Journal articles: 'CO₂ detection'

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Langenfeld-Heyser, R., Bruno Schella, Kirsten Buschmann, and Frieder Speck. "Microautoradiographic detection of CO." Trees 10, no. 4 (1996): 255. http://dx.doi.org/10.1007/s004680050031.

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Fu, Huazhu, Xiaochun Cao, and Zhuowen Tu. "Cluster-Based Co-Saliency Detection." IEEE Transactions on Image Processing 22, no. 10 (October 2013): 3766–78. http://dx.doi.org/10.1109/tip.2013.2260166.

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Pardo Pedraza, Diana. "Sensory Co-laboring." Environmental Humanities 15, no. 3 (November 1, 2023): 30–51. http://dx.doi.org/10.1215/22011919-10745968.

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Abstract Demining has not been an exclusively human affair. Mine detection dogs have been indispensable in the work of detection and in the slow but essential effort to regain trust in mine-suspected landscapes. Famously renowned for their extraordinary sensory perception, physical strength, and mental traits, they are part of human-nonhuman units training and working together to perceive explosives’ odors. This article considers the role of these units, known in Colombia as binomios caninos, in the strenuous task of mine clearance. Drawing on eighteen months of ethnographic engagement with global and local humanitarian demining efforts in Colombia, it examines detection choreographies and daily interactions, proposing to think of their joint work in terms of sensory co-laboring. Bringing anthropological work on collaboration between worlds, sensory labor, and animal work into dialogue, this composite term foregrounds detection as labor and as a result of human-nonhuman cooperation. It also highlights the asymmetrical field in which these collaborators converge and the divergent desires, affects, and attachments that mobilize their participation in demining. Mine detection is conceptualized as a sensory task through which dogs and humans intra-act, both together and apart. Recognizing this partial connection allows us to rethink how humans and other creatures are ontologically reconstituted and how overlapping histories of warfare and humanitarianism, legacies of animal behavioral practice, and instrumental-affective interactions shape these reconstitutions.

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Lu, Xiaofei, Jingjing Jia, Zonghua Wang, and Wenjing Wang. "MXene/Carbon Dots Nanozyme Composites for Glutathione Detection and Tumor Therapy." Nanomaterials 14, no. 13 (June 25, 2024): 1090. http://dx.doi.org/10.3390/nano14131090.

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Co-N-CDs-based MXene nanocomposites (MXene@PDA/Co-N-CDs) were constructed by decorating Co-N-CDs on polydopamine-functionalized MXene nanosheets. Both Co-N-CDs and MXene nanosheets have peroxidase-like activity; when the two materials are combined to form MXene@PDA/Co-N-CDs nanocomposites, the peroxide-like activity can be further enhanced. MXene@PDA/Co-N-CDs could oxidize the substrate 3,3′5,5′-tetramethylbenziline (TMB) to form ox-TMB, as confirmed by detecting the absorption of the blue products. A highly selective colorimetric biosensor was developed for the determination of glutathione (GSH) in the concentration range of 0.3 to 20 µM with a lower detection limit (LOD) of 0.12 µM, which realized the accurate detection of GSH in human serum and urine samples. Moreover, in the tumor microenvironment, MXene@PDA/Co-N-CDs could catalyze hydrogen peroxide to produce hydroxyl free radicals and produce a photothermal effect under the exposure of NIR-I irradiation. The catalytic activity of MXene@PDA/Co-N-CD nanocomposites was fully achieved for the death of cancer cells through photothermal/photodynamic synergistic therapy. The MXene@PDA/Co-N-CDs nanozyme offers multiple applications in GSH detection and tumor therapy.

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YANG Ming-yu, 杨名宇. "Detecting of photoelectric peeping devices based on active laser detection." Chinese Optics 8, no. 2 (2015): 255–62. http://dx.doi.org/10.3788/co.20150802.0255.

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Dananché, Cédric, Gláucia Paranhos-Baccalà, Mélina Messaoudi, Mariam Sylla, Shally Awasthi, Ashish Bavdekar, Jean-William Pape, et al. "Nasopharyngeal Viral and Bacterial Co-Detection among Children from Low- and Middle-Income Countries with and without Pneumonia." American Journal of Tropical Medicine and Hygiene 106, no. 4 (April 6, 2022): 1086–93. http://dx.doi.org/10.4269/ajtmh.21-0980.

