Search

Abstract To examine the clinical effect of different conservative therapies (Glisson traction, soft and rigid cervical collar) in the treatment of children with acute atlantoaxial rotatory subluxation. One hundred and forty-four children with acute atlantoaxial rotatory subluxation treated conservatively at our hospital from June 2017 to June 2022 were retrospectively analyzed. The children were divided into three groups consisting of patients treated with Glisson traction (n = 37), patients treated with soft cervical collar (n = 55), and patients treated with rigid cervical collar (n = 52). Clinical and functional results were compared among the three groups. Success outcomes were achieved at the end of treatment in the Glisson traction group (94.59%, 35/37), soft cervical collar group (83.64%, 46/55), and rigid cervical collar group (92.31%, 48/52). There was no significant difference between the success rates among the three groups (P > 0.05). At the last follow-up, the overall scores based on our scoring scale for the Glisson traction, soft cervical collar and rigid cervical collar groups were 95.95 ± 6.11 (range: 75–100), 94.64 ± 6.30 (range: 75–100) and 95.00 ± 6.02 (range: 70–100), respectively. There was no significant difference in the overall scores among the three groups (P > 0.05). All three conservative therapies for the treatment of acute atlantoaxial rotatory subluxation can attain a good clinical outcome. Treatment using a cervical collar should be given priority as it does not require the child to be hospitalized. Rigid cervical collar provides good immobilization and can possibly lead to better clinical outcomes compared to a soft cervical collar.

Abstract Purpose This study investigated the clinical and functional outcomes of children with distal both-bone forearm fractures treated by fixation of the radius only compared to fixation of both the radius and ulna. Methods A total of 71 patients from two centres with distal both-bone forearm fractures (30 in the ulna-yes group, 41 in the ulna-no group) who underwent closed reduction and percutaneous pinning treatment were retrospectively analysed. Operation duration, number of fluoroscopic exposures, loss of reduction rate and angulation based on radiographic assessment were compared between the two groups. Evaluation of wrist function including Gartland-Werley Score and Mayo Wrist Score were compared at the last follow-up. Results Ulna angulation upon bone healing on the posteroanterior and the lateral plane of ulna-no group (6.11 ± 1.56°; 6.51 ± 1.69°) was significantly greater than that of ulna-yes group (4.49 ± 1.30°; 5.05 ± 2.18°) (p 0.05). At last follow-up, no significant difference was found between the Gartland-Werley Scores of the ulna-yes group (1.83 ± 3.25, range: 0–16) and ulna-no group (1.85 ± 2.72, range: 0-11.5) (p > 0.05). No significant difference was found between the Mayo Wrist Scores of the ulna-yes group (92.60 ± 6.20) and ulna-no group (92.15 ± 7.58) (p > 0.05). Conclusions For distal both-bone forearm fractures in children, fixation of only the radius appears to be a viable method with equivalent clinical outcomes compared to fixation of both the radius and ulna.

BackgroundTraditional observational studies exploring the association between air pollution and infections have been limited by small sample sizes and potential confounding factors. To address these limitations, we applied Mendelian randomization (MR) to investigate the potential causal relationships between particulate matter (PM2.5, PM2.5–10, and PM10), nitrogen dioxide, and nitrogen oxide and the risks of infections.MethodsSingle nucleotide polymorphisms (SNPs) related to air pollution were selected from the genome-wide association study (GWAS) of the UK Biobank. Publicly available summary data for infections were obtained from the FinnGen Biobank and the COVID-19 Host Genetics Initiative. The inverse variance weighted (IVW) meta-analysis was used as the primary method for obtaining the Mendelian randomization (MR) estimates. Complementary analyses were performed using the weighted median method, MR-Egger method, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test.ResultsThe fixed-effect IVW estimate showed that PM2.5, PM2.5–10 and Nitrogen oxides were suggestively associated with COVID-19 [for PM2.5: IVW (fe): OR 3.573(1.218,5.288), PIVW(fe) = 0.021; for PM2.5–10: IVW (fe): OR 2.940(1.385,6.239), PIVW(fe) = 0.005; for Nitrogen oxides, IVW (fe): OR 1.898(1.318,2.472), PIVW(fe) = 0.010]. PM2.5, PM2.5–10, PM10, and Nitrogen oxides were suggestively associated with bacterial pneumonia [for PM2.5: IVW(fe): OR 1.720 (1.007, 2.937), PIVW(fe) = 0.047; for PM2.5–10: IVW(fe): OR 1.752 (1.111, 2.767), P IVW(fe) = 0.016; for PM10: IVW(fe): OR 2.097 (1.045, 4.208), PIVW(fe) = 0.037; for Nitrogen oxides, IVW(fe): OR 3.907 (1.209, 5.987), PIVW(fe) = 0.023]. Furthermore, Nitrogen dioxide was suggestively associated with the risk of acute upper respiratory infections, while all air pollution were not associated with intestinal infections.ConclusionsOur results support a role of related air pollution in the Corona Virus Disease 2019, bacterial pneumonia and acute upper respiratory infections. More work is need for policy formulation to reduce the air pollution and the emission of toxic and of harmful gas.

