Effects of water content change path on laboratory and field compaction of lime stabilized expansive soil

Generally, the soil requires to be compacted in highway construction. The expansive soil is a special type of soil that is highly susceptible to variations in water content, which affects the degree of compaction at the same compaction energy. In the present study, a series of wet compaction tests and dry compaction tests were carried out in the laboratory. Laboratory test results show that dry compaction will produce a higher optimum water content and a higher maximum dry unit weight compared to wet compaction, because its matric suction is smaller. Field compaction tests were also conducted, the results showed that there might be a risk of under-compacting soils during construction caused by different water content change path in actual field conditions

Neoatherosclerosis after Drug-Eluting Stent Implantation: Roles and Mechanisms

In-stent neoatherosclerosis (NA), characterized by a relatively thin fibrous cap and large volume of yellow-lipid accumulation after drug-eluting stents (DES) implantation, has attracted much attention owing to its close relationship with late complications, such as revascularization and late stent thrombosis (ST). Accumulating evidence has demonstrated that more than one-third of patients with first-generation DES present with NA. Even in the advent of second-generation DES, NA still occurs. It is indicated that endothelial dysfunction induced by DES plays a critical role in neoatherosclerotic development. Upregulation of reactive oxygen species (ROS) induced by DES implantation significantly affects endothelial cells healing and functioning, therefore rendering NA formation. In light of the role of ROS in suppression of endothelial healing, combining antioxidant therapies with stenting technology may facilitate reestablishing a functioning endothelium to improve clinical outcome for patients with stenting

Asymptotic CRB Analysis of Random RIS-Assisted Large-Scale Localization Systems

This paper studies the performance of a randomly RIS-assisted multi-target localization system, in which the configurations of the RIS are randomly set to avoid high-complexity optimization. We first focus on the scenario where the number of RIS elements is significantly large, and then obtain the scaling law of Cram\'er-Rao bound (CRB) under certain conditions, which shows that CRB decreases in the third or fourth order as the RIS dimension increases. Second, we extend our analysis to large systems where both the number of targets and sensors is substantial. Under this setting, we explore two common RIS models: the constant module model and the discrete amplitude model, and illustrate how the random RIS configuration impacts the value of CRB. Numerical results demonstrate that asymptotic formulas provide a good approximation to the exact CRB in the proposed randomly configured RIS systems

Bis[bis­(4,4′-dimethyl-2,2′-bipyridine)(10,11,12,13-tetra­hydro­dipyrido[3,2-a:2′,3′-c]phenazine)ruthenium(II)] tetra­kis(perchlorate) acetonitrile disolvate monohydrate

The asymmetric unit of the title compound, [Ru(C12H12N2)2(C18H14N4)]2(ClO4)4·2CH3CN·H2O, contains two RuII complex cations, four perchlorate counter-anions, two uncoord­inated acetonitrile mol­ecules and one water mol­ecule. The RuII ions are chelated by one 10,11,12,13-tetra­hydro­dipyrido[3,2-a:2′,3′-c]phenazine (dpqc) and two 4,4′-dimethyl-2,2′-bipyridine (dmb) ligands in a distorted octa­hedral geometry. The uncoordinated water mol­ecule is disordered over three positions, with occupancy factors of 0.398 (9), 0.312 (8) and 0.290 (8). A supra­molecular structure is formed by weak π–π inter­actions between neighbouring mol­ecules, with face-to-face distances of 3.51 (1) Å [centroid–centroid distance 3.81 (1) Å]

