Search results for: plant detection

Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

Procedia PDF Downloads 190

Authors: Mohammadreza Delfieh, Seyed Ali Mohammad Modarres Sanavy, Rouzbeh Farhoudi

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Fennel is of most important medicinal plants which is widely used in food and pharmaceutical industries. In order to investigate the effect of different nitrogen nutritional systems including chemical, organic and biologic ones at different plant densities on yield, yield components and seed essential oil content and yield of this valuable medicinal plant, a field experiment was carried out in 2013-2014 agricultural season at Islamic Azad University of Shoushtar agricultural college in split plot design with 18 treatments and based on completely randomized blocks design. Different nitrogen system treatments consisting of: 1. N1 or control (Uniformly spreading urea fertilizer in the plot, 50% at planting time and 50% at stem elongation), 2. N2 (Uniformly spreading 50% of urea fertilizer in the plot at planting time and spraying the other 50% of urea fertilizer at stem elongation on fennel foliage), 3. N3 or cow manure, 4. N4 or biofertilizer (Inoculation of fennel seeds with Azotobacter and Azospirillum), 5. N5 or Integrated-1 (Cow manure + uniformly spreading urea fertilizer in the plot at stem elongation), 6. N6 or Integrated-2 (Cow manure + Inoculation of fennel seeds with Azotobacter and Azospirillum) were applied to the main plots. Three fennel densities consisting of: 1. FD1 (60 plant/m2), 2. FD2 (80 plant/m2) and 3. FD3 (100 plant/m2) were applied to subplots. Results showed that all of the traits were significantly affected by applied treatments (P 0.01). The interaction between treatments also were significant at 5 percent level for shoot dry weight and at 1 percent level for other traits. Based on the results, using the Integrated-1 treatment at 100 plant per m2 produced 94.575 g/m2 seed yield containing 3.375 percent of essential oil. Utilization of such combination not only could lead to a desirable fennel quantity and quality, but also is more consistent with environment.

Keywords: fennel (foeniculum vulgare mill.), nutritional system, nitrogen, biofertilizer, organic fertilizer, chemical fertilizer, density

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Authors: Gabriela-Andreea Despescu, Mădălina-Elena Mavrodin, Gheorghe Lăzăroiu, Florin Adrian Grădinaru

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The purpose of this paper is to contribute to the enhancement of a hydroelectric plant protection by coordinating protection measures and existing security and introducing new measures under a risk management process. Also, the plan identifies key critical elements of a hydroelectric plant, from its level vulnerabilities and threats it is subjected to in order to achieve the necessary protection measures to reduce the level of risk.

Keywords: critical infrastructure, risk analysis, critical infrastructure protection, vulnerability, risk management, turbine, impact analysis

Procedia PDF Downloads 528

Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

Procedia PDF Downloads 159

Authors: Ravi P. Agrahari

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The present study was undertaken with the objective of evaluating kitchen waste as an alternative organic material for biogas production in community level biogas plant. The field study was carried out for one month (January 19, 2012– February 17, 2012) at Centre for Energy Studies, IIT Delhi, New Delhi, India. This study involves the uses of greenhouse canopy to increase the temperature for the production of biogas in winter period. In continuation, a semi-continuous study was conducted for one month with the retention time of 30 days under batch system. The gas generated from the biogas plant was utilized for cooking (burner) and lighting (lamp) purposes. Gas productions in the winter season registered lower than other months. It can be concluded that the solar greenhouse assisted biogas plant can be efficiently adopted in colder region or in winter season because temperature plays a major role in biogas production. 

Keywords: biogas, green house chamber, organic material, solar intensity, batch system

Procedia PDF Downloads 375

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

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Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

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Authors: Wiwat Onnakklum

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Clarification is the turbidity removal process of water treatment plant. This paper was to study the factors affecting on energy consumption in order to control energy saving strategy. The factors studied were raw water turbidity in the range of 26-40 NTU and production rate in the range of 3.76-5.20 m³/sec. Clarifiers were sludge blanket and sludge recirculation clarifier. Experimental results found that the raw water turbidity was not affected significantly by energy consumption, while the production rate was affected significantly by energy consumption. Sludge blanket clarifier provided lower energy consumption than sludge recirculation clarifier about 32-37%. Subsequently, the operating pattern in production rate can be arranged to decreased energy consumption. The results showed that it can be reduced about 5.09 % of energy saving of clarification process about 754,655 Baht per year.