To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of Object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

Abstract An integrity monitoring of integrated navigation in the presence of slowly growing errors (SGEs) is proposed. SGEs with gradual growth over time are the worst category of latent failures. Tightly coupled inertial navigation system (INS)/Global Navigation Satellite System integration is presented by taking INS errors, bias and drift of receiver clock, baro‐altitude, pseudo‐range errors and ionospheric errors into consideration. Based on the RTCA performance standards DO‐229 and DO‐384, the simplified multi‐filters solution separation is employed for fault detection and calculation of corresponding outputs. For the fault exclusion part, the authors introduce a post pseudo‐range residual and measurement averaging method that is designed to quickly and effectively address SGEs and large measurement noises. Furthermore, the authors have designed a reconstruction logic for the filters that guarantees the continuity of the civil aviation system by considering the variation of visible satellites and historical information. The simulation results demonstrate that the proposed approach satisfies the performance requirements for civil aviation of En‐route and is able to correctly exclude faults and respond quickly in the presence of different levels of SGEs and measurement noises.

EL CVn–type systems represent a rare evolutionary stage in binary star evolution, providing ideal laboratories for investigating stable mass-transfer processes and the formation of extremely low-mass white dwarfs (ELM WDs). The Transiting Exoplanet Survey Satellite (TESS) has delivered an extensive collection of high-precision time-domain photometric data, which is invaluable for studying EL CVn binaries. In this study, we identified 29 EL CVn systems from the TESS eclipsing binary catalogs (sectors 1–65), 11 of which are newly discovered. These systems consist of smaller, hotter prehelium WDs and A/F main-sequence stars. The orbital periods of these binaries range from 0.64 to 2.5 days. Utilizing TESS light curves, Gaia distances, and multiband photometric data (e.g., Galaxy Evolution Explorer, Two Micron All Sky Survey, Wide-field Infrared Survey Explorer, SkyMapper), we modeled the light curves and spectral energy distributions to derive system parameters, including effective temperatures, masses, and radii. These systems were then compared with the WD mass–period relation and the evolutionary tracks of ELM WDs. The comparison reveals that these binaries are consistent with the expected mass–period relation for WDs and align well with the evolutionary tracks on the T _eff − $\mathrm{log}g$ diagram for ELM WDs. This result suggests that these EL CVn systems likely formed through stable mass-transfer processes. We provide a catalog of complete parameters for 29 EL CVn systems identified from the TESS survey. This catalog will serve as an essential resource for studying binary mass transfer, WD formation, and pulsation phenomena in EL CVn–type systems.

Currently, the world is rapidly advancing in terms of the construction of new power systems, and planning suitable distribution network planning while also considering renewable energy has become a hot issue. Based on this background, this paper studies the distribution network planning problem. Compared with the traditional planning method, the paper considers the impact of load growth and renewable energy penetration and uses the multi-stage planning method to build the planning model; at the same time, in the scenarios selection, the affinity propagation (AP) clustering algorithm is adopted, which can automatically obtain the number of clusters. Based on the proposed model, an IEEE 33-node is used for simulation. The simulation results show that, compared with the traditional static planning method, the total economic cost of the proposed method is reduced by 4.87% and the wind–solar curtailment rate is reduced by 59.01%; in addition, according to the proposed method, the impact of energy storage equipment and wind–solar permeability on the planning results is studied. It is found that, when considering energy storage, the amount of abandoned wind and light decreases by 22.35% and the total cost first decreases and then increases with the increase in wind–solar permeability, while the total economic cost reaches the minimum at about 40%. The impact of load growth rate on the planning results is also studied. Finally, the generalizability of the proposed method is investigated while using the IEEE 69-node system as an example.

Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.

Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value.

