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Manipulating the thermal emission in the infrared (IR) range significantly impacts both fundamental scientific research and various technological applications, including IR thermal camouflage, information encryption, and radiative cooling. While prior research has put forth numerous materials and structures for these objectives, the significant challenge lies in attaining spatially resolved and dynamically multilevel control over their thermal emissions. In this study, a one-step ultrafast laser writing technique is experimentally demonstrated to achieve position-selective control over thermal emission based on the phase-change material Ge2Sb2Te5 (GST). Ultrafast laser writing technique enables direct fabrication and manipulation of laser-induced crystalline micro/nano-structures on GST films. Thermal emission can be precisely controlled by adjusting the pulse energy of the ultrafast laser, achieving a high thermal emissivity modulation precision of 0.0014. By controlling thermal emission, the ultrafast laser writing technique enables multilevel patterned processing. This provides a promising approach for multilevel IR thermal camouflage, which is demonstrated with emissivity-modulated GST emitters. Remarkably, ultrafast laser-induced crystalline micro/nano-structures display geometric grating features, resulting in a diffraction-based structural color effect. This study demonstrates the effective use of laser-printed patterns for storing information in both visible and infrared spectrum.

A negative index metamaterial (NIM) at ultraviolet range is constructed with stacked plasmonic waveguides. Based on the waveguides performing antisymmetric modes, the negative refractions of both wavevector and energy flow are realized when a TM-polarized light with a wavelength of 365 nm incidents on the plane of the layers. It is proved that the NIM could be introduced into subwavelength photolithography for extending working distance. Both theoretical and experimental results indicate that the patterns with a feature size of 160 nm can be reproduced in photoresist with a 100 nm-thick air working distance. Moreover, arbitrary two-dimensional patterns with a depth reach 160 nm can be obtained without diffraction fringe by employing a nonpolarized light. This design gives new insights into the manipulation of light. The improved working distance, well-shaped patterns over large area present an innovative method for improving subwavelength photolithography.

Background: The mechanical properties of cellular microenvironments play important roles in regulating cellular functions. Studies of the molecular response of endothelial cells to alterations in substrate stiffness could shed new light on the development of cardiovascular disease. Quantitative real-time PCR is a current technique that is widely used in gene expression assessment, and its accuracy is highly dependent upon the selection of appropriate reference genes for gene expression normalization. This study aimed to evaluate and identify optimal reference genes for use in studies of the response of endothelial cells to alterations in substrate stiffness. Methodology/Principal Findings: Four algorithms, GeNormPLUS, NormFinder, BestKeeper, and the Comparative ΔCt method, were employed to evaluate the expression of nine candidate genes. We observed that the stability of potential reference genes varied significantly in human umbilical vein endothelial cells on substrates with different stiffness. B2M, HPRT-1, and YWHAZ are suitable for normalization in this experimental setting. Meanwhile, we normalized the expression of YAP and CTGF using various reference genes and demonstrated that the relative quantification varied according to the reference genes. Conclusion/Significance:: Consequently, our data show for the first time that B2M, HPRT-1, and YWHAZ are a set of stably expressed reference genes for accurate gene expression normalization in studies exploring the effect of subendothelial matrix stiffening on endothelial cell function. We furthermore caution against the use of GAPDH and ACTB for gene expression normalization in this experimental setting because of the low expression stability in this study. [ABSTRACT FROM AUTHOR]

C6H5BrFNO3, orthorhombic, P212121 (no. 19), a = 3.9894(4) Å, b = 13.6128(11) Å, c = 14.7495(12) Å, V = 801.00(12) Å3, Z = 4, Rgt(F) = 0.0213, wRref(F2) = 0.0465, T = 150(2) K.

Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we propose a lossless compression method for SRS images based on deep learning. First, we use the difference algorithm to reduce the linear correlation within the image sequence. Then we propose a reversible truncated mapping method to reduce the range of the pixel value distribution. Thirdly, we train a deep learning model to learn the nonlinear relationship within the image sequence. Finally, we use the probability distribution predicted by the deep leaning model combined with arithmetic coding to fulfil lossless compression. Test result based on SRS images shows that our method can further decrease 20% of the data size compared to PNG, JPEG2000 and FLIF.

We discuss the state-of-the-art and remaining challenges in the fundamental understanding and technology development for controlling light-matter interactions in nanophotonic environments in and away from thermal equilibrium. The topics covered range from the basics of the thermodynamics of light emission and absorption to applications in solar thermal energy generation, thermophotovoltaics, optical refrigeration, personalized cooling technologies, development of coherent incandescent light sources, and spinoptics.