Keywords: sludge blanket clarifier, sludge recirculation clarifier, water treatment plant, energy

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Authors: Ifedapo Francis Awolowo

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The unrelenting series of embarrassing audit failures should stimulate a paradigm shift in accounting. And in this age of information revolution, there is need for a constant improvement on the products or services one offers to the market in order to be relevant. This study explores the perceptions of external auditors, forensic accountants and accounting academics on whether a paradigm shift to forensic accounting can reduce financial statement frauds. Through Neo-empiricism/inductive analytical approach, findings reveal that a paradigm shift to forensic accounting might be the right step in the right direction in order to increase the chances of fraud prevention and detection in the financial statement. This research has implication on accounting education on the need to incorporate forensic accounting into present day accounting curriculum. Accounting professional bodies, accounting standard setters and accounting firms all have roles to play in incorporating forensic accounting education into accounting curriculum. Particularly, there is need to alter the ISA 240 to make the prevention and detection of frauds the responsibilities of bot those charged with the management and governance of companies and statutory auditors.

Keywords: financial statement fraud, forensic accounting, fraud prevention and detection, auditing, audit expectation gap, corporate governance

Procedia PDF Downloads 341

Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: Siti Aishah Hasbullah

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Studies on identification of pork content in food have grown rapidly to meet the Halal food standard in Malaysia. The used mitochondria DNA (mtDNA) approaches for the identification of pig species is thought to be the most precise marker due to the mtDNA genes are present in thousands of copies per cell, the large variability of mtDNA. The standard method commonly used for DNA detection is based on polymerase chain reaction (PCR) method combined with gel electrophoresis but has major drawback. Its major drawbacks are laborious, need longer time and toxic to handle. Therefore, the need for simplicity and fast assay of DNA is vital and has triggered us to develop DNA biosensors for porcine DNA detection. Therefore, the aim of this project is to develop electrochemical DNA biosensor based on ruthenium (II) complex, [Ru(bpy)2(p-PIP)]2+ as DNA hybridization label. The interaction of DNA and [Ru(bpy)2(p-HPIP)]2+ will be studied by electrochemical transduction using Gold Screen-Printed Electrode (GSPE) modified with gold nanoparticles (AuNPs) and succinimide acrylic microspheres. The electrochemical detection by redox active ruthenium (II) complex was measured by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The results indicate that the interaction of [Ru(bpy)2(PIP)]2+ with hybridization complementary DNA has higher response compared to single-stranded and mismatch complementary DNA. Under optimized condition, this porcine DNA biosensor incorporated modified GSPE shows good linear range towards porcine DNA.

Keywords: gold, screen printed electrode, ruthenium, porcine DNA

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Authors: Renata Gerhardt, Detlev Belder