Study region: Southwestern China of Hubei, Hunan, Guangxi, Guangdong, Yunnan, Guizhou, Sichuan, and Chongqing. Study focus: Compound hot-dry extreme events (CHDEs) are escalating disproportionately worldwide, posing significant challenges across various sectors. Yet, their driving mechanisms and characteristics at a regional scale remain largely uncertain. This study used a copula-based approach to construct the daily Standardized Compound Hot-Dry Index (SCHDI) to discern the intensifying trends of hot-dry extremes from 1963 to 2022, especially under different karst landscapes in Southwestern China. New hydrological insights for the region: SCHDI variations show an escalation of hot-dry conditions across most areas, with a notable increase in extreme hot, dry, and hot-dry events. There was a consistent increase in the frequency, duration, severity, and intensity of CHDEs, with larger magnitudes observed in non-karst areas. However, karst regions exhibited a higher escalation rate in intensity, especially over the most recent three decades. Land areas affected by less severe CHDEs (e.g., abnormal: −0.31%/year) show decreasing trends over 1993–2022, while the most extreme category (e.g., extreme: 0.34%/year) has increased. The areas affected by CHDEs showed a greater increase in trend slope compared to hot or dry events alone. Additionally, these trends are further influenced by large-scale circulation patterns. Our study highlights the role of karst landscapes in escalating hot-dry extremes in Southwestern China Karst.

Highlights MPCF@VG@SiNDs/C, constructed by uniformly dispersing amorphous Si nanodots in carbon nanospheres that are welded on the wall of the macroporous carbon frameworks by vertical graphene, is synthesized and has achieved a few kilogram production per batch. Finite element imitation reveals that amorphous Si nanodots with high dispersity in carbon nanosphere can achieve ultra-low stress and strain values during lithiation. Unique low-strain property and fast-charging capability are achieved under industrial electrode conditions.

Abstract Background Clopidogrel resistance profoundly increases the risk of major cardiovascular events in coronary artery disease (CAD) patients. Here, we comprehensively analyse global m6A modification alterations in clopidogrel-resistant (CR) and non-CR patients. Methods After RNA isolation, the RNA transcriptome expression (lncRNA, circRNA, and mRNA) was analysed via RNA-seq, and m6A peaks were identified by MeRIP-seq. The altered m6A methylation sites on mRNAs, lncRNAs, and circRNAs were identified, and then, GO and KEGG pathway analyses were performed. Through joint analysis with RNA-seq and MeRIP-seq data, differentially expressed mRNAs harbouring differentially methylated sites were identified. The changes in m6A regulator levels and the abundance of differentially methylated sites were measured by RT-PCR. The identification of m6A-modified RNAs was verified by m6A-IP-qPCR. Results The expression of 2919 hypermethylated and 2519 hypomethylated mRNAs, 192 hypermethylated and 391 hypomethylated lncRNAs, and 375 hypermethylated and 546 hypomethylated circRNAs was shown to be altered in CR patients. The m6A peaks related to CR indicated lower mark density at the CDS region. Functional enrichment analysis revealed that inflammatory pathways and insulin signalling pathways might be involved in the pathological processes underlying CR. The expression of mRNAs (ST5, KDM6B, GLB1L2, and LSM14B), lncRNAs (MSTRG.13776.1 and ENST00000627981.1), and circRNAs (hsa_circ_0070675_CBC1, hsa-circRNA13011-5_CBC1, and hsa-circRNA6406-3_CBC1) was upregulated in CR patients, while the expression of mRNAs (RPS16 and CREG1), lncRNAs (MSTRG.9215.1), and circRNAs (hsa_circ_0082972_CBC1) was downregulated in CR patients. Moreover, m6A regulators (FTO, YTHDF3, and WTAP) were also differentially expressed. An additional combined analysis of gene expression and m6A peaks revealed that the expression of mRNAs (such as ST5, LYPD2, and RPS16 mRNAs) was significantly altered in the CR patients. Conclusion The expression of m6A regulators, the RNA transcriptome, and the m6A landscape was altered in CR patients. These findings reveal epitranscriptomic regulation in CR patients, which might be novel therapeutic targets in future.

Abstract Power quality disturbance (PQD) is an important problem affecting the safe and stable operation of power system. Traditional single modal methods not only have a large number of parameters, but also usually focus on only one type of feature, resulting in incomplete information about the extracted features, and it is difficult to identify complex and diverse PQD types in modern power systems. In this regard, this paper proposes a multi-modal parallel feature extraction and classification model. The model pays attention to both temporal and spatial features of PQD, which effectively improves classification accuracy. And a lightweight approach is adopted to reduce the number of parameters of the model. The model uses Long Short Term Memory Neural Network (LSTM) to extract the temporal features of one-dimensional temporal modes of PQD. At the same time, a lightweight residual network (LResNet) is designed to extract the spatial features of the two-dimensional image modality of PQD. Then, the two types of features are fused into multi-modal spatio-temporal features (MSTF). Finally, MSTF is input to a Support Vector Machine (SVM) for classification. Simulation results of 20 PQD signals show that the classification accuracy of the multi-modal model proposed in this paper reaches 99.94%, and the parameter quantity is only 0.08 MB. Compared with ResNet18, the accuracy of the proposed method has been improved by 2.55% and the number of parameters has been reduced by 99.25%.

