Zeitschriftenartikel: „Fumes extraction“

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Andrews, Ronnee N., Michael Keane, Kevin W. Hanley, H. Amy Feng, and Kevin Ashley. "Manganese speciation of laboratory-generated welding fumes." Analytical Methods 7, no. 15 (2015): 6403–10. http://dx.doi.org/10.1039/c5ay01147g.

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Guillem-Llobat, Ximo. "Medical Experts and Agnotology in the Fumes Controversy of the Huelva Copper Mines (1888–1890)." Medical History 61, no. 3 (2017): 424–43. http://dx.doi.org/10.1017/mdh.2017.36.

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Huelva’s copper mines (Spain) have been active for centuries but in the second half of the nineteenth century extractive activities in Riotinto, Tharsis, and other mines in the region were intensified in order to reach world leadership. The method used in these mines for copper extraction from low grade ores generated continuous emissions of fumes that were extremely controversial. The inhabitants had complained about the fumes for decades but as activity intensified so did complaints. The killing of anti-fumes demonstrators in 1888 led to the passing of a Royal Decree banning the open-air roasting of ore and to the drafting of numerous reports on the hazards of the fumes. Major state and provincial medical institutions, as well as renowned hygienists and engineers, took part in the assessment, contributing to a scientific controversy especially rich in content. In my paper I will analyse the production and circulation of knowledge and ignorance about the impact of fumes on public health, as well as the role of medical experts and expertise in the controversy. The analysis will focus on the reports drafted between the 1888 ban and its 1890 repeal, and will show the changing nature of the expert assessment and the numerous paths followed by experts in producing ignorance. The paper will conclude by considering other stakeholders, who may shed some light on the reasons behind the performance of the medical experts.

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Brand, Peter, Benjamin Ebert, André Esser, and Rahul Sharma. "Direct Exposure of Welders to Welding Fumes and Effect of Fume Extraction Systems Under Controlled Conditions." Journal of Occupational & Environmental Medicine 63, no. 6 (2021): 490–502. http://dx.doi.org/10.1097/jom.0000000000002167.

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Vassie, L., and J. R. Tyrer. "Extraction and filtration of laser produced fumes and degradation products." Optics & Laser Technology 25, no. 2 (1993): 62. http://dx.doi.org/10.1016/0030-3992(93)90094-v.

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Saunders, C. J., D. Pocock, and G. Carter. "Controlling Gas Shielded Arc Welding Fumes using an On-Gun Extraction System." International Journal of Ventilation 9, no. 1 (2010): 77–82. http://dx.doi.org/10.1080/14733315.2010.11683869.

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Carrillo, A., and J. I. Drever. "Environmental assessment of the potential for arsenic leaching into groundwater from mine wastes in Baja Cali- fornia Sur, Mexico." Geofísica Internacional 37, no. 1 (1998): 35–39. http://dx.doi.org/10.22201/igeof.00167169p.1998.37.1.2158.

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   Arsenolite (As203)was historically produced as a byproduct of gold and silver extraction in southernmost Baja California Peninsula. There are in the San Antonio-El Triunfo area more than 800,000 tonnes of mine waste material with an average content of 4% arsenic oxide. The chemical reaction to produce arsenic trioxide (arsenolite) was the oxidation of arsenopyrite (AsFeS) with gold (ore) to produce iron oxides (Fe203) with gold and releasing SO2and As203 fumes. During the process the arsenic fumes sublimated onto the inner walls of old, mineral processing plants. When the chambers filled up, they were periodically emptied out on big piles of oxidized tailings. But once the plants were abandoned, the chambers remain half filled with approximately 600 tonnes of pure arsenolite. X-ray diffraction and scanning electron microscopy/energy dispersive X-ray spectrometry (EDS) indicate that the trace element content in the arsenolite and its banded structure may be due to the use of different ore for gold and silver extraction. A rough but conservative analysis of the potential leaching, based on results of the humidity cell test on mine waste material (arsenolite and arsenolite-bearing oxidized tailings) indicates that from 0.5 to 2.0 mg/l arsenic could be released into the groundwater system of the region.    Real world data from the local aquifer show a close match with the calculations, indicating the close relation between arsenic content in tailings and groundwater.     

