Neutron Energy Spectrum Measurements with a Compact Liquid Scintillation Detector on EAST

A neutron detector based on EJ301 liquid scintillator has been employed at EAST to measure the neutron energy spectrum for D-D fusion plasma. The detector was carefully characterized in different quasi-monoenergetic neutron fields generated by a 4.5 MV Van de Graaff accelerator. In recent experimental campaigns, due to the low neutron yield at EAST, a new shielding device was designed and located as close as possible to the tokamak to enhance the count rate of the spectrometer. The fluence of neutrons and gamma-rays was measured with the liquid neutron spectrometer and was consistent with 3He proportional counter and NaI (Tl) gamma-ray spectrometer measurements. Plasma ion temperature values were deduced from the neutron spectrum in discharges with lower hybrid wave injection and ion cyclotron resonance heating. Scattered neutron spectra were simulated by the Monte Carlo transport Code, and they were well verified by the pulse height measurements at low energies.Comment: 19 pages,10 figures, 1 tabl

Activity of Platinum/Carbon and Palladium/Carbon Catalysts Promoted by Ni2P in Direct Ethanol Fuel Cells

Ethanol is an alternative fuel for direct alcohol fuel cells, in which the electrode materials are commonly based on Pt or Pd. Owing to the excellent promotion effect of Ni2P that was found in methanol oxidation, we extended the catalyst system of Pt or Pd modified by Ni2P in direct ethanol fuel cells. The Ni2P-promoted catalysts were compared to commercial catalysts as well as to reference catalysts promoted with only Ni or only P. Among the studied catalysts, Pt/C and Pd/C modified by Ni2P (30 wt%) showed both the highest activity and stability. Upon integration into the anode of a homemade direct ethanol fuel cell, the Pt-Ni2P/C-30% catalyst showed a maximum power density of 21 mWcm^-2, which is approximately two times higher than that of a commercial Pt/C catalyst. The Pd- Ni2P/C-30% catalyst exhibited a maximum power density of 90 mWcm^-2. This is approximately 1.5 times higher than that of a commercial Pd/C catalyst. The discharge stability on both two catalysts was also greatly improved over a 12 h discharge operation

Deposition of Metallic Nanoparticles on Carbon Nanotubes Via a Fast Evaporation Process

A new technique was developed for the deposition of colloidal metal nanoparticles on carbon nanotubes. It involves fast evaporation of a suspension containing sonochemically functionalized carbon nanotubes and colloidal nanoparticles. It was demonstrated that metallic nanoparticles with different sizes and concentrations can be deposited on the carbon nanotubes with only a few agglomerates. the technique does not seem to be limited by what the nanoparticles are, and therefore would be applicable to the deposition of other nanoparticles on carbon nanotubes. PtPd and CoPt3 alloy nanoparticles were used to demonstrate the deposition process. It was found that the surfactants used to disperse the nanoparticles can hinder the nanoparticle deposition. When the nanoparticles were washed with ethanol, they could be well deposited on the carbon nanotubes. the obtained carbon nanotube supported metal nanoparticles were characterized by transmission electron microscopy, energy dispersive x-ray spectroscopy, x-ray photoelectron spectroscopy, and cyclic voltammetry. © IOP Publishing Ltd

Development of P-I Diagrams for Framed PVB-Laminated Glass Windows

This paper investigates isodamage criteria for framed PVB (polyvinyl butyral) laminated glass panels subjected to blast load. Isodamage criteria are presented in the form of pressure-impulse (P-I) diagrams, and a methodology for the generation of the P-I diagrams for laminated glass was developed based on numerical simulation studies and the energy method. Three damage levels were classified in accordance with the conditions identified in standards, namely (1) the glass crack limit, (2) the PVB rupture limit, and (3) overall detachment with a specific velocity after the PVB ruptures. Based on nonlinear finite-element analysis, the governing failure modes of the glass panel in both impulsive and quasi-static regions for each damage level were identified and the corresponding deflection functions were determined. A simplified PVB tensile bar model is proposed to describe the local tensile failure of PVB laminated glass corresponding to Damage Level III under impulsive loading. The pressure and impulse asymptotes of framed PVB-laminated glass for different damage levels were derived using the energy balance principle. The proposed method was validated through comparison with published experimental data and further numerical results. This method can provide a reference for engineering design and hazard estimation of framed PVB-laminated glass against blast loading and can be extended to laminated glazing with other interlayers.<br/

