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Abstract Background Head and neck squamous cell carcinoma(HNSCC) is the sixth most common malignancy worldwide, with more than 890,000 new cases and 450,000 deaths annually. Its major risk factors include smoking, alcohol abuse, aging, and poor oral hygiene. Due to the lack of early and effective detection and screening methods, many patients are diagnosed at advanced stages with a five-year survival rate of less than 50%. In this study, we deeply explored the expression of Aging-related genes(ARGs) in HNSCC and analyzed their prognostic significance using single-cell sequencing and spatial transcriptomics analysis. This research aims to provide new theoretical support and directions for personalized treatment. Annually, more than 890,000 new cases of head and neck squamous cell carcinoma (HNSCC) are diagnosed globally, leading to 450,000 deaths, making it the sixth most common malignancy worldwide. The primary risk factors for HNSCC include smoking, alcohol abuse, aging, and poor oral hygiene. Many patients are diagnosed at advanced stages due to the absence of early and effective detection and screening methods, resulting in a five-year survival rate of less than 50%. In this research, single cell sequencing and spatial transcriptome analysis were used to investigate the expression of Aging-related genes (ARGs) in HNSCC and to analyse their prognostic significance. This research aims to provide new theoretical support and directions for personalized treatment. Methods In this study, we investigated the association between HNSCC and AGRs by utilizing the GSE139324 series in the GEO database alongside the TCGA database, combined with single-cell sequencing and spatial transcriptomics analysis. The data were analyzed using Seurat and tSNE tools to reveal intercellular communication networks. For the spatial transcriptome data, SCTransform and RunPCA were applied to examine the metabolic activities of the cells. Gene expression differences were determined through spacerxr and RCTD tools, while the limma package was employed to identify differentially expressed genes and to predict recurrence rates using Cox regression analysis and column line plots. These findings underscore the potential importance of molecular classification, prognostic assessment, and personalized treatment of HNSCC. Results This study utilized HNSCC single-cell sequencing data to highlight the significance of ARGs in the onset and prognosis of HNSCC. It revealed that the proportion of monocytes and macrophages increased, while the proportion of B cells decreased. Notably, high expression of the APOE gene in monocytes was closely associated with patient prognosis. Additionally, a Cox regression model was developed based on GSTP1 and age to provide personalized prediction tools for clinical use in predicting patient survival. Conclusions We utilized single-cell sequencing and spatial transcriptomics to explore the cellular characteristics of HNSCC and its interaction with the tumor microenvironment. Our findings reveal that HNSCC tissues show increased mononuclear cells and demonstrate enhanced activity in ARGs, thereby advancing our understanding of HNSCC development mechanisms.

BackgroundAcute myeloid leukemia (AML) is a hematologic tumor with poor prognosis and significant clinical heterogeneity. By integrating transcriptomic data, single-cell RNA sequencing data and independently collected RNA sequencing data this study aims to identify key genes in AML and establish a prognostic assessment model to improve the accuracy of prognostic prediction.Materials and methodsWe analyzed RNA-seq data from AML patients and combined it with single-cell RNA sequencing data to identify genes associated with AML prognosis. Key genes were screened by bioinformatics methods, and a prognostic assessment model was established based on these genes to validate their accuracy.ResultsThe study identified eight key genes significantly associated with AML prognosis: SPATS2L, SPINK2, AREG, CLEC11A, HGF, IRF8, ARHGAP5, and CD34. The prognostic model constructed on the basis of these genes effectively differentiated between high-risk and low-risk patients and revealed differences in immune function and metabolic pathways of AML cells.ConclusionThis study provides a new approach to AML prognostic assessment and reveals the role of key genes in AML. These genes may become new biomarkers and therapeutic targets that can help improve prognostic prediction and personalized treatment of AML.

