Carbon black as a dulling agent for nylon 6 filaments and the lightfastness of dyes on dulled filaments

Nylon 6 filaments containing 0–1.5% carbon-black pigment are dyed to different depths of shade. The colour on the substrate is evaluated on CIELAB a* b* diagrams at given concentrations of carbon black and dye. The carbon-black pigment acts essentially as a neutral dulling agent and only with a few dyes does it appear to alter the hue angle slightly. Thus, it is possible to use nylon 6 filaments containing carbon-black pigment to produce dull shades by the overdyeing process. The dyed samples thus produced are exposed to mercury-lamp light as well as daylight. The lightfastness is evaluated visually using the Blue Wool Scale. There is a remarkable improvement in the lightfastness of all dyes on nylon 6 containing carbon-black pigment. The carbon black in the filament appears to protect both the dye and the polymer from the degrading action of actinic radiation. However, the remarkable improvement in the lightfastness is essentially due to the optical effect.     

Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.     

Analysis of trace residues of tetracyclines in dark-colored honeys by high-performance liquid chromatography using polymeric cartridge and metal chelate affinity chromatography

An efficient clean-up procedure was developed for the trace residue determination of tetracyclines (TCs) in dark-colored honeys. TCs were extracted from samples with McIlvaine buffer (pH 4.0) containing 0.01 mol/L Na(2)EDTA. The extracts were treated with both a polymeric cartridge (GL-Pak PLS-2) and a metal chelate affinity column (MCAC) preloaded with copper(II). TCs were eluted and analyzed by high-performance liquid chromatography (HPLC) using fluorescence detection. The method was evaluated for the determination of oxytetracycline (OTC), tetracycline (TC), and chlortetracycline (CTC) in buckwheat honey, because its color is the darkest. The mean recoveries of OTC, TC and CTC from spiked samples, at three fortification levels, were >70%, and the relative standard deviations (RSDs) were <10%. Limits of quantitation (LOQs) of OTC, TC, and CTC were estimated to be 0.015 mg/kg, 0.019 mg/kg, and 0.024 mg/kg, respectively.     

Efficient decomposition of environmentally persistent perfluorooctanesulfonate and related fluorochemicals using zerovalent iron in subcritical water

Decomposition of perfluorooctanesulfonate (PFOS) and related chemicals in subcritical water was investigated. Although PFOS demonstrated little reactivity in pure subcritical water, addition of zerovalent metals to the reaction system enhanced the PFOS decomposition to form F-ions, with an increasing order of activity of no metal approximately equal Al < Cu < Zn < Fe. Use of iron led to the most efficient PFOS decomposition: When iron powder was added to an aqueous solution of PFOS (93-372 microM) and the mixture was heated at 350 degrees C for 6 h, PFOS concentration in the reaction solution fell below 2.2 microM (detection limit of HPLC with conductometric detection), with formation of F-ions with yields [i.e., (moles of F- formed)/(moles of fluorine content in initial PFOS) x 100] of 46.2-51.4% and without any formation of perfluorocarboxylic acids. A small amount of CHF3 was detected in the gas phase with a yield [i.e., (moles of CHF3)/(moles of carbon content in initial PFOS) x 100] of 0.7%, after the reaction of PFOS (372 microM) with iron at 350 degree C for 6 h. Spectroscopic measurements indicated that PFOS in water markedly adsorbed on the iron surface even at room temperature, and the adsorbed fluorinated species on the iron surface decomposed with rising temperature, with prominent release of F- ions to the solution phase above 250 degrees C. This method was also effective in decomposing other perfluoroalkylsulfonates bearing shorter chain (C2-C6) perfluoroalkyl groups and was successfully applied to the decomposition of PFOS contained in an antireflective coating agent used in semiconductor manufacturing.     

Persistent perfluorinated acids in seafood collected from two cities of China

As an initial survey of human exposure to perfluorinated acids through food consumption in China, seven types of seafood collected from fish markets in two coastal cities were analyzed. Nine perfluorinated compounds were determined using HPLC coupled with ESI-MS/MS. Perfluorooctane sulfonate (PFOS) was the predominant fluorochemical and was found in all 27 seafood samples, including fish, molluscs, crabs, shrimp, oysters, mussels, and clams. Concentrations of PFOS in seafood samples ranged from 0.3 to 13.9 ng/g wet weight, with the highest concentration in mantis shrimp. The hazard ratios of noncancer risk through seafood consumption based on PFOS and perfluorooctanoic acid concentrations were calculated and were less than unity.     