Continuous Attributes Discretization Algorithm based on FPGA

The paper addresses the problem of Discretization of continuous attributes in rough set. Discretization of continuous attributes is an important part of rough set theory because most of data that we usually gain are continuous data. In order to improve processing speed of discretization, we propose a FPGA-based discretization algorithm of continuous attributes making use of the speed advantage of FPGA. Combined attributes dependency degree of rough ret, the discretization system was divided into eight modules according to block design. This method can save much time of pretreatment in rough set and improve operation efficiency. Extensive experiments on a certain fighter fault diagnosis validate the effectiveness of the algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2811

MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization

Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information especially under low light conditions. Most of the available multispectral pedestrian detectors are based on non-end-to-end detectors, while in this paper, we propose MultiSpectral pedestrian DEtection TRansformer (MS-DETR), an end-to-end multispectral pedestrian detector, which extends DETR into the field of multi-modal detection. MS-DETR consists of two modality-specific backbones and Transformer encoders, followed by a multi-modal Transformer decoder, and the visible and thermal features are fused in the multi-modal Transformer decoder. To well resist the misalignment between multi-modal images, we design a loosely coupled fusion strategy by sparsely sampling some keypoints from multi-modal features independently and fusing them with adaptively learned attention weights. Moreover, based on the insight that not only different modalities, but also different pedestrian instances tend to have different confidence scores to final detection, we further propose an instance-aware modality-balanced optimization strategy, which preserves visible and thermal decoder branches and aligns their predicted slots through an instance-wise dynamic loss. Our end-to-end MS-DETR shows superior performance on the challenging KAIST, CVC-14 and LLVIP benchmark datasets. The source code is available at https://github.com/YinghuiXing/MS-DETR

Analysis of Kinase Gene Expression in the Frontal Cortex of Suicide Victims: Implications of Fear and Stress†

Suicide is a serious public health issue that results from an interaction between multiple risk factors including individual vulnerabilities to complex feelings of hopelessness, fear, and stress. Although kinase genes have been implicated in fear and stress, including the consolidation and extinction of fearful memories, expression profiles of those genes in the brain of suicide victims are less clear. Using gene expression microarray data from the Online Stanley Genomics Database1 and a quantitative PCR, we investigated the expression profiles of multiple kinase genes including the calcium calmodulin-dependent kinase (CAMK), the cyclin-dependent kinase, the mitogen-activated protein kinase (MAPK), and the protein kinase C (PKC) in the prefrontal cortex (PFC) of mood disorder patients died with suicide (N = 45) and without suicide (N = 38). We also investigated the expression pattern of the same genes in the PFC of developing humans ranging in age from birth to 49 year (N = 46). The expression levels of CAMK2B, CDK5, MAPK9, and PRKCI were increased in the PFC of suicide victims as compared to non-suicide controls (false discovery rate, FDR-adjusted p < 0.05, fold change >1.1). Those genes also showed changes in expression pattern during the postnatal development (FDR-adjusted p < 0.05). These results suggest that multiple kinase genes undergo age-dependent changes in normal brains as well as pathological changes in suicide brains. These findings may provide an important link to protein kinases known to be important for the development of fear memory, stress associated neural plasticity, and up-regulation in the PFC of suicide victims. More research is needed to better understand the functional role of these kinase genes that may be associated with the pathophysiology of suicide

Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently proposed method named Context Optimization (CoOp) introduces a set of learnable vectors as text prompt from the language side. However, tuning the text prompt alone can only adjust the synthesized "classifier", while the computed visual features of the image encoder can not be affected , thus leading to sub-optimal solutions. In this paper, we propose a novel Dual-modality Prompt Tuning (DPT) paradigm through learning text and visual prompts simultaneously. To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information. Extensive experimental results on 11 datasets demonstrate the effectiveness and generalization ability of the proposed method. Our code is available in https://github.com/fanrena/DPT.Comment: 12 pages, 7 figure