BackgroundTryptophan Metabolism-associated Genes (TMGs), such as ECHS1 and ALDH2, are crucial in cancer progression through immunosuppressive mechanisms, particularly in Gastric Cancer (GC). This study explores their effects on the Tumor Microenvironment (TME). Additionally, it examines their potential as novel immunotherapy targets.MethodsWe utilized single-cell and bulk transcriptomic technologies to analyze the heterogeneity of GC. Non-negative Matrix Factorization (NMF) clustering identified key TMGs, and extensive RNA-seq analyses were performed to pinpoint prognostic genes and potential immunotherapy targets. Furthermore, through PCR analyses we found that ECHS1 and ALDH2 gene expression plays a regulatory role in the migration, invasion and inflammatory factor in AGS and SNU-1 cell lines. The interference effect of si-ECHS1 and ad-ALDH2 was validated using cell scratch assay in AGS and SNU-1 cell line.ResultsWe observed a statistically significant correlation between ECHS1 and ALDH2 expression and increased TME heterogeneity. Our findings also revealed that ECHS1 down-regulation and ALDH2 up-regulation contribute to reduced TME heterogeneity, decreased inflammation, and inhibited AGS and SNU-1 tumor cells migration and proliferation. GSVA enrichment analysis highlighted the NF-kappa B(NF-κB) signaling pathway as specifically regulated by TMGs. Furthermore,ECHS1 and ALDH2 modulated CD8+ and CD4+ T cell activities, impacting GC progression. In vitro experiments further solidified our conclusions by showcasing the inhibitory effects of Si-ECHS1 and ad-ALDH2 on the invasive and proliferative capabilities of AGS and SNU-1 cells. Moreover, Si-ECHS1 and ad-ALDH2 gene expression effectively reduced the expression of inflammatory factors IL-10,IL-7,CXCL8 and IL-6, leading to a remarkable alleviation of chronic inflammation and the heterogeneous nature of the TME.ConclusionThis research highlights the importance of ECHS1 and ALDH2 in GC progression and immune modulation, suggesting that targeted therapies focusing on these genes offer promising avenues for personalized immunotherapy in GC. These findings hold potential for improving patient survival and quality of life. Future studies on the NF-κB signaling pathway’s role in this context are warranted to further elucidate the mechanisms underlying TMG-mediated immune modulation in GC.

BackgroundColorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development.MethodsWe analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics.ResultsMR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene.ConclusionThis study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

Abstract With the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack samples, such as zero-day, leaves a short response time for security protectors, making it substantially more difficult to protect industrial control systems from new types of malicious attacks. Traditional supervised intrusion detection models rely on a large number of samples for training and their performance needs to be improved. Therefore, there is an urgent need for few-shot intrusion detection. Aiming at the above problems, this paper proposes a detection model based on a meta-learning framework, which aims to effectively improve the accuracy and real-time performance of intrusion detection, and designs a meta-learning intrusion detection model containing a sample generation module, a feature mapping module and a feature metric module. Among them, the sample generation module introduces the residual block into the Natural GAN and proposes a new method to generate high-quality antagonistic samples—Res-Natural GAN, which is used to enhance the antagonism of the generated samples and the feature mining degree, to improve the accuracy of malicious traffic detection; the feature mapping module proposes a new attention mechanism, the multi-head fast attention mechanism, which is applied to the encoder structure of the transformer and combined with a parameter optimization algorithm based on particle swarm mutation to shorten the mapping time and improve the real-time performance of the model while mapping the features effectively; the feature metric module proposes a prototype structure based on a prototype storage update algorithm and combines it with a prototype network to achieve correct classification by measuring the Euclidean distance between the detected samples and the class of prototypes, and to shorten the inference time while ensuring the detection accuracy; finally, the three modules are combined to form a real-time meta-learning intrusion detection model. To evaluate the proposed model, five different types of experiments are conducted on multiple public datasets. The experimental results show that the model has higher detection accuracy than the traditional model for both few-shot and zero-shot malicious attacks, and is not only applicable to 5G-enabled industrial internet, but also generalized to different network environments and attack types.

BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies.MethodsComprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments.ResultsCompared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control.ConclusionThis study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

PurposeIn recent years, traditional Chinese medicine has received widespread attention in the field of cancer pain treatment. This meta-analysis is the first to evaluate the effectiveness and safety of acupuncture point stimulation in the treatment of stomach cancer pain.MethodsFor this systematic review and meta-analysis, we searched PubMed, Web of Science, Cochrane Library, Embase, WANFANG, China National Knowledge Infrastructure (CNKI), and Chinese Journal of Science and Technology (VIP) databases as well as forward and backward citations to studies published between database creation to July 27, 2023. All randomized controlled trials (RCTs) on acupuncture point stimulation for the treatment of patients with stomach cancer pain were included without language restrictions. We assessed all outcome indicators of the included trials. The evidence from the randomized controlled trials was synthesized as the standardized mean difference (SMD) of symptom change. The quality of the evidence was assessed using the Cochrane Risk of Bias tool. This study is registered on PROSPERO under the number CRD42023457341.ResultsEleven RCTs were included. The study included 768 patients, split into 2 groups: acupuncture point stimulation treatment group (n = 406), medication control group (n = 372). The results showed that treatment was more effective in the acupuncture point stimulation treatment group than in the medication control group (efficacy rate, RR = 1.63, 95% CI 1.37 to 1.94, p

With the formation and popularity of the Internet of Things(IoT), the difficulty of protecting IoT infrastructure and smart devices from a few-shot of ever-changing malicious attacks has increased significantly. Traditional intrusion detection models in static mode cannot defend against intelligent attacks that change in real time and are good at reconnaissance, and it is difficult to achieve effective detection of few-shot attacks. Therefore, to solve the above problems, this paper proposes a variable few-shot intrusion detection model GDE Model for IoT, which contains a data processing module consisting of an improved Gramian Angular Fields(GAF), a data augmentation module consisting of an improved Denoising Diffusion Probabilistic Models(DDPM), and an image classification module consisting of a variable network ETNet V2 obtained from a neural architecture search based on efficient net v2. The one-dimensional network traffic data collected from a real network environment is converted into two-dimensional images and fed into the above model for learning and training, so as to construct a variable intrusion detection model that combines accuracy and inference speed. To evaluate the work of this paper, different categories of evaluation metrics were selected and experiments were conducted on each module and the overall model separately. The experimental results show that the proposed intrusion detection model achieves variable functionality with an accuracy of up to 99.20% for few-shot detection and an average inference time as low as 23ms, which can be used not only for IoT but also for different network environments and attack types.

The condensation of moist air is a common process in industry and increase its heat transfer coefficient is of great importance for improving energy efficiency. This study used 3-D finned tube to enhance moist air condensation, and the condensation process was calculated through numerical method aligned with Diffusion Layer model and Eulerian Wall Film model. The total heat transfer, sensible heat transfer and latent heat transfer during the condensation process were discussed. The results indicated that 3-D finned tube increased heat transfer amount and the condensation was mainly on fin surface. Besides, the influence of 3-D fin height was also obtained. The results indicated that the increase of fin height in circumferential direction can increase heat transfer in the tail of tube, and sensible heat transfer process was more sensitive to the area increment.

Three dimensional pin fin tube is an effective heat transfer element. It has been widely used in industry process. However, condensation heat transfer characteristic of moist air outside it has not been experimentally studied. The wettability of tube surface has an important effect on this process. In this study, experiments were carried out, in which moist air condensing outside three dimensional pin fin tube with different wettability. Smooth tube is also tested to make comparison. Effects of steam mole fraction, moist air temperature, and wall subcooling are analysed in detail. Super-hydrophobic surface is obtained using oxidation and self-assembling monolayer method. The results show that heat transfer coefficient increases with the increase of steam mole fraction, wall subcooling and the decrease of moist air temperature. Heat transfer coefficient ranges from 55 to 2,035 W/m2K within the study of this paper, and hydrophilic three dimensional finned tube can achieve 2 times heat transfer coefficient compared with smooth tube and superhydrophobic three dimensional pin fin tube.