Gravitational Waves (GWs) provide a unique way to explore our Universe. The ongoing ground-based detectors, e.g., LIGO, Virgo, and KAGRA, and the upcoming next-generation detectors, e.g., Cosmic Explorer and Einstein Telescope, as well as the future space-borne GW antennas, e.g., LISA, TianQin, and TaiJi, cover a wide range of GW frequencies from ∼10−4Hz to ∼103Hz and almost all types of compact objects in close orbits serve as the potential target sources for these GW detectors. The synergistic multi-band GW and EM observations would allow us to study fundamental physics from stars to cosmology. The formation of stellar GW sources has been extensively explored in recent years, and progress on physical processes in binary interaction has been made as well. Furthermore, some studies have shown that the progress in binary evolution may significantly affect the properties of the stellar GW sources. In this article, we review the formation channels of compact objects in close orbits and discuss their implications for GW observations.

Abstract Fe3O4 is widely investigated as an anode for ambient sodium‐ion batteries (SIBs), but its electrochemical properties in the wide operation‐temperature range have rarely been studied. Herein, the Fe3O4 nanoparticles, which are well encapsulated by carbon nanolayers, are uniformly dispersed on the graphene basal plane (named Fe3O4/C@G) to be used as the anode for SIBs. The existence of graphene can reduce the size of Fe3O4/C nanoparticles from 150 to 80 nm and greatly boost charge transport capability of electrode, resulting in an obvious size decrease of superparamagnetic Fe nanoparticles generated from the conversion reaction from 5 to 2 nm. Importantly, the ultra‐small superparamagnetic Fe nanoparticles (≈2 nm) can induce a strong spin‐polarized surface capacitance effect at operating temperatures ranging from −40 to 60 °C, thus achieving highly efficient Na‐ion transport and storage in a wide operation‐temperature range. Consequently, the Fe3O4/C@G anode shows high capacity, excellent fast‐charging capability, and cycling stability ranging from −40 to 60 °C in half/full cells. This work demonstrates the viability of Fe3O4 as anode for wide operation‐temperature SIBs and reveals that spin‐polarized surface capacitance effects can promote Na‐ion storage over a wide operation temperature range.

Tillage practices can greatly influence the temporal variation in soil erosion, deposition, and redistribution, and thus soil magnetic susceptibility (MS). In-depth investigation of the distribution characteristics of soil magnetic susceptibility (MS) is helpful to understand the dynamic process of soil redistribution and environmental changes. With the aid of the 137Cs and 210Pbex deposition chronologies, the objective of this study is to investigate the historical changing pattern of MS in a karst depression, and to further evaluate the effects of soil properties, tillage practice, and soil erosion on the temporal variation in soil MS. With collecting representative soil profile samples from cropland and undisturbed bamboo forest land in the karst depression, the MS, soil particle distribution, nutrients, radionuclides and heavy metals were measured. The results showed that mass-specific low-frequency MS (χlf) was positively correlated with percentage frequency-dependent MS (χfd%) (P

An initial orbit determination (IOD) solution from angles-only observations of a single short orbit arc is often required for applications such as tracklet association and fast reacquisition of a newly detected space object. Modern optical observations can collect tens or even hundreds of data points over a short arc, thus enabling a large number of IOD solutions to be determined when using an IOD algorithm of 3 lines of sight (3-LOSs), such as the Gooding algorithm. It is necessary but difficult to find an optimal solution from a solution pool, particularly in the case of too short arc (TSA). Another issue in using 3-LOSs IOD methods is the neglect of perturbation effects on the observations. That is, 3-LOSs IOD methods are developed in the 2-body frame, but the observations are perturbed. Thus, the IOD solutions may have additional errors if the observations are not corrected for perturbation effects. In this study, we investigate the distribution of the semi-major axis and eccentricity of IOD solutions in a pool and find that choosing the solution with the maximum kernel density in the distribution is a much better way to determine the final solution from the pool. We also propose a technique to correct J2 secular effects on observed angle data. We use the Gooding algorithm as the basic 3-LOSs IOD algorithm to demonstrate the effectiveness of the proposed techniques in improving the IOD accuracy in the cases of short-arc ground-based observations and space-based simulation data.

