Search results for: fault detection and isolation (FDI)

Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Nagendra P. Luitel, Mark J. D. Jordans

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Globally, there is a significant gap between the number of individuals in need of mental health care and those who actually receive treatment. The evidence is accumulating that mental health services can be delivered effectively by primary health care workers through community-based programs and task-sharing approaches. Changing the role of specialist mental health workers from service delivery to building clinical capacity of the primary health care (PHC) workers could help in reducing treatment gap in low and middle-income countries (LMICs). We developed a comprehensive mental health care plan in 2012 and evaluated its feasibility and effectiveness over the past three years. Initially, a mixed method formative study was conducted for the development of mental health care plan (MHCP). Routine monitoring and evaluation data, including client flow and reports of satisfaction, were obtained from beneficiaries (n=135) during the pilot-testing phase. Repeated community survey (N=2040); facility detection survey (N=4704) and the cohort study (N=576) were conducted for evaluation of the MHCP. The resulting MHCP consists of twelve packages divided over the community, health facility, and healthcare organization platforms. Detection of mental health problems increased significantly after introducing MHCP. Service implementation data support the real-life applicability of the MHCP, with reasonable treatment uptake. Currently, MHCP has been implemented in the entire Chitwan district where over 1400 people (438 people with depression, 406 people with psychosis, 181 people with epilepsy, 360 people with alcohol use disorder and 51 others) have received mental health services from trained health workers. Key barriers were identified and addressed, namely dissatisfaction with privacy, perceived burden among health workers, high drop-out rates and continue the supply of medicines. The results indicated that involvement of PHC workers in detection and management of mental health problems is an effective strategy to minimize treatment gap on mental health care in Nepal.

Keywords: mental health, Nepal, primary care, treatment gap

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Tuba Zahra, Aakash Parekh

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Hyperspectral remote sensing refers to imaging of objects or materials in narrow conspicuous spectral bands. Hyperspectral images (HSI) enable the extraction of spectral signatures for objects or materials observed. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. It enables the acquisition of data simultaneously in hundreds of spectral bands with narrow bandwidths and can provide detailed contiguous spectral curves that traditional multispectral sensors cannot offer. The contiguous, narrow bandwidth of hyperspectral data facilitates the detailed surveying of Earth's surface features. This would otherwise not be possible with the relatively coarse bandwidths acquired by other types of imaging sensors. Hyperspectral imaging provides significantly higher spectral and spatial resolution. There are several use cases that represent the commercial applications of hyperspectral remote sensing. Each use case represents just one of the ways that hyperspectral satellite imagery can support operational efficiency in the respective vertical. There are some use cases that are specific to VNIR bands, while others are specific to SWIR bands. This paper discusses the different commercially viable use cases that are significant for HSI application areas, such as agriculture, mining, oil and gas, defense, environment, and climate, to name a few. Theoretically, there is n number of use cases for each of the application areas, but an attempt has been made to streamline the use cases depending upon economic feasibility and commercial viability and present a review of literature from this perspective. Some of the specific use cases with respect to agriculture are crop species (sub variety) detection, soil health mapping, pre-symptomatic crop disease detection, invasive species detection, crop condition optimization, yield estimation, and supply chain monitoring at scale. Similarly, each of the industry verticals has a specific commercially viable use case that is discussed in the paper in detail.

Keywords: agriculture, mining, oil and gas, defense, environment and climate, hyperspectral, VNIR, SWIR

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Authors: A. Bahieldin, A. Atef, S. Edris, N. O. Gadalla, S. M. Hassan, M. A. Al-Kordy, A. M. Ramadan, A. S. M. Al- Hajar, F. M. El-Domyati