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Englund, Finn, and Ralph M. Nussbaum. "Monoterpenes in Scots Pine and Norway Spruce and their Emission during Kiln Drying." Holzforschung 54, no. 5 (2000): 449–56. http://dx.doi.org/10.1515/hf.2000.075.

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Summary Scots pine (Pinus silvestris) and Norway spruce (Picea abies) were dried in a laboratory kiln, using conventional schedules with temperatures up to 60°C as well as schedules reaching 110°C. The losses of terpenes, which completely dominate the fugitive emissions from kiln drying of softwoods, were estimated by extraction of pooled subsamples from each batch, before and after drying. The original contents of terpenes were found to be in the ranges of 0.4–0.5% in pine sapwood, 0.8–1.1% in pine heartwood, and 0.02–0.08% in spruce, all calculated relative to the dry weight of the wood. The emissions from pine corresponded to 25–50% of the original content and those from spruce to 10–50%. High temperature drying in the laboratory was accompanied by larger losses, but this tendency was not found in the full-scale drying of spruce. No consistent difference was found between timber from the north of Sweden and from the south. Sampling of the vent exhaust fumes did not give results of sufficient reliability to confirm the extraction analyses.

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Vasconcelos, M. Teresa S. D., Carlos A. R. Gomes, and Adélio A. S. C. Machado. "Ion chromatographic determination of fluoride in welding fumes with elimination of high contents of iron by solid-phase extraction." Journal of Chromatography A 685, no. 1 (1994): 53–60. http://dx.doi.org/10.1016/0021-9673(94)00664-4.

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Spearman, John, George Tsavellas, and Paul Nichols. "Current Attitudes and Practices Towards Diathermy Smoke." Annals of The Royal College of Surgeons of England 89, no. 2 (2007): 162–65. http://dx.doi.org/10.1308/003588407x155752.

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INTRODUCTION The hazards of surgical smoke are well documented and electrosurgical units (ESUs) are an integral part of surgical practice. The aim of this study was to gauge the opinions of general surgical consultants, specialist registrars and senior theatre nurses in the Wessex Region towards the hazards of ESU smoke. MATERIALS AND METHODS A literature search was carried out using Ovid Medline. A questionnaire was sent to 169 consultants, SpRs and nurses in the 14 hospitals across the Wessex Region, exploring current practices, perceived hazards and whether adequate precautions were currently in use. RESULTS Only 3 of 98 surgeons used dedicated smoke extractors, despite the fact the majority (72%) felt that, currently, inadequate precautions were taken to protect staff and patients from surgical smoke. There was also uncertainty about the hazards amongst the respondents. CONCLUSIONS The use of smoke extraction equipment is very limited. Greater awareness of the hazards and available technology to extract fumes from the theatre environment might lead to greater uptake.

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PURCARO, GIORGIA, JOSÉ A. NAVAS, FRANCESC GUARDIOLA, LANFRANCO S. CONTE, and SABRINA MORET. "Polycyclic Aromatic Hydrocarbons in Frying Oils and Snacks." Journal of Food Protection 69, no. 1 (2006): 199–204. http://dx.doi.org/10.4315/0362-028x-69.1.199.

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The high incidence of lung cancer observed among Chinese women has been associated with exposure to fumes from cooking oil. Polycyclic aromatic hydrocarbons (PAHs) are a class of potentially mutagenic substances emitted from cooking oils heated at high temperatures. The objective of this study was to investigate whether deep frying with different oils under different conditions leads to the development of PAHs either in the oil or in the fried product (snacks). PAH analysis was carried out with solid-phase extraction followed by reverse-phase high-performance liquid chromatography and spectrofluorometric detection. Different oils were used to fry chips and extruded snacks in different industrial plants (continuous frying) at temperatures between 170 and 205°C, and peanut oil was used to fry French fries and fish (discontinuous frying) at temperatures between 160 and 185°C. No appreciable differences in PAH load was observed in the same oil before and after frying. Both before and after frying, the benzo[a]pyrene concentration in oils ranged from trace to 0.7 ppb. All the analyzed samples, including oils from fried snacks, had benzo[a]pyrene concentrations well below the 2 ppb limit recently proposed by the European Community.

