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LoRaWAN is a popular technology in Low Power Wide Area Networks(LPWANs). It can be applied to the smart agriculture, smart building, logistics tracking, disaster monitoring, and so on. The Class A terminal device in LoRaWAN adopts the pure ALOHA access mode. It has the problems of low channel utilization, high access delay and high collision probability. In this paper, we mainly study the access process of Class A devices in LoRaWAN, and derive the mathematical analytical model. To solve this problem, we propose a enhanced access mechanism, dynamic backoff mechanism. The backoff window size would be adaptively adjusted according to the access user attempts. The results show that our model is correct and the proposed scheme can effectively improve the successful access probability for LoRaWAN nodes.

The currently reported optical-phonon-scattering-limited carrier mobility of MoS

We report the thermal conductance induced by few-layered graphene (G) sandwiched between β-phase tungsten (β-W) films of 15, 30 and 40 nm thickness. Our differential characterization is able to distinguish the thermal conductance of β-W film and β-W/G interface. The cross-plane thermal conductivity (k) of β-W films is determined at 1.69~2.41 Wm−1K−1 which is much smaller than that of α-phase tungsten (174 Wm−1K−1). This small value is consistent with the large electrical resistivity reported for β-W in literatures and in this work. The β-W/β-W and β-W/G interface thermal conductance (G W/W and G W/G ) are characterized and compared using multilayered β-W films with and without sandwiched graphene layers. The average G W/W is found to be at 280 MW m−2K−1. G W/G features strong variation from sample to sample, and has a lower-limit of 84 MW m−2K−1, taking into consideration of the uncertainties. This is attributed to possible graphene structure damage and variation during graphene transfer and W sputtering. The difference between G2W/G and G W/W uncovers the finite thermal resistance induced by the graphene layer. Compared with up-to-date reported graphene interface thermal conductance, the β-W/G interface is at the high end in terms of local energy coupling.

We report on the discovery of asymmetries of Raman scattering along one scanning direction, between two scanning directions, and by structure variation of the sample in space. Asymmetry of Raman shift along the x direction, and the asymmetry of Raman shift and linewidth between the two scanning directions (x and y) are found for a 1210 nm diameter silica particle. The observed asymmetries are confirmed by further 2D Raman scanning of the same particle. To further explore the asymmetry of Raman scattering, glass fibers of three diameters (0.53, 1.00, and 3.20 μm) are scanned along two directions. The asymmetry of Raman shift along each direction, the asymmetry of linewidth along the y direction, and the asymmetry of Raman shift and linewidth between the two scanning directions are discovered. Additionally, 11 nm-thick MoSe2 nanosheets on silicon are used to discover whether an asymmetry of Raman scattering exists at the edge of the nanosheets. One edge of the nanosheet is scanned in four directions and the asymmetry of Raman scattering caused by the step variation is also detected. All the observed Raman scattering asymmetries are explained soundly by the Raman signal diffraction and image shift on the CCD detector arrays of the Raman spectrometer. In practice, to use scanning Raman for surface structure study, great measure has to be taken to consider the structure-induced asymmetries to uncover the real Raman wave number variation by intrinsic material structure. We propose a signal processing method by averaging the scanning points along four directions to eliminate the interference of the edge. This method works well to significantly suppress the asymmetries of Raman properties and uncover the real Raman signal change by structure variation.

Current polarized Raman-based techniques for identifying the crystalline orientation of black phosphorus suffer significant uncertainty and unreliability because of the complex interference involving excitation laser wavelength, scattering light wavelength, and sample thickness. Herein, for the first time, we present a new method, optothermal Raman spectroscopy (OT-Raman), for identifying crystalline orientation. With a physical mechanism based on the anisotropic optical absorption of the polarized laser and the resulting heating, the OT-Raman can identify the crystalline orientation explicitly, regardless of excitation wavelength and sample thickness, by Raman frequency-power differential Φ (=∂ω/∂P). The parameter Φ has the largest (smallest) value when the laser polarization is along the armchair (zigzag) direction. The OT-Raman technique is robust and is able to identify the crystalline orientation of BP samples with thicknesses up to 300 nm at a minimum and potentially as high as 1200 nm.

We report a novel approach for non-contact simultaneous determination of the hot carrier diffusion coefficient (D) and interface thermal resistance (R) of sub-10 nm virgin mechanically exfoliated MoS

Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.

Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.