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The idea of this work is based on a natural exciting phenomenon suggesting that suppression of genes related to the program cell death (or PCD) mechanism might help the plant cells to efficiently tolerate abiotic stresses. The scope of this work was the detection of PCD-related transcription factors (TFs) that might also be related to salt stress tolerance in plant. Two model plants, e.g., tobacco and Arabidopsis, were utilized in order to investigate this phenomenon. Occurrence of PCD was first proven by Evans blue staining and DNA laddering after tobacco leaf discs were treated with oxalic acid (OA) treatment (20 mM) for 24 h. A number of 31 TFs up regulated after 2 h and co-expressed with genes harboring PCD-related domains were detected via RNA-Seq analysis and annotation. These TFs were knocked down via virus induced gene silencing (VIGS), an RNA interference (RNAi) approach, and tested for their influence on triggering PCD machinery. Then, Arabidopsis SALK knocked out T-DNA insertion mutants in selected TFs analogs to those in tobacco were tested under salt stress (up to 250 mM NaCl) in order to detect the influence of different TFs on conferring salt tolerance in Arabidopsis. Involvement of a number of candidate abiotic-stress related TFs was investigated.

Keywords: VIGS, PCD, RNA-Seq, transcription factors

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Authors: A. I. Alajtal, H. G. M. Edwards, M. A. Elbagermi

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Raman spectroscopy is currently a part of the instrumentation suite of the ESA ExoMars mission for the remote detection of life signatures in the Martian surface and subsurface. Terrestrial analogues of Martian sites have been identified and the biogeological modifications incurred as a result of extremophilic activity have been studied. Analytical instrumentation protocols for the unequivocal detection of biomarkers in suitable geological matrices are critical for future unmanned explorations, including the forthcoming ESA ExoMars mission to search for life on Mars scheduled for 2018 and Raman spectroscopy is currently a part of the Pasteur instrumentation suite of this mission. Here, Raman spectroscopy using 785nm excitation was evaluated for determining various concentrations of beta-carotene in admixture with polyaromatic hydrocarbons and usnic acid have been investigated by Raman microspectrometry to determine the lowest levels detectable in simulation of their potential identification remotely in geobiological conditions in Martian scenarios. Information from this study will be important for the development of a miniaturized Raman instrument for targetting Martian sites where the biosignatures of relict or extant life could remain in the geological record.

Keywords: raman spectroscopy, mars-analog, beta-carotene, PAHs

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Authors: A. Zh. Aupova, A. Ulankyzy, A. Sarsenova, A. Kussayin, Sh. Turarbek, N. Moldagulova, A. Kurmanbayev

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One of the methods of organic waste bioconversion into environmentally-friendly fertilizer is composting. Microorganisms that produce hydrolytic enzymes play a significant role in accelerating the process of organic waste composting. We studied the enzymatic potential (amylase, protease, cellulase, lipase, urease activity) of bacteria isolated from the sewage sludge of Nur-Sultan, Rudny, and Fort-Shevchenko cities, the dacha soil of Nur-Sultan city, and freshly cut grass from the dacha for processing organic waste and identifying active strains. Microorganism isolation was carried out by the cultures enrichment method on liquid nutrient media, followed by inoculating on different solid media to isolate individual colonies. As a result, sixty-one microorganisms were isolated, three of which were thermophiles (DS1, DS2, and DS3). The highest number of isolates, twenty-one and eighteen, were isolated from sewage sludge of Nur-Sultan and Rudny cities, respectively. Ten isolates were isolated from the wastewater of the sewage treatment plant in Fort-Shevchenko. From the dacha soil of Nur-Sultan city and freshly cut grass - 9 and 5 isolates were revealed, respectively. The lipolytic, proteolytic, amylolytic, cellulolytic, ureolytic, and oil-oxidizing activities of isolates were studied. According to the results of experiments, starch hydrolysis (amylolytic activity) was found in 2 isolates - CB2/2, and CB2/1. Three isolates - CB2, CB2/1, and CB1/1 were selected for the highest ability to break down casein. Among isolated 61 bacterial cultures, three isolates could break down fats - CB3, CBG1/1, and IL3. Seven strains had cellulolytic activity - DS1, DS2, IL3, IL5, P2, P5, and P3. Six isolates rapidly decomposed urea. Isolate P1 could break down casein and cellulose. Isolate DS3 was a thermophile and had cellulolytic activity. Thus, based on the conducted studies, 15 isolates were selected as a potential for sewage sludge composting - CB2, CB3, CB1/1, CB2/2, CBG1/1, CB2/1, DS1, DS2, DS3, IL3, IL5, P1, P2, P5, P3. Selected strains were identified on a mass spectrometer (Maldi-TOF). The isolate - CB 3 was referred to the genus Rhodococcus rhodochrous; two isolates CB2 and CB1 / 1 - to Bacillus cereus, CB 2/2 - to Cryseobacterium arachidis, CBG 1/1 - to Pseudoxanthomonas sp., CB2/1 - to Bacillus megaterium, DS1 - to Pediococcus acidilactici, DS2 - to Paenibacillus residui, DS3 - to Brevibacillus invocatus, three strains IL3, P5, P3 - to Enterobacter cloacae, two strains IL5, P2 - to Ochrobactrum intermedium, and P1 - Bacillus lichenoformis. Hence, 60 isolates were isolated from the wastewater of the cities of Nur-Sultan, Rudny, Fort-Shevchenko, the dacha soil of Nur-Sultan city, and freshly cut grass from the dacha. Based on the highest enzymatic activity, 15 active isolates were selected and identified. These strains may become the candidates for bio preparation for sewage sludge processing.

