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Paper-based fluorescence devices, with smartphone aids, bring considerable operation convenience for tetracycline (TC) sensing. Nevertheless, they must meet the challenge in real determination against complicated backgrounds. Considering that, we present a programmable-printing paper-based device and then apply it to TC determination for various natural samples. MoS2 NPs and Gmp/Eu-Cit are synthetized as composite probes. A static quenching process is found with MoS2 NP fluorescence at 430 nm, while significant magnification of Gmp/Eu-Cit emission is obtained at 617 nm, establishing a valuable ratiometric indicator. Remarkably, two-stage programmable printing maximizes the proposed sensing capability. A transitive device, containing a gradually changing amount of a certain probe, is prepared to sense TC. With a homemade smartphone application and 3D-printed measurement chamber, the corresponding signals are examined to explore optimal setups. These setups are automatically processed to prepare the final-version device, not requiring manual operations. Benefitting from this interesting feature, the proposed device gains many rewards in performances. It effectively senses TC in a wide range from 12.7 nM to 80 μM and simultaneously provides naked eye-legible signals and smartphone-based readouts with confident selectivity and stability. This device is consequently applied for various samples of soil, river water, milk, and serum and meets well with HPLC-MS and recovery tests.

This study utilized equivalent umami concentrations (EUC) to characterize umami intensity in salmon with different freeze-thaw times. A rapid and non-destructive method was established to determine EUC values in salmon which is based on hyperspectral imaging (HSI) system combined with multiple characteristic variable screening methods. The established CARS-PLS model showed greater advantages in correlating the reference values of spectral data with EUC in salmon with Rc of 0.9012, Rp of 0.9009, RMSECV of 0.82, and RMSEP of 0.88. The model was employed pixel-wise to visualize the distribution of EUC with different freeze-thaw times, which demonstrated the reduction of EUC value with the increasing of freeze-thaw times. Therefore, this reseearch showed hyperspectral imaging (HSI) system combined with chemometrics possesses a substantial capability to predict and visualize the EUC of salmon, which would provide an intuitive understanding of salmon quality prediction and detection.

Inputs of organic materials are recommended to increase soil carbon (C) sequestration and crop growth. However, organic amendments may increase greenhouse gas (GHG) emissions. To comprehensively evaluate the effect of organic amendments on the net ecosystem C budget (NECB) and net global warming potential (net GWP) in a successive tobacco cultivation system, methane (CH4) and nitrous oxide (N2O) fluxes and main components of the NECB under different treatments were measured in a pot experiment. The experiment with cultivated tobacco plants included five treatments: no fertiliser (NF), NPK, NPK plus wheat straw, NPK plus sesame cake (NPKC) and NPK plus biochar made from tobacco stalks. The results of NECB and estimated SOC changes implied that the tobacco-cultivated ecosystems acted as a C source under the NF, NPK and NPKC treatments, whereas the systems acted as a C sink with the application of straw and biochar. N2O emissions were significantly increased by 53.7% and 96.2% in soils amended with straw and sesame cake respectively. Biochar addition had no effect on N2O emissions. CH4 uptake was not affected by organic amendments. As a result, the application of straw and biochar significantly decreased the net GWP and GHG intensity, but sesame cake amendments had no such significant effects. Therefore, in comparison to the other treatments, the straw and biochar treatments were more effective GHG mitigation practices that increased the yield and NECB in the tobacco cultivation system, and this was especially true for the biochar treatment.

Paper-based fluorescence devices, with smartphone aids, bring considerable operation convenience for tetracycline (TC) sensing. Nevertheless, they must meet the challenge in real determination against complicated backgrounds. Considering that, we present a programmable-printing paper-based device and then apply it to TC determination for various natural samples. MoS

In this paper, a two-phase stepper motor drive controller which can be used in 6000m deep-sea electric Pan&Tilt is designed. The two-phase stepper motor drive controller adopts the architecture of MCU combined with Universal high voltage driver of two-phase bipolar stepper motor, interacts with external control unit through RS-485 communication bus, and uses Hall magnetic angle sensor to perform the position closed loop control of the stepper motor, thereby achieving high precision positioning of the deep-sea electric Pan&Tilt under water. The hydrostatic pressure test shows that the two-phase stepper motor drive controller has withstood the test of 75Mpa, and has the feasibility of application on the 6000m deep-sea electric Pan&Tilt.

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales. The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB). Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less. The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method. In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone. When testing with a lower resolution (300 × 300), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.

A simple and sensitive electrochemical sensor is designed for evaluating nitrite (NO2−) in cured meat by using the functionalized mesoporous carbon composites (CMK-8) as sensing interface. Herein, the synthesized CMK-8 with 3-D bicontinuous cubic (Ia3d) space groups has obvious electrochemical superiority over the usual CMK-3 with 2-D hexagonal (p6mm) space groups, and offers a powerful platform for gold nanoparticles (AuNPs) decoration inside and outside the ordered carbon pores via electrostatic adsorption and size matching. Benefitting from the synergistic effects of AuNPs and CMK-8 under suitable ratio, AuNPs-CMK-8 provides a favorable biocompatible microenvironment for hemoglobin (Hb) attachment, and also accelerates electron transfer between NO2− and active centers of Hb. The electrochemical determination is accomplished through one-step catalytic process without complicated operation or multiple reagents, greatly improving temporal efficiency. The limit of detection (LOD) as low as 2.1 nM (corresponding to 0.097 ppb by weight) is well qualified for the requirement for rapid monitoring nitrite in cured meat. Overall, the sensing strategy possesses the characteristics of excellent sensitivity, anti-interference ability, and single-step fashion, giving a promising solution to evaluate nitrite residual for ensuring cured meat safety.

Object detection across different scales is challenging as the variances of object scales. Thus, a novel detection network, Top-Down Feature Fusion Single Shot MultiBox Detector (TDFSSD), is proposed. The proposed network is based on Single Shot MultiBox Detector (SSD) using VGG-16 as backbone with a novel, simple yet efficient feature fusion module, namely, the Top-Down Feature Fusion Module. The proposed module fuses features from higher-level features, containing semantic information, to lower-level features, containing boundary information, iteratively. Extensive experiments have been conducted on PASCAL VOC2007, PASCAL VOC2012, and MS COCO datasets to demonstrate the efficiency of the proposed method. The proposed TDFSSD network is trained end to end and outperforms the state-of-the-art methods across the three datasets. The TDFSSD network achieves 81.7% and 80.1% mAPs on VOC2007 and 2012 respectively, which outperforms the reported best results of both one-stage and two-stage frameworks. In the meantime, it achieves 33.4% mAP on MS COCO test-dev, especially 17.2% average precision (AP) on small objects. Thus all the results show the efficiency of the proposed method on object detection. Code and model are available at: https://github.com/dongfengxijian/TDFSSD .