Calcium bismuth niobate (CBN) ceramic, as a core element of high-temperature piezoelectric sensors, has attracted widespread attention due to its high Curie temperature within the class of Aurivillius compounds. However, CBN usually faces two shortcomings: poor piezoelectric constant and low resistivity. In this work, CBN-based ceramics with donor–acceptor ions (W/Co) co-substituted at B-site were prepared by solid-state reaction method, and structure–property relationship of ceramics was studied in detail. Co-substitution of W/Co ions effectively improved the electrical property and hardness of CBN ceramics. CaBi2Nb[Formula: see text](W[Formula: see text]Co[Formula: see text]O9 exhibits enhanced electrical and mechanical properties including high resistivity of [Formula: see text]cm at 500°C, piezoelectric constant of [Formula: see text] pC/N and hardness value of [Formula: see text][Formula: see text]GPa. These values are two orders of magnitude, over two times, and 1.36 times higher than those of pure CBN ceramic, respectively. This work provides a reference for exploring other bismuth-layered structural ceramics.

This is an article in the field of earth sciences. The newly discovered Maoping graphite deposit is a super-large crystalline graphite deposit that is located in the middle-northern section of the Kang-Dian fault-uplift belt on the western margin of the Upper Yangtze paleo-continent, and the research on the source of mineralization and its mineralization age is relatively weak. The orebodies occur in the epimetamorphic rocks of Mesoproterozoic Tianbaoshan Formation. In this paper, the geochemical characteristics of major ores, trace amounts and rare earth elements were mainly studied. The results show that the original rock of the ore-bearing rock layer is parametamorphic rock, a set of carbonaceous quartz-bearing siltstone, which is deposited in an oxygen-deficient environment. The characteristics of trace elements indicate that the original rock of the ore body is formed by the deposition of offshore terrigenous clasts. The ores show sedimentary characteristics of coastal tidal flat facies, with an average ΣREE of 158 g/t, average δ Ce value of 0.63 (weak negative anomaly), and an average δ Eu value of 0.69 (negative abnormaly). The graphite ore body has a δ13C value of -28.35‰～-29.08‰, with an average of -28.59‰, indicating that the mineralized carbon is dominated by organic carbon. The LA-MC-ICP-MS Zircon U-Pb of amphibole zircon from Xijie hornblende diorite Zircon is (823.4±3.6) Ma, which is later than the formation era of volcanic rocks of the Tianbaoshan Formation (954 Ma), representing the time of superposition metamorphism of graphite. In summary,the genetic type of the ore deposit is sedimentary- metamorphism and its metamorphism may include the regional dynamic metamorphism of Jinning period that is superimposed by contact metamorphism.

Since ancient times, infectious diseases have been a major source of harm to human health. Therefore, scientists have established many mathematical models in the history of fighting infectious diseases to study the law of infection and then analyzed the practicability and effectiveness of various prevention and control measures, providing a scientific basis for human prevention and research of infectious diseases. However, due to the great differences in the transmission mechanisms and modes of many diseases, there are many kinds of infectious disease dynamic models, which make the research more and more difficult. With the continuous progress of infectious disease research technology, people have adopted more ways to prevent and interfere with the derivation and spread of infectious disease, which will make the state of infectious disease system change in an instant. The mutation of this state can be described more scientifically and reasonably by the mathematical impulse dynamic system, which makes the research more practical. Based on this, a time-delay differential system model of infectious disease under impulse effect was established by means of impulse differential equation theory. A class of periodic boundary value problems for impulsive integrodifferential equations of mixed type with integral boundary conditions was studied. The existence of periodic solutions of these equations was obtained by using the comparison theorem, upper and lower solution methods, and the monotone iteration technique. Finally, combined with the practical application, the established time-delay differential system model was applied to the prediction of the stability and persistence of the infectious disease dynamic system, and the correctness of the conclusion was further verified. This study provides some reference for the prevention and treatment of infectious diseases.