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Raman spectroscopy has attracted much attention as a structurally descriptive and label-free detection method. It is particularly suited for chemical analysis given as it is non-destructive and molecules can be identified via the fingerprint region of the spectra. In this work possibilities are investigated how to integrate Raman spectroscopy as a detection method for chip-based chromatography, making use of a droplet interface. A demanding task in lab-on-a-chip applications is the specific and sensitive detection of low concentrated analytes in small volumes. Fluorescence detection is frequently utilized but restricted to fluorescent molecules. Furthermore, no structural information is provided. Another often applied technique is mass spectrometry which enables the identification of molecules based on their mass to charge ratio. Additionally, the obtained fragmentation pattern gives insight into the chemical structure. However, it is only applicable as an end-of-the-line detection because analytes are destroyed during measurements. In contrast to mass spectrometry, Raman spectroscopy can be applied on-chip and substances can be processed further downstream after detection. A major drawback of Raman spectroscopy is the inherent weakness of the Raman signal, which is due to the small cross-sections associated with the scattering process. Enhancement techniques, such as surface enhanced Raman spectroscopy (SERS), are employed to overcome the poor sensitivity even allowing detection on a single molecule level. In SERS measurements, Raman signal intensity is improved by several orders of magnitude if the analyte is in close proximity to nanostructured metal surfaces or nanoparticles. The main gain of lab-on-a-chip technology is the building block-like ability to seamlessly integrate different functionalities, such as synthesis, separation, derivatization and detection on a single device. We intend to utilize this powerful toolbox to realize Raman detection in chip-based chromatography. By interfacing on-chip separations with a droplet generator, the separated analytes are encapsulated into numerous discrete containers. These droplets can then be injected with a silver nanoparticle solution and investigated via Raman spectroscopy. Droplet microfluidics is a sub-discipline of microfluidics which instead of a continuous flow operates with the segmented flow. Segmented flow is created by merging two immiscible phases (usually an aqueous phase and oil) thus forming small discrete volumes of one phase in the carrier phase. The study surveys different chip designs to realize coupling of chip-based chromatography with droplet microfluidics. With regards to maintaining a sufficient flow rate for chromatographic separation and ensuring stable eluent flow over the column different flow rates of eluent and oil phase are tested. Furthermore, the detection of analytes in droplets with surface enhanced Raman spectroscopy is examined. The compartmentalization of separated compounds preserves the analytical resolution since the continuous phase restricts dispersion between the droplets. The droplets are ideal vessels for the insertion of silver colloids thus making use of the surface enhancement effect and improving the sensitivity of the detection. The long-term goal of this work is the first realization of coupling chip based chromatography with droplets microfluidics to employ surface enhanced Raman spectroscopy as means of detection.

Keywords: chip-based separation, chip LC, droplets, Raman spectroscopy, SERS

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Authors: Sylwia Baluta, Joanna Cabaj, Karol Malecha

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The evolution of biosensors was driven by the need for faster and more versatile analytical methods for application in important areas including clinical, diagnostics, food analysis or environmental monitoring, with minimum sample pretreatment. Rapid and sensitive neurotransmitters detection is extremely important in modern medicine. These compounds mainly occur in the brain and central nervous system of mammals. Any changes in the neurotransmitters concentration may lead to many diseases, such as Parkinson’s or schizophrenia. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements.

Keywords: adrenaline, biosensor, dopamine, laccase, tyrosinase

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Authors: Joel Bonales, Venecia Solorzano, Carlos Garcia

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A great percentage of the wastewater generated in developing countries don’t receive any treatment, which leads to numerous environmental impacts. In response to this, a paradigm change in the current wastewater treatment model based on large scale plants towards a small and medium scale based model has been proposed. Nevertheless, small scale wastewater treatment (SS-WTTP) with novel technologies such as anaerobic digesters, as well as the utilization of derivative co-products such as biogas, still presents diverse environmental impacts which must be assessed. This study consisted in a Life Cycle Assessment (LCA) performed to a SS-WWTP which treats wastewater from a small commercial block in the city of Morelia, Mexico. The treatment performed in the SS-WWTP consists in anaerobic and aerobic digesters with a daily capacity of 5,040 L. Two different scenarios were analyzed: the current plant conditions and a hypothetical energy use of biogas obtained in situ. Furthermore, two different allocation criteria were applied: full impact allocation to the system’s main product (treated water) and substitution credits for replacing Mexican grid electricity (biogas) and clean water pumping (treated water). The results showed that the analyzed plant had bigger impacts than what has been reported in the bibliography in the basis of wastewater volume treated, which may imply that this plant is currently operating inefficiently. The evaluated impacts appeared to be focused in the aerobic digestion and electric generation phases due to the plant’s particular configuration. Additional findings prove that the allocation criteria applied is crucial for the interpretation of impacts and that that the energy use of the biogas obtained in this plant can help mitigate associated climate change impacts. It is concluded that SS-WTTP is a environmentally sound alternative for wastewater treatment from a systemic perspective. However, this type of studies must be careful in the selection of the allocation criteria and replaced products, since these factors have a great influence in the results of the assessment.

