Paramagnetic behaviour of silver nanoparticles generated by decomposition of silver oxalate

Silver oxalate Ag2C2O4, was already proposed for soldering applications, due to the formation when it is decomposed by a heat treatment, of highly sinterable silver nanoparticles. When slowly decomposed at low temperature (125 °C), the oxalate leads however to silver nanoparticles isolated from each other. As soon as these nanoparticles are formed, the magnetic susceptibility at room temperature increases from -3.14 10-7 emu.Oe-1.g-1 (silver oxalate) up to -1.92 10-7 emu.Oe-1.g-1 (metallic silver). At the end of the oxalate decomposition, the conventional diamagnetic behaviour of bulk silver, is observed from room temperature to 80 K. A diamagnetic-paramagnetic transition is however revealed below 80 K leading at 2 K, to silver nanoparticles with a positive magnetic susceptibility. This original behaviour, compared to the one of bulk silver, can be ascribed to the nanometric size of the metallic particles

Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis

Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution. In this paper, we propose a novel deep graph clustering framework with temporal consistency regularization on inter-community structures, inspired by the concept of minimal network topological changes within short intervals. Specifically, to address the representation collapse problem, we first introduce MFC, a matrix factorization-based deep graph clustering algorithm that preserves node embedding. Based on static clustering results, we construct probabilistic community networks and compute their persistence homology, a robust topological measure, to assess structural similarity between them. Moreover, a novel neural network regularization TopoReg is introduced to ensure the preservation of topological similarity between inter-community structures over time intervals. Our approach enhances temporal consistency and clustering accuracy on real-world datasets with both fixed and varying numbers of communities. It is also a pioneer application of TDA in temporally persistent community detection, offering an insightful contribution to field of network analysis. Code and data are available at the public git repository: https://github.com/kundtx/MFC_TopoRegComment: AAAI 202

Memristor Neural Network Design

Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system–based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy‐efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. In this chapter, the design of different neural network architectures based on memristor is introduced, including spiking neural networks, multilayer neural networks, convolution neural networks, and recurrent neural networks. And the brief introduction, the architecture, the computing circuits, and the training algorithm of each kind of neural networks are presented by instances. The potential applications and the prospects of memristor‐based neural network system are discussed

The rotation effect on the thermodynamics of the QCD matter

In this study, we investigate the impact of rotation on the thermodynamic characteristics of QCD matter using the three-flavor NJL model. We examine the temperature, quark chemical potential, and angular velocity dependencies of key thermodynamic quantities, such as the trace anomaly, specific heat, speed of sound, angular momentum, and moment of inertia. As the main finding of our analysis, we observe that the speed of sound exhibits a nonmonotonic behavior as the angular velocity changes.Comment: 18 pages, 19 figure

Perioperative immunotherapy for stage II-III non-small cell lung cancer: a meta-analysis base on randomized controlled trials

BackgroundIn recent years, we have observed the pivotal role of immunotherapy in improving survival for patients with non-small cell lung cancer (NSCLC). However, the effectiveness of immunotherapy in the perioperative (neoadjuvant + adjuvant) treatment of resectable NSCLC remains uncertain. We conducted a comprehensive analysis of its antitumor efficacy and adverse effects (AEs) by pooling data from the KEYNOTE-671, NADIM II, and AEGEAN clinical trials.MethodsFor eligible studies, we searched seven databases. The randomized controlled trials (RCTs) pertaining to the comparative analysis of combination neoadjuvant platinum-based chemotherapy plus perioperative immunotherapy (PIO) versus perioperative placebo (PP) were included. Primary endpoints were overall survival (OS) and event-free survival (EFS). Secondary endpoints encompassed drug responses, AEs, and surgical outcomes.ResultsThree RCTs (KEYNOTE-671, NADIM II, and AEGEAN) were included in the final analysis. PIO group (neoadjuvant platinum-based chemotherapy plus perioperative immunotherapy) exhibited superior efficacy in OS (hazard ratio [HR]: 0.63 [0.49-0.81]), EFS (HR: 0.61 [0.52, 0.72]), objective response rate (risk ratio [RR]: 2.21 [1.91, 2.54]), pathological complete response (RR: 4.36 [3.04, 6.25]), major pathological response (RR: 2.79 [2.25, 3.46]), R0 resection rate (RR: 1.13 [1.00, 1.26]) and rate of adjuvant treatment (RR: 1.08 [1.01, 1.15]) compared with PP group (neoadjuvant platinum-based chemotherapy plus perioperative placebo). In the subgroup analysis, EFS tended to favor the PIO group in almost all subgroups. BMI (>25), T stage (IV), N stage (N1-N2) and pathological response (with pathological complete response) were favorable factors in the PIO group. In the safety assessment, the PIO group exhibited higher rates of serious AEs (28.96% vs. 23.51%) and AEs leading to treatment discontinuation (12.84% vs. 5.81%). Meanwhile, although total adverse events, grade 3-5 adverse events, and fatal adverse events tended to favor the PP group, the differences were not statistically significant.ConclusionPIO appears to be superior to PP for resectable stage II-III NSCLC, demonstrating enhanced survival and pathological responses. However, its elevated adverse event (AE) rate warrants careful consideration.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42023487475