Keywords: sewage sludge, composting, bacteria, enzymatic activity

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Authors: Indu Verma, Santanu Kumar Pal

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Interactions between DNA and adsorbed Poly (L-lysine) (PLL) on liquid crystal (LC) droplets were investigated using polarizing optical microcopy (POM) and epi-fluorescence microscopy. Earlier, we demonstrated that adsorption of PLL to the LC/aqueous interface resulted in homeotropic orientation of the LC and thus exhibited a radial configuration of the LC confined within the droplets. Subsequent adsorption of DNA (single stranded DNA/double stranded DNA) at PLL coated LC droplets was found to trigger a LC reorientation within the droplets leading to pre-radial/bipolar configuration of those droplets. To our surprise, subsequent exposure of complementary ssDNA (c-ssDNA) to ssDNA/ adsorbed PLL modified LC droplets did not cause the LC reorientation. This is likely due to the formation of polyplexes (DNA-PLL complex) as confirmed by fluorescence microscopy and atomic force microscopy. In addition, dsDNA adsorbed PLL droplets have been found to be effectively used to displace (controlled release) propidium iodide (a model drug) encapsulated within dsDNA over time. These observations suggest the potential for a label free droplet based LC detection system that can respond to DNA and may provide a simple method to develop DNA-based drug nano-carriers.

Keywords: DNA biosensor, drug delivery, interfaces, liquid crystal droplets

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Authors: Mohd Sukri Hassan, Ahlam Inayatullah Badrul Munir, M. Husaini A. Rahman

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Halal authentication and verification in supplement capsules are highly required as the gelatine available in the market can be from halal or non-halal sources. It is an obligation for Muslim to consume and use the halal consumer goods. At present, real-time polymerase chain reaction (RT-PCR) is the most common technique being used for the detection of porcine and bovine DNA in gelatine due to high sensitivity of the technique and higher stability of DNA compared to protein. In this study, twenty samples of supplements capsules from different products with different Halal logos were analyzed for porcine and bovine DNA using RT-PCR. Standard bovine and porcine gelatine from eurofins at a range of concentration from 10^-1 to 10^-5 ng/µl were used to determine the linearity range, limit of detection and specificity on RT-PCR (SYBR Green method). RT-PCR detected porcine (two samples), bovine (four samples) and mixture of porcine and bovine (six samples). The samples were also tested using FT-IR technique where normalized peak of IR spectra were pre-processed using Savitsky Golay method before Principal Components Analysis (PCA) was performed on the database. Scores plot of PCA shows three clusters of samples; bovine, porcine and mixture (bovine and porcine). The RT-PCR and FT-IR with chemometrics technique were found to give same results for porcine gelatine samples which can be used for Halal authentication.

