances and its sensing properties in a crossed ring-shaped metasurface by use of the finite-different time-domain (FDTD) simulation. The results show that the dual-Fano resonances in the proposed crossed ring-shaped metasurface are caused by the interaction among three local surface plasmon resonances (LSPRs), and spectra of the dual-Fano resonances can be tuned by the radius of the circular ring (CR) nanostructure, the distance between the center of the two CRs in x direction, and the polarization of the incident light. Interestingly, single Fano resonance splits into dual-Fano resonances in the case of the asymmetric ring structure arrangement or the non-y-axis polarized incident or the distance d

The purpose of this study was to investigate the synergistic and bactericidal effects of combinations of colistin with meropenem or amikacin in vitro and provide laboratory data needed for development of therapeutic strategies for the treatment of carbapenem-resistant Klebsiella pneumoniae (CRKP) infection. We found that minimum inhibitory concentration (MIC) of colistin, meropenem and amikacin were 2~32, 4~256, and 1~16384 μg/ml, respectively. The minimum bactericidal concentration of the antibiotics was either 1× or 2×MIC. Treatments of 6 CRKP isolates at 1 μg/ml colistin completely killed 2 of them and suppressed 4 others growth. 4 CRKP isolates at 16 μg/ml meropenem or amikacin completely killed and suppressed 2 others growth. 2 CRKP isolates showed synergic effects in all colistin combination and 3 CRKP isolates showed synergic effects in part of colistin combination. Our data suggest that colistin in combination with either meropenem or amikacin could be a valid therapeutic option against colistin-resistant CRKP isolates. Moreover, the combination of colistin-amikacin is less expensive to treat CRKP infections in Eastern Heilongjiang Province.

Regarding the ice periods of the Yellow River, it is difficult to obtain ice data information. To effectively grasp the ice evolution process in the ice periods of the typical reach of the Yellow River, a fixed-point air-coupled radar remote monitoring device is proposed in this paper. The device is mainly composed of an air-coupled radar ice thickness measurement sensor, radar water level measurement sensor, temperature measurement sensor, high-definition infrared night vision instrument, remote switch control, telemetry communication machine, solar and wind power supply, lightning protection, and slewing arm steel tower. The integrated monitoring device can monitor ice thickness, water level, air temperature, ice surface temperature, and other related parameters in real time. At present, devices have obtained the ice change process of fixed points in ice periods from 2020 to 2021. Through a comparison with manual data, the mean error of the monitoring results of the water level and ice thickness was approximately 1 cm. The device realizes the real-time monitoring of ice thickness and water level change in the whole cycle at the fixed position. Through video monitoring, it can take pictures and videos regularly and realize the connection between the visual river and monitoring data. The research results provide a new model and new technology for hydrological monitoring in the ice periods of the Yellow River, which has broad application prospects.

Frazil particles, ice crystals or slushy granules that form in turbulent water, change the freezing properties of ice to create “frazil ice”. To understand the microstructural characteristics of these particles and the physical properties of frazil ice in greater depth, an in situ sampler was designed to collect frazil particles in the Yellow River. The ice crystal microstructural characteristics of the frazil particles (morphology, size, air bubble, and sediment) were observed under a microscope, and their nucleation mechanism was analyzed according to its microstructure. The physical properties of frazil ice (ice crystal microstructure, air bubble, ice density, and sediment content) were also observed. The results showed that these microstructures of frazil particles can be divided into four types: granular, dendritic, needle-like, and serrated. The size of the measured frazil particles ranged from 0.1 to 25 mm. Compared with columnar ice, the crystal microstructure of frazil ice is irregular, with a mean crystal diameter less than 5 mm extending in all directions. The crystal grain size and ice density of frazil ice are smaller than columnar ice, but the bubble and sediment content are larger.