The Tongde Graphite Deposit in Panzhihua is located in the accretionary zone on the western margin of the Yangtze plate. In this paper, the whole rock geochemical analysis, C isotope determination and Re-Os isotope dating of the ore in Tongde graphite mining area are carried out, the original rock of the ore is restored, and the metallogenic environment is discussed. Research shows that the ore SiO2 is 55.65~61.68%, SiO2/Al2O3 ratio is 4.59~5.42, and Ni/Co ratio is 6.23~12.88. It is rich in Ba, Rb, Sr and other large ion lithophile elements and Nb, Zr, Hf, Th, U and other high field strength elements. Total rare earth elements of ore ΣREE is 149.13×10-6～195.37×10-6, with weak Ce negative anomaly and Eu negative anomaly, representing the anoxic marine sedimentary environment. The Carbon isotope δ13CV-PDB in ore is -25.0‰~-23.5‰, which is within the range of biogenic organic carbon, indicating that the ore-forming carbon mainly comes from organic matter. The Re-Os isotopic age of graphite is 983 ± 72 Ma (MSWD=1.7), belonging to the early Neoproterozoic, earlier than the intrusion age of magmatic rocks around Tongde. The content of re is between 27.66×10-9～79.81×10-9, the contents of ordinary Os and 187Os are 0.52 ×10-9～2.16×10-9, 0.28 × 10-9～0.83×10-9,respectively ,it is significantly enriched relative to the abundance of Re and Os in the crust. The 187re/188os ratio is 122.9~350.5, and the initial Os isotope ratio 187Os/188Os=0.31±0.21. Combined with the field geological survey, the original rock of ore and ore bearing mica quartz schist in Tongde area is restored, and it is concluded that the original rock is sedimentary rock.

Background: In recent years, identifying players with injury risk through physical fitness assessment has become a hot topic in sports science research. Although practitioners have conducted many studies on the relationship between physical fitness and the likelihood of injury, the relationship between the two remains indeterminate. Consequently, this study utilized machine learning to preliminary investigate the relationship between individual physical fitness tests and injury risk, aiming to identify whether patterns of physical fitness change have an impact on injury risk.Methods: This study conducted a retrospective analysis by extracting the records of 17 young female basketball players from the sport-specific physical fitness monitoring and injury registration database in Fujian Province. Sports-specific physical fitness tests included physical performance, physiological, biochemical, and subjective perceived responses. The data for each player was standardized individually using Z-scores. Synthetic minority over-sampling techniques and edited nearest neighbor algorithms were used to sample the training set to address the negative impact of class imbalance on model performance. Feature extraction was performed on the dataset using linear discriminant analysis, and the prediction model was constructed using the cost-sensitive neural network.Results: The 10 replicate 5-fold stratified cross-validation showed that the lower limb non-contact injury prediction model based on the cost-sensitive neural network had achieved good discrimination and calibration (average Precision: 0.6360; average Recall: 0.8700; average F2-Score: 0.7980; average AUC: 0.8590; average Brier-score: 0.1020), which could be well applied in training practice. According to the attribution analysis, agility and speed were important physical attributes that affect youth female basketball players’ non-contact lower limb injury risk. Specifically, there was enhance in the performance of the 1-min double under, accompanied by an increase in urinary ketone and urinary blood levels following the agility test. The 3/4 basketball court sprint performance improved, while urinary protein and RPE levels decreased after the speed test.Conclusion: The sport-specific physical fitness change pattern can impact the lower limb non-contact injury risk of young female basketball players in Fujian Province, specifically in terms of agility and speed. These findings will provide valuable insights for planning athletes’ physical training programs, managing fatigue, and preventing injuries.

In the field of biological control, there are a large number of systems that gradually evolve over a certain period of time. However, due to some natural or human intervention behavior, the system state will be subjected to some relatively short time interference, so that the system state changes in an instant. This sudden change of state makes the system not simply described by continuous or discrete dynamical systems, but by means of impulse dynamical systems. In this paper, the multi-point boundary value problem of a class of second-order nonlinear impulsive integral differential equations is studied on the basis of impulsive differential equation theory. The main results of this kind of equation are obtained by using the fixed point theorem of strict set contraction operator. Under certain assumptions, the existence of the solution of the equation is proved by constructing the operator on the special cone. Finally, combined with the practical application, the theory was applied to the stability prediction of biological ecosystem, and the correctness of the conclusion was verified.

The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.