Keywords: halal, real-time PCR, gelatine, chemometrics

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Authors: F. J. Puertas, M. Castro, A. Tebar, P. J. Fito, R. Gadea, J. M. Monzó, R. J. Colom

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There is a great diversity of diseases that affect the integrity of the walls of the esophagus, generally of a digestive nature. Among them, gastroesophageal reflux is a common disease in the general population, affecting the patient's quality of life; however, there are still unmet diagnostic and therapeutic issues. The consequences of untreated or asymptomatic acid reflux on the esophageal mucosa are not only pain, heartburn, and acid regurgitation but also an increased risk of esophageal cancer. Currently, the diagnostic methods to detect problems in the esophageal tract are invasive and annoying, as 24-hour impedance-pH monitoring forces the patient to be uncomfortable for hours to be able to make a correct diagnosis. In this work, the development of a sensor able to measure in depth is proposed, allowing the detection of liquids circulating in the esophageal tract. The multisensor detection system is based on radiofrequency photospectrometry. At an experimental level, consumers representative of the population in terms of sex and age have been used, placing the sensors between the trachea and the diaphragm analyzing the measurements in vacuum, water, orange juice and saline medium. The results obtained have allowed us to detect the appearance of different liquid media in the esophagus, segregating them based on their ionic content.

Keywords: bioimpedance, dielectric spectroscopy, gastroesophageal reflux, GERD

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Authors: René Vieroth, Christian Dils, M. V. Krshiwoblozki, Christine Kallmayer, Martin Schneider-Ramelow, Klaus-Dieter Lang

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Green urban mobility in commercial and private contexts is one of the great challenges for the continuously growing cities all over the world. Bicycle based solutions are already and since a long time the key to success. Modern developments like e-bikes and high-end cargo-bikes complement the portfolio. Weight, aerodynamic drag, and security for the transported goods are the key factors for working solutions. Recent achievements in the field of smart-textiles allowed the creation of a totally new generation of intelligent textile cargo containers, which fulfill those demands. The fusion of technical textiles, design and electrical engineering made it possible to create an ecological solution which is very near to become a product. This paper shows all the details of this solution that includes an especially developed sensor textile for cut detection, a protective textile layer for intrusion prevention, an universal-charging-unit for energy harvesting from diverse sources and a low-energy alarm system with GSM/GPRS connection, GPS location and RFID interface.

Keywords: cargo-bike, cut-detection, e-bike, energy-harvesting, green urban mobility, logistics, smart-textiles, textile-integrity sensor

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Sugandha Asthana, Geetika Vajpayee, Pratibha Kumari, Shanthy Sundaram

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An economically important disease of wheat in North Western region of India is Karnal Bunt caused by smut fungus Tilletia indica. This fungal pathogen spreads by air, soil and seed borne sporodia at the time of flowering, which ultimately leads to partial bunting of wheat kernels with fishy odor and taste to wheat flour. It has very serious effects due to quarantine measures which have to be applied for grain exports. Chemical fungicides such as mercurial compounds and Propiconazole applied to the control of Karnal bunt have been only partially successful. Considering the harmful effects of chemical fungicides on man as well as environment, many countries are developing biological control as the superior substitute to chemical control. Repeated use of fungicides can be responsible for the development of resistance in fungal pathogens against certain chemical compounds. The present investigation is based on the isolation and evaluation of antifungal properties of some isolated (from natural manure) and commercial bacterial strains against Tilletia indica. Total 23 bacterial isolates were obtained and antagonistic activity of all isolates and commercial bacterial strains (Bacillus subtilis MTCC8601, Bacillus pumilus MTCC 8743, Pseudomonas aeruginosa) were tested against T. indica by dual culture plate assay (pour plate and streak plate). Test for the production of antifungal volatile organic compounds (VOCs) by antagonistic bacteria was done by sealed plate method. Amongst all s1, s3, s5, and B. subtilis showed more than 80% inhibition. Production of extracellular hydrolytic enzymes such as protease, beta 1, 4 glucanase, HCN and ammonia was studied for confirmation of antifungal activity. s1, s3, s5 and B. subtilis were found to be the best for protease activity and s5 and B. subtilis for beta 1, 4 glucanase activity. Bacillus subtilis was significantly effective for HCN whereas s3, s5 and Bacillus subtilis for ammonia production. Isolates were identified as Pseudomonas aeruginosa (s1) and B. licheniformis (s3, s5) by various biochemical assays and confirmed by16s rRNA sequencing. Use of microorganisms or their secretions as biocontrol agents to avoid plant diseases is ecologically safe and may offer long term of protection to crop. The above study reports the promising effects of these strains in better pathogen free crop production and quality maintenance as well as prevention of the excessive use of synthetic fungicides.

