Search results for: aspirating concentration detection

Authors: Rovina Kobun, Shafiquzzaman Siddiquee

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A novel and simple electrochemical sensor for the determination of melamine was developed based on modified gold electrode (AuE) with chitosan (CHIT) nanocomposite membrane, zinc oxide nanoparticles (ZnONPs) and ionic liquids ([EMIM][Otf]) to enhance the potential current response of melamine. Cyclic voltammetry and differential pulse voltammetry were used to investigate the electrochemical behaviour between melamine and modified AuE in the presence of methylene blue as a redox indicator. The experimental results indicated that the interaction of melamine with CHIT/ZnONPs/([EMIM][Otf])/AuE were based on the strong interaction of hydrogen bonds. The morphological characterization of modified AuE was observed under scanning electron microscope. Under optimal conditions, the current signal was directly proportional to the melamine concentration ranging from 9.6 x 10-5 to 9.6 x 10-11 M, with a correlation coefficient of 0.9656. The detection limit was 9.6 x 10-12 M. Finally, the proposed method was successfully applied and displayed an excellent sensitivity in the determination of melamine in milk samples.

Keywords: melamine, gold electrode, zinc oxide nanoparticles, cyclic voltammetries, differential pulse voltammetries

Procedia PDF Downloads 417

Authors: Behnaz Mahdavi, Hamidreza Khodaei, Alireza Banitaba

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Conjugated Linoleic Acid is a group of long-chain unsaturated fatty acids with more than one double bond and a mixture of 28 isomers of Linoleic acid (C 18:2) and it is counted as one of the essential acids. The main purpose of this study was to investigate the effect of CLA on some reproductive hormones in rams. In this study, six rams 3 to 4 years old with an average weight of 90 kg were selected. Rams were randomly divided into 3 groups and were treated by CLA treatment for 30 days. The first group (as a control group) did not receive CLA, The second group received 0.5 gr and the third group received 1 gram of CLA. The blood testing was done on rams every 15 days using a 20 ml syringe. Data analysis was performed by SAS software. Also mean comparison was done using Duncan's test method (p<0.05). Obtained results showed that the serum concentration of testosterone hormone was decreased numerically as well as the concentration of FSH hormone however the concentration of LH was increased. Also, the CLA had a significant effect on Leptin concentration. CLA in oral form can reduce the concentration of testosterone in rams.

Keywords: CLA, ram, testosterone, conjugated linoleic acid

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Authors: Patel P. Kailash, Patel M. Parimal

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An investigation was conducted to study the different concentration of coal fly ash solution on morphological and biochemical parameters of Helianthus annuus L. The seeds of Helianthus annuus L. were placed in petri dishes in three replicates and allowed to grow for 16 days in different concentration of coal fly ash solution. Shoot length, root length and fresh weight, dry weight declined with increasing concentration of fly ash. Semidiluted and concentrated fly ash solution exhibited significant reduction in chlorophyll, protein,sugar and ascorbic acid. Concentration dependent changes were observed in most of parameters. Diluted solution of fly ash revealed the maximum increase morphological and biochemical changes of seedlings.

Keywords: Helianthus annuus L., protein, sugar, chlorophyll, coal fly ash

Procedia PDF Downloads 348

Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba

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Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.

Keywords: high level synthesis, canny edge detection, hardware accelerators, computer vision