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ABSTRACT. The role of microbial coinfection in the pathogenesis of pneumonia in children is not well known. The aim of this work was to describe the prevalence of microorganism co-detection in nasopharyngeal samples (NPS) of pneumonia cases and control subjects and to study the potential association between nasopharyngeal microorganism co-detection and pneumonia. A case-control study was carried out from 2010 to 2014 in nine study sites located in low- or middle-income countries. The data from 888 children under 5 years of age with pneumonia (cases) and 870 children under 5 without pneumonia (controls) were analyzed. Nasopharyngeal samples were collected; reverse transcription polymerase chain reaction (RT-PCR) enabled the detection of five bacteria and 19 viruses. Multiple, mixed-effects logistic regression modeling was undertaken to evaluate the association between microorganism co-detection and pneumonia. A single Streptococcus pneumoniae colonization was observed in 15.2% of the controls and 10.1% of the cases (P = 0.001), whereas S. pneumoniae and a single virus co-detection was observed in 33.3% of the cases and in 14.6% of the controls (P < 0.001). Co-detections with rhinovirus, respiratory syncytial virus, parainfluenza virus, human metapneumovirus, and influenza virus were more frequent in the cases compared with the controls (P < 0.001) and were significantly associated with pneumonia in multiple regression analysis. The proportion of single virus detection without bacterial co-detection was not different between cases and controls (13.6% versus 11.3%, P = 0.13). This study suggests that coinfection of S. pneumoniae and certain viruses may play a role in the pathophysiology of pneumonia in children.

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Ye, Linwei, Zhi Liu, Junhao Li, Wan-Lei Zhao, and Liquan Shen. "Co-Saliency Detection via Co-Salient Object Discovery and Recovery." IEEE Signal Processing Letters 22, no. 11 (November 2015): 2073–77. http://dx.doi.org/10.1109/lsp.2015.2458434.

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Probst, Varvara, Bhinnata Piya, Laura Stewart, Susan Gerber, Brian Rha, Joana Yu, Suman Das, Angela P. Campbell, John V. Williams, and Natasha B. Halasa. "741. Impact of Adenovirus Co-detections on Illness Severity." Open Forum Infectious Diseases 5, suppl_1 (November 2018): S266. http://dx.doi.org/10.1093/ofid/ofy210.748.

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Abstract Background Human adenovirus (AdV) is a common pathogen among children with acute respiratory illnesses (ARI) and is often associated with co-detection with other respiratory viral pathogens. We sought to compare demographic and clinical characteristics in children with ARI who had single-AdV vs. AdV-co-detection with other viruses. Methods Children <18 years with fever and/or ARI were enrolled in Vanderbilt Children’s Hospital inpatient setting from 2015 to 2018 and emergency department from 2016 to 2018. Interviews were conducted using standardized case report forms. Nose and throat swab specimens were collected and tested by RT-qPCR for common respiratory pathogens (AdV, RSV, HRV, hMPV, PIV1–4 and Influenza). Results Of 2,740 ARI cases, 174 were positive for AdV [88 (51%) single detection], with 53% male, 47% White, 36% Black, 30% Hispanic and median age of 17.2 months. Co-detected pathogens in AdV-positive specimens were RSV(15%), HRV(14%), influenza(5%), PIV1(1%), PIV2(0.6%), PIV3(1.7%), and PIV4(0.6%), hMPV(3%), >1 co-pathogens(9%). Subjects with single-AdV detection were more likely to have an underlying medical condition (42% vs. 24%, P < 0.05). Table 1 compares clinical presentation and severity of single-AdV and AdV-co-detection cases. Conclusion Patients with single-AdV detection were less likely to present with ARI symptoms and require oxygen, but were more likely to have underlying medical conditions compared with AdV-co-detection. Further studies to type AdV isolates will help elucidate the role of specific adenovirus types associated with co-detections and illness severity and inform epidemiological information for future vaccine initiatives. Disclosures J. V. Williams, Quidel: Board Member, Consulting fee. GlaxoSmithKline: Consultant, Consulting fee. N. B. Halasa, sanofi pasteur: Investigator, Research support. GSK: Consultant, Consulting fee. Moderna: Consultant, Consulting fee.

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Encrenaz, Th, E. Lellouch, P. Drossart, H. Feuchtgruber, G. S. Orton, and S. K. Atreya. "First detection of CO in Uranus." Astronomy & Astrophysics 413, no. 2 (December 18, 2003): L5—L9. http://dx.doi.org/10.1051/0004-6361:20034637.

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Beheshtian, Javad, Zargham Bagheri, Mohammad Kamfiroozi, and Ali Ahmadi. "Toxic CO detection by B12N12 nanocluster." Microelectronics Journal 42, no. 12 (December 2011): 1400–1403. http://dx.doi.org/10.1016/j.mejo.2011.10.010.