The facile pyrolysis of a bipyridyl metal-organic framework, MOF-253, produces N-doped porous carbons (Cz-MOF-253), which exhibit excellent catalytic activity in the Knoevenagel condensation reaction and outperform other nitrogen-containing MOF-derived carbons. More importantly, by virtue of their high Lewis basicity and porous nature, Cz-MOF-253-supported Pd nanoparticles (Pd/Cz-MOF-253-800) show excellent performance in a one-pot sequential Knoevenagel condensation-hydrogenation reaction.

Heat dissipation issues are the emerging challenges in the field of flexible electronics. Thermal management of flexible electronics creates a demand for flexible materials with highly anisotropic thermal conductivity, which work as heat spreaders to remove excess heat in the in-plane direction and as heat shields to protect human skin or device components under them from heating. This study proposes a self-assembled silver nanowire network with high thermal and electrical anisotropy with the potential to solve these challenges. The in-plane thermal conductivity of the network along the axial direction of silver nanowires is measured as 37 W/m-K while the cross-plane thermal conductivity is only 0.36 W/m-K. The results of measurements of electrical and thermal conductivities suggest that abundant wire-wire contacts strongly impede thermal transport. The excellent alignment of nanowires results in the same anisotropy ratio of 3 for both thermal and electrical conduction in the two in-plane directions. The ratio remains unchanged as the temperature decrease to 50 K, which indicates that wire-wire contacts lower the thermal and electrical conduction in the two directions to the same extent and their effect is independent of temperature. In addition, phonon softening markedly reduces the Debye temperatures of the network, which are fitted from the electrical resistivity data. As a result of phonon thermal conduction, the Lorenz numbers of the film in the two directions, which are approximately the same, are larger than the Sommerfeld value at room temperature and decrease as temperature decreases because of small angle scattering and the reduced phonon contribution. This nanowire network provides a solution to the emerging challenges of thermal management of flexible electronics.

Frequency-resolved Raman spectroscopy (FR-Raman) is a new technique for nondestructive thermal characterization. Here, we apply this new technique to measure the anisotropic thermal conductivity of suspended nm-thick black phosphorus samples without the need of optical absorption and temperature coefficient. Four samples with thicknesses between 99.8 and 157.6 nm are studied. Based on steady state laser heating and Raman measurement of samples with a specifically designed thermal transport path, the thermal conductivity ratio (κZZ/κAC) is determined to be 1.86–3.06. Based on the FR-Raman measurements, the armchair thermal conductivity is measured as 14–22 W m−1 K−1, while the zigzag thermal conductivity is 40–63 W m−1 K−1. FR-Raman has great potential for studying the thermal properties of various nanomaterials. This study significantly advances our understanding of thermal transport in black phosphorus and facilitates the application of black phosphorus in novel devices.

The assembly line is two-sided with multi-parallel stations assembly line for large products such as heavy vehicles or loaders due to its large space and assembly space, which is very complicated. In order to minimize the assembly stations, a mixed integer linear programming model is set up and CPLEX algorithm based on branch and bound algorithm is presented. Taking a two sided with multi-parallel stations assembly line for loaders as example, the balancing scheme is achieved using the algorithm, and workshop space is saved, and the total time of whole line and transmission time loss are reduced, which is of important instruction significance to the actual manufacturing layout for the two-side with multi-parallel station assembly line.

Black phosphorus (BP) is one of new 2D materials that have attracted wide attention. This work reports the interfacial thermal conductance between BP flake and SiOx using Raman spectroscopy. From 293 K down to the 223 K, eight BP flakes in a thickness range of 16.6–113.7 nm are characterized. At 293 K, the largest interfacial thermal conductance is 1.14 × 108 W m−2 K−1 for a 82.1 nm thick BP flake, and the smallest one is 2.17 × 107 W m−2 K−1 for a 26.6 nm thick BP flake. Such large interfacial thermal conductance can be attributed to the excellent interface contact and strong phonon coupling between BP and SiOx. The measured interfacial thermal conductance has a one-fold up to around four-fold increase with decreased temperature from 293 to 223 K, which is a result of thermal-expansion-mismatch induced variation in the morphology of BP flakes. Additionally, it demonstrates no thickness-dependent behavior. It is speculated the intrinsic thickness dependence is weak and is overshadowed by the large variation in the interface contact of different samples. As a new 2D material, BP shows great potential to be a thermal interface material for heat dissipation in electronics.