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Farfán, Ricardo Fabricio Muñoz, Telly Yarita Macías Zambrano, Fausto Roberto Andrade Badillo, and Adrián Adalberto Hernández Solís. "Design and construction of an industrial ship conditioning system." International journal of physical sciences and engineering 4, no. 1 (2020): 29–38. http://dx.doi.org/10.29332/ijpse.v4n1.423.

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This project is based on the design and construction of an industrial air conditioning system for the improvement of the working thermal comfort of workers that for various reasons there is the trend of the increase in body temperature are these by machines, equipment or the same work activities, which affects the productive performance and possible health risks. During development, the selection of mechanical equipment such as the fan, water pump, ventilation transport ducts is studied with high rates of energy efficiency. In the light of the above, the environmental economic partner alternative is chosen to implement the evaporative conditioning system, conducive to working in open places to lower the temperature by labor and technology installed in the production areas, as well as the extraction of fumes derived from production processes. The importance of the evaporative conditioning system is to derive the appropriate mechanisms to take advantage of the surface heat transfer of a panel and copper and aluminum coil using water, and thus take advantage of its temperature differential reaching 25oC, with an average humidity of 66% and energy consumption of 0.29 KW/h.

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Li, Xinde, Pei Li, Mohammad Omar Khyam, Xiangheng He, and Shuzhi Sam Ge. "A welding seam identification method based on cross-modal perception." Industrial Robot: the international journal of robotics research and application 46, no. 3 (2019): 453–59. http://dx.doi.org/10.1108/ir-09-2018-0182.

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Purpose As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception. Design/methodology/approach First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method. Findings It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best. Originality/value This paper proposes a robust method for automatically identifying and tracking a welding seam.

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Narkhede, Parag, Rahee Walambe, Shruti Mandaokar, Pulkit Chandel, Ketan Kotecha, and George Ghinea. "Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion." Applied System Innovation 4, no. 1 (2021): 3. http://dx.doi.org/10.3390/asi4010003.

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With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

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Klugmann-Radziemska, Ewa, and Krzysztof Ciunel. "Rapeseed pellet - a byproduct of biodiesel production - as an excellent renewable energy source / Wytłoki rzepakowe - produkt uboczny w produkcji biodiesla - jako doskonałe źródło energii odnawialnej." Chemistry-Didactics-Ecology-Metrology 18, no. 1-2 (2013): 109–19. http://dx.doi.org/10.2478/cdem-2013-0024.

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Abstract Vegetable oils are renewable feedstock currently being used for production of biofuels from sustainable biomass resources. The existing technology for producing diesel fuel from plant oils, such as rapeseed, soybean, canola and palm oil are largely centered on transesterification of oils with methanol to produce fatty acid methyl esters (FAME) or biodiesel. Rapeseed pellet - crushed seed residue from oil extraction is a byproduct of biodiesel production process. As other types of biomass, it can either be burned directly in furnaces or processed to increase its energetic value. The interest to use different types of biomass as fuels has grown rapidly during the last years as a mean to reduce the CO2 emissions of energy production. Biomass is renewable, abundant and has domestic usage, the sources of biomass can help the world reduce its dependence on petroleum products and natural gas. Energetically effective utilization of rapeseed pellet could substantially improve the economic balance of an individual household in which biodiesel for fulfilling the producer’s own energetic demand is obtained. In this article the experimental results of analyzing the emissions levels of different pollutants in exhaust fumes during different stages of biomass boiler operation were presented. It has been proved that that the pellet, a byproduct of biodiesel production, is an excellent renewable and environmentally-friendly energy source, especially viable for use in household tap water heating installations.

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Narkhede, Parag, Rahee Walambe, Shruti Mandaokar, Pulkit Chandel, Ketan Kotecha, and George Ghinea. "Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion." Applied System Innovation 4, no. 1 (2021): 3. http://dx.doi.org/10.3390/asi4010003.

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With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

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Sakhno, Nataliya Nikolaevna. "July 25 — the Day of the Dental Technician." Spravočnik vrača obŝej praktiki (Journal of Family Medicine), no. 8 (July 12, 2021): 73–78. http://dx.doi.org/10.33920/med-10-2108-11.