High energy physics (HEP) experiments, such as LHAASO, produce a large amount of data, which is usually stored and processed on distributed sites. Nowadays, the distributed data management system faces some challenges such as global file namespace, efficient data access and storage. Focusing on those problems, this paper proposed a cross-domain data access file system (CDFS), applying data deduplication and compression as the storage-optimized engine, aiming at dynamically building an aggregate view of multiple distributed storages and accessing data in a fast and efficient way. The test based on the raw data of LHAASO experiment showed that the CDFS could present a unique repository based on distributed sites in LHAASO. And the storage-optimized engine reduces the storage consumption of the raw data by more than 50%.

High-energy physics computing is a typical data-intensive calculation. Each year, petabytes of data needs to be analyzed, and data access performance is increasingly demanding. The tiered storage system scheme for building a unified namespace has been widely adopted. Generally, data is stored on storage devices with different performances and different prices according to different access frequency. When the heat of the data changes, the data is then migrated to the appropriate storage tier. At present, heuristic algorithms based on artificial experience are widely used in data heat prediction. Due to the differences in computing models of different users, the accuracy of prediction is low. A method for predicting future access popularity based on file access characteristics with the help of LSTM deep learning algorithm is proposed as the basis for data migration in hierarchical storage. This paper uses the real data of high-energy physics experiment LHAASO as an example for comparative testing. The results show that under the same test conditions, the model has higher prediction accuracy and stronger applicability than existing prediction models.

C26H27NO4, monoclinic, P21/c (no. 14), a = 12.034(4) Å, b = 15.247(5) Å, c = 12.991(4) Å, β = 115.379(6)°, V = 2153.6(12) Å3, Z = 4, Rgt(F) = 0.0531, wRref(F2) = 0.1447, T = 296 K.

C21H16BrNO5, monoclinic, P21/c (no. 14), a = 10.146(5) Å, b = 8.188(4) Å, c = 22.204(4) Å, β = 90.720(9)°, V = 1844.4(12) Å3, Z = 4, Rgt(F) = 0.051, wRref(F2) = 0.142, T = 296(2) K.

The tendency of the waveform curve can directly reflect the deformation and failure process of specimen in the SHPB (Split Hopkinson Pressure Bar) test of concrete. Different loading rates will result in the different ultimate failure modes, waveform curves. Furthermore, these differences are obviously characterized by some feature points of waveform or stress-strain curves. It is to say for concrete-like damage softening materials, the waveform features contains lots of information of material response. In this study, large dimension (Ф120mm) SHPB tests of concrete specimens have been conducted. Four typical failure patterns of concrete specimens are classified, as well as some typical waveform features, e.g. the “double-peak” and“compression wave” phenomena of reflection wave, etc. On the other hand, the numerical simulations corresponding to the experimental tests are performed by means of the 3D meso-scale model of concrete material. In the numerical results, waveform features observed in experiment are reliably reproduced and predicted. Associating with waveform features, the violation indicator of the specimen stress equilibrium in the SHPB test is first identified for concrete-like damage softening materials. The concrete material behaviors forstress non-equilibrium are further analyzed, e.g. DIF and damage development, etc.

A research has been conducted to develop an 8MW electron cyclotron resonance heating and current drive (ECRH/ECCD) system on HL-2M tokamak. The ECRH system compromise eight 1MW gyrotrons, eight evacuated transmission lines and three launchers. The main purpose of the ECRH system was to suppress the neo-classical tearing modes and control the plasma profile. This paper presents an overview of the design and studies performed in this framework. Some primary test results of the critical components have been released in this paper, e.g. polarizers, power monitor and fast steering launchers.

Electron Cyclotron Resonance Heating (ECRH) is one of the main auxiliary heating systems for HL-2A tokamak. The ECRH system with total output power 5MW has been equipped on HL-2A which include 6 sets of 0.5MW/1.0s at a frequency of 68GHz and 2 sets of 1MW/3s at a frequency of 140GHz. The power is one of important parameters in ECRH system. In this paper, the method for measuring the power of ECRH system on HL-2A is introduced which include calorimetric techniques and directional coupler. Calorimetric techniques is an existing method, which is used successfully in ECRH commissioning and experiment, and the transmission efficiency of ECRH system is achieved by measuring the absorbed microwave power in the Match Optical Unit (MOU), gyrotron output window and tours window of the EC system use this method. Now base on the theory of electromagnetic coupling through apertures, directional couplers are being designed, which is a new way for us.

C15H12N2O2 monoclinic, P21/n (no. 14), a = 12.343(4) Å, b = 8.172(3) Å, c = 13.375(5) Å, β = 114.466(4)°, V = 1228.0 Å3, Z = 4, Rgt(F) = 0.048, wRref(F2) = 0.127, T = 296 K.