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Li, Zun, Congyan Lang, Jiashi Feng, Yidong Li, Tao Wang, and Songhe Feng. "Co-saliency Detection with Graph Matching." ACM Transactions on Intelligent Systems and Technology 10, no. 3 (May 31, 2019): 1–22. http://dx.doi.org/10.1145/3313874.

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Liu, Yan, Tengpeng Li, Yang Wu, Huihui Song, and Kaihua Zhang. "Self-supervised image co-saliency detection." Computers and Electrical Engineering 105 (January 2023): 108533. http://dx.doi.org/10.1016/j.compeleceng.2022.108533.

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Anca Lascu, Anca Lascu, Anca Palade Anca Palade, and Mihaela Birdeanu and Eugenia Fagadar Cosma Mihaela Birdeanu and Eugenia Fagadar Cosma. "Procaine Detection Using Hybrids of Cobalt-Metalloporphyrin with Gold and Silver Nanoparticles." Journal of the chemical society of pakistan 41, no. 1 (2019): 43. http://dx.doi.org/10.52568/000725/jcsp/41.01.2019.

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Two organic-inorganic nanomaterials derived from Co(II)-tetra(3-hydroxyphenyl)porphyrin (Co-3OHPP) and noble metal nanoparticles, namely: Co-porphyrin-nanoAu (Co-3OHPP/nAu) and Co-porphyrin-nanoAg (Co-3OHPP/nAg) were obtained and tested for their capacity to detect procaine. Both hybrids present changes in UV-vis spectroscopy after interaction with procaine, in the same range of procaine concentration. The silver colloid is more easily obtainable and more economically affordable and its hybrid has a wider range of procaine detection from 5.39 x 10-5 mol l-1 to 28.04 x 10-5 mol l-1 concentration, thus being useful for the monitoring of the anesthesic remanence in patients’ plasma. Besides, the plasmonic band of the hybrid is more clearly defined. AFM studies were performed to compare the morphology of the two hybrid materials, Co-3OHPP/nAu and Co-3OHPP/nAg after being exposed to procaine. Both materials display triangular geometries that in case of Co-3OHPP/nAg are not very well defined taking also the form of kvatarons and these two coexisting forms generate pyramidal architectures.

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Zeng, Junyi, Yanting Yang, Xiaoyu Lei, Jinan Deng, Ning Hu, and Jun Yang. "Tuning Co/Ni Ratio in Co–Ni Bimetallic Hybrid for Electrochemical Detection of Glucose." Chemosensors 12, no. 3 (March 4, 2024): 38. http://dx.doi.org/10.3390/chemosensors12030038.

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Transition metallic binary alloys have attracted enormous attention in regard to the non-enzymatic detection of glucose due to their high electrocatalytic activities induced by the synergistic effect between the individual metallic species. However, the easy aggregation of the bimetallic particles has limited their performance. Herein, a facile metal–organic framework (MOF)-derived strategy is developed to synthesize a hybrid containing binary Co–Ni nanoparticles decorated on an N-doped porous carbon matrix (CoxNiy/N-C) for the non-enzymatic detection of glucose. The Co/Ni ratio in the hybrid is investigated to regulate its electrocatalytic behaviors for glucose sensing. A hybrid with the optimal Co/Ni ratio of 1:1 displays two linear detection ranges (0.5 µM to 1 mM and 1 mM to 10 mM) with a detection limit of 0.11 µM for glucose. The feasibility of using this hybrid-modified SPE for glucose detection in real serum samples has also been validated.

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Kim, Hyeonmin, Jung-Taek Kim, Jaehyuk Eoh, and Dong-Won Lim. "Development of a Physics-Based Monitoring Algorithm Detecting CO2 Ingress Accidents in a Sodium-Cooled Fast Reactor." Energies 12, no. 1 (December 20, 2018): 1. http://dx.doi.org/10.3390/en12010001.

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One of the benefits of the supercritical CO 2 Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO 2 with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO 2 and liquid sodium make detecting CO 2 ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurements of two identical heat exchangers for the early detection. The CO 2 ingress occurs owing to a crack at the pressure boundary wall, a certain self-recovery of structural damage does not happen over time, and an accident probabilistically starts at only one component of two. The proposed physics-based method with the probabilistic analysis was compared to the pure data-based method. Finally, the damage degradation was developed with a simplified mass and energy transfer model, and the proposed algorithm was verified with experimental data. The results show that a 99.99% detection probability can be achieved for the air ingress of 30 cc/s, which is equivalent to the 0.12 g/s CO 2 ingress, in a 70 s detection time, limiting down to 0.1% false alarms due to sensor noise.

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Chowdhury, Aditta, Mehdi Hasan Chowdhury, Diba Das, Sampad Ghosh, and Ray Chak Chung Cheung. "Hardware and software co-design for detecting hypertension from photoplethysmogram." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (June 1, 2024): 2647. http://dx.doi.org/10.11591/ijece.v14i3.pp2647-2654.