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Healthy teeth are the key to the normal functioning of the whole organism; people should start taking care of the condition of their teeth from early childhood. It is the teeth that are the only organ of the human body that is incapable of regeneration, so they need special care and the most careful attitude. Not everyone regularly visits dentists, and very few are closely familiar with the work of their right-hand people — dental technicians. At the beginning of the 18th century, serious changes took place in the field of dentistry, i.e. the transition from conservative tooth extraction to the desire for their maximum preservation and restoration. Pierre Fauchard is considered the ideological inspirer of scientific dentistry. By the way, it is dentists who are most directly related to the formation of another scientific discipline — anesthesiology. The first experiments with anesthetics — nitrous oxide (laughing gas) and sulfuric ether — began to be used by dentists as early as the 19th century. It was then that the English naturalist H. Davy experienced the pain-dulling effect of nitrous oxide fumes, and in the 40s of the same century, another dentist had a tooth removed for the first time under the influence of «laughing gas». Many people are used to calling stomatologists dentists, but this broad concept unites not only dentists and surgeons, but also feldshers and technicians. Each individual direction requires a highly qualified specialist, serious practical experience and excellent work skills.

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Shen, Qi, Wen-xiang Wang, Qiu-ping Xu, et al. "Association of Single-Nucleotide Polymorphism REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 with Primary Lung Cancer Susceptibility: A Case-Control Study in a Chinese Population." Disease Markers 2019 (May 2, 2019): 1–23. http://dx.doi.org/10.1155/2019/4150263.

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The purpose of the current study is to explore the contribution of single-nucleotide polymorphisms (SNPs) of REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 to the risk of lung cancer. A questionnaire survey was used to obtain basic information of the included subjects. A case control study was performed in 1121 patients and 1121 controls. All subjects were subjected to blood sampling for genomic DNA extraction and genotyping of the cancer stem cell-associated gene SNPs, including REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 by real-time PCR. The association with the risk of primary lung cancer and interaction with environmental factors were assessed using unconditional logistic regression for the odds ratios and corresponding 95% confidence intervals. The genotype frequency distribution of OCT4 rs13409 loci was statistically significant, but there was no significant difference in the rest of the loci between lung cancer patients and healthy controls. The OCT4 gene was also related with lung cancer susceptibility in the genetic model after adjusting for lung cancer-related factors. Despite the presence of the dominant or recessive model, the four loci polymorphisms were associated with pollution near the place of residence, house type, worse ventilation situation, smoking, passive smoking, cooking oil fumes (COF), and family history of cancer, which increased the risk of lung cancer. Nonmarried status, 18.5≤BMI, COF, smoking, passive smoking, family history of cancer, and history of lung disease were independent risk factors of lung cancer susceptibility. Additionally, college degree or above, no pollution near the place of residence, protective genotype 1 or 2, and well ventilation can reduce the occurrence of lung cancer. There is an interaction between the four loci and environmental factors, and OCT4 rs13409 is a risk factor of primary lung cancer.

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Blomberg, Jan, Paul C. de Groot, Henk C. A. Brandt, Jan J. B. van der Does, and Peter J. Schoenmakers. "Development of an on-line coupling of liquid–liquid extraction, normal-phase liquid chromatography and high-resolution gas chromatography producing an analytical marker for the prediction of mutagenicity and carcinogenicity of bitumen and bitumen fumes." Journal of Chromatography A 849, no. 2 (1999): 483–94. http://dx.doi.org/10.1016/s0021-9673(99)00583-x.

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Zhang, Pei, Changyuan Wang, and Hongbo Jia. "Mask R-CNN Method for Dashboard Feature Extraction in Eye Tracking." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2020): 2055017. http://dx.doi.org/10.1142/s0218001420550174.

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The traditional information extraction technology of dashboard is easily affected by external factors, and the robustness is poor. To improve the safety of the pilot’s performance on the dashboard, this paper proposes a way for extracting the dashboard feature information in eye tracking, which acquires the line of sight point in simulated dashboard. It then uses the Mask R-CNN method to detect the gaze area and then extracts the target feature information. Finally, it fuses two sets of data to get the result of the pilot who extracts the target gaze area in the scene. Experiment results show that the method of new dashboard information extraction proposed in this paper has a better accuracy.

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Chute, Daniel O., and Bradley W. Christ. "The National Shipbuilding Research Program Welding Fume Study." Journal of Ship Production 17, no. 01 (2001): 35–39. http://dx.doi.org/10.5957/jsp.2001.17.1.35.