A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Some works are inspired by $k$-WL/FWL (Folklore WL) and design the corresponding neural versions. Despite the high expressive power, there are serious limitations in this line of research. In particular, (1) $k$-WL/FWL requires at least $O(n^k)$ space complexity, which is impractical for large graphs even when $k=3$; (2) The design space of $k$-WL/FWL is rigid, with the only adjustable hyper-parameter being $k$. To tackle the first limitation, we propose an extension, $(k,t)$-FWL. We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k,t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem. To tackle the second problem, we propose $k$-FWL+, which considers any equivariant set as neighbors instead of all nodes, thereby greatly expanding the design space of $k$-FWL. Combining these two modifications results in a flexible and powerful framework $(k,t)$-FWL+. We demonstrate $(k,t)$-FWL+ can implement most existing models with matching expressiveness. We then introduce an instance of $(k,t)$-FWL+ called Neighborhood$^2$-FWL (N$^2$-FWL), which is practically and theoretically sound. We prove that N$^2$-FWL is no less powerful than 3-WL, and can encode many substructures while only requiring $O(n^2)$ space. Finally, we design its neural version named N$^2$-GNN and evaluate its performance on various tasks. N$^2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%. Moreover, N$^2$-GNN achieves new SOTA results on the BREC dataset (71.8%) among all existing high-expressive GNN methods.Comment: Accepted to NeurIPS 202

Perioperative Dexmedetomidine Improves Outcomes of Cardiac Surgery

BACKGROUND: Cardiac surgery is associated with a high risk of cardiovascular and other complications that translate into increased mortality and healthcare costs. This retrospective study was designed to determine whether the perioperative use of dexmedetomidine could reduce the incidence of complications and mortality following cardiac surgery. METHODS AND RESULTS: 1,134 patients who underwent CABG and CABG plus valvular and/or other procedures were included. 568 received intravenous dexmedetomidine infusion and 566 did not. Data were adjusted with propensity scores and multivariate logistic regression was used. The primary outcomes measured included mortality and postoperative major adverse cardiocerebral events (MACE: stroke, coma, perioperative myocardial infarction, heart block or cardiac arrest). Secondary outcomes included renal failure, sepsis, delirium, postoperative ventilation hours, length of hospital stay and 30-day readmission. Dexmedetomidine use significantly reduced postoperative in-hospital [1.23% vs. 4.59%; adjusted odds ratio (OR), 0.34; 95% confidence intervals (CI), 0.192 to 0.614; P < 0.0001], 30-day (1.76% vs. 5.12%; adjusted OR, 0.39; 95% CI, 0.226 to 0.655; P <0.0001) and 1-year (3.17% vs. 7.95%; adjusted OR, 0.47; 95% CI, 0.312 to 0.701; P = 0.0002) mortalities. Perioperative dexmedetomidine therapy also reduced the risk of overall complications (47.18 vs. 54.06%; adjusted OR, 0.80, 95% CI, 0.68 to 0.96; p= 0.0136) and delirium (5.46% vs. 7.42%; adjusted OR, 0.53; 95% CI, 0.37 to 0.75; p=0.0030). CONCLUSIONS: Perioperative dexmedetomidine use was associated with a decrease in postoperative mortality up to one year and decreased incidence of postoperative complications and delirium in patients undergoing cardiac surgery

Bibliometric and visual analysis of RAN methylation in cardiovascular disease

BackgroundRNA methylation is associated with cardiovascular disease (CVD) occurrence and development. The purpose of this study is to visually analyze the results and research trends of global RNA methylation in CVD.MethodsArticles and reviews on RNA methylation in CVD published before 6 November 2022 were searched in the Web of Science Core Collection. Visual and statistical analysis was performed using CiteSpace 1.6.R4 advanced and VOSviewer 1.6.18.ResultsThere were 847 papers from 1,188 institutions and 63 countries/regions. Over approximately 30 years, there was a gradual increase in publications and citations on RNA methylation in CVD. America and China had the highest output (284 and 259 papers, respectively). Nine of the top 20 institutions that published articles were from China, among which Fudan University represented the most. The International Journal of Molecular Sciences was the journal with the most studies. Nature was the most co-cited journal. The most influential writers were Zhang and Wang from China and Mathiyalagan from the United States. After 2015, the primary keywords were cardiac development, heart, promoter methylation, RNA methylation, and N6-methyladenosine. Nuclear RNA, m6A methylation, inhibition, and myocardial infarction were the most common burst keywords from 2020 to the present.ConclusionsA bibliometric analysis reveals research hotspots and trends of RNA methylation in CVD. The regulatory mechanisms of RNA methylation related to CVD and the clinical application of their results, especially m6A methylation, are likely to be the focus of future research

Generation of dual-gRNA library for combinatorial CRISPR screening of synthetic lethal gene pairs

Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps fo