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Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.

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Tsai, Chung-Chi, Weizhi Li, Kuang-Jui Hsu, Xiaoning Qian, and Yen-Yu Lin. "Image Co-Saliency Detection and Co-Segmentation via Progressive Joint Optimization." IEEE Transactions on Image Processing 28, no. 1 (January 2019): 56–71. http://dx.doi.org/10.1109/tip.2018.2861217.

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Wei, Weiyi, Wenxia Chen, and Mengyu Xu. "Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph." Symmetry 14, no. 11 (November 12, 2022): 2393. http://dx.doi.org/10.3390/sym14112393.

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For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based on edge consistency, and introduced the optimized depth map into the SLIC algorithm to obtain the better superpixel segmentation results of RGBD images. Second, the color features, optimized depth features and global spatial features of superpixels were extracted to construct a weighted hypergraph model to generate saliency maps. Finally, we constructed a weighted hypergraph model for co-saliency detection based on the relationship of color features, global spatial features, optimized depth features and saliency features among images. In addition, in order to verify the impact of the symmetry of the optimized depth information on the co-saliency detection results, we compared the proposed method with two types of models, which included considering depth information and not considering depth information. The experimental results on Cosal150 and Coseg183 datasets showed that our improved algorithm had the advantages of suppressing the background and detecting the integrity of the common salient region, and outperformed other algorithms on the metrics of P-R curve, F-measure and MAE.

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A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.

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Wang, Chong, Zheng-Jun Zha, Dong Liu, and Hongtao Xie. "Robust Deep Co-Saliency Detection with Group Semantic." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8917–24. http://dx.doi.org/10.1609/aaai.v33i01.33018917.

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High-level semantic knowledge in addition to low-level visual cues is essentially crucial for co-saliency detection. This paper proposes a novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning highlevel group-wise semantic representation as well as deep visual features of a given image group. The inter-image interaction at semantic-level as well as the complementarity between group semantics and visual features are exploited to boost the inferring of co-salient regions. Specifically, the proposed approach consists of a co-category learning branch and a co-saliency detection branch. While the former is proposed to learn group-wise semantic vector using co-category association of an image group as supervision, the latter is to infer precise co-salient maps based on the ensemble of group semantic knowledge and deep visual cues. The group semantic vector is broadcasted to each spatial location of multi-scale visual feature maps and is used as a top-down semantic guidance for boosting the bottom-up inferring of co-saliency. The co-category learning and co-saliency detection branches are jointly optimized in a multi-task learning manner, further improving the robustness of the approach. Moreover, we construct a new large-scale co-saliency dataset COCO-SEG to facilitate research of co-saliency detection. Extensive experimental results on COCO-SEG and a widely used benchmark Cosal2015 have demonstrated the superiority of the proposed approach as compared to the state-of-the-art methods.

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Amarin, Justin Z. "#66 Clinical characteristics and outcomes of children with single or co-detected rhinovirus-associated acute respiratory infection in Middle Tennessee." Journal of the Pediatric Infectious Diseases Society 11, Supplement_1 (June 14, 2022): S9. http://dx.doi.org/10.1093/jpids/piac041.035.

