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Stock investment is an economic activity characterized by high risks and high returns. Therefore, the prediction of stock prices or fluctuations is of great importance to investors. Stock price prediction is a challenging task due to the nonlinearity and high volatility of stock time series. Existing deep learning models may not capture the periodic and non-periodic features of stock data effectively. In this paper, we propose a novel model that leverages Complete Ensemble Empirical Mode Decomposition (CEEMD), Time2Vec, and Transformer to better capture and utilize various patterns in stock data for enhanced prediction performance, and we call it ETT. CEEMD decomposes the stock data into different frequency components based on their intrinsic scales. Time2Vec provides a time vector representation that captures both periodic and non-periodic patterns while being invariant to time scaling. Transformer learns the long-term dependencies and global information from the data. We apply ETT to predict stock prices in the Chinese A-share market and compare it with several baseline models. The results show that ETT reduces the mean squared error (MSE) by an average of 4% and increases the average cumulative return by 58% on the CSI 100 and Hushen 300 datasets.

Close-kin mark-recapture (CKMR) methods have recently been used to infer demographic parameters for several aquatic and terrestrial species. For mosquitoes, the spatial distribution of close-kin pairs has been used to estimate mean dispersal distance, of relevance to vector-borne disease transmission and genetic biocontrol strategies. Close-kin methods have advantages over traditional mark-release-recapture (MRR) methods as the mark is genetic, removing the need for physical marking and recapturing that may interfere with movement behavior. Here, we extend CKMR methods to accommodate spatial structure alongside life history for mosquitoes and comparable insects. We derive kinship probabilities for parent-offspring and full-sibling pairs in a spatial context, where an individual in each pair may be a larva or adult. Using the dengue vector Aedes aegypti as a case study, we use an individual-based model of mosquito life history to test the effectiveness of this approach at estimating parameters such as mean dispersal distance, daily staying probability, and the strength of a barrier to movement. Considering a simulated population of 9,025 adult mosquitoes arranged on a 19-by-19 grid, we find the CKMR approach provides unbiased and precise estimates of mean dispersal distance given a total of 2,500 adult females sampled over a three-month period using 25 traps evenly spread throughout the landscape. The CKMR approach is also able to estimate parameters of more complex dispersal kernels, such as the daily staying probability of a zero-inflated exponential kernel, or the strength of a barrier to movement, provided the magnitude of these parameters is greater than 0.5. These results suggest that CKMR provides an insightful characterization of mosquito dispersal that is complementary to conventional MRR methods.

Background The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to miss meaningful genetic interactions. Gene co-expression analysis addresses this limitation by identifying coordinated changes in gene expression in response to cellular conditions, such as developmental or temporal trajectories. Existing approaches to gene co-expression analysis often assume restrictive linear relationships. However, gene co-expression can change in complex, non-linear ways, which suggests the need for more flexible and accurate methods. Results We propose a copula-based framework, TIME-CoExpress, with proper data-driven smoothing functions to model non-linear changes in gene co-expression along cellular temporal trajectories. Our method provides the flexibility to incorporate characteristics commonly observed in scRNAseq data, such as over-dispersion and zero-inflation, into the modeling framework. In addition to modeling gene co-expression, TIME-CoExpress captures dynamic changes in gene-level zero-inflation rates and mean expression levels, providing a more comprehensive analysis of scRNAseq data. Through a series of simulation analyses, we evaluated the performance of the proposed approach. We further demonstrated its implementation using a scRNAseq dataset and identified differentially co-expressed gene pairs along the cellular temporal trajectory during pituitary embryonic development, comparing and wild-type mice. Conclusions The proposed framework enables flexible and robust identification of dynamic, non-linear changes in gene co-expression, zero-inflation rates, and mean expression levels along temporal trajectories in scRNAseq data. Detecting these changes provides deeper insights into the biological processes and offers a better understanding of gene regulation throughout cellular development.