Keywords: biogas, life cycle assessment, small scale treatment, wastewater treatment

Procedia PDF Downloads 106

Authors: Maomao Cao

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Objective: To test the performance of ctDNA methylation for the detection of liver cancer. Methods: A total of 1233 individuals have been recruited in 2017. 15 male and 15 female samples (including 10 cases of liver cancer) were randomly selected in the present study. CfDNA was extracted by MagPure Circulating DNA Maxi Kit. The concentration of cfDNA was obtained by Qubit™ dsDNA HS Assay Kit. A pre-constructed predictive model was used to analyze methylation data and to give a predictive score for each cfDNA sample. Individuals with a predictive score greater than or equal to 80 were classified as having liver cancer. CT tests were considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the diagnosis of liver cancer were calculated. Results: 9 patients were diagnosed with liver cancer according to the prediction model (with high sensitivity and threshold of 80 points), with scores of 99.2, 91.9, 96.6, 92.4, 91.3, 92.5, 96.8, 91.1, and 92.2, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of ctDNA methylation for the diagnosis of liver cancer were 0.70, 0.90, 0.78, and 0.86, respectively. Conclusions: ctDNA methylation could be an acceptable diagnostic modality for the detection of liver cancer.

Keywords: liver cancer, ctDNA methylation, detection, diagnostic performance

Procedia PDF Downloads 134

Authors: Kareem Kalo Qassim, Hassan A. M. Mezori

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The bioaccumulation of heavy metals in the environment has become a matter of public interest because it persists in the soil longer than other components of the biosphere. Bioremediation has emerged as the ideal alternative environmentally friendly and ecological sound technology for removing pollutants from polluted sites. Phytoremediation is an attractive remediation technology that makes use of plants to remove contaminants from the environment. A pot experiment was conducted under lath house conditions to evaluate the ability of plants (H. Annuus, S. Bicolor, and Z. Mays) to phytoextract heavy metals from artificially polluted soils by different concentrations of Cadmium, Lead, and Copper (0, 100, 200, 200 + EDTA). The Seed germination was influenced by the presence of heavy metals and inhibition increased by increasing the heavy metals concentration. A significant difference was observed in the effect of lead and copper. Generally, the length of root, shoot, and intact plant was reduced by all the concentrations used in the experiments. The root system was affected more than the shoot system of the same plants. All heavy metals concentrations caused a reduction in the dry weight and chlorophyll content of all tested plant species; the reduction was increased by increasing the concentration of all heavy metals, especially when EDTA was added. The Bioaccumulation of heavy metals concentration of all the tested plants increased by increasing the concentration. The highest accumulation of cadmium was (81.77mg kg⁻¹), and copper was ( 65.07 mg kg⁻¹) in S. bicolor, while lead-in H. annuus was (60.74 mg kg⁻¹). The order of accumulation of heavy metals in all the tested plant species in the root system and the intact plant was as follows: H. annuus ˃ S. bicolor ˃ Z. mays and shoot system was: H. annuus ˃ Z. mays ˃ S. bicolor. The highest TF of cadmium was (0.41) in H. annuus; lead was (0.72) in Z. mays and S. bicolor, and copper was (0.44) in Z. mays. The tested plant species varied in their response to the heavy metals and the inhibition was concentration depended. In general, the roots system was more affected by heavy metals toxicity than the shoots system; the roots system accumulated more heavy metals in the roots than the shoots system. The addition of EDTA to the last concentration of heavy metals facilitated the availably and absorption of heavy metals from the polluted soil by all tested plant species.

Keywords: phytoextyraction, phytoremediation, translocation, heavy metals, soil pollution

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Authors: Murray L. Ireland, Kevin J. Worrall, Rebecca Mackenzie, Thaleia Flessa, Euan McGookin, Douglas Thomson

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Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing input residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances.

Keywords: fault detection, ground robot, inverse simulation, rover

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Authors: S. Murugan, Bohumil Horak

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Pyrolysis is one of the possible technologies to derive energy from waste organic substances. In recent years, pilot level and demonstrated plants have been installed in few countries. The heat energy lost during the process is not effectively utilized resulting in less savings of energy and money. This paper proposes a novel approach to integrate a combined heat and power unit(CHP) and reduce the primary energy consumption in a tyre pyrolysis pilot plant. The proposal primarily uses the micro combined heat and power concept that will help to produce both heat and power in the process.

