Simultaneous Determination of Ultraviolet Absorbers and Antibacterial Agents in Textiles by Ultra-High Performance Liquid Chromatography/Orbitrap High Resolution Mass Spectrometry

This paper reported a new analytical method for the simultaneous determination of seven benzotriazole ultraviolet absorbers and seven antibacterial agents in textiles. After ultrasonic extraction for the textile samples in methanol, the solutions were analyzed by ultra-high performance liquid chromotagraphy/orbitrap high resolution mass spectrometry (UPLC/Orbitrap HRMS). It showed that a good chromatographic separation for these target compounds was achieved by a Hypersil GOLD column (100 mm × 2.1 mm × 1.9 μm) with a gradient elution of methanol and 0.1% aqueous formic acid solution (containing 0.5 mmol/L ammonium acetate). Triclosan and 4-chloro-3,5-dimethyl phenol (PCMX) were detected by the orbitrap HRMS in an electrospray ionization (ESI) negative mode while the other twelve target compounds were detected by orbitrap HRMS in ESI positive mode. Full scan experiment was performed over the range from m/z 100 to m/z 500. These target compounds were routinely detected with mass accuracy below 2 × 10-6 (2 ppm) at the optimized conditions. The results showed that the limits of detection (LODs) were in the range from 0.1 to 0.3 μg/kg. The blank samples were spiked at three levels and their average recoveries varied from 80.5% to 96.3% while the relative standard deviation (RSD) changed from 3.2% to 9.9%. The present method was also applied for the determination of those ultraviolet absorbers and antibacterial agents in the commercial textiles.

Application of Ultra-High Resolution STXM-NEXAFS and Nano-SIMS on the Mapping of Carbon Related Samples

Nano-Scale mapping of minerals and organic compounds give unprecedented high resolution information on the origin and nature of substances,and provide new insight on their correlative distribution and interaction,thus present a powerful tool to study the progressive changes of geological samples,and may even be applied to study extraterrestrial samples in search of life.One example we present here explore the use of elemental microprobe,X-Ray Photon Spectroscopy(XPS),and synchrotron-based Scanning Transmission X-ray Microscopy(STXM) coupled with Near Edge X-ray Absorption Fine Structure(NEXAFS) Spectroscopy to investigate the surface properties and stability of micron-size organic carbonaceous particles from Central Amazon,Brazil,specifically focusing on black carbon in Kaolinitic Oxisol originated from anthropogenic processes,and their interaction with cations.

超高精度桩柱结合体施工关键问题及对策

在逆作法中,变截面变强度超高精度桩柱结合体得到了越来越广泛的应用,但是,实践中会遇到3个关键问题:准确定位和顺利下放钢管柱,确保立柱桩桩身和钢管柱混凝土完整性,确保钢管柱垂直度达到1/1200~1/1000的超高精度要求。基于超高分辨率倾角仪、综合平台系统和辅助对策,解决了上述3个关键问题,而且对策成本低,使用方便快捷,值得推广。

Ultra-high resolution microwave photonic radar with post-bandwidth synthesis

We demonstrate microwave photonic radar with post-bandwidth synthesis, which can realize target detection with ultra-high range resolution using relatively small-bandwidth radio frequency(RF) frontends. In the proposed radar, two temporal-overlapped linear frequency-modulated(LFM) signals with the same chirp rate and different center frequencies are transmitted. By post-processing the de-chirped echoes in the receiver, a signal equivalent to that de-chirped from an LFM signal with the combined bandwidth is achieved. In a proof-ofconcept experiment, two LFM signals with bandwidths of 8.4 GHz are exploited to achieve radar detection with an equivalent bandwidth of 16 GHz, and a range resolution of 1 cm is obtained.

Ultra-high range resolution demonstration of a photonics-based microwave radar using a high-repetition-rate mode-locked fiber laser

We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser（AMLL）. The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression（MLS） algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.

新型岩土工程FBG测斜传感器的开发与验证

边坡内部角度的测量是决定边坡安全预警的重要因素,本文考虑边坡上覆压力的几种工况,结合GEO-studio软件加载实验的模拟边坡加载实验结果,设计了一种使用3D打印技术封装的FBG测斜传感器,通过标定实验得到角度与FBG波长的线性关系。模拟边坡加载实验分析了上部荷载对新型FBG传感器波长变化的影响,并将结果与软件模拟结果进行对比分析,发现两种边坡加载实验的结果趋于一致。该新型传感器成功的消除了温度变化的影响。

高精度倾斜仪探头信号采集处理电路的设计

为实现倾斜仪探头高线性度、高分辨率和高稳定度的角度测量要求,以石英挠性加速度计作为传感器,设计了一种以STM32为主控制器的倾斜仪探头信号采集处理电路,详细阐述了硬件设计和软件设计。加速度计感应到的重力加速度信号完成信号调理和A/D转换,对转换得到的数字信号进行倾斜角度解算、数字滤波和温度补偿后,通过D/A转换得到倾斜角度值(模拟电压信号)。经试验验证,该倾斜仪探头测量角度值的非线性偏差小于0.001°,分辨率达0.000 3°,最大温度漂移为0.010 9°。

MRm-DLDet:a memory-resident malware detection framework based on memory forensics and deep neural network

Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent years.One popular evasion method is to execute malicious code and perform malicious actions only in memory.Mali-cious programs that use this attack method are called memory-resident malware,with excellent evasion capability,and have posed huge threats to cyber security.Traditional static and dynamic methods are not effective in detect-ing memory-resident malware.In addition,existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory analysis.This paper proposes MRm-DLDet,a state-of-the-art memory-resident malware detection framework,to overcome these drawbacks.MRm-DLDet first builds a virtual machine environment and captures memory dumps,then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping,followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect them.MRmNet receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18,which extracts the features of the sub-images.Then trains a network of gated recurrent units with an attention mechanism.Finally,it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting layer.We created a high-quality dataset consisting of 2,060 benign and memory-resident programs.In other words,the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB images.We implement MRm-DLDet for Windows 10,and it performs better than the latest methods,with a detection accuracy of up to 98.34%.Moreover,we measured the effects of mimicry and adversarial attacks on MRm-DLDet,and the experimental results demonstrated the robustness of MRm-DLDet.

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography(UHR-OCT)imaging data.Methods:A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups:normal(50 eyes),with keratoconus(38 eyes)or with subclinical keratoconus(33 eyes).All eyes were imaged with a Scheimpflug camera and UHR-OCT.Corneal morphological features were extracted from the imaging data.A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes.Fisher’s score was used to rank the differentiable power of each feature.The receiver operating characteristic(ROC)curves were calculated to obtain the area under the ROC curves(AUCs).Results:The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus(AUC=0.93).The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.Conclusion:The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes.The epithelial features extracted from the OCT images were the most valuable in the discrimination process.This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.