Measurement of elemental speciation by liquid chromatography: inductively coupled plasma mass spectrometry (LC-ICP-MS) with the direct injection nebulizer (DIN)

A new version of the direct injection nebulizer (DIN) is used to interface liquid chromatographic (LC) separations with element - selective detection by inductively coupled plasma - mass spectrometry (ICP-MS). The advantages of using LC-DIN-ICP-MS include little dead volume, low sample and solvent consumption, excellent plasma stability when nebulizing samples containing concentrated organic solvent, excellent absolute detection limits, excellent precision, superior chromatographic resolution, and reduced memory effects from memory-prone elements (e.g., Hg);Various compounds containing arsenic (AsO[subscript]2[superscript]-, HAsO[subscript]4[superscript]2-, CH[subscript]3AsO[subscript]3[superscript]2- and (CH[subscript]3)[subscript]2AsO[subscript]2[superscript]-), tin (CH[subscript]3Sn[superscript]3+, (CH[subscript]3)[subscript]2Sn[superscript]2+, (C[subscript]2H[subscript]5)[subscript]2Sn[superscript]2+ and (CH[subscript]3)[subscript]3Sn[superscript]+), mercury (Hg[superscript]+2, CH[subscript]3Hg[superscript]+, C[subscript]2H[subscript]5Hg[superscript]+ and C[subscript]6H[subscript]5Hg[superscript]+) and lead (Pb[superscript]+2, (CH[subscript]3)[subscript]3Pb[superscript]+ and (C[subscript]2H[subscript]5)[subscript]3Pb[superscript]+) are separated by reversed-phase ion-pairing LC. Selenium species (SeO[subscript]3[superscript]2- and SeO[subscript]4[superscript]2-) are separated by ion chromatography (IC). Several metalloproteins containing Na, Fe, Cu, Zn, Cd, Ba, and Pb are separated by size-exclusion chromatography (SEC). Some of these compounds are separated and measured in biological and environmental samples such as human urine and human serum. Detection limits are ≈3, 0.7, 1, 0.5, 5, 0.5, 15, 7, and 0.2 pg for Fe, Cu, Zn, As, Se, Cd, Sn, Hg, and Pb, respectively. Microbore (1-2 mm i.d.) packed columns are employed because the liquid flow rates used (30-100 [mu]L min[superscript]-1) are compatible with the DIN;Aerosol droplet sizes and velocities from a DIN are measured with radial and axial spatial resolution by phase doppler particle analysis (PDPA). The droplets on the central axis of the spray become finer and their size becomes more uniform when ≈20% methanol is added to the usual aqueous solvent. This could explain why the analyte signal is a maximum at this solvent composition when the DIN is used for ICP-MS. Mean droplet velocities are 12 to 22 m s[superscript]-1 with standard deviations of ±4 to ±7 m s[superscript]-1. The outer fringes of the aerosol plume tend to be enriched in large droplets. The Sauter mean diameter (D[subscript]3,2) and velocity of the droplets also vary substantially with axial position in the aerosol plume. These findings are valuable for improving the analytical performance of the DIN

Understanding Chinese gamblers’ adoption of online casinos based on e-marketing mix model

This paper presents a quantitative study of online casino adoption based on the e-marketing mix model. The Internet has changed the business context of many industries. Online casino is one such rapidly growing industry. Different e-marketing approaches have been widely adopted by online casinos to attract more customers. In China, there are twice as many online gamblers as there are online shoppers. Due to the high population in China, the market potential is huge. The purpose of this study is to evaluate the impact of Chinese gamblers’ perceptions of e-marketing mix elements on their adoption of online casinos. The results can provide a reference for investors to develop more effective online casino businesses

Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate

Simultaneous Measurement Of 3-D Displacement Components Caused By Crack Growth Using Circular Gratings Moire Fringes And Graphical Analysis

Cracking is well-known to be a major cause of failures in concrete structures. By monitoring the growth of cracks, early remedial measure can be taken to save the structure from failure. To sense all the three crack modes namely, opening mode, sliding mode and tearing mode, a method that is able to measure the 3-D displacement components simultaneously with a single setting is needed. The moiré pattern formed by overlapping two circular gratings with slightly different pitches is proposed as a measurement tool because the moiré pattern is repeatable and unique for a relative in-plane (2-D) displacement between the two gratings

Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities

Bingham sealing and application in vacuum clamping

Vacuum clamping is extensively used in shell machining. In this paper a Bingham Sealing (BS) is presented and formulized based on Bingham plastic performance. The sealing capability of BS is evaluated in various cases. A new Bingham plastic is developed and the yield stress is measured. The performances of "O"ring sealing and BS with the developed Bingham plastic are compared to the static experiment. In this experiment the same vacuum is achieved and the distortion of the blade with BS is better than that with "O" ring sealing

Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities

Online synchronous inspection and system optimization of flexible food packaging bags by using machine vision and sensing technique

Flexible food packaging in the market is increasingly favored, and its quality is essential and indispensable for safety and convenience.  However, quality inspection still stays in the manual stage, or partially manual inspection remains, in production, leading low efficiency, lack and even false inspection, hardly meeting the requirements of the modern output.  This paper proposes and optimizes the design of an automatic detection system with intelligence for flexible food packaging bag, which can effectively be adopted to check the quality of packaging trademark patterns, fillers, and sealing quality.  The inspection system runs with two-stage structure, machine vision, pressure sensing and synchronization to improve efficiency and ensure the normal production beat. Simplex Method is adopted to determine the best synchronous speeds online to achieve the best expectation. Comparison has been made between the manual inspection and our automatic operation, the sample of 10000 was statistically analyzed and results have shown that two workers were saved and the correctness rate of inspection raised up to 999.8‰

Pegylated derivatives of recombinant human arginase (rhArg1) for sustained in vivo activity in cancer therapy: preparation, characterization and analysis of their pharmacodynamics in vivo and in vitro and action upon hepatocellular carcinoma cell (HCC)

<p>Abstract</p> <p>Background</p> <p>Protein used in medicine, e.g. interferon, are immunogenic and quickly broken down by the body. Pegylation is a recognized way of preserving their integrity and reducing immune reactions, and works well with enzymes used to degrade amino acids, a recent focus of attention in controlling cancer growth. Of the two arginine-degrading enzymes being explored clinically, arginine deiminase is a decidedly foreign mycoplasm-derived enzyme, whereas human arginase 1 is a native liver enzyme. Both have been pegylated, the former with adjuncts of 20 kD, the latter with 5 kD PEG. Pegylation is done by several different methods, not all of which are satisfactory or desirable.</p> <p>Methods</p> <p>The preparation of novel polyethylene glycol (PEG) derivatives for modifying proteins is described, but directed specifically at pegylation of recombinant human arginase 1 (rhArg1). rhArg1 expressed in <it>Escherichia coli </it>was purified and coupled in various ways with 5 different PEG molecules to compare their protective properties and the residual enzyme activity, using hepatocellular cell lines both in vitro and in vivo.</p> <p>Results</p> <p>Methoxypolyethylene glycol-succinimidyl propionate (mPEG-SPA 5,000) coupled with very high affinity under mild conditions. The resulting pegylated enzyme (rhArg1-peg_{5,000 mw}) had up to 6 PEG chains of 5K length which not only protected it from degradation and any residual immunogenicity, but most importantly let it retain >90% of its native catalytic activity. It remained efficacious in depleting arginine in rats after a single ip injection of 1,500 U of the conjugate as the native enzyme, plasma arginine falling to >0.05 μM from ~170 μM within 20 min and lasting 6 days. The conjugate had almost the same efficacy as unpegylated rhArg1 on 2 cultured human liver cancer (HCC) cell lines. It was considerably more effective than 4 other pegylated conjugates prepared.</p> <p>Conclusion</p> <p>Valuable data on the optimization of the pegylation procedure and choice of ligand that best stabilizes the enzyme arginase 1 are presented, a protocol that should equally fit many other enzymes and proteins. It is a long lasting arginine-depleting enzyme in vivo which will greatly improve its use in anti-cancer therapy.</p