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The Dabei deep high-pressure condensate gas field occupies the paramount position in the Tarim Oilfield in China, the exploration and developments of which have been progressing. Since the initial development, the wax deposition and plugging in the wellbore and gathering pipeline have been the most bothering issues, resulting in the reduction or even shutdown of condensate gas well production. Therefore, the wax appearance temperature of Dabei condensate oil was studied using the capillary viscometer, differential scanning calorimetry (DSC), and polarizing microscope observation. The wax content was tested by using the DSC and crystallization separation test method. Finally, the wax appearance temperatures of degassed condensate oil and equilibrium condensate oil under different pressures were tested. Experimental results show that the wax appearance temperature measured by polarizing microscope observation was higher than that measured by the DSC and capillary viscometer, the lag of which can be recorded as the cloud point. The wax appearance temperature measured by polarizing microscope observation is of high accuracy. Secondly, the DSC method is not sufficient for measuring wax precipitation at low temperatures, showing a lower wax content than the crystallization separation test method. Thus, the wax content of Dabei condensate oil can be better measured by using the crystallization separation test method. Additionally, the wax precipitation law of equilibrium condensate oil is opposite to that of degassed condensate oil. The wax appearance temperature of equilibrium condensate oil increases as the pressure decreases. The results of wax appearance temperature of equilibrium condensate oil provide a useful and quick index to judge the potential risk of wax precipitation in the Tarim Oilfield, which can provide an efficient strategy for the development of waxy condensate gas reservoirs and the optimization of wax prevention and treatment technology.

With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model’s feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model’s generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios.

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attacks and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify attack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and can achieve superior effectiveness compared to existing methods and models.

•This study investigates the lateralization of thalamocortical white matter connections in relation to reading abilities.•Significant microstructural differences in thalamocortical fiber tracts are observed between individuals with reading difficulties and controls.•Adults with a history of reading difficulties show reduced neural density in frontal and occipital thalamocortical connections.•Greater rightward lateralization of frontal-thalamic tracts is associated with poorer early reading performance in those with reading difficulties.•The findings suggest that early reading experiences have a long-term impact on the lateralization of thalamocortical connectivity. Previous research has shown that the thalamus is crucial in reading, with its function depending largely on its connections with the cortex. However, the relationship between the lateralization of thalamocortical connections and reading has not been well-explored. This study investigates the microstructure and its lateralization differences in thalamocortical white matter fiber tracts in individuals with varying reading abilities and explores their relationship with reading skills and early reading performances. The study involved 26 Mandarin-speaking adults with a history of reading difficulties and 35 typically developing Mandarin-speaking adults. Severity of reading difficulties were accessed via the Chinese Adult Reading History Questionnaire (C-ARHQ) self-reported by participants. Reading-related abilities including reading accuracy, phonological awareness, and rapid automatized naming were assessed. Neuroimaging data, including T1-weighted and diffusion-weighted images, were collected. Thalamocortical white matter fiber tracts were reconstructed using the constrained spherical deconvolution (CSD) model and grouped into six regions based on connections with bilateral brain areas. The Neurite Orientation Dispersion and Density Imaging (NODDI) model was employed to evaluate the microstructural properties of these tracts, calculating lateralization indices for the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (VISO). Results revealed that individuals with reading difficulties had significantly lower NDI values in the left and right frontal-thalamic and occipital-thalamic fiber tracts compared to good readers. Additionally, greater rightward lateralization of frontal-thalamic white matter fiber tracts was linked to poorer early reading performance in those with reading difficulties. Our study reveals atypical thalamocortical white matter connections in adults with a history of reading difficulties, and the lateralization of these connections is influenced by severity of early reading difficulties.

Sparse Bayesian learning (SBL) is applied to the coprime array for underdetermined wideband direction of arrival (DOA) estimation. Using the augmented covariance matrix, the coprime array can achieve a higher number of degrees of freedom (DOFs) to resolve more sources than the number of physical sensors. The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be off the search grid no matter how fine the grid is. This dictionary mismatch problem can be well resolved by the SBL using fixed point updates. The SBL can automatically choose sparsity and approximately resolve the non-convex optimizaton problem. Numerical simulations are conducted to validate the effectiveness of the underdetermined wideband DOA estimation via SBL based on coprime array. It is clear that SBL can obtain good performance in detection and estimation compared to least absolute shrinkage and selection operator (LASSO), simultaneous orthogonal matching pursuit least squares (SOMP-LS) , simultaneous orthogonal matching pursuit total least squares (SOMP-TLS) and off-grid sparse Bayesian inference (OGSBI).

The prognostic value of the glucose-potassium ratio (GPR) in non-traumatic subarachnoid hemorrhage (SAH) remains undetermined. We investigated the association between the GPR at admission and all-cause mortality (ACM) in critically ill patients with SAH. We identified critically ill patients with SAH from the Medical Information Mart for Intensive Care database and stratified them into quartiles based on the GPR levels at admission. To evaluate mortality risk associations, we employed Cox proportional hazards models along with restricted cubic splines (RCS) to assess non-linear relationships. Survival curves were generated using the Kaplan–Meier (K–M) method. The robustness of the results was assessed through prespecified subgroup analyses and interaction tests, with effect modifications evaluated using likelihood ratio testing. The study cohort comprised 855 patients (median age: 61 years), with cumulative ACM rates of 18.5% at 30 days, 22.7% at 90 days, and 26.4% at 1 year. Cox regression analysis results revealed that higher GPR was significantly related to ACM at 30 days (hazard ratio (HR): 1.42; 95% confidence interval (CI): 1.13–1.80), 90 days (HR: 1.31; 95% CI 1.05–1.64), and 1 year (HR: 1.25; 95% CI 1.00–1.54). RCS analysis revealed a non-linear U-shaped association with an inflection point at GPR = 2.3. Below this threshold, GPR revealed no mortality association (HR: 0.96, 95% CI 0.57–1.63), while values above exhibited progressive risk elevation (HR: 1.67, 95% CI 1.25–2.22). Subgroup analyses confirmed consistent associations across patient characteristics (all interactions, p  > 0.05). Moreover, the combination of GPR and GCS performed better than GPR and GCS alone in predicting ACM. A U-shaped relationship was found between GPR and mortality in critically ill patients with SAH. This easily available biomarker holds potential for risk stratification and clinical decision-making, though optimal thresholds require prospective validation.