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Abstract Background Rhinovirus (RV) is one of the most common viral etiologies of acute respiratory infection (ARI)—the leading infectious cause of mortality in young children. Multiple respiratory viruses may be detected with RV in children with ARI, but the clinical significance of viral co-detection is unclear. We aimed to compare the clinical characteristics and outcomes of children with ARI associated with RV-only detection or RV co-detection. Method We conducted a prospective viral surveillance study (11/2015–7/2016) in Nashville, Tennessee. Children <18 years old who presented to the emergency department (ED) or were hospitalized with fever and/or respiratory symptoms of <14 days duration were eligible if they resided in one of nine selected counties in Middle Tennessee. Demographics and clinical characteristics were collected by parental interviews and medical chart abstractions. Nasal and/or throat specimens were collected and tested for RV, RSV, metapneumovirus, adenovirus, parainfluenza 1–4, and influenza A–C using real-time reverse transcriptase polymerase chain reaction assays. We compared clinical characteristics and outcomes of children with RV-only detection or RV co-detection using Pearson's χ2 test for categorical variables and linear regression with robust standard errors for continuous variables. Results Of 1,250 children who were enrolled, 904 (72.3%) were positive for at least one virus. RV was detected in 406 virus-positive children (44.9%), of whom 117 (28.8%) had RV co-detection. The most common RV co-detection pair was RV/RSV (n=36). The mean age of children with RV co-detection was lower than those with RV-only detection (2.6 ± 2.7 years vs. 4.3 ± 4.4 years; p<0.001). The signs and symptoms of children with RV-only detection and those with RV co-detection are compared in Table 1. A total of 283 children with RV were discharged from the ED (69.7%), of whom 89 (31.4%) had RV co-detection. Children with RV co-detection who were discharged from the ED were less likely to have a diagnosis of asthma/reactive airway disease (RAD) than those with RV-only detection (10.1% vs. 21.6%; p=0.019) but more likely to be diagnosed with bronchiolitis (18.0% vs. 1.5%; p<0.001). Of the 123 hospitalized children with RV, 28 (22.8%) had RV co-detection. Hospitalized children with RV co-detection were less likely to have an admission diagnosis of asthma/RAD than those with RV-only detection (7.1% vs. 43.2%; p<0.001) but more likely to be diagnosed with bronchiolitis (32.1% vs. 14.7%; p=0.038) and pneumonia (32.1% vs. 8.4%; p<0.001). We did not identify differences in hospitalization, intensive care unit admission, supplemental oxygen use, or length of stay between children with RV-only detection and RV co-detection. Conclusion We did not find evidence that RV co-detection was associated with worse outcomes in children with ARI. However, the clinical presentation of children with RV co-detection differs from that of children with RV-only detection. These distinctions warrant consideration in the diagnostic evaluation and management of RV-positive children. Further studies are needed to describe the distinct clinical presentations of RV/non-RV pairs and RV species.

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Lee, Dong-Yun, Kyi-Yeol Park, Deok-Ho Kim, Il-Hyu Seong, and Bong-Seok Park. "Development of Fire Receiver System for Camper on Controling Disaster Prevention Device And Auxiliary Battery Power by Linking Standalone CO Gas Alarm And Fire Detection Signal." Korea Industrial Technology Convergence Society 27, no. 3 (September 30, 2022): 97–107. http://dx.doi.org/10.29279/jitr.2022.27.3.97.

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A receiver capable of detecting and providing alerts about fire and CO gas during camper van accidents and operating a disaster prevention device was developed. The function of the proposed disaster prevention device was verified by designing and testing a receiver for fire detection. It includes a function for controlling windows and ventilation fans by interlocking a fire signal and a stand-alone CO gas alarm and cutting off the auxiliary-battery power. The proposed camper fire-detection receiver is considered a primary model in the development of additional disaster prevention facilities for campers considering that there are currently no commercialized products.

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Prasanth, K. "Image Co-Segmentation and Efficiency Saliency Detection." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 637–43. http://dx.doi.org/10.22214/ijraset.2018.4109.

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LI Bin, 李. 斌., 刘燕德 LIU Yan-de, and 谢锋云 XIE Feng-yun. "Coarse co-phasing detection of segmented mirrors." Optics and Precision Engineering 26, no. 11 (2018): 2647–53. http://dx.doi.org/10.3788/ope.20182611.2647.

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ZHENG Bin, 郑. 彬., 陈永和 CHEN Yong-he, and 傅雨田 FU Yu-tian. "Co-focus error detection of segmented mirrors." Optics and Precision Engineering 27, no. 1 (2019): 26–33. http://dx.doi.org/10.3788/ope.20192701.0026.

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., M. Sreenavya, and Chandra Mohan Reddy Sivappagari. "Fusion of Saliency Based Co-Saliency Detection." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 578–83. http://dx.doi.org/10.26438/ijcse/v6i7.578583.

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Zheng Bin, 郑. 彬., 陆培芬 Lu Peifen, 陈永和 Chen Yonghe, and 傅雨田 Fu Yutian. "Co-Phase Error Detection of Segmented Mirrors." Acta Optica Sinica 37, no. 11 (2017): 1112002. http://dx.doi.org/10.3788/aos201737.1112002.

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Li, Yue. "Structural Resolution with Co-inductive Loop Detection." Electronic Proceedings in Theoretical Computer Science 258 (September 13, 2017): 52–67. http://dx.doi.org/10.4204/eptcs.258.4.

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Wang, Chuan, Huazhu Fu, Liang Yang, and Xiaochun Cao. "Text Co-Detection in Multi-View Scene." IEEE Transactions on Image Processing 29 (2020): 4627–42. http://dx.doi.org/10.1109/tip.2020.2973511.

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Guan, Xin, and Yue-Fang Wu. "Detection of CO Outflow in Rotating Cores." Chinese Journal of Astronomy and Astrophysics 8, no. 2 (April 2008): 230–36. http://dx.doi.org/10.1088/1009-9271/8/2/10.