Resting-state electroencephalogram (EEG) microstate analysis resolves EEG signals into topographical maps representing discrete, sequential network activations. These maps can be used to identify patterns in EEGs that may be indicative of underlying neurological conditions. One such pattern is observed in EEGs of patients with Alzheimer's disease (AD), where a global microstate disorganization is evident. We initially investigated the classification efficacy of microstate parameters as markers for AD classification. Subsequently, we compared the classification efficacy of EEG conventional features to ascertain the superiority of microstate features. We extracted raw EEG data from a public, independent database, OpenNeuro EEG. The raw EEG was subjected to preprocessing and band-pass filtering to obtain five distinct frequency bands. The SVM classifier was used to input the microstate feature set to determine the one with the best classification effect as the main band. In order to verify the advantage of the microstate features, the AD group and the healthy control group were filtered for the main frequency bands respectively. Then the microstate feature set and the regular feature set were extracted. The two feature sets were input into four different conventional machine learning classifiers, namely SVM, KNN, RF, and LR, in order to avoid the classifiers as the dependent variable. And the comparison of the classification results of simply two feature sets as the dependent variable can be obtained. The results show that in the Alpha (8-13 Hz) sub-band, the microstate feature set as model input to SVM is optimal for the recognition of AD, with a classification accuracy of 99.22%. The Alpha band, as the main frequency band, the microstate feature set as model input to the four classifiers obtains an average classification accuracy of 98.61%, and the average classification accuracy obtained by the conventional EEG feature set as model is 91.19%. Based on four different classifiers, microstate parameters can be served as markers to effectively classify the EEG of AD patients. The microstate feature set outperforms the conventional EEG feature set after excluding the effect of classifiers.

Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection.

Abstract Background The purpose of this study was to investigate the clinical and functional outcomes of early versus delayed treatment of pediatric lateral condylar fractures of the humerus with a displacement greater than 2 mm. Methods Sixty-seven children treated surgically at our hospital from March 2016 to September 2021 for lateral condylar fracture of the humerus with displacement > 2 mm were retrospectively analyzed. The children were divided into two groups where early surgery consisted of patients being operated on within 24-h post-injury (n = 36) and delayed surgery consisted of children operated after 24-h post-injury (n = 31). Clinical and functional results were compared between the two groups. Results There were no significant differences between the two groups in terms of operation time, blood loss and incidences of perioperative complications. However, mean length of incision was significantly greater (P 2 mm in children. However, delayed open reduction and pinning may increase the incision length possibly due to increased edema.

A virtual-center-atom (VCA) structural descriptor was proposed to construct machine learning potentials which can be applied in studying alloy materials. The descriptor performs by describing the spatial features of atomic environments centered on a defined virtual atom within crystalline structure. Computational algorithms were developed to determine the position of virtual center atom. To verify the generality of this descriptor, crystal structures from both molecular dynamics trajectory and online accessible databases are utilized to check their machine learning (ML) performances. The challenge of the ML lies in increasing the transferability of the derived weight parameters to cover a broad range of structure distribution. Forces are found difficult to train the ML model under simple optimization methods. We thus suggest deep learning techniques for improving the applicability of the VCA potential. The descriptor is simple compared to other reported schemes and has promising applications in accelerating material discovery.

The protection of forests and the mitigation of pest damage to trees play a crucial role in mitigating the greenhouse effect. In this paper, we first establish a delayed differential equation model for Ips subelongatus Motschulsky-Larix spp., where the delay parameter represents the time required for trees to undergo curing. Second, we analyze the stability of the equilibrium of the model and derive the normal form of Hopf bifurcation using a multiple-time-scales method. Then, we analyze the stability and direction of Hopf bifurcating periodic solutions. Finally, we conduct simulations to analyze the changing trends in pest and tree populations. Additionally, we investigate the impact of altering the rate of artificial planting on the system and provide corresponding biological explanations.

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

Prefabricated installation, a pivotal study in the realm of contemporary construction practices, delves into the utilization of prefabrication within mechanical, electrical, and plumbing (MEP) systems. Despite its ascending prominence, the domain grapples with ambiguities in application pathways, uncertain developmental trajectories, and the absence of a holistic technical paradigm. This research endeavors to bridge these gaps by conducting a thorough and multidimensional investigation into the current landscape of prefabricated MEP installation initiatives. This study meticulously dissects the paradigm from five critical vantage points: historical evolution, standards and regulations, life cycle analysis, technological applications, and corporate implementation strategies. At present, there is still a lack of standards and specifications specifically for the field of assembled MEP installation. The analysis reveals a trend towards intelligent and sustainable installation practices in prefabricated MEP projects. The research predominantly focuses on the design, production, and installation stages. Notably, building information modeling (BIM) emerges as the most prominent technology, followed by the Internet of Things (IoT) and 3D laser scanning, with extended reality (XR) technologies gaining traction. Large, state-owned construction firms are spearheading innovative applications in this realm. In summary, this paper provides an overview and outlook for the development direction and the application of cutting-edge technologies in prefabricated MEP installation projects, with the aim of supporting the industry’s advancement.