Pest management has long been a critical aspect of crop protection. Insect behavior is of great research value as an important indicator for assessing insect characteristics. Currently, insect behavior research is increasingly based on the quantification of behavior. Traditional manual observation and analysis methods can no longer meet the requirements of data volume and observation time. In this paper, we propose a method based on region localization combined with an improved 3D convolutional neural network for six grooming behaviors of Bactrocera minax: head grooming, foreleg grooming, fore-mid leg grooming, mid-hind leg grooming, hind leg grooming, and wing grooming. The overall recognition accuracy reached 93.46%. We compared the results obtained from the detection model with manual observations; the average difference was about 12%. This shows that the model reached a level close to manual observation. Additionally, recognition time using this method is only one-third of that required for manual observation, making it suitable for real-time detection needs. Experimental data demonstrate that this method effectively eliminates the interference caused by the walking behavior of Bactrocera minax, enabling efficient and automated detection of grooming behavior. Consequently, it offers a convenient means of studying pest characteristics in the field of crop protection.

Glass products are important evidence of early East–West cultural exchanges. Ancient glass in China mostly consisted of lead glass, and potassium glass is widely believed to be imported abroad. In order to figure out the origin of glass artefacts, it is crucial to define the type of glass products accurately. In contemporary research on the chemical composition of ancient glass products, potassium glass is separated from lead glass primarily by the weight ratio of oxides or the proportion of lead-containing compounds. This approach can be excessively subjective and prone to mistakes while calculating the mass fraction of compounds containing potassium. So, it is better to find out the link between the proportion of glass’s chemical composition and its classifications during the weathering process of the glass products, to develop an effective classification model using machine learning techniques. In this research, we suggest employing the slime mould approach to optimise the parameters of a support vector machine and examine a 69-group glass chemical composition dataset. In addition, the results of the proposed algorithm are compared to those of commonly used classification models: decision trees (DT), random forests (RF), support vector machines (SVM), and support vector machines optimised by genetic algorithms (GA-SVM). The results of this research indicated that the support vector machine method with the sticky slime mould algorithm strategy is the most effective. On the training set, 100% accuracy was attained, while on the test set, 97.50% accuracy was attained in this research. The research results demonstrate that the support vector machine algorithm combining the slime mould algorithm strategy is capable of providing a trustworthy classification reference for future glass artefacts.

In-air signatures are promising applications that have been investigated extensively in the past decades; an in-air signature involves gathering datasets through portable devices, such as smartwatches. During the signing process, individuals wear smartwatches on their wrists and sign their names in the air. The dataset we used in this study collected in-air signatures from 22 participants, resulting in a total of 440 smartwatch in-air signature signals. The dynamic time warping (DTW) algorithm was applied to verify the usability of the dataset. This paper analyzes and compares the performances of multiple convolutional neural networks (CNN) and the transformer using median-sized smartwatch in-air signatures. For the four CNN models, the in-air digital signature data were first transformed into visible three-dimensional static signatures. For the transformer, the nine-dimensional in-air signature signals were concatenated and downsampled to the desired length and then fed into the transformer for time sequence signal multi-classification. The performance of each model on the smartwatch in-air signature dataset was thoroughly tested with respect to 10 optimizers and different learning rates. The best testing performance score in our experiment was 99.8514% with ResNet by using the Adagrad optimizer under a 1×10−4 learning rate.

To explore the effect and mechanism of bivalent ion doping on yttrium iron garnet (YIG), Zn-YIG (Zn-doped YIG) nanoparticles with a size of 60~70 nm were prepared by the sol-gel method. It was proven that Zn ion doping resulted in lattice expansion and internal stress due to crystallite size shrinkage. A Raman spectroscopic analysis proved the influence of Zn doping on the crystal structure and peak intensity by analyzing Raman vibration modes. The characteristics and chemical mechanism of mass loss and phase evolution in each temperature region were explored through TG-DSC measurements. Moreover, it was revealed that the antiferromagnetic coupling, pinning mechanisms and particle aggregation lead to coercivity, exhibiting different variation trends. A saturation magnetization (Ms) curve variation mechanism was further revealed, which was due to the thermal effects, super-exchange effect, and coupling effect between sub-lattices. Meanwhile, the influence of the thermal effect on Ms and its mechanism were explored by spin theory, and it was proven that it was mainly caused by the random arrangement of magnetic moments and thermal vibration. These results provide theoretical support for the wider application of YIG devices in microwave and high-temperature fields.

