The stability of soil aggregates in sweet cherry ( L.) orchards of different ages and varieties.

In recent years, Sweet Cherry ( L.) have been in high demand and planted in large quantities due to their nutritional value and appealing organoleptic properties. The management mode and species characteristics in the tillage process lead to a decrease in soil quality, and the stability of soil aggregates and decrease in soil nutrients indicate this.

Accessing a Carboxyl-Anhydride Molecular Switch-Mediated Recyclable PECT Through Upcycling End-of-Use PET.

Poly(ethylene terephthalate) (PET), with an annual production of exceeding 70 million tons, is mainly utilized in disposable fields and subsequently contribute to severe environmental pollution. Conventional chemical recycling, which typically involves depolymerizing polymer into monomers, is limited due to the intricate recycling process, excess using unrecyclable solvents and low polymer conversion. Inspired by protein's molecular switches, we propose a novel polymer-to-polymer recycling strategy based on polycondensation principles upcycling waste PET to high-value recyclable poly(ethylene-co-1,4-cyclohexanedimethanol terephthalate) derivatives containing molecular switches.

Examining the effectiveness of motor imagery combined with non-invasive brain stimulation for upper limb recovery in stroke patients: a systematic review and meta-analysis of randomized clinical trials.

Background: Transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) are common non-invasive brain stimulation (NIBS) methods for functional recovery after stroke. Motor imagery (MI) can be used in the rehabilitation of limb motor function after stroke, but its effectiveness remains to be rigorously established. Furthermore, there is a growing interest in the combined application of NIBS with MI, yet the evidence regarding its impact on the recovery of upper limb function after stroke is inconclusive.

Study on Springback Behavior in Hydroforming of Micro Channels for a Metal Bipolar Plate.

Bipolar plates are one of the most important components of proton exchange membrane fuel cells. With the miniaturization of bipolar plate flow channel sizes and the increasing demand for precision, springback has become a key focus of research in the bipolar plate forming process. In this paper, the hydroforming process for 316L stainless steel bipolar plates was studied, and an FEM model was built to examine the stress and strain at various locations on the longitudinal section of the plate.

Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.

Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the influence of PAHs on environmental bacterial communities in different habitats (soil, water, and sediment) remains a major challenge. We collected and reanalyzed 1924 16S rRNA sequencing samples to determine the effects of PAHs on bacterial communities in different habitats and used machine learning to predict potential degrading bacteria.

Soil microbial community construction under revegetation in newly created land.

Revegetation represents the primary method for restoring the ecological balance of the Loess Plateau. The assessment of revegetation efficacy on degraded lands can be facilitated through the utilization of soil microorganisms as indicators. We chose Medicago sativa L.

Hydrocarbon Transportation in Heterogeneous Shale Pores by Molecular Dynamic Simulation.

Shale oil in China is widely distributed and has enormous resource potential. The pores of shale are at the nanoscale, and traditional research methods encounter difficulty in accurately describing the fluid flow mechanism, which has become a bottleneck restricting the industrial development of shale oil in China. To clarify the distribution and migration laws of fluid microstructure in shale nanopores, we constructed a heterogeneous inorganic composite shale model and explored the fluid behavior in different regions of heterogeneous surfaces.

Effect and Mechanism of Rhein-praseodymium Complex on Intestinal Uric Acid Excretion in Rats with Renal Injury and Hyperuricemia.

Background: Hyperuricemia (HUA) is a disease characterized by excessive uric acid production and/or insufficient uric acid excretion caused by abnormal purine metabolism in the human body. Uric acid deposition caused by hyperuricemia can cause complications, such as kidney damage. The current therapeutic drugs for HUA are not very targeted and usually have specific toxic side effects.

TBK1-stabilized ZNF268a recruits SETD4 to methylate TBK1 for efficient interferon signaling.

Sufficient activation of interferon signaling is critical for the host to fight against invading viruses, in which post-translational modifications have been demonstrated to play a pivotal role. Here, we demonstrate that the human KRAB-zinc finger protein ZNF268a is essential for virus-induced interferon signaling. We find that cytoplasmic ZNF268a is constantly degraded by lysosome and thus remains low expressed in resting cell cytoplasm.

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass.

The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (m ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected m is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms.

Conditional normalizing flow for multivariate time series anomaly detection.

Multivariate time series data is becoming increasingly ubiquitous in various fields such as servers, industrial applications, and healthcare. However, detecting anomalies in such data is challenging due to its complex time-dependent, high-dimensional, and label scarcity. Aiming at this problem, this paper proposes an Attention Factorization Normalizing Flow (AFNF) algorithm for unsupervised multivariate time series anomaly detection.

On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning Approaches.

Driving behavior recognition can provide an important reference for the intelligent vehicle industry and probe vehicle-based traffic estimation. The identification of driving behavior using mobile sensing techniques such as smartphone- and vehicle-mounted terminals has gained significant attention in recent years. The present work proposed the monitoring of longitudinal driving behavior using a machine learning approach with the support of an on-board unit (OBU).

Atmospheric antibiotic resistome driven by air pollutants.