In the era of multi-messenger astronomy, gamma-ray bursts (GRBs) with known redshifts, especially high-redshift GRBs, are a powerful tool for studying the structure and evolution of the early Universe. We review the background, the history, and the application of measurement methods of GRB redshifts in astronomy. Based on different observation targets, two measurement methods are mainly introduced. One is on GRB afterglow, the other is on GRB host galaxy. There are various processing methods belonging to measurement methods based on afterglow, including spectral measurement method of afterglow and afterglow spectral energy distribution fitting method with improved methods. There are also numerous measurement methods based on host galaxy, such as spectral measurement method of host galaxy, template matching method of host galaxy, some automatic spectroscopic redshift measurement methods, and machine learning methods. We subsequently introduce the principles, effects, and performance of these methods. We enumerate several detection and measurement instruments, which have been used in observation. The characteristics, advantages, disadvantages, and applicability of the GRB redshift measurement methods are summarized and analyzed. Furthermore, we provide a data set of 611 GRBs with measured redshift. The data set has been collected since 1997. Analysis and statistics are presented based on this data set. We summarize the characteristics of GRBs such as location, time, and accuracy. Finally, we introduce Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission dedicated to searching high redshift GRBs. We also introduce the application prospect of various redshift measurement methods in SVOM mission.

Abstract Single‐layered MoS2 is a promising anode material for lithium‐ion batteries (LIBs), sodium‐ion batteries (SIBs), and potassium‐ion batteries (PIBs) due to its high capacity and isotropic ion transport paths. However, the low intrinsic conductivity and easy‐agglomerated feature hamper its applications. Here, a charge‐driven interlayer expansion strategy that Co2+ replaces Mo4+ in the doping form to endow MoS2 layers with negative charges, thus inducing electrostatic repulsion, together with the insertion of gaseous groups, to drive interlayer expansion which once breaks the confinement of interlayer van der Waals force, single‐layered MoS2 is obtained and uniformly dispersed into carbon matrix arising from the transformation of carbonaceous gaseous groups under high vapor pressure, is proposed. Co atom doping helps enhance the intrinsic conductivity of single‐layered MoS2. Carbon matrix effectively prevents agglomeration of single‐layered MoS2. The doped Co atoms can be fully transformed into ultrasmall Co nanoparticles during conversion reaction, which enables strong spin‐polarized surface capacitance and thus significantly boosts ion transport and storage. Consequently, the prepared material delivers superb Li/Na/K‐ion storage performances, which are best in the reported MoS2‐based anodes. The proposed charge‐driven interlayer expansion strategy provides a novel perspective for preparing single‐layered MoS2, which shows huge potential for energy storage.

Abstract Voltage unbalance is a frequent problem in the power system, and using renewable energy power generation devices to achieve unbalance compensation is a promising solution compared to adding additional equipment. Most of the existing unbalance compensation schemes for inverters are implemented based on cascaded linear controllers, which have obvious disadvantages of complex structure and cumbersome parameter adjustment process. In order to develop a simpler unbalance compensation scheme, this paper first derives the equivalent sequence network (ESN) model of the unbalanced three‐phase power system, and it reveals the important role of transmission line impedance in the unbalance problem. Inspired by the ESN model, an unbalance compensation scheme based on negative sequence virtual impedance (NSVI) is determined. Then the NSVI is embedded in the inverter mathematical model, and the virtual model predictive control (VMPC) of the inverter to achieve unbalance compensation is proposed. Finally, the proposed VMPC method is validated by experiments, and a comparative analysis is carried out with linear control methods. The development process and validation results show that, compared with the unbalance compensation scheme based on cascaded linear controllers, the VMPC method proposed in this paper has a simpler structure and stable performance.

Abstract In the past few years, the increasing amount of space debris has triggered the demand for distributed surveillance systems. Long exposure time can effectively improve the target detection capability of the wide-area surveillance system. Problems that also cause difficulties in space-target detection include large amounts of data, countless star points, and discontinuous or nonlinear targets. In response to these problems, this paper proposes a high-precision space-target detection and tracking pipeline that aims to automatically detect debris data in space. First, a guided filter is used to effectively remove the stars and noise, then Hough transform is used to detect space debris, and finally Kalman filter is applied to track the space debris target. All experimental images are from Jilin Observatory, and the telescope is in star-tracking mode. Our method is practical and effective. The results show that the proposed automatic extraction channel of space debris can accurately detect and track space targets in a complex background.