Perfluorinated compounds in the plasma of loggerhead and Kemp's ridley sea turtles from the southeastern coast of the United States

Perfluorinated compounds (PFCs) have been measured in blood of humans and wildlife and are considered globally distributed contaminants. We examined 12 PFCs in the plasma of 73 loggerhead sea turtles (Caretta caretta) and 6 Kemp's ridley sea turtles (Lepidochelys kempii) captured from inshore waters of Core Sound, North Carolina (NC), and offshore waters of South Carolina, Georgia, and Florida (SC-FL). Perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) were the dominant compounds, with respective mean concentrations of 11.0 ng/mL and 3.20 ng/mL for loggerhead turtles and 39.4 ng/mL and 3.57 ng/mL for Kemp's ridley turtles. Mean PFOS concentrations were 2- to 12-fold higher than typical mean sigmaPCB concentrations (approximately 5 ng/g wet mass) measured previously in sea turtle blood. More than 79% of the samples had detectable levels of perfluorocarboxylates (PFCAs) with 8-12 carbons, whereas only 17% or less of samples had detectable levels of PFCAs with 6 or 7 carbons. No samples had detectable levels of PFCAs with 4 or 5 carbons. In loggerhead turtles, sigmaPFC concentrations were not influenced by sex (p > 0.05), but were higher in turtles captured from inshore waters of NC than in turtles from offshore waters of SC-FL (p = 0.009). A backward stepwise multiple regression model showed that sigmaPFC concentrations were (1) significantly higher in Kemp's ridley turtles than loggerhead turtles (p < 0.0001), (2) higher in larger turtles (p = 0.018; carapace length used as a proxy for age), and (3) higher in turtles captured toward the north (p = 0.006). These findings suggest that bioaccumulation of PFCs in sea turtles is influenced by species, age, and habitat.     

Analysis of perfluorinated acids at parts-per-quadrillion levels in seawater using liquid chromatography-tandem mass spectrometry

Perfluorinated acids (PFAs) and their salts have emerged as an important class of global environmental contaminants. Determination of sub-parts-per-trillion or parts-per-quadrillion concentrations of perfluorinated acids in aqueous media has been impeded by relatively high background levels arising from procedural or instrumental blanks. To understand the role of the oceans in the transport and fate of perfluorinated acids, methods to determine ultratrace levels of these compounds in seawater are needed. In this study, sources of procedural and instrumental blank contamination by perfluorinated acids have been identified and eliminated, to reduce background levels in blanks and thereby improve limits of quantitation. The method developed in this study is capable of detecting perfluorooctanesulfonate (PFOS), perfluorohexanesulfonate (PFHS), perfluorobutanesulfonate (PFBS), perfluorooctanoate (PFOA), perfluorononanoate (PFNA), and perfluorooctanesulfonamide (PFOSA) at low pg/L levels in oceanic waters. PFOA is the major perfluorinated compound detected in oceanic waters, followed by PFOS. Further studies are being conducted to elucidate the distribution and fate of perfluorinated acids in oceans.     

Nonlinear inferential multi-rate control of Kappa number at multiple locations in a continuous pulp digester

The continuous pulp digester represents a large-scale, distributed parameter system. Control of the spatial profile of degree of cooking, characterized by the Kappa number, rather than its endpoint value can effectively control properties that are dependent on the history of cooking. However, profile control of such large-scale distributed parameter systems throws up new challenges in estimation and control. We design a nonlinear model predictive controller using a multi-rate extended Kalman filter to infer and control discrete points along the Kappa number profile. Both, the plant and controller models are based on first principles. The design is tested for significant mismatches in parameters, initial state errors, and stochastic disturbances in the entering wood composition.     

Automated Artificial Intelligence Empowered White Blood Cells ClassificationModel

White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes data normalization and data augmentation process (rotation and zooming) at the initial stage. In addition, the residual network (ResNet) approach was used for feature extraction in which the initial hyperparameter values of the ResNet model are tuned by the use of AO algorithm. Finally, Bayesian neural network (BNN) classification technique has been implied for the identification of WBC images into distinct classes. The experimental validation of the AOTL-WBCC methodology is performed with the help of Kaggle dataset. The experimental results found that the AOTL-WBCC model has outperformed other techniques which are based on image processing and manual feature engineering approaches under different dimensions.