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Song, E. H., and Y. F. Zhu. "Titanium Decorated Graphene as CO Detection Sensor." Nanoscience and Nanotechnology Letters 5, no. 2 (February 1, 2013): 198–203. http://dx.doi.org/10.1166/nnl.2013.1523.

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Liu, Zhi, Wenbin Zou, Lina Li, Liquan Shen, and Olivier Le Meur. "Co-Saliency Detection Based on Hierarchical Segmentation." IEEE Signal Processing Letters 21, no. 1 (January 2014): 88–92. http://dx.doi.org/10.1109/lsp.2013.2292873.

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Courbat, J., M. Pascu, D. Gutmacher, D. Briand, J. Wöllenstein, U. Hoefer, K. Severin, and N. F. de Rooij. "A colorimetric CO sensor for fire detection." Procedia Engineering 25 (2011): 1329–32. http://dx.doi.org/10.1016/j.proeng.2011.12.328.

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Su, Hang, Shaogang Gong, and Xiatian Zhu. "Scalable logo detection by self co-learning." Pattern Recognition 97 (January 2020): 107003. http://dx.doi.org/10.1016/j.patcog.2019.107003.

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Zhang, Dingwen, Huazhu Fu, Junwei Han, Ali Borji, and Xuelong Li. "A Review of Co-Saliency Detection Algorithms." ACM Transactions on Intelligent Systems and Technology 9, no. 4 (February 21, 2018): 1–31. http://dx.doi.org/10.1145/3158674.

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Luo, X., F. Y. Shi, and J. X. Lin. "CO-laser photoacoustic detection of phosgene (COCl2)." International Journal of Infrared and Millimeter Waves 12, no. 2 (February 1991): 141–47. http://dx.doi.org/10.1007/bf01009888.

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Li, Hongliang, Fanman Meng, and King Ngi Ngan. "Co-Salient Object Detection From Multiple Images." IEEE Transactions on Multimedia 15, no. 8 (December 2013): 1896–909. http://dx.doi.org/10.1109/tmm.2013.2271476.

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Sage, L. J., and J. M. Wrobel. "Detection of CO emission from S0 galaxies." Astrophysical Journal 344 (September 1989): 204. http://dx.doi.org/10.1086/167789.

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Yu, Ning. "Exploring Co-training strategies for opinion detection." Journal of the Association for Information Science and Technology 65, no. 10 (March 10, 2014): 2098–110. http://dx.doi.org/10.1002/asi.23111.

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Yang, Wu, Guo-Wei Shen, Wei Wang, Liang-Yi Gong, Miao Yu, and Guo-Zhong Dong. "Anomaly Detection in Microblogging via Co-Clustering." Journal of Computer Science and Technology 30, no. 5 (September 2015): 1097–108. http://dx.doi.org/10.1007/s11390-015-1585-3.

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Shi, Lan, and Yan Rui Zhang. "A New Research on Instrusion Detection System Based on Artificial Immune." Applied Mechanics and Materials 380-384 (August 2013): 2728–31. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2728.

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The paper proposed a new model by applying biological immune into intrusion detection system, in this new model, generated algorithm of the mature detection get improved, the self-et realized dynamic, co-evolution module can effectively find the system potential vulnerabilities and generate the corresponding patch to strengthen the system. As of result, simulation experiment for this new model is did, through the analysis of the result for simulation experiment, it shows that the new model and method has higher rate in making matured detector than the traditional model and method, and new model also has higher detecting rate on intrusion detection. To sum up, the co-evolution method is able to strengthen the system effectively.

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Sayago, Isabel, Carlos Sánchez-Vicente, and José Pedro Santos. "Highly Sensitive and Selective SnO2-Gr Sensor Photoactivated for Detection of Low NO2 Concentrations at Room Temperature." Nanomaterials 14, no. 24 (December 12, 2024): 1994. https://doi.org/10.3390/nano14241994.

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Chemical nanosensors based on nanoparticles of tin dioxide and graphene-decorated tin dioxide were developed and characterized to detect low NO2 concentrations. Sensitive layers were prepared by the drop casting method. SEM/EDX analyses have been used to investigate the surface morphology and the elemental composition of the sensors. Photoactivation of the sensors allowed for detecting ultra-low NO2 concentrations (100 ppb) at room temperature. The sensors showed very good sensitivity and selectivity to NO2 with low cross-responses to the other pollutant gases tested (CO and CH4). The effect of humidity and the presence of graphene on sensor response were studied. Comparative studies revealed that graphene incorporation improved sensor performance. Detections in complex atmosphere (CO + NO2 or CH4 + NO2, in humid air) confirmed the high selectivity of the graphene sensor in near-real conditions. Thus, the responses were of 600%, 657% and 540% to NO2 (0.5 ppm), NO2 (0.5 ppm) + CO (5 ppm) and NO2 (0.5 ppm) + CH4 (10 ppm), respectively. In addition, the detection mechanisms were discussed and the possible redox equations that can change the sensor conductance were also considered.