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

Procedia PDF Downloads 117

Authors: Anil P. Dewani, Ravindra L. Bakal, Anil V. Chandewar

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Present work demonstrates the applicability of high-performance liquid chromatography (HPLC) with UV detection for the determination of malondialdehyde as malondialdehyde-thiobarbituric acid complex (MDA-TBA) in-vivo in rats. The HPLC-UV method for MDA-TBA was achieved by isocratic mode on a reverse-phase C18 column (250mm×4.6mm) at a flow rate of 1.0mLmin−1 followed by UV detection at 278 nm. The chromatographic conditions were optimized by varying the concentration and pH followed by changes in percentage of organic phase optimal mobile phase consisted of mixture of water (0.2% Triethylamine pH adjusted to 2.3 by ortho-phosphoric acid) and acetonitrile in ratio (80:20 % v/v). The retention time of MDA-TBA complex was 3.7 min. The developed method was sensitive as limit of detection and quantification (LOD and LOQ) for MDA-TBA complex were (standard deviation and slope of calibration curve) 110 ng/ml and 363 ng/ml respectively. The method was linear for MDA spiked in plasma and subjected to derivatization at concentrations ranging from 100 to 1000 ng/ml. The precision of developed method measured in terms of relative standard deviations for intra-day and inter-day studies was 1.6–5.0% and 1.9–3.6% respectively. The HPLC method was applied for monitoring MDA levels in rats subjected to chronic treatment of ciprofloxacin (CFL) (5mg/kg/day) for 21 days. Results were compared by findings in control group rats. Mean peak areas of both study groups was subjected for statistical treatment to unpaired student t-test to find p-values. The p value was < 0.001 indicating significant results and suggesting increased MDA levels in rats subjected to chronic treatment of CFL of 21 days.

Keywords: MDA, TBA, ciprofloxacin, HPLC-UV

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Authors: Ahmadu Maidorawa, Idrissa Djibo, Muhammad Tella

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This paper presents a method for detecting flow chart and figure plagiarism based on shape of image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets. Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offense that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide these checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts result in look holes that people can take advantage. That means people can plagiarize figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts.

Keywords: flowchart, multimedia retrieval, figures similarity, image comparison, figure retrieval

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Authors: Ahmad H. Abdelgwad, Ahmed A. Nashat

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Landmine detection is an important and yet challenging problem remains to be solved. Ground Penetrating Radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. The detection methodology of GPR depends mainly on the contrast of the dielectric properties of the searched target and its surrounding soil. This contrast produces a partial reflection of the electromagnetic pulses that are being transmitted into the soil and then being collected by the GPR.  One of the most critical hardware components for the performance of GPR is the antenna system. The current paper explores the design and simulation of a pyramidal horn antenna operating at L-band frequencies (1- 2 GHz) to detect a landmine. A prototype model of the GPR system setup is developed to simulate full wave analysis of the electromagnetic fields in different soil types. The contrast in the dielectric permittivity of the landmine and the sandy soil is the most important parameter to be considered for detecting the presence of landmine. L-band horn antenna is proved to be well-versed in the investigation of landmine detection.

Keywords: full wave analysis, ground penetrating radar, horn antenna design, landmine detection

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Authors: Maysoon Khalil Askar

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The delineation of possible areas of corrosion along the external face of an underground oil pipeline in Trunk line of Al- Garraf oil field was investigated using the horizontal electrical resistivity profiling technique and study the contribution of pH, Moisture Content in Soil and Presence chlorides, sulfates and total dissolve salts in soil and water. The test sites represent a physical and chemical properties of soils. The hydrogen-ion concentration of soil and groundwater range from 7.2 to 9.6, and the resistivity values of the soil along the pipeline were obtained using the YH302B model resistivity meter having values between 1588 and 720 Ohm-cm. the chloride concentration in soil and groundwater is high (more than 1000 ppm), total soulable salt is more than 5000 ppm, and sulphate range from 0.17% and 0.98% in soil and more than 600 ppm in groundwater. The soil is poor aeration, the soil texture is fine (clay and silt soil), the water content is high (the groundwater is close to surface), the chloride and sulphate is high in the soil and groundwater, the total soulable salt is high in ground water and finally the soil electric resistivity is low that the soil is very corrosive and there is the possibility of the pipeline failure. These methods applied in the study are quick, economic and efficient for detecting along buried pipelines which need to be protected. Routine electrical geophysical investigations along buried oil pipelines should be undertaken for the early detection and prevention of pipeline failure with its attendant environmental, human and economic consequences.