Background: Pseudoexstrophy is a rare variant of the exstrophy-epispadias complex, which comprises musculoskeletal defects associated with bladder exstrophy without any urinary tract defects. However, only a few pregnancy complications have been reported in patients with pseudoexstrophy.Case Presentation: This report presents the case of a woman with pseudoexstrophy, who survived recurrent placental abruption during the second trimester of her pregnancy. The patient presented with a bicornuate uterus and survived placental abruption twice, which may have resulted from the malformation of the uterus. Placental abruption occurred at 20 weeks during her first pregnancy, and because she was already in labor, uterine contraction was augmented until vaginal delivery was achieved. The second pregnancy, however, could not be terminated quickly enough; therefore, a cesarean section was performed to save the patient's life.Conclusions: Our study makes a significant contribution to the literature although pregnancy complications have been reported in patients with pseudoexstrophy. Our findings show that in female patients with pseudoexstrophy who are or wish to become pregnant, detailed imaging studies must be performed to identify any deformities of the pelvis or reproductive organs, in order to make a pregnancy-related risk assessment. Our experience also indicates that if surgery is inevitable, the obstetrician must be more careful when entering the abdominal cavity during the surgery to avoid secondary injury. Furthermore, the peritoneum and fascia layers must be sutured more firmly when closing the abdomen to avoid an abdominal wall hernia, because of the lack of abdominal muscle and fat tissue in such patients.

We theoretically study the multiple sharp Fano resonances produced by the near-field coupling between the multipolar narrow plasmonic whispering-gallery modes (WGMs) and the broad-sphere plasmon modes supported by a deep-subwavelength spherical hyperbolic metamaterial (HMM) cavity, which is constructed by five alternating silver/dielectric layers wrapping a dielectric nanosphere core. We find that the linewidths of WGMs-induced Fano resonances are as narrow as 7.4–21.7 nm due to the highly localized feature of the electric fields. The near-field coupling strength determined by the resonant energy difference between WGMs and corresponding sphere plasmon modes can lead to the formation of the symmetric-, asymmetric-, and typical Fano lineshapes in the far-field extinction efficiency spectrum. The deep-subwavelength feature of the proposed HMM cavity is verified by the large ratio (~5.5) of the longest resonant wavelength of WGM1,1 (1202.1 nm) to the cavity size (diameter: 220 nm). In addition, the resonant wavelengths of multiple Fano resonances can be easily tuned by adjusting the structural/material parameters (the dielectric core radius, the thickness and refractive index of the dielectric layers) of the HMM cavity. The narrow linewidth, multiple, and tunability of the observed Fano resonances, together with the deep-subwavelength feature of the proposed HMM cavity may create potential applications in nanosensors and nanolasers.

Hydrogel-enabled skin bioelectronics that can continuously monitor health for extended periods is crucial for early disease detection and treatment. However, it is challenging to engineer ultrathin gas-permeable hydrogel sensors that can self-adhere to the human skin for long-term daily use (>1 week). Here, we present a ~10-micrometer-thick polyurethane nanomesh-reinforced gas-permeable hydrogel sensor that can self-adhere to the human skin for continuous and high-quality electrophysiological monitoring for 8 days under daily life conditions. This research involves two key steps: (i) material design by gelatin-based thermal-dependent phase change hydrogels and (ii) robust thinness geometry achieved through nanomesh reinforcement. The resulting ultrathin hydrogels exhibit a thickness of ~10 micrometers with superior mechanical robustness, high skin adhesion, gas permeability, and anti-drying performance. To highlight the potential applications in early disease detection and treatment that leverage the collective features, we demonstrate the use of ultrathin gas-permeable hydrogels for long-term, continuous high-precision electrophysiological monitoring under daily life conditions up to 8 days. [ABSTRACT FROM AUTHOR]