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This paper presents the results of a study evaluating the impact of anticipated reductions in OSHA worker exposure limits for airborne contaminants, nickel (Ni), manganese (Mn), and hexavalent chromium (Cr6) on the shipbuilding industry. As part of the study field evaluation, air monitoring and data analysis were conducted to measure the effectiveness of a series of exposure control methods including fume extractor guns, ixed fume extraction systems, portable fume extraction systems, low fume welding wires, downdraft/backdraft tables, and fume iltration devices. Six shipyards participated in this study. The study concluded that the cost of compliance increases greatly with lower exposure limits. It was recommended that the shipbuilding industry continue to develop and evaluate feasible control measures in anticipation of revised standards.

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Wei, Shan, Brynn Levy, Nataly Hoffman, et al. "A rapid and simple bead-bashing-based method for genomic DNA extraction from mammalian tissue." BioTechniques 68, no. 5 (2020): 240–44. http://dx.doi.org/10.2144/btn-2019-0172.

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Conventional genomic DNA (gDNA) extraction methods can take hours to complete, may require fume hoods and represent the most time-consuming step in many gDNA-based molecular assays. We systematically optimized a bead bashing-based (BBB) approach for rapid gDNA extraction without the need for a fume hood. Human tissue specimens (n = 34) subjected to the 12-min BBB method yielded 0.40 ± 0.17 (mean ± SD) μg of gDNA per milligram of tissue, sufficient for many downstream applications, and 3- and 6-min extensions resulted in an additional 0.43 ± 0.23 μg and 0.48 ± 0.43 μg per milligram of tissue, respectively. The BBB method provides a simple and rapid method for gDNA extraction from mammalian tissue that is applicable to time-sensitive clinical applications.

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Yoo, Na-Kyung, Ye-Rin Jeon, and Soo-Jin Choi. "Determination of Two Differently Manufactured Silicon Dioxide Nanoparticles by Cloud Point Extraction Approach in Intestinal Cells, Intestinal Barriers and Tissues." International Journal of Molecular Sciences 22, no. 13 (2021): 7035. http://dx.doi.org/10.3390/ijms22137035.

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Food additive amorphous silicon dioxide (SiO2) particles are manufactured by two different methods—precipitated and fumed procedures—which can induce different physicochemical properties and biological fates. In this study, precipitated and fumed SiO2 particles were characterized in terms of constituent particle size, hydrodynamic diameter, zeta potential, surface area, and solubility. Their fates in intestinal cells, intestinal barriers, and tissues after oral administration in rats were determined by optimizing Triton X-114-based cloud point extraction (CPE). The results demonstrate that the constituent particle sizes of precipitated and fumed SiO2 particles were similar, but their aggregate states differed from biofluid types, which also affect dissolution properties. Significantly higher cellular uptake, intestinal transport amount, and tissue accumulation of precipitated SiO2 than of fumed SiO2 was found. The intracellular fates of both types of particles in intestinal cells were primarily particle forms, but slowly decomposed into ions during intestinal transport and after distribution in the liver, and completely dissolved in the bloodstream and kidneys. These findings will provide crucial information for understanding and predicting the potential toxicity of food additive SiO2 after oral intake.

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Neidel, A., V. Hartanto, S. Riesenbeck, and T. Ullrich. "Erosion Damage to Impeller of Welding Fume Extraction System." Practical Metallography 57, no. 8 (2020): 569–75. http://dx.doi.org/10.3139/147.110647.

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Sam, Kar Yen, Siong Hoong Lee, and Zhen Hong Ban. "Computational fluid dynamics (CFD) modelling on effect of fume extraction." Journal of Chemical Health and Safety 26, no. 6 (2019): 20–31. http://dx.doi.org/10.1016/j.jchas.2019.04.004.

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Mahanta, Priyakshi, Hasin Afzal Ahmed, Dhruba Kumar Bhattacharyya, and Ashish Ghosh. "FUMET: A fuzzy network module extraction technique for gene expression data." Journal of Biosciences 39, no. 3 (2014): 351–64. http://dx.doi.org/10.1007/s12038-014-9423-2.