Keywords: soil resistivity, corrosion, cathodic protection, chloride concentration, water content

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Authors: Liu Linxin

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As system complexity increases, log parsing and anomaly detection become more and more important in ensuring system stability. However, traditional methods often face the problems of insufficient adaptability and decreasing accuracy when dealing with rapidly changing log contents and unknown domains. To this end, this paper proposes an approach LogRAG, which combines RAG (Retrieval Augmented Generation) technology with Prompt Engineering for Large Language Models, applied to log analysis tasks to achieve dynamic parsing of logs and intelligent anomaly detection. By combining real-time information retrieval and prompt optimisation, this study significantly improves the adaptive capability of log analysis and the interpretability of results. Experimental results show that the method performs well on several public datasets, especially in the absence of training data, and significantly outperforms traditional methods. This paper provides a technical path for log parsing and anomaly detection, demonstrating significant theoretical value and application potential.

Keywords: log parsing, anomaly detection, retrieval-augmented generation, prompt engineering, LLMs

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Authors: Sahar Ehsani, David James, Vernon Hodge

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In this experimental study, total selenium in solution was measured with Graphite Furnace Atomic Absorption Spectroscopy, GFAAS, then chemical reactions with sodium borohydride were used to reduce selenite to hydrogen selenide. Hydrogen selenide was then stripped from the solution by purging the solution with nitrogen gas. Since the two main speciations in oxic waters are usually selenite, Se(IV) and selenate, Se(VI), it was assumed that after Se(IV) is removed, the remaining total selenium was Se(VI). Total selenium measured after stripping gave Se(VI) concentration, and the difference of total selenium measured before and after stripping gave Se(IV) concentration. An additional step of reducing Se(VI) to Se(IV) was performed by boiling the stripped solution under acidic conditions, then removing Se(IV) by a chemical reaction with sodium borohydride. This additional procedure of removing Se(VI) from the solution is useful in rare cases where the water sample is reducing and contains selenide speciation. In this study, once Se(IV) and Se(VI) were both removed from the water sample, the remaining total selenium concentration was zero. The method was tested to determine Se(IV) and Se(VI) in both purified water and synthetic irrigation water spiked with Se(IV) and Se(VI). Average recovery of spiked samples of diluted synthetic irrigation water was 99% for Se(IV) and 97% for Se(VI). Detection limits of the method were 0.11 µg L⁻¹ and 0.32 µg L⁻¹ for Se(IV) and Se(VI), respectively.

Keywords: Analytical Method, Graphite Furnace Atomic Absorption Spectroscopy, Selenate, Selenite, Selenium Speciations

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Authors: Belkacem Soundes, Guezouli Larbi

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This paper presents a robust and accurate method for text detection and localization over lecture videos. Frame regions are classified into text or background based on visual feature analysis. However, lecture video shows significant degradation mainly related to acquisition conditions, camera motion and environmental changes resulting in low quality videos. Hence, affecting feature extraction and description efficiency. Moreover, traditional text detection methods cannot be directly applied to lecture videos. Therefore, robust feature extraction methods dedicated to this specific video genre are required for robust and accurate text detection and extraction. Method consists of a three-step process: Slide region detection and segmentation; Feature extraction and non-text filtering. For robust and effective features extraction moment functions are used. Two distinct types of moments are used: orthogonal and non-orthogonal. For orthogonal Zernike Moments, both Pseudo Zernike moments are used, whereas for non-orthogonal ones Hu moments are used. Expressivity and description efficiency are given and discussed. Proposed approach shows that in general, orthogonal moments show high accuracy in comparison to the non-orthogonal one. Pseudo Zernike moments are more effective than Zernike with better computation time.

