Effect of CO Concentration on the Performance of Polymer-Enhanced Foam at the Steam Front.

This study examines the impact of CO concentration on the stability and plugging performance of polymer-enhanced foam (PEF) under high-temperature and high-pressure conditions representative of the steam front in heavy oil reservoirs. Bulk foam experiments were conducted to analyze the foam performance, interfacial properties, and rheological behavior of CHSB surfactant and Z364 polymer in different CO and N gas environments. Additionally, core flooding experiments were performed to investigate the plugging performance of PEF in porous media and the factors influencing it.

Study on the Control of Steam Front Mobility in High-Temperature and High-Salinity Conditions Using Polymer-Enhanced Foam.

This study investigated the enhancing effects of the temperature-resistant polymer Poly(ethylene-co-N-methylbutenoyl carboxylate-co-styrenesulfonate-co-pyrrolidone) (hereinafter referred to as Z364) on the performance of cocamidopropyl hydroxy sulfobetaine (CHSB) foam under high-temperature and high-salinity conditions. The potential of this enhanced foam system for mobility control during heavy oil thermal recovery processes was also evaluated. Through a series of experiments, including foam stability tests, surface tension measurements, rheological assessments, and parallel core flooding experiments, we systematically analyzed the interaction between the Z364 polymer and CHSB surfactant on foam performance.

Semi-Supervised Graph Structure Learning via Dual Reinforcement of Label and Prior Structure.

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental assumption that the graph structure along which information propagates is perfect. However, the real-world graphs are inevitably incomplete or noisy, which violates the assumption, thus resulting in limited performance.

Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction.

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction.

Molecular dynamics simulation on the displacement behaviour of crude oil by CO/CH mixtures on a silica surface.

Produced gas re-injection is an effective and eco-friendly approach for enhancing oil recovery from shale oil reservoirs. However, the interactions between different gas phase components, and the oil phase and rocks are still unclear during the re-injection process. This study aims to investigate the potential of produced gas re-injection, particularly focusing on the effects of methane (CH) content in the produced gas on shale oil displacement.

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance.

Surgical evidence-based comparison of [Ga]Ga-FAPI-04 PET and MRI-DWI for assisting debulking surgery in ovarian cancer patients.

Purpose: Imaging assessment of abdominopelvic tumor burden is crucial for debulking surgery decision in ovarian cancer patients. This study aims to compare the efficiency of [Ga]Ga-FAPI-04 FAPI PET and MRI-DWI in the preoperative evaluation and its potential impact to debulking surgery decision.

Methods: Thirty-six patients with suspected/confirmed ovarian cancer were enrolled and underwent integrated [Ga]Ga-FAPI-04 PET/MRI.

Indirect estimation of pediatric reference interval via density graph deep embedded clustering.

Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals.

Adaptive-weighted deep multi-view clustering with uniform scale representation.

Multi-view clustering has attracted growing attention owing to its powerful capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally fail to distinguish the unequal importance of multiple views to the clustering task and overlook the scale uniformity of learned latent representation among different views, resulting in blurry physical meaning and suboptimal model performance. To address these issues, in this paper, we propose a joint learning framework, termed Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU).

Clustering Enhanced Multiplex Graph Contrastive Representation Learning.

Multiplex graph representation learning has attracted considerable attention due to its powerful capacity to depict multiple relation types between nodes. Previous methods generally learn representations of each relation-based subgraph and then aggregate them into final representations. Despite the enormous success, they commonly encounter two challenges: 1) the latent community structure is overlooked and 2) consistent and complementary information across relation types remains largely unexplored.

Constrained Maximum Cross-Domain Likelihood for Domain Generalization.

As a recent noticeable topic, domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. Great efforts have been made to learn domain-invariant features by aligning distributions across domains. However, existing works are often designed based on some relaxed conditions which are generally hard to satisfy and fail to realize the desired joint distribution alignment.

Semisupervised Progressive Representation Learning for Deep Multiview Clustering.

Multiview clustering has become a research hotspot in recent years due to its excellent capability of heterogeneous data fusion. Although a great deal of related works has appeared one after another, most of them generally overlook the potentials of prior knowledge utilization and progressive sample learning, resulting in unsatisfactory clustering performance in real-world applications. To deal with the aforementioned drawbacks, in this article, we propose a semisupervised progressive representation learning approach for deep multiview clustering (namely, SPDMC).

Consistency and adversarial semi-supervised learning for medical image segmentation.

Medical image segmentation based on deep learning has made enormous progress in recent years. However, the performance of existing methods generally heavily relies on a large amount of labeled data, which are commonly expensive and time-consuming to obtain. To settle above issue, in this paper, a novel semi-supervised medical image segmentation method is proposed, in which the adversarial training mechanism and the collaborative consistency learning strategy are introduced into the mean teacher model.

Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor.

Low-rank self-representation based subspace learning has confirmed its great effectiveness in a broad range of applications. Nevertheless, existing studies mainly focus on exploring the global linear subspace structure, and cannot commendably handle the case where the samples approximately (i.e.

Analysis of clinical diagnosis and treatment of intestinal volvulus.

Background: The aim of this study is to investigate the clinical characteristics and treatment experience of intestinal volvulus, and to analyze the incidence of adverse events and related risk factors of intestinal volvulus.

Methods: Thirty patients with intestinal volvulus admitted to the Digestive Emergency Department of Xijing Hospital from January 2015 to December 2020 were selected. The clinical manifestations, laboratory tests, treatment and prognosis were retrospectively analyzed.

Neoadjuvant study of niraparib in patients with HER2-negative, BRCA-mutated, resectable breast cancer.

This single-arm pilot study (NCT03329937) evaluated neoadjuvant niraparib antitumor activity and safety in patients with localized HER2-negative, BRCA-mutated breast cancer. Twenty-one patients received niraparib 200 mg once daily in 28-day cycles. After 2 cycles, tumor response (≥30% reduction from baseline) by MRI was 90.

Clinicopathologic and molecular features of denosumab-treated giant cell tumour of bone (GCTB): Analysis of 21 cases.

Unlabelled: GCTB is an osteolytic, locally-aggressive, rarely-metastasizing tumour, characterized by abundance of osteoclast-like giant cells, induced by neoplastic mononuclear cells expressing high-levels of the receptor activator of nuclear factor Kappa-B ligand (RANKL), a mediator of osteoclast activation. Although the mainstay of treatment is complete tumour removal with preservation of bone, therapy with denosumab, an inhibitor of RANKL, has been introduced for selected cases.

Objectives: Denosumab-treated GCTB (DT-GCTB) was reported to show a wide spectrum of histological changes such as depletion of osteoclast-like giant cells and intralesional bone deposition, which may lead to diagnostic difficulties.

Complex survival trial design by the product integration method.

Nonproportional hazards (NPHs) are often observed in survival trials such as the immunotherapy cancer trials. Under NPH, the classical log-rank test can be inefficient, and the estimated hazards ratio from the Cox model is difficult to interpret. The weighted log-rank test, and the tests for comparing the restricted mean survival time or the milestone survival become increasingly popular in handling NPH.

MOVER confidence intervals for a difference or ratio effect parameter under stratified sampling.

Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted difference, or the CI for the weighted effect in each group, or the variance for the weighted difference is calculated as the weighted sum of the corresponding stratum-specific statistics.