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Al balawi, Aisha Nawaf, Nor Azah Yusof, Sazlinda Kamaruzaman, Faruq Mohammad, Helmi Wasoh, and Hamad A. Al-Lohedan. "DNA Adsorption Studies of Poly(4,4′-Cychlohexylidene Bisphenol Oxalate)/Silica Nanocomposites." Materials 12, no. 7 (2019): 1178. http://dx.doi.org/10.3390/ma12071178.

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The present study deals with the synthesis, characterization, and DNA extraction of poly(4,4′-cyclohexylidene bisphenol oxalate)/silica (Si) nanocomposites (NCs). The effects of varying the monomer/Si (3.7%, 7%, and 13%) ratio towards the size and morphology of the resulting NC and its DNA extraction capabilities have also been studied. For the NC synthesis, two different methods were followed, including the direct mixing of poly(4,4′-cyclohexylidene bisphenol oxalate) with fumed Si, and in situ polymerization of the 4,4′-cyclohexylidene bisphenol monomer in the presence of fumed silica (11 nm). The formed NCs were thoroughly investigated by using different techniques such as scanning electron microscopy (SEM), fourier transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), powdered X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis where the results supported that there was the successful formation of poly(4,4′-cyclohexylidene bisphenol oxalate)/Si NC. Within the three different NC samples, the one with 13% Si was found to maintain a very high surface area of 12.237 m2/g, as compared to the other two samples consisting of 7% Si (3.362 m2/g) and 3.7% Si (1.788 m2/g). Further, the solid phase DNA extraction studies indicated that the efficiency is strongly influenced by the amount of polymer (0.2 g > 0.1 g > 0.02 g) and the type of binding buffer. Among the three binding buffers tested, the guanidine hydrochloride/EtOH buffer produced the most satisfactory results in terms of yield (1,348,000 ng) and extraction efficiency (3370 ng/mL) as compared to the other two buffers of NaCl (2 M) and phosphate buffered silane. Based on our results, it can be indicated that the developed poly(4,4′-cyclohexylidene bisphenol oxalate)/Si NC can serve as one of the suitable candidates for the extraction of DNA in high amounts as compared to other traditional solid phase approaches.

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Wang, Hai, Lei Dai, Yingfeng Cai, Long Chen, and Yong Zhang. "Saliency Detection by Multilevel Deep Pyramid Model." Journal of Sensors 2018 (August 14, 2018): 1–11. http://dx.doi.org/10.1155/2018/8249180.

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Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

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Yang, Xiao Ping, and Yan Ru Chen. "Research on the Furnace Fume Treatment and Control System." Advanced Materials Research 518-523 (May 2012): 2775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.2775.

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Silicon metal is known as a metalloid or semi-metal with several characteristics of metals. Silicon metal also has a large influence on the modern world economy. But the production of the silicon metal consumes high energy and discharges huge furnace fume. According to the technical demands of the fume cleaning and micro-silicon powder recovery system to the silicon metal furnace, this paper studies the control scheme with the structure mode of Schneider PLC and the Kingview software, develops the relevant control program and monitor program, and optimizes the control strategy through the on-line debugging. The practical application indicates that the control system is stable and reliable with the best effect of dust extraction and the longest service life to the dust settling bag.

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Zin, Thi Thi, Ye Htet, Yuya Akagi, et al. "Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera." Sensors 21, no. 17 (2021): 5895. http://dx.doi.org/10.3390/s21175895.

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Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

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Jamal, Miftah Bedru, Jiang Zhengang, and Fang Ming. "An Improved Deep Mutual-Attention Learning Model for Person Re-Identification." Symmetry 12, no. 3 (2020): 358. http://dx.doi.org/10.3390/sym12030358.

