Search results for: microorganisms detection

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: 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: 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: Kubra Dogan, Fatih Tornuk

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Due to increasing consumer demand for the high-quality food products and awareness regarding the health benefits of different nutrients in food minimal processing becomes more popular in modern food preservation. To date, heat treatment is often used for inactivation of spoilage microorganisms in foods. However, it may cause significant changes in the quality and nutritional properties of food. In order to overcome the detrimental effects of heat treatment, several alternatives of non-thermal microbial inactivation processes have been investigated. Ultraviolet (UV) inactivation is a promising and feasible method for better quality and longer shelf life as an alternative to heat treatment, which aims to inhibit spoilage and pathogenic microorganisms and to inactivate the enzymes in vegetable juice production. UV-C is a sub-class of UV treatment which shows the highest microcidal effect between 250-270 nm. The wavelength of 254 nm is used for the surface disinfection of certain liquid food products such as vegetable juice. Effects of UV-C treatment on microbiological load and quality parameter of vegetable juice which is a mix of celery, carrot, lemon and orange was investigated. Our results showed that storing of UV-C applied vegetable juice for three months, reduced the count of TMAB by 3.5 log cfu/g and yeast-mold by 2 log cfu/g compared to control sample. Total phenolic content was found to be 514.3 ± 0.6 mg gallic acid equivalent/L, and there wasn’t a significant difference compared to control. The present work suggests that UV-C treatment is an alternative method for disinfection of vegetable juice since it enables adequate microbial inactivation, longer shelf life and has minimal effect on degradation of quality parameters of vegetable juice.

Keywords: heat treatment, phenolic content, shelf life, ultraviolet (UV-C), vegetable juice

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

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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

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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

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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

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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

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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: Islam Zeb

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Water of poor quality has a potential of probable contamination and a way to spread pollutant in the field and surrounding environment. A number of comprehensive reviews articles have been published which highlight irrigation water as a source of pathogenic microorganisms and heavy metals toxicity that leads to chronic diseases in human. Here a study was plan to determine the microbial status of irrigation water collected from various location of district Swat in various months. The analyses were carried out at Environmental Horticulture Laboratory, Department of Horticulture, The University of Agriculture Peshawar, during the year 2018 – 19. The experiment was laid out in Randomized Complete Block Design (RCBD) with two factors and three replicates. Factor A consist of different locations, and factor B represent various months. The results of microbial status for various locations in irrigation water showed the highest value for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella, and Listeria (9.05, 8.54, 6.01, 5.84, and 5.03 log cfu L-1 respectively) for samples collected from mingora location, whereas the lowest values for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella and Listeria (6.70, 6.38, 4.47, 4.42 and 3.77 log cfu L-1 respectively) were observed for matta location. Data for various months showed maximum Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella, and Listeria (12.01, 11.70, 8.46, 8.41, and 6.88 log cfu L-1, respectively) were noted for the irrigation water samples collected in May/June whereas the lowest range for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella and Listeria (4.41, 4.08, 2.61, 2.55 and 3.39 log cfu L-1 respectively) were observed in Jan/Feb. A significant interaction was found for all the studied parameters it was concluded that maximum bacterial groups were recorded in the months of May/June from Mingora location, it might be due to favorable weather condition.

Keywords: contamination, irrigation water, microbes, SWAT, various months

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Authors: Naydelis Brito Suárez, Deni Librado Torres Román, Fernando Hermosillo Reynoso

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Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time.

Keywords: convex hull, dynamic ROI detection, pixel entropy, time series, moving objects

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Authors: Wanwisa Deenin, Abdulhadee Yakoh, Chahya Kreangkaiwal, Orawon Chailapakul, Kanitha Patarakul, Sudkate Chaiyo

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LipL32 is an outer membrane protein present only on pathogenic Leptospira species, which are the causative agent of leptospirosis. Leptospirosis symptoms are often misdiagnosed with other febrile illnesses as the clinical manifestations are non-specific. Therefore, an accurate diagnostic tool for leptospirosis is indeed critical for proper and prompt treatment. Typical diagnosis via serological assays is generally performed to assess the antibodies produced against Leptospira. However, their delayed antibody response and complicated procedure are undoubtedly limited the practical utilization especially in primary care setting. Here, we demonstrate for the first time an early-stage detection of LipL32 by an integrated lateral-flow immunoassay with electrochemical readout (eLFIA). A ferrocene trace tag was monitored via differential pulse voltammetry operated on a smartphone-based device, thus allowing for on-field testing. Superior performance in terms of the lowest detectable limit of detection (LOD) of 8.53 pg/mL and broad linear dynamic range (5 orders of magnitude) among other sensors available thus far was established. Additionally, the developed test strip provided a straightforward yet sensitive approach for diagnosis of leptospirosis using the collected human sera from patients, in which the results were comparable to the real-time polymerase chain reaction technique.