Keywords: pyrolysis, waste tyres, waste plastics, biomass, waste heat

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Simin Khodabakhshi, Hossein Rassi

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Hyperlipidemia refers to any of several acquired or genetic disorders that result in a high level of lipids circulating in the blood. Helicobacter pylori infection is a contributing factor in the progression of hyperlipidemia with serum lipid changes. The aim of this study was to detect of Helicobacter pylori by PCR and serological methods in patients with hyperlipidemia. In this case-control study, 174 patients with hyperlipidemia and 174 healthy controls were studied. Also, demographics, physical and biochemical parameters were performed in all samples. The DNA extracted from blood specimens was amplified by H pylori cagA specific primers. The results show that H. pylori cagA positivity was detected in 79% of the hyperlipidemia and in 56% of the control group by ELISA test and 49% of the hyperlipidemia and in 24% of the control group by PCR test. Prevalence of H. pylori infection was significantly higher in hyperlipidemia as compared to controls. In addition, patients with hyperlipidemia had significantly higher values for triglyceride, total cholesterol, LDL-C, waist to hip ratio, body mass index, diastolic and systolic blood pressure and lower levels of HDL-C than control participants (all p < 0.0001). Our result detected the ELISA was a rapid and cost-effective detection and considering the high prevalence of cytotoxigenic H. pylori strains, cag A is suggested as a promising target for PCR and ELISA tests for detection of infection with toxigenic strains. In general, it can be concluded that molecular analysis of H. pylori cagA and clinical parameters are important in early detection of hyperlipidemia and atherosclerosis with H. pylori infection by PCR and ELISA tests.

Keywords: Helicobacter pylori, hyperlipidemia, PCR, ELISA

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Authors: Olesya Bolkhovskaya, Alexander Maltsev

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Antenna arrays are widely used in modern radio systems in sonar and communications. The solving of the detection problems of a useful signal on the background of noise is based on the GLRT method. There is a large number of problem which depends on the known a priori information. In this work, in contrast to the majority of already solved problems, it is used only difference spatial properties of the signal and noise for detection. We are analyzing the influence of the degree of non-coherence of signal and noise unhomogeneity on the performance characteristics of different GLRT statistics. The description of the signal and noise is carried out by means of the spatial covariance matrices C in the cases of different number of known information. The partially coherent signal is simulated as a plane wave with a random angle of incidence of the wave concerning a normal. Background noise is simulated as random process with uniform distribution function in each element. The results of investigation of degradation of performance characteristics for different cases are represented in this work.

Keywords: GLRT, Neumann-Pearson’s criterion, Test-statistics, degradation, spatial processing, multielement antenna array

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Authors: Dharshini M., Hema N. S.

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The project for the production of aniline is done by providing 295.46 tons per day of nitrobenzene as feed. The material and energy balance calculations for the different equipment like distillation column, heat exchangers, reactor and mixer are carried out with simulation via DWSIM. The conversion of nitrobenzene to aniline by hydrogenation process is considered to be 96% and the total production of the plant was found to be 215 TPD. The cost estimation of the process is carried out to estimate the feasibility of the plant. The net profit and percentage return of investment is estimated to be ₹27 crores and 24.6%. The payback period was estimated to be 4.05 years and the unit production cost is ₹113/kg. A techno-economic analysis was performed for the production of aniline; the result includes economic analysis and sensitivity analysis of critical factors. From economic analysis, larger the plant scale increases the total capital investment and annual operating cost, even though the unit production cost decreases. Uncertainty analysis was performed to predict the influence of economic factors on profitability and the scenario analysis is one way to quantify uncertainty. In scenario analysis the best-case scenario and the worst-case scenario are compared with the base case scenario. The best-case scenario was found at a feed rate of 120 kmol/hr with a unit production cost of ₹112.05/kg and the worst-case scenario was found at a feed rate of 60 kmol/hr with a unit production cost of ₹115.9/kg. The base case is closely related to the best case by 99.2% in terms of unit production cost. since the unit production cost is less and the profitability is more with less payback time, it is feasible to construct a plant at this capacity.

Keywords: aniline, nitrobenzene, economic analysis, unit production cost

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Authors: Bin Liu

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As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors.

Keywords: protein remote homology detection, protein fold recognition, top frequency profile, support vector machines

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Authors: M. S. Nikitenko, S. A. Kizilov, D. Y. Khudonogov

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The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated.