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Person re-identification is the task of matching pedestrian images across a network of non-overlapping camera views. It poses aggregated challenges resulted from random human pose, clutter from the background, illumination variations, and other factors. There has been a vast number of studies in recent years with promising success. However, key challenges have not been adequately addressed and continue to result in sub-optimal performance. Attention-based person re-identification gains more popularity in identifying discriminatory features from person images. Its potential in terms of extracting features common to a pair of person images across the feature extraction pipeline has not been be fully exploited. In this paper, we propose a novel attention-based Siamese network driven by a mutual-attention module decomposed into spatial and channel components. The proposed mutual-attention module not only leads feature extraction to the discriminative part of individual images, but also fuses mutual features symmetrically across pairs of person images to get informative regions common to both input images. Our model simultaneously learns feature embedding for discriminative cues and the similarity measure. The proposed model is optimized with multi-task loss, namely classification and verification loss. It is further optimized by a learnable mutual-attention module to facilitate an efficient and adaptive learning. The proposed model is thoroughly evaluated on extensively used large-scale datasets, Market-1501 and Duke-MTMC-ReID. Our experimental results show competitive results with the state-of-the-art works and the effectiveness of the mutual-attention module.

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Firth, Nigel L., Deborah A. Ross, and Michael L. Thonney. "Comparison of Ether and Chloroform for Soxhlet Extraction of Freeze-Dried Animal Tissues." Journal of AOAC INTERNATIONAL 68, no. 6 (1985): 1228–31. http://dx.doi.org/10.1093/jaoac/68.6.1228.

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Abstract Chloroform was a satisfactory replacement for ether in solvent extraction for lipid determination in freeze-dried animal tissues, although values obtained were not identical. Fat content of tissues used in this study ranged from less than 10 to more than 95%. Chloroform has the great advantage of being fireproof, but proper fume hoods should be used. An antifoaming agent should be added to chloroform, both in the Soxhlet apparatus and when it is redistilled.

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Marconi, Mario, and Albano Bravaccini. "Capture Efficiency of Integral Fume Extraction Torches for GMA Welding — Part 1." Welding in the World 54, no. 1-2 (2010): 3–15. http://dx.doi.org/10.1007/bf03263477.

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Marconi, Mario, and Albano Bravaccini. "Capture Efficiency of Integral Fume Extraction Torches for GMA Welding — Part 2." Welding in the World 54, no. 3-4 (2010): 15–33. http://dx.doi.org/10.1007/bf03263486.

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Satheesh Kumar, K. V., M. Dharmaraj, P. Thangavel, K. A. Srikishore, R. Sudharsan, and M. Vimal. "A Critical Review on Fume Extraction System for Pipe and Plate Welding." IOP Conference Series: Materials Science and Engineering 1084, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1757-899x/1084/1/012064.

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35

Zhang, Chao, Xiaopei Wu, Jianchao Lu, et al. "RICA-MD: A Refined ICA Algorithm for Motion Detection." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–17. http://dx.doi.org/10.1145/3416492.

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With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.

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Stubberud, Stephen C., and Kathleen A. Kramer. "Feature object extraction applied to activity-based intelligence threat understanding." SIMULATION 93, no. 9 (2017): 727–36. http://dx.doi.org/10.1177/0037549717717805.

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Activity-based intelligence, the assessment of both the situation and potential threats, relies on several sources of information. Some of these sources provide information, such as time and position, that is easily represented mathematically. Other pieces of information may come in the form of patterns or imagery. The intelligence problem eventually requires a human analyst. However, the ever-increasing amount of data that must be investigated can overwhelm the analyst and, thus, necessitates the development of automated tools to process the information. An intelligent evidence accrual technique is proposed as a means to combine information in order to identify potential threats. The technique is able to provide both a measure of evidence and a level of uncertainty for the state. The approach is based upon a fuzzy Kalman filter that fuses several types of observations to monitor activities over a duration of time and provide an assessment of the situation. A data-to-image mapping and correlation that allows the combination of disparate elements is incorporated.

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Kuang, Hongwu, Bei Wang, Jianping An, Ming Zhang, and Zehan Zhang. "Voxel-FPN: Multi-Scale Voxel Feature Aggregation for 3D Object Detection from LIDAR Point Clouds." Sensors 20, no. 3 (2020): 704. http://dx.doi.org/10.3390/s20030704.

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Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts and fuses multi-scale voxel information in a bottom-up manner, whereas decoder fuses multiple feature maps from various scales by Feature Pyramid Network in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.

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Li, Chengming, Xiaoyan Wang, Zheng Wu, Zhaoxin Dai, Jie Yin, and Chengcheng Zhang. "An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data." Sustainability 13, no. 9 (2021): 5042. http://dx.doi.org/10.3390/su13095042.

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Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.