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Sun, Jian, and Wei Guo Lin. "Features Extraction of CO₂ Signal with Operational Conditions Adaptability." Applied Mechanics and Materials 109 (October 2011): 131–35. http://dx.doi.org/10.4028/www.scientific.net/amm.109.131.

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A method for indirect online over-temperature detection of isolators in high voltage switchgear is proposed，which is based on Na Superionic Conductor CO2 sensor. The over-temperature detecting principle is put forward and an over-temperature detecting system based on the method is introduced. The reliability of over-temperature detection depends on signal noise ratio (SNR), but the sensor and signal processing determine the SNR of CO2 signal. Based on quantitative analysis, it is concluded that Na Superionic Conductor CO2 sensor has higher SNR. By signal processing, the necessity of moving average filter for SNR is demonstrated. A temperature compensation circuit is adopted to eliminate the influence of temperature. In view of the characteristics of rude signal, an appropriate method of real-time digital filtering is used to improve the Signal-to-Noise Ratio. The different features of the signal under over-temperature condition have been analyzed and compared to those under normal conditions. The paper introduces a reliable algorithm for identifying over-temperature detection based on sequential section differences. Experiments prove that the method is feasible.

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Li, Jun, Gengyu Yang, and Yanhua Shao. "Ransomware Detection Model Based on Adaptive Graph Neural Network Learning." Applied Sciences 14, no. 11 (May 27, 2024): 4579. http://dx.doi.org/10.3390/app14114579.

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Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection technology has become a focus of research on information security risk detection methods. However, current ransomware detection techniques have high false positive and false negative rates, and traditional methods ignore global word co-occurrence and correlation information between key node steps in the entire process. This poses a significant challenge for accurately identifying and detecting ransomware. We propose a ransomware detection model based on co-occurrence information adaptive diffusion learning using a Text Graph Convolutional Network (ADC-TextGCN). Specifically, ADC-TextGCN first assign self-weights to word nodes based on sensitive API call functions and preserve co-occurrence information using Point Mutual Information Theory (COIR-PMI); then our model automatically learn the optimal neighborhood through an Adaptive Diffusion Convolution (ADC) strategy, thereby improving the ability to aggregate long-distance node information across layers and enhancing the network’s ability to represent ransomware behavior. Experimental results show that our method achieves an accuracy of over 96.6% in ransomware detection, proving its effectiveness and superiority compared to traditional methods based on CNN and RNN in ransomware detection.

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Tan, Zhenshan, Xiaodong Gu, and Qingrong Cheng. "Co-saliency detection with two-stage co-attention mining and individual calibration." Engineering Applications of Artificial Intelligence 127 (January 2024): 107201. http://dx.doi.org/10.1016/j.engappai.2023.107201.

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Nandy, Turja, Ronald Coutu, and Cristinel Ababei. "Carbon Monoxide Sensing Technologies for Next-Generation Cyber-Physical Systems." Sensors 18, no. 10 (October 13, 2018): 3443. http://dx.doi.org/10.3390/s18103443.

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Carbon monoxide (CO) is a toxic gas, and environmental pollutant. Its detection and control in residential and industrial environments are necessary in order to avoid potentially severe health problems in humans. In this review paper, we discuss the importance of furthering research in CO sensing technologies for finding the proper material with low-range detection ability in very optimum condition. We build our discussion through the perspective of a cyber-physical system (CPS) modeling framework, because it provides a comprehensive framework to model and develop automated solutions for detection and control of poisonous chemical compounds, such as the CO. The most effective CO sensors, then, can be used in CPS network to provide a pathway for real-time monitoring and control in both industrial and household environment. In this paper, first, we discuss the necessity of CO detection, the proposal of a basic CPS framework for modeling and system development, how the CPS-CO model can be beneficiary to the environment, and a general classification of the various CO detection mechanisms. Next, a broad overview emphasizes the sensitivity, selectivity, response and recovery time, low concentration detection ability, effects of external parameters and other specifications that characterize the performance of the sensing methods proposed so far. We will discuss recent studies reported on the use of metal oxide semiconductor (MOS) sensing technologies for the detection of CO. MOS based micro-sensors play an important role in the measurement and monitoring of various trace amounts of CO gas. These sensors are used to sense CO through changes in their electrical properties. In addition to MOS based sensors, optical sensing methods have recently become popular, due to their increased performance. Hence, a brief overview of newly proposed optical based CO detection methods is provided as well.