Keywords: leptospirosis, electrochemical detection, lateral flow immunosensor, point-of-care testing, early-stage detection

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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We describe an innovative method for highly specific detection of miRNAs using a specially modified method of poly(A) adaptor RT-qPCR. We use uniquely designed specific extension sequence, which plays important role in providing an opportunity to affect high specificity of miRNA detection. This method involves two steps of reactions as like previously reported and which are poly(A) tailing and reverse-transcription followed by real-time PCR. Firstly, miRNAs are extended by a poly(A) tailing reaction and then converted into cDNA. Here, we remarkably reduced the reaction time by the application of short length of poly(T) adaptor. Next, cDNA is hybridized to the 3’-end of a specific extension sequence which contains miRNA sequence and results in producing a novel PCR template. Thereafter, the SYBR Green-based RT-qPCR progresses with a universal poly(T) adaptor forward primer and a universal reverse primer. The target miRNA, miR-106b in human brain total RNA, could be detected quantitatively in the range of seven orders of magnitude, which demonstrate that the assay displays a dynamic range of at least 7 logs. In addition, the better specificity of this novel extension-based assay against well known poly(A) tailing method for miRNA detection was confirmed by melt curve analysis of real-time PCR product, clear gel electrophoresis and sequence chromatogram images of amplified DNAs.

Keywords: microRNA(miRNA), specific extension sequence, RT-qPCR, poly(A) tailing assay, reverse transcription

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Khaldon Lweesy, Feryal Alskafi, Rabaa Hammad, Shaker Khanfar, Yara Alsukhni

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Electronic noses are devices designed to emulate the humane sense of smell by characterizing and differentiating odor profiles. In this study, we build a low-cost e-nose using an array module containing four different types of metal oxide semiconductor gas sensors. We used this system to create a profile for a meat specimen over three days. Then using a pattern recognition software, we correlated the odor of the specimen to its age. It is a simple, fast detection method that is both non-expensive and non-destructive. The results support the usage of this technology in food control management.

Keywords: e-nose, low cost, odor detection, food safety

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Authors: Thi Huong Vu, Vijay Jayasena, Zhongxiang Fang, Gary Dykes

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Carob kibble has recently received attention due to the presence of high level of polyphenol antioxidants. The capacity of microorganisms to improve antioxidant activities and total phenolics in carob kibble was investigated in the study. Two types of microorganisms including lactic acid bacteria Lactobacillus plantarum (L. plantarum) and yeast Saccharomyces cerevisiae (S. cerevisiae) were used in single and in their combination as starters. The total phenolic content was determined by the Folin–Ciocalteu method. Antioxidant activities were assessed scavenging capacity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS). The study found that S. cerevisiae alone considerably improved 55% total phenolics content at 15 h, while L. plantarum caused in a loss of 20% through the process. Antioxidant capacity of the yeast-fermented samples significantly increased by 43 % and 10 % in ABTS and DPPH assays, respectively. However, reduction of 13 % and 32 % inhibition were recorded in the carob treated with L. plantarum. In the combination of S. cerevisiae and L. plantarum (1:1), both total phenolic content and antioxidant activity of carob kibble were a similar trend as these of S. cerevisiae single, but a lower improvement. The antioxidant power of the extracts was linearly correlated to their total phenolic contents (R=0.75). The results suggested that S. cerevisiae alone was the better for enhancement of both total phenolic content and antioxidant activity in carob kibble using submerged fermentation. The efficiency of fermentation reached the highest at 15h. Thus submerged fermentation with S. cerevisiae offers a tool with simple and cost effective to further increase the bioactive potential of carob kibble, which is in use for food, cosmetic and pharmaceutical industries.

Keywords: antioxidant activity, carob kibble, lactobacillus plantarum, saccharomyces cerevisiae, total phenolics

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Authors: Goutham Kumar Dogiparti, D. R. Seshu

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In the present study, wavelet analysis was used for locating damage in simply supported and cantilever beams. Study was carried out varying different levels and locations of damage. In numerical method, ANSYS software was used for modal analysis of damaged and undamaged beams. The mode shapes obtained from numerical analysis is processed using MATLAB wavelet toolbox to locate damage. Effect of several parameters such as (damage level, location) on the natural frequencies and mode shapes were also studied. The results indicated the potential of wavelets in identifying the damage location.