Many scholars have conducted in-depth analysis on relevant factors that affect the accuracy of jet dispensing. However, research on the influence of needle impact velocity on jetting performance is limited. Thus, a piezoelectric needle-collision dispenser for micro-droplet jetting is designed in this study, and the influence of the needle impact velocity on jetting effect is deeply analyzed. Initially, the principle of droplet formation and injection is analyzed by Fluent simulation. Thereafter, the mathematical model of the droplet velocity in the nozzle orifice is derived, and the influence of the needle impact velocity on the jetting performance is simulated and analyzed. Results show that the needle impact velocity has an influence on the jetting effect. A prototype of the jetting dispenser is subsequently fabricated, and the needle impact velocity is tested with laser micrometer. The relationship between the needle impact velocity and droplet weight and its corresponding ejected effect are obtained through experimental research. Finally, a UV glue with viscosity of 5,000 cps can be ejected with a minimum average diameter of 272.1 μm by using a needle with a radius of 0.5 mm, a nozzle with a diameter of 50 μm, a taper angle of 90°, a supply pressure of 0.2 Mpa, and a needle impact velocity of 0.13 m/s. The variation of the droplet diameter is within ±2.5%.

The unidirectional reflectionless (UR) light propagation is investigated in the waveguide coupled to gain and loss resonators by using a developed coupled mode-scattering matrix theory. The results show that there is almost no reflection in the case of the backward incidence, but total reflection in the case of the forward incidence under the condition of balancing gain and loss in the gain resonator for the proposed waveguide when the indirect coupling phase θ ranges from 0.8 rad to 2.3 rad and from 4 rad to 5.5 rad. Moreover, the coherent perfect absorption (CPA) can be observed at the same time. Especially, the UR light propagation appears when the absolute value of detuning δ is smaller than 1 × 10 ^13 rad s ^−1 . Based on the findings above, we propose a metal–insulator–metal non-parity-time symmetric plasmonic waveguide and obtain the UR plasmonic propagation and CPA. The theoretical results are in excellent agreement with the finite-difference time-domain simulations. These results will provide a new pathway for the realization of unidirectional propagation and absorption of light at the nanoscale.

We investigate Fano resonances and sensing enhancements in a simple Au/TiO2 hybrid metasurface through the finite-different time-domain (FDTD) simulation and coupled mode theory (CMT) analysis. The results show that the Fano resonance in the proposed simple metasurface is caused by the destructive interaction between the surface plasmon polaritons (SPPs) and the local surface plasmon resonances (LSPRs), the quality factor and dephasing time for the Fano resonance can be effectively tuned by the thickness of Au and TiO2 structures, the length of each unit in x and y directions, as well as the structural defect. In particular, single Fano resonance splits into multiple Fano resonances caused by a stub-shaped defect, and multiple Fano resonances can be tuned by the size and position of the stub-shaped defect. Moreover, we also find that the sensitivity in the Au/TiO2 hybrid metasurface with the stub-shaped defect can reach up to 330 nm/RIU and 535 nm/RIU at the Fano resonance 1 and Fano resonance 2, which is more than three times as sensitive in the Au/TiO2 hybrid metasurface without the stub-shaped defect, and also higher than that in the TiO2 metasurface reported before. These results may provide further understanding of Fano resonances and guidance for designing ultra-high sensitive refractive index sensors.

We develop a novel nonlinear radiation oscillator theory, which can describe nonlinear optical responses in symmetric and anti-symmetric damping graphene coupled metamaterials. An ultra-high quality factor of induced transparency can be realized in the proposed model. The results show that the spectral response and quality factor can be effectively tuned by the resonant detuning, the phase difference, the damping factor as well as the field intensity of the pump light. Moreover, the quality factor of induced transparency is increased by more than 60 times when the field intensity of the pump light and the damping factor are changed. Especially, the single induced transparency window splits into double induced transparency windows as the damping factors of dark mode and bright mode show anti-symmetric damping case. These results may pave the way for designing the high performance of nano-optical devices in terahertz.