Double helium white dwarfs (He WDs) are a type of gravitational wave source and are greatly important in studies of binary interaction, particularly in common envelope (CE) ejection physics. Most double He WDs with mass ratios of q ∼ 1 are formed through a particular channel. In this channel, one He WD is initially produced from a red giant (RG) with a degenerate core via stable Roche lobe overflow, and another He WD is formed from an RG with a degenerate core via CE ejection. They may have significant implications for binary evolution processes but have not received specific studies, especially for the CE phase. This paper adopts a semianalytic method and a detailed stellar evolution simulation to model the formation of double He WDs. We find that most double He WDs show mass ratios slightly greater than 1, and their orbital period–mass ratio relations are broadly consistent with observations. There is also a relation between the mass ratios and progenitor masses of the He WDs produced via CE ejection for double He WDs with determined WD masses. Based on this relation, the mass of the He WD progenitor can be inferred from the mass ratio. Then, the CE ejection efficiency can be constrained with the orbital period. In addition, we constrain the CE ejection efficiency for two double He WDs, J1005-2249 and WD0957-666. The results show that the CE ejection efficiencies increase with the WD progenitor masses.

Common envelope evolution (CEE) is vital for forming short-orbital-period compact binaries. It covers many objects, such as double compact merging binaries, Type Ia supernovae progenitors, binary pulsars, and X-ray binaries. Knowledge of the common envelope (CE) ejection efficiency still needs to be improved, though progress has been made recently. Short-orbital-period hot subdwarf B star (sdB) plus white dwarf (WD) binaries are the most straightforward samples with which to constrain CEE physics. We apply the known orbital period–WD mass relation to constrain the sdB progenitors of seven sdB+WD binaries with a known inclination angle. The average CE efficiency parameter is 0.32. This is consistent with previous studies. However, the CE efficiency need not be constant, but a function of the initial mass ratio, based on well-constrained sdB progenitor mass and evolutionary stage. Our results can be used as physical inputs for binary population synthesis simulations of related objects. A similar method can also be applied to study other short-orbital-period WD binaries.

The real-time light curve classification of transients is helpful in searching for rare transients. We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow’s architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve. We also use the TLW algorithm to classify ZTF real transients. The real-time classification results for ZTF transients show that the accuracy of the TLW algorithm is higher than the other six algorithms.

The hot subdwarf O/B stars (sdO/Bs) are known as extreme horizontal branch stars, which is of great importance in stellar evolution theory. The sdO/Bs are generally thought to have a helium-burning core and a thin hydrogen envelope ( M _env < 0.02 M _⊙ ). In the canonical binary evolution scenario, sdO/Bs are considered to be the stripped cores of red giants. However, such a scenario cannot explain the recently discovered sdO/B binary SMSS J1920, where the strong Ca H and K lines in the spectrum are found. It suggests that this binary likely originated from the recent ejection of the common envelope (CE). In this work, we propose a new formation channel of massive sdO/Bs, namely, sdO/Bs produced from a CE ejection process with an asymptotic giant branch (AGB) star (hereafter the AGB CE channel). We constructed the evolutionary model of sdO/Bs and successfully explained most of the important observed parameters of the sdO/B star in SMSS J1920, including the evolutionary age, sdO/B mass, effective temperature, surface gravity, and surface helium abundance. The minimum sdO/B mass produced from the AGB CE channel is about 0.48 M _⊙ . The evolutionary tracks in the $\mathrm{log}{T}_{\mathrm{eff}}\mbox{--}\mathrm{log}g$ plane may explain a fraction of the observational samples with high $\mathrm{log}{T}_{\mathrm{eff}}$ and low $\mathrm{log}g$ . Considering the wind mass loss of sdO/Bs, the model could produce helium-rich hot subdwarfs with $\mathrm{log}({n}_{\mathrm{He}}/{n}_{{\rm{H}}})\gtrsim -1$ .

Type Ia supernovae (SNe Ia) discovered at redshift z ≲ 2.5 are presumed to be produced from Population I/II stars. In this work, we investigate the production of SNe Ia from Population III binaries in the cosmological framework. We derive the SN Ia rate as a function of redshift in a theoretical context for the production of first-generation stars and evaluate the likelihood of their detection by the James Webb Space Telescope (JWST). Assuming the initial stellar mass function (IMF) favors low-mass stars, as from recent numerical simulations, we found that Population III stars may give rise to a considerable number of SNe Ia at high redshift, and Population III stars may even be the dominant SN Ia producer at z ≳ 6. In an optimistic scenario, we expect ∼1(2) SNe Ia from Population III stars at z ≈ 4(5) for a survey of area $300\,{\mathrm{arcmin}}^{2}$ during a 3 yr period with JWST. The same survey may record more than ∼400 SNe Ia at lower redshift ( z ≲ 2.5) but with only about one of them from Population III progenitors. There will be ∼six Population III SNe Ia in the same field of view at redshifts of 5–10. Observational constraints on SN Ia rates at the redshift range of 5–10 can place crucial constraints on the IMF of Population III stars.