Keywords: text detection, text localization, lecture videos, pseudo zernike moments

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Authors: Ismail Elkhrachy

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Determination of land use changing is an important component of regional planning for applications ranging from urban fringe change detection to monitoring change detection of land use. This data are very useful for natural resources management.On the other hand, the technologies and methods of change detection also have evolved dramatically during past 20 years. So it has been well recognized that the change detection had become the best methods for researching dynamic change of land use by multi-temporal remotely-sensed data. The objective of this paper is to assess, evaluate and monitor land use change surrounding the area of Najran city, Kingdom of Saudi Arabia (KSA) using Landsat images (June 23, 2009) and ETM+ image(June. 21, 2014). The post-classification change detection technique was applied. At last,two-time subset images of Najran city are compared on a pixel-by-pixel basis using the post-classification comparison method and the from-to change matrix is produced, the land use change information obtained.Three classes were obtained, urban, bare land and agricultural land from unsupervised classification method by using Erdas Imagine and ArcGIS software. Accuracy assessment of classification has been performed before calculating change detection for study area. The obtained accuracy is between 61% to 87% percent for all the classes. Change detection analysis shows that rapid growth in urban area has been increased by 73.2%, the agricultural area has been decreased by 10.5 % and barren area reduced by 7% between 2009 and 2014. The quantitative study indicated that the area of urban class has unchanged by 58.2 km〗^2, gained 70.3 〖km〗^2 and lost 16 〖km〗^2. For bare land class 586.4〖km〗^2 has unchanged, 53.2〖km〗^2 has gained and 101.5〖km〗^2 has lost. While agriculture area class, 20.2〖km〗^2 has unchanged, 31.2〖km〗^2 has gained and 37.2〖km〗^2 has lost.

Keywords: land use, remote sensing, change detection, satellite images, image classification

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Authors: Yushen Dai, Tao Deng, Miaoying Chen, Baoqin Huang, Yan Ji, Yongshen Feng, Shaofei Liu, Dongmei Zhong, Tao Zhang, Lifeng Zhang

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Background: Detection and diagnosis are prerequisites for early interventions in the care of children with Autism Spectrum Disorder (ASD). However, few studies have focused on this topic. Aim: This study aims to characterize the timing from symptom detection to intervention in children with ASD and to identify the potential predictors of early detection, diagnosis, and intervention. Methods and procedures: A cross-sectional survey was conducted with 314 parents of children with ASD in Guangzhou, China. Outcomes and Results: This study found that most children (76.24%) were diagnosed within one year after detection, and 25.8% of them did not receive the intervention after diagnosis. Predictors to ASD diagnosis included ASD-related symptoms identified at a younger age, more serious symptoms, and initial symptoms with abnormal development and sensory anomalies. ASD-related symptoms observed at an older age, initial symptoms with the social deficit, sensory anomalies, and without language impairment, parents as the primary caregivers, family with lower income and less social support utilization increased the odds of the time lag between detection and diagnosis. Children whose fathers had a lower level of education were less likely to receive the intervention. Conclusions and Implications: The study described the time for detection, diagnosis, and interventions of children with ASD. Findings suggest that the ASD-related symptoms, the timing at which symptoms first become a concern, primary caregivers’ roles, father’s educational level, and the family economic status should be considered when offering support to improve early detection, diagnosis, and intervention. Helping children and their families take full advantage of support is also important.

Keywords: autism spectrum disorder, child, detection, diagnosis, intervention, social support

Procedia PDF Downloads 89

Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

Procedia PDF Downloads 356

Authors: Walid Mousa, Mohamed Nayel, Ahmed Zaghawa, Akram Salama, Ahmed El-Sify, Hesham Rashad, Dina El-Shafey