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Swaminathan, G. S. "Assessment of Efficiency of a Fume Extraction System During Gas Metal Arc Welding." Indian Welding Journal 29, no. 1 (1996): 35. http://dx.doi.org/10.22486/iwj.v29i1.182641.

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Bai, Xiao Ming, and Cheng Zhang Wang. "Class Dependent Face Recognition with 3D Deformable Model." Advanced Materials Research 211-212 (February 2011): 460–64. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.460.

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Both class information of face subjects and color information of face images are very important cues for recognition. In this paper, a novel class dependent color face recognition approach based on block diagonal discriminant NMF is proposed. The approach employs block diagonal matrix to encode color information of face images simultaneously. Block diagonal constraint is imposed on discriminant NMF algorithm to construct feature extraction approach, which fuses class and color information at the same time into the extracted facial features. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Experimental results on CVL and CMU PIE face databases verify the effectiveness of the proposed approach.

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Liu, Wei, MengYuan Yang, Meng Xie, et al. "Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network." Remote Sensing 11, no. 24 (2019): 2912. http://dx.doi.org/10.3390/rs11242912.

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Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and "speckle" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries.

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Khoshboresh Masouleh, M., and M. R. Saradjian. "ROBUST BUILDING FOOTPRINT EXTRACTION FROM BIG MULTI-SENSOR DATA USING DEEP COMPETITION NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 615–21. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-615-2019.

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Abstract. Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.

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Bonthoux, Francis. "Factors Affecting the Capture Efficiency of a Fume Extraction Torch for Gas Metal Arc Welding." Annals of Occupational Hygiene 60, no. 6 (2016): 761–70. http://dx.doi.org/10.1093/annhyg/mew016.

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Witt, P. J., C. B. Solnordal, L. J. Mittoni, S. Finn, and J. Pluta. "Optimising the design of fume extraction hoods using a combination of engineering and CFD modelling." Applied Mathematical Modelling 30, no. 11 (2006): 1167–79. http://dx.doi.org/10.1016/j.apm.2006.02.003.

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Wu, Hao, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, and Yuning Jiang. "DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1037–45. http://dx.doi.org/10.1609/aaai.v34i01.5453.

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Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information flows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised refinement decoder to generate the prediction in a coarse-to-fine way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy.

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Chen, Linlin, Zhihui Wei, and Yang Xu. "A Lightweight Spectral–Spatial Feature Extraction and Fusion Network for Hyperspectral Image Classification." Remote Sensing 12, no. 9 (2020): 1395. http://dx.doi.org/10.3390/rs12091395.

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Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification.

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Li, Chenming, Yongchang Wang, Xiaoke Zhang, Hongmin Gao, Yao Yang, and Jiawei Wang. "Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data." Sensors 19, no. 1 (2019): 204. http://dx.doi.org/10.3390/s19010204.

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With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

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Wallace, Marjorie, Stanley Shulman, and John Sheehy. "Comparing Exposure Levels by Type of Welding Operation and Evaluating the Effectiveness of Fume Extraction Guns." Applied Occupational and Environmental Hygiene 16, no. 8 (2001): 771–79. http://dx.doi.org/10.1080/10473220117155.

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Godbole, Ajit, Paul Cooper, and John Norrish. "Design and Optimization of a Novel on-Torch Fume Extraction Device using CFD and Fluid Simulation." Welding in the World 54, no. 3-4 (2010): R80—R86. http://dx.doi.org/10.1007/bf03263491.

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Karthikeyan, D., V. P. Arumbu, P. Divya, K. Vijayakumar, and K. Mani Rahulan. "Energy conservation at high power consuming holding furnace with modified coil design and electrical circuits." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (2021): 803. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp803-810.

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This paper deals with high energy consuming induction holding furnace. The improved version of coil design along with additional electrical interlocks and alarm, modified hydraulic controls, strengthening mechanical structures and energy saving proposals at holding furnace fume extraction system ensures trouble free operation of furnace and hence continuity of production at all the planned plant operational days. This project involves in purchasing of D-section copper extruded coil from Europe–Switzerland for its extended length from existing 3 meters long to 9 Meters which enables in reduction of coil joints. The performance of the holding furnace has improved in terms ofits energy consumption per ton of liquid metal. The payback period for the estimated investment will be less than 2 years.

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