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We report results of magnetization and 19F NMR measurements in the normal state of as-grown vacuum-annealed LaO0.5⁢F0.5⁡BiS2. The magnetization is dominated by a temperature-independent diamagnetic component and a field- and temperature-dependent paramagnetic contribution 𝑀𝜇⁡(𝐻,𝑇) from a ∼1000 ppm concentration of local moments, an order of magnitude higher than can be accounted for by measured rare-earth impurity concentrations. 𝑀𝜇⁡(𝐻,𝑇) can be fit by the Brillouin function 𝐵𝐽⁡(𝑥) or, perhaps more realistically, a two-level tanh⁡(𝑥) model for magnetic Bi 6⁢𝑝 ions in defect crystal fields. Both fits require a phenomenological Curie-Weiss argument 𝑥=𝜇eff⁢𝐻⁡/(𝑇+𝑇𝑊), 𝑇𝑊≈1.7 K. There is no evidence for magnetic order down to 2 K, and the origin of 𝑇𝑊 is not clear. 19F frequency shifts, linewidths, and spin-lattice relaxation rates are consistent with purely dipolar 19F/defect-spin interactions. The defect-spin correlation time 𝜏𝑐⁡(𝑇) obtained from 19F spin-lattice relaxation rates obeys the Korringa relation 𝜏𝑐⁢𝑇=const, indicating the relaxation is dominated by conduction-band fluctuations. 

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs. 

A new matrix framework is presented in this studyfor the improved ionization efficiency of complex mixtures by matrix-assisted laser desorption ionization (MALDI) mass spectrometry/imaging. Five nitro indole (NI) derivatives [3-methyl-4-nitro-1H-indole (3,4-MNI), 3-methyl-6-nitro-1H-indole(3,6-MNI), 2,3-dimethyl-4-nitro-1H-indole (2,3,4-DMNI), 2,3-dimethyl-6-nitro-1H-indole (2,3,6-DMNI), and 4-nitro-1H-indole(4-NI)] were synthesized and shown to produce both positive and negative ions with a broad class of analytes as MALDI matrices. NI matrices were compared to several common matrices, such as 2,5-dihydroxybenzoic acid (DHB), alpha-cyano-4-hydroxylcinnamicacid (CHCA), sinapinic acid (SA), 1,5-diaminonaphthelene (1,5-DAN), and 9-aminoacridine (9-AA), for the analysis of lipid, peptide, protein, glycan, and perfluorooctanesulfonic acid (PFOS) compounds. 3,4-MNI demonstrated the best performance among the NI matrices. This matrix resulted in reduced ion suppression and better detection sensitivity for complex mixtures, for example, egg lipids/milk proteins/PFOS in tap water, while 2,3,6-DMNI was the best matrix for blueberry tissue imaging. Several important aspects of this work are reported: (1) dual-polarity ion production with NI matrices and complex mixtures; (2) quantitative analysis of PFOS with a LOQ of 0.5 ppb in tap water and 0.05 ppb in MQ water (without solid phase extraction enrichment), with accuracy and precision within 5%; (3) MALDI imaging with 2,3,6-DMNI as a matrix for plant metabolite/lipid identification with ionization enhancement in the negative ion mode m/z 600−900 region; and (4) development of a thin film deposition under/above tissue method for MALDI imaging with a vacuum sublimation matrix on a high-vacuum MALDI instrument. 

Understanding the development and breakup of interfacial waves in a two-phase mixing layer between the gas and liquid streams is paramount to atomization. Due to the velocity difference between the two streams, the shear on the interface triggers a longitudinal instability, which develops to interfacial waves that propagate downstream. As the interfacial waves grow spatially, transverse modulations arise, turning the interfacial waves from quasi-two-dimensional to fully three-dimensional. The inlet gas turbulence intensity has a strong impact on the interfacial instability. Therefore, parametric direct numerical simulations are performed in the present study to systematically investigate the effect of the inlet gas turbulence on the formation, development and breakup of the interfacial waves. The open-source multiphase flow solver, PARIS, is used for the simulations and the mass–momentum consistent volume-of-fluid method is used to capture the sharp gas–liquid interfaces. Two computational domain widths are considered and the wide domain will allow a detailed study of the transverse development of the interfacial waves. The dominant frequency and spatial growth rate of the longitudinal instability are found to increase with the inlet gas turbulence intensity. The dominant transverse wavenumber, determined by the Rayleigh–Taylor instability, scales with the longitudinal frequency, so it also increases with the inlet gas turbulence intensity. The holes formed in the liquid sheet are important to the disintegration of the interfacial waves. The hole formation is influenced by the inlet gas turbulence. As a result, the sheet breakup dynamics and the statistics of the droplets formed also change accordingly. 

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.