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Kral, Quentin, Luca Matrà, Grant M. Kennedy, Sebastian Marino, and Mark C. Wyatt. "Survey of planetesimal belts with ALMA: gas detected around the Sun-like star HD 129590." Monthly Notices of the Royal Astronomical Society 497, no. 3 (July 11, 2020): 2811–30. http://dx.doi.org/10.1093/mnras/staa2038.

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ABSTRACT Gas detection around main-sequence stars is becoming more common with around 20 systems showing the presence of CO. However, more detections are needed, especially around later spectral type stars to better understand the origin of this gas and refine our models. To do so, we carried out a survey of 10 stars with predicted high likelihoods of secondary CO detection using ALMA in band 6. We looked for continuum emission of mm-dust as well as gas emission (CO and CN transitions). The continuum emission was detected in 9/10 systems for which we derived the discs’ dust masses and geometrical properties, providing the first mm-wave detection of the disc around HD 106906, the first mm-wave radius for HD 114082, 117214, HD 15745, HD 191089, and the first radius at all for HD 121191. A crucial finding of our paper is that we detect CO for the first time around the young 10–16 Myr old G1V star HD 129590, similar to our early Sun. The gas seems colocated with its planetesimal belt and its total mass is likely in the range of (2–10) × 10−5 M⊕. This first gas detection around a G-type main-sequence star raises questions as to whether gas may have been released in the Solar system as well in its youth, which could potentially have affected planet formation. We also detected CO gas around HD 121191 at a higher signal-to-noise ratio than previously and find that the CO lies much closer-in than the planetesimals in the system, which could be evidence for the previously suspected CO viscous spreading owing to shielding preventing its photodissociation. Finally, we make estimates for the CO content in planetesimals and the HCN/CO outgassing rate (from CN upper limits), which we find are below the level seen in Solar system comets in some systems.

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Xu, Zeshan, Min Xiao, and Jie Chen. "Analysis of characteristics of underground pipeline detection by co-polarization and cross-polarization of ground penetrating radar." Journal of Physics: Conference Series 2887, no. 1 (November 1, 2024): 012061. http://dx.doi.org/10.1088/1742-6596/2887/1/012061.

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Abstract The polarization mode of ground penetrating radar includes co-polarization mode and cross-polarization mode. The co-polarization mode is more commonly used, mainly because of its good polarization matching effect and good detection effect, so the importance of cross-polarization is ignored. In order to achieve better detection effect in underground pipeline detection, this paper compares the co-polarization and cross-polarization experiments, The results show that when the surface roughness of the pipeline is low, the detection effect of VV polarization of the co-polarization mode is better, and when the surface roughness of the pipeline is high, the detection effect of cross polarization mode is better.

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Ashgar, Sami. "Increase AmpC co-producer ESBL phenotypic detection by using Tazobactam with Cefepime Increase AmpC co-producer ESBL Phenotypic Detection." Egyptian Academic Journal of Biological Sciences. C, Physiology and Molecular Biology 15, no. 1 (January 23, 2023): 49–54. http://dx.doi.org/10.21608/eajbsc.2023.281335.

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Zhang, Daojun, Xiaobei Zhang, Yingping Bu, Jingchao Zhang, and Renchun Zhang. "Copper Cobalt Sulfide Structures Derived from MOF Precursors with Enhanced Electrochemical Glucose Sensing Properties." Nanomaterials 12, no. 9 (April 19, 2022): 1394. http://dx.doi.org/10.3390/nano12091394.

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Nonenzymatic electrochemical detection of glucose is popular because of its low price, simple operation, high sensitivity, and good reproducibility. Co-Cu MOFs precursors were synthesized via the solvothermal way at first, and a series of porous spindle-like Cu-Co sulfide microparticles were obtained by secondary solvothermal sulfurization, which maintained the morphology of the MOFs precursors. Electrochemical studies exhibit that the as-synthesized Cu-Co sulfides own excellent nonenzymatic glucose detection performances. Compared with CuS, Co (II) ion-doped CuS can improve the conductivity and electrocatalytic activity of the materials. At a potential of 0.55 V, the as-prepared Co-CuS-2 modified electrode exhibits distinguished performance for glucose detection with wide linear ranges of 0.001–3.66 mM and high sensitivity of 1475.97 µA·mM−1·cm−2, which was much higher than that of CuS- and Co-CuS-1-modified electrodes. The constructed sulfide sensors derived from MOF precursors exhibit a low detection limit and excellent anti-interference ability for glucose detection.

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Journal articles on the topic 'CO₂ detection'

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