Numerical simulation technology is a crucial tool for reducing costs and increasing efficiency in the wind power industry. However, with the development of large-scale wind turbines, the computational cost of numerical simulation has gradually increased. This paper uses the geometric similarity, structural similarity criterion, Reynolds similarity and boundary layer theory to establish a scaled model of the geometric three-dimensional shape, composite material, and finite element mesh of large wind turbine blades. The study analyzes the aerodynamic, gravitational, and centrifugal load variations within the scaled model. The proportional relationship between the scaled model’s operating parameters, the numerical simulation’s environmental parameters, and the mechanical response parameters is established. These parameters are coordinated to ensure the similarity of the blade structure and the fluid dynamics. For a geometric scale factor of 0.316, the relative difference in maximum deflection is 4.52%, with a reduction in calculation time by 48.1%. On the premise of ensuring the calculation accuracy of the aerodynamic and structural response of the blade, the calculation efficiency is effectively improved.

Meningioma resection can involve massive intraoperative blood loss due to an abundant blood supply in structures. [...]the routine use of mannitol during meningioma resection may affect hemodynamic stability. The Fisher's exact test or χ2 test was used to analyze dichotomous data, and the student's t-test was used for normally distributed continuous data. [...]the Mann–Whitney U test was used for non-parametric ordinal data. According to a two-tailed power analysis with an α of 5% and β of 10%, at least 39 patients are required per group. Variables GDFT (n = 42) Control (n = 42) Statistics values P values Postoperative complications Composite complications ≥1 29 (69) 37 (88) 4.525* 0.033 ≥2 16 (38) 26 (62) 4.762* 0.029 ≥3 5 (12) 13 (31) 4.525* 0.033 Overall complications Hypertension, 7 (17) 5 (12) 0.389* 0.533 Myocardial ischemia or infarction 0 2 (5) ‡ 0.494 Intubation >24 h 0 2 (5) ‡ 0.494 ALI or ARDS 0 0 NA Respiratory infection 4 (10) 7 (17) 0.418* 0.518 Stroke 0 0 NA Intracranial hemorrhage 1 (2) 0 ‡ 1.000 Intracranial infection 2 (5) 1 (2) ‡ 1.000 Hemiplegia 2 (5) 3 (7) ‡ 1.000 Aphasia 1 (2) 0 ‡ 1.000 Severe encephaledema 2 (5) 11 (26) 5.824* 0.016 Constipation 19 (45) 22 (52) 0.429* 0.513 PONV 16 (38) 19 (45) 0.441* 0.507 Gastrointestinal hemorrhage 0 0 NA Ileus 0 1 (2) ‡ 1.000 Urinary infection 0 0 NA Creatinine elevation 0 1 (2) ‡ 1.000 Thrombocytopenia (Platelet <100,000) 0 3 (7) ‡ 0.241 Coagulopathy (INR >1.5) 0 1 (2) ‡ 1.000 Wound infection 0 0 NA Hospital course Length of hospital stay (days) 14 (13–15) 15 (12–17) –0.966† 0.334 Number of ICU admission 8 (19) 7 (17) 0.081* 0.776 Get out of bed to walk (days) 2 (2–3) 3 (2–4) –1.481† 0.139 Postoperative GI function Postoperative exhaust time (h) 14 (5–24) 20 (11–29) –2.070† 0.038 Take solid food (days) 2 (1–3) 3 (2–5) –3.209† 0.001 Data are presented as median (interquartile range) for continuous variables.

Bionic structures have been widely utilized in the crashworthiness design of thin-walled structures due to their superior energy absorption capabilities. This study constructed a bionic-shell thin-walled tube (BST) with excellent crashworthiness based on the structural bionic principle using the shell shape cross-section as the prototype. First, the theoretical model of the mean crushing force ( MCF ) for BST under axial compression was developed. An experiment was conducted and the reliability of the finite element model was verified. Then, the effects of structural parameters, such as the number of ribs, wall thickness, and inner tube diameter on the crashworthiness of the BST were investigated using the finite element method. Finally, to obtain the ideal configuration of structural parameters, the BST was optimized using the response surface method (RSM) with specific energy absorption ( SEA ) and crushing force efficiency ( CFE ) as the optimization objectives and peak crushing force ( PCF ) as the constraint condition. The results showed that the BST with six ribs exhibited the best crashworthiness under the same mass. The optimized BST-6 was found to have better energy absorption performance than the double circular tube (DCT) and the bionic-horsetail thin-walled tube (BHT). Compared with the DCT, the SEA and CFE increased by 35.15 % and 32.23 %, respectively.

