Chemometric approach to the optimisation of LC-FL and GC-MS methods for the determination of nitrite and nitrate in some biological,food and environmental samples

Gas chromatography–mass spectrometry (GC-MS) method and a liquid chromatography–fluorescence (LC-FL) detection method using experimental design and optimisation approach were improved for the quantitative determination of nitrite and nitrate in biological, food and environmental samples. The obtained recoveries of nitrite and nitrate ions from samples based on both GC-MS and LC-FL results ranged from 98.5% to 98.9% for nitrite and 97.9% to 98.4% for nitrate. The precision of these methods, as indicated by the relative standard deviations (RSDs), was within the range from 2.4% to 3.6% for nitrite and 2.5% to 3.8% for nitrate, respectively. The limits of detection of nitrite and nitrate ions from samples based on GC-MS and LC-FL results ranged from 0.01 to 0.14 ng L^?1 for nitrite and 0.02 to 0.71 ng L^?1 for nitrate, respectively. The optimised isolation procedure by central composite design was successfully applied to real samples. The results revealed that the proposed procedure combined with GC-MS and LC-FL techniques is more sensitive, reliable and selective compared to the other methods available for the precise determination of trace levels of nitrite and nitrate in biological, food and environmental samples.     

Toll-like receptors for pathogen detection in water: challenges and benefits

The need for effective and efficient methods for pathogen detection in water is as serious as ever due to the health risk posed to human population by the consumption of pathogen-contaminated water. One of the important research streams which have been focused on by researchers for development of novel techniques for this purpose is biosensor technologies. Using different bio-recognition elements and transduction methodologies, biosensors have the potential to detect their analyte of interest in a fast and highly specific manner. Different pathogenic agents can be recognised by toll-like receptors (TLRs). The innate immune system of higher organisms employs TLRs for triggering intracellular signalling and induction of the expression of immune response genes. In this report, we explore the challenges associated with employing TLRs for pathogen detection in water samples. Although methods using TLR expressing cells also have been discussed, the focus of this review is on using TLR proteins as the bio-recognition elements in biosensors.     

Rapid determination of trace polycyclic aromatic hydrocarbons in particulate matter using accelerated solvent extraction followed by ultra high performance liquid chromatography with fluorescence detection

A method has been developed for the trace analysis of polycyclic aromatic hydrocarbons, which are known as persistent organic pollutants and ubiquitous constituents of fine particulate matter that causes growing airborne pollution. The method, which was especially for samples of airborne particulate matter less than 2.5 μm in diameter, utilized accelerated solvent extraction and ultra high performance liquid chromatography with fluorescence detector. Four principal parameters of accelerated solvent extraction were optimized to obtain maximum extraction efficiency. Using the established synergetic programs of gradient elution and fluorescence wavelength switching, a rapid separation was achieved in 6.56 min with good linearity for 15 polycyclic aromatic hydrocarbons (coefficient of determination above 0.999). The limits of detection ranged from 0.833 to 10.0 pg/m³. The precision of the method expressed as inter‐day relative standard deviation ranged from 0.2 to 1%, which was calculated from nine repetitive measurements of 8.00 μg/L analytes. Average spiked recoveries ranged from 71.6 to 97.7%, with the exception of naphthalene. The rapid, sensitive, and accurate method can meet the pressing needs of health risk assessment and increasingly heavy daily tasks of air quality monitoring.     

Current status of automotive fuel cells for sustainable transport

Automotive proton-exchange membrane fuel cells (PEMFCs) have finally reached a state of technological readiness where several major automotive companies are commercially leasing and selling fuel cell electric vehicles, including Toyota, Honda, and Hyundai. These now claim vehicle speed and acceleration, refueling time, driving range, and durability that rival conventional internal combustion engines and in most cases outperform battery electric vehicles. The residual challenges and areas of improvement which remain for PEMFCs are performance at high current density, durability, and cost. These are expected to be resolved over the coming decade while hydrogen infrastructure needs to become widely available. Here, we briefly discuss the status of automotive PEMFCs, misconceptions about the barriers that platinum usage creates, and the remaining hurdles for the technology to become broadly accepted and implemented.     

导电聚苯胺线膜修饰ZnO纳米棒阵列的可控制备及其紫外探测性能

基于p-n结的光生伏特效应可构筑性能优异的UV探测器,本文采用水热法可控制备竖直排列的氧化锌纳米棒阵列(n型ZnO-NRs),利用原位聚合法在ZnO-NRs表面上修饰p型聚苯胺线膜(PANI-NWs),再组装成ZnO-NRs与ZnO-NRs/PANI-NWs紫外探测器.通过扫描电镜(SEM)、X射线衍射仪(XRD)、紫...     

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.     

HMD-Net: A Vehicle Hazmat Marker Detection Benchmark

Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.     

Blind Speech Watermarking Method with Frame Self-Synchronization Based on Spread-Spectrum Using Linear Prediction Residue

State-of-the-art speech watermarking techniques enable speech signals to be authenticated and protected against any malicious attack to ensure secure speech communication. In general, reliable speech watermarking methods must satisfy four requirements: inaudibility, robustness, blind-detectability, and confidentiality. We previously proposed a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction (LP) scheme to solve the first two issues (inaudibility and robustness) due to distortion by spread spectrum. This method not only effectively embeds watermarks with small distortion but also has the same robustness as the DSS method. There are, however, two remaining issues with blind-detectability and confidentiality. In this work, we attempt to resolve these issues by developing an approach called the LP-DSS scheme, which takes two forms of data embedding for blind detection and frame synchronization. We incorporate blind detection with frame synchronization into the scheme to satisfy blind-detectability and incorporate two forms of data embedding process, front-side and back-side embedding for blind detection and frame synchronization, to satisfy confidentiality. We evaluated these improved processes by carrying out four objective tests (PESQ, LSD, Bit-error-rate, and accuracy of frame synchronization) to determine whether inaudibility and blind-detectability could be satisfied. We also evaluated all combinations with the two forms of data embedding for blind detection with frame synchronization by carrying out BER tests to determine whether confidentiality could be satisfied. Finally, we comparatively evaluated the proposed method by carrying out ten robustness tests against various processing and attacks. Our findings showed that an inaudible, robust, blindly detectable, and confidential speech watermarking method based on the proposed LP-DSS scheme could be achieved.     

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.