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Mycoplasmosis in small ruminants constitutes a serious contagious problem in smallholders causing severe economic losses worldwide. This study was conducted to determine the clinical, Minimum Inhibitory Concentration (MIC) and molecular characterization of Mycoplasma species associated in sheep breeding herds in Menoufiya governorate, Egypt. Out of the examination of 400 sheep, 104 (26%) showed respiratory manifestations, nasal discharges, cough and conjunctivitis with systemic body reaction. Meanwhile, out of these examined sheep, only 56 (14%) were positive for mycoplasma isolation onto PPLO(Pleuropneumonia-like organisms) specific medium. The MIC for evaluating the efficacy of sensitivity of Mycoplasma isolates against different antibiotics groups revealed that both the Linospectin and Tylosin with 2ug, 0.25ug/ml concentration were the most effective antibiotics for Mycoplasma isolates. The application of PCR was the rapid, specific and sensitive molecular approach for detection of M. ovipneumoniae, and M. arginine at 390 and 326 bp, respectively, in all tested isolates. In conclusion, the diagnosis of Mycoplsamosis in sheep is important to achieve effective control measures and minimizing the disease dissemination among sheep herds.

Keywords: MIC, mycoplasmosis, PCR, sheep

Procedia PDF Downloads 226

Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

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This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

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Authors: Sloka Bhushan

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Dark matter constitutes a majority of matter in the universe, yet very little is known about it due to its extreme lack of interaction with regular matter and the fundamental forces. Weakly Interacting Massive Particles, or WIMPs, have been contested to be one of the strongest candidates for dark matter due to their promising theoretical properties. However, various endeavors to detect these elusive particles have failed. This paper explores the various particles which may be WIMPs and the detection techniques being employed to detect WIMPs (such as underground detectors, LHC experiments, and so on). There is a special focus on the reasons for the lack of detection of WIMPs so far, and the possibility of limits in detection being a reason for the lack of physical evidence of the existence of WIMPs. This paper also explores possible inconsistencies within the WIMP particle theory as a reason for the lack of physical detection. There is a brief review on the possible solutions and alternatives to these inconsistencies. Additionally, this paper also reviews the supersymmetry theory and the possibility of the supersymmetric neutralino (A possible WIMP particle) being detectable. Lastly, a review on alternate candidates for dark matter such as axions and MACHOs has been conducted. The explorative study in this paper is conducted through a series of literature reviews.

Keywords: dark matter, particle detection, supersymmetry, weakly interacting massive particles

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Authors: K. L. Kabemba, R. F. Sandenberg

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The usual way to desorb gold is by elution with a hot concentrated alkaline solution of sodium cyanide. The high temperature is necessary because the dielectric constant of water decreases with increasing temperature hence the electrostatic forces between charcoal and the gold cyanide complex decreases. High alkalinity and a high concentration of cyanide are necessary for gold desorption because both OH- and CN- ions are preferentially adsorbed. The rate of elution increases with increasing anion concentration but decreases with increasing cation concentration that means the rate of elution passes through a maximum as the concentration of the eluting salt (NaCN, for example) is increased. The anion that gives the best results, the cyanide ion, decomposes fairly rapidly at elevated temperatures (40% in 6 hours, 90% in 24 hours at 95°C).

Keywords: caustic, cyanide, gold, temperature

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Authors: Rana Muhammad Armaghan Ayaz, Yigit Uysallı, Nima Bavili, Berna Morova, Alper Kiraz

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Traditional optical methods for example resonance wavelength shift and cavity ring-down spectroscopy used for material detection and sensing have disadvantages, for example, less resistance to laser noise, temperature fluctuations and extraction of the required information can be a difficult task like ring downtime in case of cavity ring-down spectroscopy. Phase shift cavity ring down spectroscopy is not only easy to use but is also capable of overcoming the said problems. This technique compares the phase difference between the signal coming out of the cavity with the reference signal. Detection of any material is made by the phase difference between them. By using this technique, air, water, and isopropyl alcohol can be recognized easily. This Methodology has far-reaching applications and can be used in air pollution detection, human breath analysis and many more.