Perioperative neurocognitive disorder (PND) is a common condition in elderly patients undergoing surgery. Sedation, analgesia, regional cerebral oxygen saturation (rSO 2 ), and body temperature are known to be associated with PND, but few studies have examined the contribution of these factors combined in detail. This prospective, randomized, controlled, double-blinded study investigated whether anesthesia management based on multimodal brain monitoring—an anesthesia management algorithm designed by our group—could improve the post-operative cognitive function and brain functional connectivity (FC) in elderly patients undergoing elective spinal surgery with general anesthesia. The patients (aged ≥65 years) were randomized into two groups [control (Group C), n = 12 and intervention (Group I), n = 14]. Patients in Group I were managed with multimodal brain monitoring (patient state index, spectral edge frequency, analgesia nociception index, rSO 2 , and temperature), and those in Group C were managed with routine anesthesia management. All patients were pre- and post-operatively evaluated (7 days after surgery) with the Montreal Cognitive Assessment (MoCA). Amplitude of low-frequency fluctuation (ALFF) and FC were analyzed after resting-state functional MRI. Serum C-reactive protein (CRP) and lipopolysaccharide levels were measured, and the correlation between FC and changes in inflammatory marker levels was analyzed. Mean post-operative MoCA score was higher in Group I (24.80 ± 2.09) than in Group C (22.56 ± 2.24) ( p = 0.04), with no difference in PND incidence between groups (28.57 vs. 16.67%; p = 0.47). Group I also showed significantly increased ALFF values in several brain regions after surgery ( p < 0.05), and FC between the left hippocampus and left orbital inferior frontal gyrus (FG), left middle FG, left superior temporal gyrus, and left precentral gyrus was enhanced ( p < 0.05), which was negatively correlated with the change in serum CRP (pre vs. post-intervention) ( R = −0.58, p = 0.01). These results suggest that management of elderly patients undergoing surgery by multimodal brain monitoring may improve post-operative neurocognition and FC by reducing systemic inflammation. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx , identifier: ChiCTR1900028024.

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.

Introduction Esophageal cancer (EC) remains highly lethal due to tumor microenvironment (TME)-mediated immune evasion. While natural killer (NK) cells are central to antitumor immunity, their functional states in EC are poorly characterized. Methods We integrated bulk RNA-seq (TCGA/GEO) and single-cell data to construct an NK cell-derived prognostic signature (NK score) via LASSO-Cox regression. Immunofluorescence was applied to assess the clinical relevance of SAMD3 + NK cells in EC. Using both xenograft mouse models and in vitro co-culture procedures, the impact of SAMD3 on NK cell function was confirmed. Results In EC patients, the prognostic NK score—which is generated from important NK cell markers including SAMD3—was substantially correlated with a worse chance of survival. NK cells within the TME had significant levels of SAMD3 expression, as seen by immunofluorescence labeling. Moreover, NK cells with SAMD3 knockdown exhibited enhanced antitumor activity, leading to decreased tumor development in the xenograft model. Discussion Our results demonstrate the predictive significance of NK cell markers in EC and pinpoint SAMD3 as a critical modulator of NK cell activity. We pioneer SAMD3 + NK cells as architects of TME immunosuppression in EC. Our findings nominate SAMD3 inhibition as a combinatorial strategy to overcome immune checkpoint blockade resistance.

The loosening and fracture of the blade root bolt, a crucial link between the blade and hub, significantly affect the wind turbine's safe operation. To address this issue, the load redistribution after loosening and fracture of the blade root bolts is considered first. The theoretical model of axial force calculation of the blade root bolts is then deduced and verified through tests. Subsequently, a finite element model of the blade root bolt connection structure is established, and its effectiveness is analyzed. Finally, based on the finite element model, the effects of the preload force, the number of loosened or fractured, and the area of loosened or fractured on the loading characteristics of blade root bolts are investigated. Results show that when the preload force of the loosened blade root bolts is not zero, its axial stress variation law is the same as its preload force variation law. When the preload force is zero, the axial stress of the blade root bolts with zero preload force increases, whereas the axial stress of the non-loosened blade root bolts decreases. Exceeding the material's ultimate strength causes the blade root bolt to fracture. The axial stress in the loosened blade root bolts around the center of the fractured decreases, and the magnitude of the axial stresses of the surrounding non-loosened blade root bolts increases and then decreases along the fractured center to both sides. The findings can offer theoretical guidance for predicting the fatigue life of blade root bolts and their online monitoring.