The atmosphere is an important reservoir and habitat for antibiotic resistance genes (ARGs) and is a main pathway to cause potential health risks through inhalation and ingestion. However, the distribution characteristics of ARGs in the atmosphere and whether they were driven by atmospheric pollutants remain unclear. We annotated 392 public air metagenomic data worldwide and identified 1863 ARGs, mainly conferring to tetracycline, MLS, and multidrug resistance.

[Expression of Tyrosine Kinase Receptor 2 in Oral Squamous Cell Carcinoma and the Effect on Cell Proliferation and Migration and Epithelial-Mesenchymal Transition Process].

Objective: To study the expression of tyrosine kinase receptor 2 (Tie2) in oral squamous cell carcinoma (OSCC) and its effect on cell proliferation and migration and the epithelial-mesenchymal transition (EMT) process.

Methods: Immunohistochemistry (IHC) tests were conducted to examine the expression of Tie2 in OSCC tissues and normal oral mucosa tissues. Western blot was performed to examine the expression of Tie2 in dysplastic oral keratinocyte (DOK) cell line and OSCC cell lines, and the cell line with high Tie2 expression was selected as the experimental cell line.

Experimental Study on the Ethanol-Assisted Catalytic Steam Reforming of Endothermic Hydrocarbon Fuel.

Thermal protection is a critical problem in the development of hypersonic aircraft. To enhance the thermal protection capability of hydrocarbon fuel, the ethanol-assisted catalytic steam reforming of endothermic hydrocarbon fuel was proposed. The result shows that the total heat sink can be significantly improved by the endothermic reactions of ethanol.

Process coupling of CO reduction and 5-HMF oxidation mediated by defect-enriched layered double hydroxides.

Aiming at the comprehensive utilization of waste carbon resources and renewable carbon resources, we put forward the photocatalytic coupling process of CO reduction and 5-hydroxymethylfurfural (5-HMF) oxidation mediated by the anionic compound of layered double hydroxides (LDHs). Specifically, a ZnNiFe-LDH was synthesized by co-precipitation method, during which CO was stored between LDH layers in the form of carbonate. Then, a certain amount of metal vacancies were introduced into LDH nanosheets by selectively etching Zn ions.

A Carbon-Negative Hydrogen Production Strategy: CO Selective Capture with H Production.

We reported a strategy of carbon-negative H production in which CO capture was coupled with H evolution at ambient temperature and pressure. For this purpose, carbonate-type Cu Mg Fe layered double hydroxide (LDH) was preciously constructed, and then a photocatalysis reaction of interlayer CO reduction with glycerol oxidation was performed as driving force to induce the electron storage on LDH layers. With the participation of pre-stored electrons, CO was captured to recover interlayer CO in presence of H O, accompanied with equivalent H production.

MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking.

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address this matching problem caused by one-stage association, we propose a novel multi-stage association method, which consists of a hierarchical matching module and a customized track management module.

Reference values of the carotid elastic modulus using shear wave elastography and arterial stiffness change in coronary slow flow.

Purpose: Shear wave elastography (SWE) accurately and sensitively evaluates arterial wall stiffness by quantifying the elastic modulus (EM); however, the absence of reference values has precluded its widespread clinical application. This prospective cohort study aimed to establish reference values for the carotid EM using SWE; investigate the main determinants of the EM; and evaluate EM changes in coronary slow flow (CSF), which is characterized by delayed coronary opacification without evident obstructive lesion in epicardial coronary artery on angiography.

Method: This study enrolled 169 healthy volunteers and 30 patients with CSF.

Advanced aqueous sodium-ion capacitors based on NiMnO nanoparticles encapsulated in electrospinning carbon nanofibers.

Manganese oxides are promising cathode material candidates with appropriate positive potential windows for low-cost and safe aqueous sodium-ion capacitors (ASICs). However, their low electrical conductivity issue and the lack of advanced binder-free manganese oxide-based electrodes severely restrict their practical capacitance and application in flexible ASICs. Here, NiMnO (NMO) nanoparticles uniformly encapsulated in carbon nanofiber films with excellent flexibility are fabricated by electrospinning and subsequent carbonization.

Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis.

Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis.

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention.

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios.

GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection.

The rapid development of smart factories, combined with the increasing complexity of production equipment, has resulted in a large number of multivariate time series that can be recorded using sensors during the manufacturing process. The anomalous patterns of industrial production may be hidden by these time series. Previous LSTM-based and machine-learning-based approaches have made fruitful progress in anomaly detection.

Light-Induced Structural Dynamic Evolution of Pt Single Atoms for Highly Efficient Photocatalytic CO Reduction.

Revealing the structural evolution of the real active site during photocatalysis is very important for understanding the catalytic mechanism, but it remains a great challenge. By employing single atoms (SAs) as the mechanism research platform, we investigated the variation of the SA structure under light and the corresponding reaction pathway controlment mechanism. In particular, taking the defect anchoring strategy, Pt SAs are anchored on the metal ion vacancy-rich ZnNiTi layered double hydroxide-etched (ZnNiTi-LDHs-E) support.