Binary stars are responsible for many unusual astrophysical phenomena, including some important explosive cosmic events. The stability criteria for rapid mass transfer and common-envelope evolution are fundamental to binary star evolution. They determine the mass, mass ratio, and orbital distribution of systems such as X-ray binaries and merging gravitational-wave sources. We use our adiabatic mass-loss model to systematically survey metal-poor and solar-metallicity donor thresholds for dynamical timescale mass transfer. The critical mass ratios q _ad are systematically explored and the impact of metallicity and nonconservative mass transfer are studied. For metal-poor radiative-envelope donors, q _ad are smaller than those for solar-metallicity stars at the same evolutionary stage. However, q _ad do the opposite for convective-envelope donors. Nonconservative mass transfer significantly decreases q _ad for massive donors. This is because it matters how conservative mass transfer is during the thermal timescale phase immediately preceding a delayed dynamical mass transfer. We apply our theoretical predictions to observed high-mass X-ray binaries that have overfilled their Roche lobes and find a good agreement with their mass ratios. Our results can be applied to study individual binary objects or large samples of binary objects with binary population synthesis codes.

The Changchun Observatory of the National Astronomical Observatories, Chinese Academy of Sciences, and the Shanghai Astronomical Observatory are used to generate very short arc (VSA) angle observations of objects in low Earth orbit (LEO) and geostationary orbit (GEO) with their ground-based electrical–optical telescope arrays (EA), the Changchun EA and SAO FocusGEO, respectively. These observations are used in this paper. The range-searching (RS) algorithm for initial orbit determination (IOD) is improved through the multiple combinations of observations and the dynamic range-searching step length. Two different computation modes (the normal mode and the refining mode) of the IOD computation process are proposed. The geometrical method for the association is used. The IOD and association methods are extended to the real optical observations for both LEO and GEO objects. The results show that the IOD success rate of arcs from the LEO objects is about 91%, the error of the semimajor axis (SMA) of the initial orbital elements is less than 50 km, and the correlation accuracy rate is about 89%. The IOD success rate of arcs from the GEO objects is higher than 88%, and the correlation accuracy rate is greater than 87%. The recent COSMOS 1408 antisatellite test (ASAT) generated a large amount of debris. The algorithm of this paper and the observations of Changchun EA are used to initially identify new debris, possibly from the ASAT through initial orbit determination and track association. Finally, 64 suspected new pieces of debris can be found. The results show the effectiveness of the IOD and the correlation algorithm, as well as the potential application of the optical–electrical array in studying space events.

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

Initial orbit determination (IOD), as a basis for initial orbit association and accurate orbit determination (OD), has a crucial role in the process of obtaining space debris orbit information. Among the traditional methods, the Gooding method has better convergence and stability. In this study, the Gooding method is enhanced to solve the issues discovered. A novel initial orbit determination (IOD) method is developed using the proposed improvement measures of the single-parameter initial value determination (SIVD) method, the fitted-curve noise suppression method, the restricted corrective value solution method, the removal of trivial solutions, etc. The experimental results verify the effectiveness of the improved method. The success rate of the initial orbit determination reached 99%, and the accuracy of the solved orbit parameters was significantly improved, especially the semi-major axis (SMA) error of less than 50 km, accounting for 88% of the total. It can be seen that the method meets the demand of space-based space debris cataloging for initial orbit association and can serve in the field of space situational awareness, which has important practical significance and application potential.

Atmospheric drag is an important influencing factor in precise orbit determination and the prediction of low-orbit space debris. It has received widespread attention. Currently, calculating atmospheric drag mainly relies on different atmospheric density models. This experiment was designed to explore the impact of different atmospheric density models on the orbit prediction of space debris. In the experiment, satellite laser ranging data published by the ILRS (International Laser Ranging Service) were used as the basis for the precise orbit determination for space debris. The prediction error of space debris orbits at different orbital heights using different atmospheric density models was used as a criterion to evaluate the impact of atmospheric density models on the determination of space-target orbits. Eight atmospheric density models, DTM78, DTM94, DTM2000, J71, RJ71, JB2006, MSIS86, and NRLMSISE00, were compared in the experiment. The experimental results indicated that the DTM2000 atmospheric density model is best for determining and predicting the orbits of LEO (low-Earth-orbit) targets.