Rapid, one-step preparation of SERS substrate in microfluidic channel for detection of molecules and heavy metal ions

On-chip fabrication of surface-enhanced Raman spectroscopy (SERS)-active materials enables continuous, real-time sensing of targets in the microfluidic chip. However, the current techniques require the time-consuming, complicated process and costly, bulky facilities. In this work, we present a novel method for synthesis of Ag nanostructures in a microfluidic channel via one-step electroless galvanic replacement reaction. The whole reaction could be achieved \u3c10 \u3emins, while the traditional methods take hours. The microfluidic channel has a Cu base, which can reduce Ag ions to Ag nanoparticles in the presence of AgNO3 solution. The new technique enables the label-free sensing of chemical molecules (i.e., methylene blue)and biomolecules (i.e., urea). Two proof-of-concept experiments are performed to verify the utilization of the prepared SERS substrate. First, the microfluidics-assisted SERS sensor is used to detect Hg ions in aqueous solution with high sensitivity and good selectivity. Second, the fabricated SERS-active material can couple with a concentration gradient generator for continuous SERS detection. This simple technique can be used in any laboratory without any bulky equipment and can realize numerous lab-on-a-chip applications with the integration of other microfluidic networks

GT2006-90211 Flow Control of Annular compressor Cascade by Synthetic Jets

ABSTRACT An experimental investigation conducted in a stationary annular cascade wind tunnel demonstrated that unsteady flow control using synthetic (zero mass-flux) jets can effectively reduce flow separation from suction side of the blade in axial compressor cascade. The synthetic jets driven by a high-power speaker were introduced through the casing radially into the flow-field just adjacent to the leading edge of compressor cascade. The experimental results revealed that the aerodynamic performance of compressor cascade could be improved amazingly by synthetic jets and the maximum relative reduction of loss coefficient was up to 27.5%. The optimal analysis of the excitation frequency, excitation location was systematically investigated at different incidences. In order to obtain detail information on flow-field structure, DPIV technique was adopted. The experimental results showed that the intensity of wake vortices became much weaker and streamlines became smoother and more uniform by synthetic jets. NOMENCLATURE A relative excitation amplitude, A = je

TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis

Over the past 35 years, studies conducted worldwide have revealed a threefold increase in the incidence of thyroid cancer. Strain elastography is a new imaging technique to identify benign and malignant thyroid nodules due to its sensitivity to tissue stiffness. However, there are certain limitations of this technique, particularly in terms of standardization of the compression process, evaluation of results and several assumptions used in commercial strain elastography modes for the purpose of simplifying imaging analysis. In this work, we propose a novel conditional generative adversarial network (TSE-GAN) for automatically generating thyroid strain elastograms, which adopts a global-to-local architecture to improve the ability of extracting multi-scale features and develops an adaptive deformable U-net structure in the sub-generator to apply effective deformation. Furthermore, we introduce a Lab-based loss function to induce the networks to generate realistic thyroid elastograms that conform to the probability distribution of the target domain. Qualitative and quantitative assessments are conducted on a clinical dataset provided by Shanghai Sixth People’s Hospital. Experimental results demonstrate that thyroid elastograms generated by the proposed TSE-GAN outperform state-of-the-art image translation methods in meeting the needs of clinical diagnostic applications and providing practical value