River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

Data-driven equation identification for dynamical systems has achieved great progress, which for static systems, however, has not kept pace. Unlike dynamical systems, static systems are time invariant, so we cannot capture discrete data along the time stream, which requires identifying governing equations only from scarce data. This work is devoted to this topic, building a data-driven method for extracting the differential-variational equations that govern static behaviors only from scarce, noisy data of responses, loads, as well as the values of system attributes if available. Compared to the differential framework typically adopted in equation identification, the differential-variational framework, due to its spatial integration and variation arbitrariness, brings some advantages, such as high robustness to data noise and low requirements on data amounts. The application, efficacy, and all the aforementioned advantages of this method are demonstrated by four numerical examples, including three continuous systems and one discrete system. [ABSTRACT FROM AUTHOR]

This study assesses the impact of secondary orientations on the tensile deformation behavior of single-crystal superalloys at mid-temperature of 760 °C. Tensile tests and stress-strain analyses were performed on specimens with primary orientation [001] and secondary orientations [100], [410], and [110]. Stress distributions were modeled using Abaqus software, while fracture behavior, slip system activation, oxidation, lattice rotation, and micro-dislocation movements were investigated using optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM). Findings reveal that the [100] orientation, due to the activation of coplanar dual slip systems, underwent significant strain hardening, exhibiting the highest strength and lowest ductility due to restricted lattice rotation. In contrast, the [410] and [110] orientations, each with a single primary slip system, showed reduced strength. The [410] orientation demonstrated enhanced ductility due to extensive lattice rotation, whereas the [110] orientation displayed the lowest strength, primarily due to premature cracking aligned with slip lines on the surface oxide film. Dominant deformation microstructures involved isolated stacking faults (SFs) shearing into γ′ precipitates, with the formation of immobile dual Lomer-Cottrell (L-C) locks at intersecting slip planes significantly contributing to microstructural strengthening.

Abstract Behçet’s disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein–protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723–0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

Abstract Since the basic rail of the switch needs to have a certain bending angle when the train changes direction, top bending is an important link in the production process of the basic rail. The three-point pressure top bending method is simple, flexible and widely used. In this study, the traditional three-point pressure bending is optimized, the influence of the pick width in the model is considered, a corresponding rebound model is established, and the model is applied to the pressure bending process of the basic rail. The bilinear strengthening model of the material was used to construct the bending moment expressions at different positions during the top bending process, and the relationship between the load and bending deflection in the elastic stage and elastic-plastic stage was obtained. The final top bending prediction model was obtained by combining the load-deflection model in the bending stage and the rebound stage. The correctness of the theoretical mathematical model was verified by establishing finite element simulations, and the theoretical calculation results were compared with the experimental results. The results showed that the top bend prediction optimization model established in this study had high feasibility and met the machining accuracy requirements.

The simplified governing equations of applied mechanics play a pivotal role and were derived based on ingenious assumptions or hypotheses regarding the displacement fields for specific problems. In this paper, we introduce a data-driven method by the name AI-Timoshenko in honor of Timoshenko, father of applied solid mechanics, to automatically discover simplified governing equations for applied mechanics problems directly from discrete data simulated by the three-dimensional (3D) finite element method. The simplified governing equations are in variational form, which is compact and advantageous for data-driven discovery. AI-Timoshenko liberates applied mechanicians from burdensome labor, including assumptions, derivation, and trial and error. The simplified governing equations for Euler-Bernoulli and Timoshenko beam theories are successfully rediscovered using the present AI-Timoshenko method, which shows that this method is capable of discovering simplified governing equations for applied mechanics problems. [ABSTRACT FROM AUTHOR]

Mathematical models of prediction provide theoretical support for basic rail automation. The three-point bending method for basic rails is characterized by its simplicity and flexibility, and, as such, it is widely used in bending processes. However, due to the significant curvature changes that occur after bending, it is not suitable for scenarios requiring large arc bending, and its range of achievable deflections is limited. This study focuses on four-point bending, dividing the bending process into three stages and using a power-law material hardening model to establish different bending moment expressions for each stage. We derived the relationships between curvature, elastic zone ratio, load, and deflection, ultimately creating a load–deflection model. Based on the simple springback law, we developed the final bending prediction model. Finite element simulations were conducted to simulate the bending process under various conditions, using top punch distances ranging from 200 mm to 400 mm and die distances ranging from 600 mm to 1000 mm. These simulations validated the advantages and accuracy of the four-point bending prediction model in large arc bending. Additionally, a four-point bending experimental setup was established under specified conditions. The experimental results were compared with the theoretical model calculations, showing errors within 0.2 mm and thus verifying the accuracy of the four-point bending prediction model. The mathematical model developed in this study provides theoretical support for the automation of basic rail bending.

Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part layout can improve work efficiency, save time, and reduce the labor intensity and production costs for the company. In this study, we studied a layout optimization algorithm, focusing on rectangular parts in the material preparation process. A mathematical model for the rectangular layout problem was constructed, and a hybrid genetic whale optimization algorithm is proposed that is a combination of the whale optimization algorithm and the genetic algorithm. Based on the “one size fits all” layout strategy, the materials are divided into strips, which are further divided into stacks, serving as the positioning strategy to determine the positional relationships of the parts. Test cases and actual engineering data were used to compare the layouts generated using different algorithms. The results show that the genetic whale algorithm proposed in this paper results in a high utilization rate and is highly effective.

This study focuses on using activated fly ash to preparate silica aerogel by the acid solution–alkali leaching method and ambient pressure drying. Additionally, to improve the performance of silica aerogel, C6H16O3Si (KH-570) and CH3Si(CH3O)3 (MTMS) modifiers were used. Finally, this paper investigated the factors affecting the desilication rate of fly ash and analyzed the structure and performance of silica aerogel. The experimental results show that: (1) The factors affecting the desilication rate are ranked as follows: hydrochloric acid concentration > solid–liquid ratio > reaction temperature > reaction time. (2) KH-570 showed the best performance, and when the volume ratio of the silica solution to it was 10:1, the density of silica aerogel reached a minimum of 183 mg/cm3. (3) The optimal process conditions are a hydrochloric acid concentration of 20 wt%, a solid–liquid ratio of 1:4, a reaction time of two hours, and a reaction temperature of 100 °C. (4) The optimal performance parameters of silica aerogel were the thermal conductivity, specific surface area, pore volume, average pore size, and contact angle values, with 0.0421 W·(m·K)−1, 487.9 m2·g−1, 1.107 cm3·g−1, 9.075 nm, and 123°, respectively. This study not only achieves the high-value utilization of fly ash, but also facilitates the effective recovery and utilization of industrial waste.

Abstract Background Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs). Methods Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein–protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed. Results Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971–1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes. Conclusion The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

In order to achieve the high-value utilization of heavy tar for the production of enhanced-performance graphite foam carbon, the carbon mesophase was ready from the heavy component of low-temperature coal tar, and the coal tar was modified by styrene-butadiene-styrene (SBS), polyethylene (PE) and ethylene-vinyl-acetate (EVA) copolymers. The order degree of the carbonite mesophase was analyzed using a polarizing microscope test, Fourier transform infrared spectroscopy and X-ray diffraction to screen out the most suitable copolymer type and addition amount. Furthermore, the mechanism of modification by this copolymer was analyzed. The results showed that adding SBS, PE and EVA to coal tar would affect the order of carbonaceous mesophase; however, at an addition rate of 10.0 wt.%, the linear-structure SBS copolymer with a styrene/butadiene ratio (S/B) of 30/70 exhibited the optimal degree of ordering in the carbonaceous mesophase. Its foam carbon prepared by polymer modification is the only one that forms a graphitized structure, with d002 of 0.3430 nm, and the maximum values of Lc and La are 3.54 nm and 2.22 nm, respectively. This is because, under elevated pressure and high-temperature conditions, SBS underwent chain scission, releasing a more significant number of methyl and other free radicals that interacted with the coal tar constituents. As a result, it reduced the affinity density of heavy coal tar molecules, enhanced fluidity, promoted the stacking of condensed aromatic hydrocarbons and increased the content of soluble carbonaceous mesophase, ultimately leading to a more favorable alignment of the carbonaceous mesophase.

This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks. By integrating the Louvain community detection algorithm into our GNN frameworks, we consistently enhanced the performance across all models tested. For example, integrating the Louvain model with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains were noted when the Louvain model was paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent increase in performance—reflected in our extensive experimentation on bipartite graphs of scientific collaborations and citations—highlights the synergistic potential of combining community detection with GNNs to overcome common link prediction challenges such as scalability and resolution limits. Our findings advocate for the integration of community structures as a significant step forward in the predictive accuracy of network science models, offering a comprehensive understanding of scientific collaboration patterns through the lens of advanced machine learning techniques.