Keywords: damage, detection, beams, wavelets

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Astorga-Trejo Rebeca, Fonseca-Peralta Héctor Manuel, Beltrán-Arredondo Laura Ivonne, Castro-Martínez Claudia

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The use of biomass as feedstock for the production of fuels and other chemicals of interest is an ever-growing accepted option in the way to the development of biorefinery complexes; in the Mexican state of Sinaloa, two million tons of residues from corn crops are produced every year, most of which can be converted to bioethanol and other products through biotechnological conversion using yeast and other microorganisms. Therefore, the objective of this work was to take advantage of corn stover and evaluate its potential as a substrate for the production of second-generation bioethanol (2G), enzymes, and xylitol. To produce bioethanol 2G, an acid-alkaline pretreatment was carried out prior to saccharification and fermentation. The microorganisms used for the production of enzymes, as well as for the production of xylitol, were isolated and characterized in our workgroup. Statistical analysis was performed using Design Expert version 11.0. The results showed that it is possible to obtain 2G bioethanol employing corn stover as a carbon source and Saccharomyces cerevisiae ItVer01 and Candida intermedia CBE002 with yields of 0.42 g and 0.31 g, respectively. It was also shown that C. intermedia has the ability to produce xylitol with a good yield (0.46 g/g). On the other hand, qualitative and quantitative studies showed that the native strains of Fusarium equiseti (0.4 IU/mL - xylanase), Bacillus velezensis (1.2 IU/mL – xylanase and 0.4 UI/mL - amylase) and Penicillium funiculosum (1.5 IU / mL - cellulases) have the capacity to produce xylanases, amylases or cellulases using corn stover as raw material. This study allowed us to demonstrate that it is possible to use corn stover as a carbon source, a low-cost raw material with high availability in our country, to obtain bioproducts of industrial interest, using processes that are more environmentally friendly and sustainable. It is necessary to continue the optimization of each bioprocess.

Keywords: biomass, corn stover, biorefinery, bioethanol 2G, enzymes, xylitol

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Authors: Sajjad Jafari

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Background and Aim: The use of herbal medicines has a long history. Today, due to the resistance of microorganisms to antibiotics and antimicrobial substances, herbal medicines have attracted attention due to their significant antimicrobial effects and low toxicity. This study aims to systematically review the antimicrobial effects of different plant extracts (Quercus infectoria) as possible candidates for treating infectious diseases. Material and Methods: The present study is a review study by searching reputable scientific databases such as PubMed, Google Scholar, Scopus, and Web of Science from 2000 to 2023 using the keywords Antimicrobial, Quercus infectoria, Medicinal herbal, Infectious diseases the latest information obtained. Results: In this study, 45 articles were found and reviewed. Quercus infectoria is a small tree native to Greece, Asia Minor, and Iran. Quercus is a plant genus in the family of Fagaceae. This species is generally known under the name ‘‘baloot” in Iran and is commonly used as a medicinal plant. The extracts used included water, hydro-alcoholic, ethanol, methanol. This plant had high inhibition activity and a lethal effect on gram-positive and gram-negative bacteria of ATCC strains, hospital, and resistant strains. Therefore, in addition to antibacterial effects, antiparasitic and antifungal effects. The seed of the plant was the most used and the most effective antimicrobial extract among the ethanol and methanol extracts. Conclusion: The findings of this study suggest that Quercus infectoria has significant antimicrobial effects against a wide range of microorganisms. This makes it a potential candidate for the development of new antimicrobial drugs. Further research is needed to confirm the efficacy and safety of Quercus infectoria in clinical trials.

Keywords: antimicrobial, Quercus infectoria, medicinal herbal, infectious diseases

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Authors: Irina Mikajlo, Jakub Elbl, Helena Dvořáčková, Antonín Kintl, Jindřich Kynický, Martin Brtnický, Jaroslav Záhora

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Present work deals with the possible use of fertigation in agriculture and its impact on the availability of mineral nitrogen (Nmin) in topsoil and subsoil horizons. The aim of the present study is to demonstrate the effect of the organic matter presence in fertigation on microbial transformation and availability of mineral nitrogen forms. The main investigation reason is the potential use of pre-treated waste water, as a source of organic carbon (Corg) and residual nutrients (Nmin) for fertigation. Laboratory experiment has been conducted to demonstrate the effect of the arable land fertilization method on the Nmin availability in different depths of the soil with the usage of model experimental containers filled with soil from topsoil and podsoil horizons that were taken from the precise area. Tufted hairgrass (Deschampsia caespitosa) has been chosen as a model plant. The water source protection zone Brezova nad Svitavou has been a research area where significant underground reservoirs of drinking water of the highest quality are located. From the second half of the last century local sources of drinking water show nitrogenous compounds increase that get here almost only from arable lands. Therefore, an attention of the following text focuses on the fate of mineral nitrogen in the complex plant-soil. Research results show that the fertigation application with Corg in a combination with mineral fertilizer can reduce the amount of Nmin leached from topsoil horizon of agricultural soils. In addition, some plants biomass production reduce may occur.