Keywords: machine vision, machine vision operating system, light markers, measuring capability, obstacle detection system, autonomous transport

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Authors: J. Joel Bright, P. Peer Mohamed, M. Aswin SAangameshwaran

Abstract:

The increasing usage of concrete produces 80% of carbon dioxide in the atmosphere. Hence, this results in various environmental effects like global warming. The amount of the carbon dioxide released during the manufacture of OPC due to the calcination of limestone and combustion of fossil fuel is in the order of one ton for every ton of OPC produced. Hence, to minimize this Geo Polymer Concrete was introduced. Geo polymer concrete is produced with 0% cement, and hence, it is eco-friendly and it also uses waste product from various industries like thermal power plant, steel manufacturing plant, and paper waste materials. This research is mainly about using Geo polymer concrete for pavement which gives very high strength than conventional concrete and at the same time gives way for sustainable development.

Keywords: activator solution, GGBS, fly ash, metakaolin

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Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

Abstract:

The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

Abstract:

The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

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Authors: Farzad Razavi, Behrooz Taheri, Mohammad Parpaei, Mehdi Mohammadi Ghalesefidi, Siamak Zarei

Abstract:

As one of the major problems in protecting large-dimension power systems, power swing and its effect on distance have caused a lot of damages to energy transfer systems in many parts of the world. Therefore, power swing has gained attentions of many researchers, which has led to invention of different methods for power swing detection. Power swing detection algorithm is highly important in distance relay, but protection relays should have general requirements such as correct fault detection, response rate, and minimization of disturbances in a power system. To ensure meeting the requirements, protection relays need different tests during development, setup, maintenance, configuration, and troubleshooting steps. This paper covers power swing scheme of the modern numerical relay protection, 7sa522 to address the effect of the different fault types on the function of the power swing blocking. In this study, it was shown that the different fault types during power swing cause different time for unblocking distance relay.

Keywords: power swing, distance relay, power system protection, relay test, transient in power system

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Authors: Djamil Guettiche, Ahmed Mekki, Tighilt Fatma-Zohra, Rachid Mahmoud

Abstract:

The aim of this study was to synthesize and characterize conducting polymer composites of polypyrrole and graphene, including pristine and surface-treated graphene (PPy/GO, PPy/rGO, and PPy/rGO-ArCOOH), for use as sensitive elements in a homemade chemiresistive module for on-line detection of nitrogen oxides vapors. The chemiresistive module was prepared, characterized, and evaluated for performance. Structural and morphological characterizations of the composite were carried out using FTIR, Raman spectroscopy, and XRD analyses. After exposure to NO and NO₂ gases in both static and dynamic modes, the sensitivity, selectivity, limit of detection, and response time of the sensor were determined at ambient temperature. The resulting sensor showed high sensitivity, selectivity, and reversibility, with a low limit of detection of 1 ppm. A composite of polypyrrole and graphene functionalized with aryl 4-carboxy benzene diazonium salt was synthesized and characterized using FTIR, scanning electron microscopy, transmission electron microscopy, UV-visible, and X-ray diffraction. The PPy-rGOArCOOH composite exhibited a good electrical resistance response to NO₂ at room temperature and showed enhanced NO₂-sensing properties compared to PPy-rGO thin films. The selectivity and stability of the NO₂ sensor based on the PPy/rGO-ArCOOH nanocomposite were also investigated.

Keywords: conducting polymers, surface treated graphene, diazonium salt, polypyrrole, Nitrogen oxide sensing

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

Abstract:

Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Ja’afar Umar, Adamu Aliyu Aliero

Abstract:

Plant growth, biochemical parameters, zinc metal concentrations were determined for Capsicum frutescens L. in response to varied concentration of zinc metal. The plant exhibited a decline in the vegetative parameters measured. Free proline and glycine betaine content increases with increasing concentration of zinc metal and differ significantly (P<0.05). It can be concluded that the osmolyte (pro and GB) accumulations, and high length of stem and wide leaf expansion are possible indicator of tolerance to heavy metals (Zinc) in Capsicum frutescens.

Keywords: zinc metal, osmolyte, Capsicum frutescens, stress

Procedia PDF Downloads 463