Keywords: antagonistic, antifungal, biocontrol, Karnal bunt

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Mohamed Al-Hazmi, Abdullah Al-Arfaj, Moussa Ihab

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Raw, unpasteurized milk can carry dangerous bacteria such as Salmonella, E. coli, and Listeria, which are responsible for causing numerous foodborne illnesses. The objective of this study was molecular characterization of shiga toxogenic E. coli in raw milk collected from different Egyptian governorates by multiplex PCR. During the period of 25th May to 25th October 2012, a total of 320 bulk-tank milk samples were collected from 10 cow farms located in different Egyptian governorates. Bacteriological examination of milk samples revealed the presence of E. coli organisms in 65 samples (20.3%), serotyping of the E. coli isolates revealed, 35 strains (10.94%) O111, 15 strains (4.69%) O157: H7, 10 strains (3.13%) O128 and 5 strains (1.56%) O119. Multiplex PCR for detection of shiga toxin type 2 and intimin genes revealed positive amplification of 255 bp fragment of shiga toxin type 2 gene and 384 bp fragment of intimin gene from all E. coli serovar O157: H7, while from serovar O111 were 25 (71.43%), 20 (57.14%) and from serovar O128 were 6 (60%), 8 (80%), respectively. The results of multiplex PCR assay are useful for identification of STEC possessing the eaeA and stx2 genes.

Keywords: raw milk, E. coli, multiplex PCR, Shiga toxin type 2, intimin gene

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Authors: T. Garba, T. O. Quddus, Y. Y. Babanyara, M. A. Modibbo

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Desertification is define as the changing of productive land into a desert as the result of ruination of land by man-induced soil erosion, which forces famers in the affected areas to move migrate or encourage into reserved areas in search of a fertile land for their farming activities. This study therefore used remote sensing imageries to determine the level of changes in the vegetative areas. To achieve that Normalized Difference of the Vegetative Index (NDVI), classified imageries and image slicing derived from landsat TM 1986, land sat ETM 1999 and Nigeria sat 1 2007 were used to determine changes in vegetations. From the Classified imageries it was discovered that there a more natural vegetation in classified images of 1986 than that of 1999 and 2007. This finding is also future in the three NDVI imageries, it was discovered that there is increased in high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007. The figures in the three histogram also indicted that there is increased in vegetative areas from 29.15 Km2 in 1986, to 60.58 Km2 in 1999 and then to 109 Km2 in 2007. The study recommends among other things that there is need to restore natural vegetation through discouraging of farming activities in and around the natural vegetation in the study area.

Keywords: vegetative index, classified imageries, change detection, landsat, vegetation

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Authors: Mona Hamdy, Iman Shaheen, Hadeel Seif Eldin, Basma Ali, Omnia Abdeldayem