Keywords: fertigation, fertilizers, mineral nitrogen, soil microorganisms

Procedia PDF Downloads 347

Authors: Meltem Agar, Maisem Laabei, Hannah Leese, Pedro Estrela

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Pathogenic bacteria and the diseases they cause have become a global problem. Their early detection is vital and can only be possible by detecting the bacteria causing the disease accurately and rapidly. Great progress has been made in this field with the use of biosensors. Molecularly imprinted polymers have gain broad interest because of their excellent properties over natural receptors, such as being stable in a variety of conditions, inexpensive, biocompatible and having long shelf life. These properties make molecularly imprinted polymers an attractive candidate to be used in biosensors. In this study it is aimed to produce an aptamer-molecularly imprinted polymer based electrochemical sensor by utilizing the properties of molecularly imprinted polymers coupled with the enhanced specificity offered by DNA aptamers. These ‘apta-MIP’ sensors were used for the detection of Staphylococcus aureus and Escherichia coli. The experimental parameters for the fabrication of sensor were optimized, and detection of the bacteria was evaluated via Electrochemical Impedance Spectroscopy. Sensitivity and selectivity experiments were conducted. Furthermore, molecularly imprinted polymer only and aptamer only electrochemical sensors were produced separately, and their performance were compared with the electrochemical sensor produced in this study. Aptamer-molecularly imprinted polymer based electrochemical sensor showed good sensitivity and selectivity in terms of detection of Staphylococcus aureus and Escherichia coli. The performance of the sensor was assessed in buffer solution and tap water.

Keywords: aptamer, electrochemical sensor, staphylococcus aureus, molecularly imprinted polymer

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Authors: Seongwon Im, Jimin Kim, Kyeongcheol Kim, Dong-Hoon Kim

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Cattle manure (CM) produced from farmhas been utilized to soils for increasing crop production owing to high nutrients content and effective microorganisms. Some cities with the concentrated activity of livestock industry have suffered from environmental problems, such as odorous gas emissions and soil and water pollution, caused by excessive use of compost. As an alternative option, the anaerobic digestion (AD) process can be utilized, which can reduce the volume of organic waste but also produce energy. According to Korea-Ministry of Trade, Industry, and Energy (KMTIE), the energy potential of CM via biogas production was estimated to be 0.8 million TOE per year, which is higher than that of other organic wastes. However, limited energy is recovered since useful organic matter, capable of converting to biogas, may be degraded during the long storage period (1-6 months).In this study, the effect of storage period on biogas potential of CM was investigated. Compared to fresh CM (VS 14±1 g/L, COD 205±5 g/L, TKN 7.4±0.8 g/L, NH4+-N 1.5±0.1), old CM has higher organic (35-37%) and nitrogen content (50-100%) due to the drying process during storage. After stabilization period, biogas potential of 0.09 L CH4/g VS was obtained in R1 (old CM supplement) at HRT of 150-100 d, and it was decreased further to 0.06 L CH4/g VS at HRT of 80 d. The drop of pH and organic acids accumulation were not observed during the whole operation of R1. Ammonia stripping and pretreatment of CM were found to be not effective to increase CH4 yield. On the other hand, a sudden increase of biogas potential to 0.19-0.22 L CH4/g VS was achieved in R2 after changing feedstock to fresh CM. The expected reason for the low biogas potential of old CM might be related with the composition of organic matters in CM. Easily biodegradable organic matters in the fresh CM were contained in high concentration, butthey were removed by microorganisms during storing CM in a farm, resulting low biogas yield. This study implies that fresh storage is important to make AD process applicable for CM.

Keywords: storage period, cattle manure, biogas potential, microbial analysis

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

Abstract:

Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Arabi Abderrazak, Bourouba Nacerdine, Ayad Mouloud, Belaout Abdeslam

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In this paper, a new test technique of analog circuits using time mode simulation is proposed for the single catastrophic faults detection in analog circuits. This test process is performed to overcome the problem of catastrophic faults being escaped in a DC mode test applied to the inverter amplifier in previous research works. The circuit under test is a second-order low pass filter constructed around this type of amplifier but performing a function that differs from that of the previous test. The test approach performed in this work is based on two key- elements where the first one concerns the unique square pulse signal selected as an input vector test signal to stimulate the fault effect at the circuit output response. The second element is the filter response conversion to a square pulses sequence obtained from an analog comparator. This signal conversion is achieved through a fixed reference threshold voltage of this comparison circuit. The measurement of the three first response signal pulses durations is regarded as fault effect detection parameter on one hand, and as a fault signature helping to hence fully establish an analog circuit fault diagnosis on another hand. The results obtained so far are very promising since the approach has lifted up the fault coverage ratio in both modes to over 90% and has revealed the harmful side of faults that has been masked in a DC mode test.

Keywords: analog circuits, analog faults diagnosis, catastrophic faults, fault detection

Procedia PDF Downloads 435

Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

Procedia PDF Downloads 269

Authors: Bhuyain Mobarok Hossain

Abstract:

Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

Procedia PDF Downloads 293