Recent relation extraction models’ architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic information in the sequence or the distance dependence problem, the internal semantic information may contain the useful knowledge which can help relation classification. Focus on these problems, this paper proposed a BERT-based relation classification method. Compare with the existing Bert-based architecture, the proposed model can obtain the internal semantic information between entity pair and solve the distance semantic dependence better. The pre-trained BERT model after fine tuning is used in this paper to abstract the semantic representation of sequence, then adopt the piecewise convolution to obtain semantic information which influence the extraction results. Compare with the existing methods, the proposed method can achieve a better accuracy on relational extraction task because of the internal semantic information extracted in the sequence. While, the generalization ability is still a problem that cannot be ignored, and the numbers of the relationships are difference between different categories. In this paper, the focal loss function is adopted to solve this problem by assigning a heavy weight to less number or hard classify categories. Finally, comparing with the existing methods, the F1 metric of the proposed method can reach a superior result 89.95% on the SemEval-2010 Task 8 dataset.

Sepsis is a life-threatening condition frequently observed in patients with intracerebral hemorrhage (ICH) who are critically ill. Early and accurate identification and prediction of sepsis are crucial. Machine learning (ML)-based predictive models exhibit promising sepsis prediction capabilities in emergency settings. However, their application in predicting sepsis among patients with ICH is still limited. The aim of the study is to develop an ML-driven risk calculator for early prediction of sepsis in patients with ICH who are critically ill and to clarify feature importance and explain the model using the Shapley Additive Explanations method. Patients with ICH admitted to the intensive care unit (ICU) from the Medical Information Mart for Intensive Care IV database between 2008 and 2022 were divided into training and internal test sets. The external test was performed using the eICU Collaborative Research Database, which includes over 200,000 ICU admissions across the United States between 2014 and 2015. Sepsis following ICU admission was identified using Sepsis-3.0 through clinical diagnosis combining elevation of the Sequential Organ Failure Assessment by ≥2 points with suspected infection. The Boruta algorithm was used for feature selection, confirming 29 features. Nine ML algorithms were used to construct the prediction models. Predictive performance was compared using several evaluation metrics, including the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanations technique was used to interpret the final model, and a web-based risk calculator was constructed for clinical practice. Overall, 2414 patients with ICH were enrolled from the Medical Information Mart for Intensive Care IV database, with 1689 and 725 patients assigned to the training and internal test sets, respectively. An external test set of 2806 patients with ICH from the eICU database was used. Among the 9 ML models tested, the categorical boosting (CatBoost) model demonstrated the best discriminative ability. After reducing features based on their importance, an explainable final CatBoost model was developed using 8 features. The final model accurately predicted sepsis in internal (AUC=0.812) and external (AUC=0.771) tests. We constructed a web-based risk calculator with 8 features based on the CatBoost model to assist clinicians in identifying people at high risk for sepsis in patients with ICH who are critically ill.

Transition metal oxides (TMOs) exhibit fascinating physicochemical properties, which originate from the diverse coordination structures between the transition metal and oxygen atoms. Accurate determination of such structure-property relationships of TMOs requires to correlate structural and electronic properties by capturing the global parameters with high resolution in energy, real, and momentum spaces, but it is still challenging. Herein, we report the determination of characteristic electronic structures from diverse coordination environments on the prototypical anatase-TiO 2 (001) with (1 × 4) reconstruction, using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/atomic force microscopy, in combination with density functional theory calculation. We unveil that the shifted positions of O 2 s and 2 p levels and the gap-state Ti 3 p levels can sensitively characterize the O and Ti coordination environments in the (1 × 4) reconstructed surface, which show distinguishable features from those in bulk. Our findings provide a paradigm to interrogate the intricate reconstruction-relevant properties in many other TMO surfaces. By measuring in energy, momentum and real space, the authors unveil diverse coordination environments and electronic structures on the reconstructed anatase TiO 2 (001), giving insights into its structure-property relationship with atomic precision.

ObjectivesStroke is a major cause of death and disability globally, especially among diabetic patients. In this study, we aim to scrutinise the effects of metformin on the clinical outcomes of stroke in diabetic patients.DesignThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Data sourcesPubMed, Embase and Web of Science databases were searched between their inception and 5 December 2023.Eligibility criteria for selecting studiesStudies investigating the effect of metformin on the clinical outcomes of stroke in patients with diabetes were included.Data extraction and synthesisThe effect of metformin on the clinical outcomes of stroke in patients with diabetes was identified using combined ORs and 95% CIs.ResultsA total of 11 studies involving 18 525 participants were included in this review. Pooled analysis has demonstrated that prestroke metformin use could reduce the probability of poor course after stroke by 34% in diabetes mellitus (DM) patients (OR=0.66, 95% CI: 0.61 to 0.72) and reduce the probability of death by 43% (OR=0.57, 95% CI: 0.51 to 0.64).ConclusionsPrestroke metformin use is beneficial for the improvement of clinical outcomes in patients who had a stroke with DM, although the potential bias should be carefully considered.PROSPERO registration numberCRD42024496056.