Keywords: materials, noise, phase shift, resonance wavelength, sensitivity, time domain approach

Procedia PDF Downloads 147

Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

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Authors: Nicole Virgili, Romolo Remetti

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The aim of this work is to characterize, from radiation protection point of view, the emission into the environment of air contaminated by argon-41. In this research work, 41Ar is produced by a TR19PET cyclotron, operated at 19 MeV, installed at 'A. Gemelli' University Hospital, Rome, Italy, for fluorine-18 production. The production rate of 41Ar has been calculated on the basis of the scheduled operation cycles of the cyclotron and by utilising proper production algorithms. Then extensive Monte Carlo calculations, carried out by MCNP code, have allowed to determine the absolute detection efficiency to 41Ar gamma rays of a Geiger Muller detector placed in the terminal part of the chimney. Results showed unsatisfactory detection efficiency values and the need for integrating the detection system with more efficient detectors.

Keywords: Cyclotron, Geiger Muller detector, MCNPX, argon-41, emission of radioactive gas, detection efficiency determination

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Aria Vitkova, Hanna Sykulska-Lawrence

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In recent years, Europa has been one of the major focus points in astrobiology due to its high potential of harbouring life in the vast ocean underneath its icy crust. However, the detection of life on Europa faces many challenges due to the harsh environmental conditions and mission constraints. Raman spectroscopy is a highly capable and versatile in-situ characterisation technique that does not require any sample preparation. It has only been used on Earth to date; however, recent advances in optical and laser technology have also allowed it to be considered for extraterrestrial exploration. So far, most efforts have been focused on the exploration of Mars, the most imminent planetary target. However, as an emerging technology with high miniaturization potential, Raman spectroscopy also represents a promising tool for the exploration of Europa. In this study, the capabilities of Raman technology in terms of life detection on Europa are explored and assessed. Spectra of biosignatures identified as high priority molecular targets for life detection on Europa were acquired at various excitation wavelengths and conditions analogous to Europa. The effects of extremely low temperatures and low concentrations in water ice were explored and evaluated in terms of the effectiveness of various configurations of Raman instruments. Based on the findings, a design of a miniaturized Raman instrument optimized for in-situ detection of life on Europa is proposed.

Keywords: astrobiology, biosignatures, Europa, life detection, Raman Spectroscopy

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Mahmoud E. Mohamed, Ahmed F. Shalash, Hanan A. Kamal

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In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, Despite the tradeoff between the noise level and the speed of the detection. In this paper, An improvement is introduced in the Kalman filter, Through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, The effect on the response to false alarms is also presented and false alarm rate show improvement.

Keywords: Kalman filter, innovation, false detection, improvement

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Rory Anthony Hutagalung, Leonardus Widjaja

Abstract:

Microalgae Meyerella planktonica is a potential biofuel source because it can grow in bulk in either autotrophic or heterotrophic condition. However, the quantitative growth of this algal type is still low as it tends to precipitates on the bottom. Beside, the lipid concentration is still low when grown in autotrophic condition. In contrast, heterotrophic condition can enhance the lipid concentration. The combination of autotrophic condition and agitation treatment was conducted to increase the density of the culture. On the other hand, a heterotrophic condition was set up to raise the lipid production. A two-stage experiment was applied to increase the density at the first step and to increase the lipid concentration in the next step. The autotrophic condition resulted higher density but lower lipid concentration compared to heterotrophic one. The agitation treatment produced higher density in both autotrophic and heterotrophic conditions. The two-stage experiment managed to enhance the density during the autotrophic stage and the lipid concentration during the heterotrophic stage. The highest yield was performed by using 0.4% v/v glycerol as a carbon source (2.9±0.016 x 106 cells w/w) attained 7 days after the heterotrophic stage began. The lipid concentration was stable starting from day 7.

Keywords: agitation, glycerol, heterotrophic, lipid productivity, Meyerella planktonica

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

Abstract:

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

Procedia PDF Downloads 102