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Summary: Bone involvement of sickle cell disease (SCD) patients varies from acute clinical manifestations of painful vaso-occlusive crises or osteomyelitis to more chronic affection of bone mineral density (BMD) and debilitating osteonecrosis and osteoporosis. Secreted klotho protein is involved in calcium (Ca) reabsorption in the kidney. This study aimed to measure serum klotho levels in children with SCD to determine the possibility of using it as a marker of low BMD in children with SCD in correlation with a dual-energy radiograph absorptiometry scan. This study included 60 sickle disease patients and 30 age-matched and sex-matched control participants without SCD. A highly statistically significant difference was found between patients with normal BMD and those with low BMD, with serum Ca and klotho levels being lower in the latter group. Klotho serum level correlated positively with both serum Ca and BMD. Serum klotho level showed 94.9% sensitivity and 95.2% specificity in the detection of low BMD. Both serum Ca and klotho serum levels may be useful markers for detection of low BMD related to SCD with high sensitivity and specificity; however, klotho may be a better indicator as it is less affected by the nutritional and endocrinal status of patients or by intake of Ca supplements.

Keywords: sickle cell disease, BMD, osteoporosis, DEXA, klotho

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Authors: Ibrahim Musa, Guy Raffin, Marie Hangouet, Nadia Zine, Nicole Jaffrezic-Renault, Abdelhamid Errachid

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Due to its application in different domains, such as fuel cell configuration and adulteration of alcoholic beverages, a miniaturized sensor for methanol detection is urgently required. A conductometric microsensor for measuring volatile organic compounds (VOC) was conceived, based on electrospun composite nanofibers of polyvinyl chloride (PVC) doped with nickel phthalocyanine(NiPc) deposited on interdigitated electrodes (IDEs) used transducers. The nanofiber's shape, structure, percent atomic content and thermal properties were studied using analytical techniques, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA), respectively. The methanol sensor showed good sensitivity (505µS/cm(v/v) ⁻¹), low LOD (15 ppm), short response time (13 s), and short recovery time (15 s). The sensor was 4 times more sensitive to methanol than to ethanol and 19 times more sensitive to methanol than to acetone. Furthermore, the sensor response was unaffected by the interfering water vapor, making it more suitable for VOC sensing in the presence of humidity. The sensor was applied for conductometric detection of methanol in rubbing alcohol.

Keywords: composite, methanol, conductometric sensor, electrospun, nanofiber, nickel phthalocyanine, PVC

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Authors: M. Amo, A. Alvaro, A. Astudillo, R. Mc Culloch, J. C. del Castillo, M. Gómez, J. M. Martín

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Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) of course comprise a range of highly toxic compounds that may exist as particulates within the air or accumulate within water supplies, soil, or vegetation. They may be created either ubiquitously or naturally within the environment as a product of forest fires or volcanic eruptions. It is only since the industrial revolution, however, that it has become necessary to closely monitor their generation as a byproduct of manufacturing/combustion processes, in an effort to mitigate widespread contamination events. Of course, the environmental concentrations of these toxins are expected to be extremely low, therefore highly sensitive and accurate methods are required for their determination. Since ionization of non-polar compounds through electrospray and APCI is difficult and inefficient, we evaluate the performance of a novel low-flow Atmospheric Pressure Photoionization (APPI) source for the trace detection of various dioxins and furans using rapid Mass Spectrometry workflows. Air, soil and biota (vegetable matter) samples were collected monthly during one year from various locations within the vicinity of an industrial incinerator in Spain. Analytes were extracted and concentrated using soxhlet extraction in toluene and concentrated by rotavapor and nitrogen flow. Various ionization methods as electrospray (ES) and atmospheric pressure chemical ionization (APCI) were evaluated, however, only the low-flow APPI source was capable of providing the necessary performance, in terms of sensitivity, required for detecting all targeted analytes. In total, 10 analytes including 2,3,7,8-tetrachlorodibenzodioxin (TCDD) were detected and characterized using the APPI-MS method. Both PCDDs and PCFDs were detected most efficiently in negative ionization mode. The most abundant ion always corresponded to the loss of a chlorine and addition of an oxygen, yielding [M-Cl+O]- ions. MRM methods were created in order to provide selectivity for each analyte. No chromatographic separation was employed; however, matrix effects were determined to have a negligible impact on analyte signals. Triple Quadrupole Mass Spectrometry was chosen because of its unique potential for high sensitivity and selectivity. The mass spectrometer used was a Sciex´s Qtrap3200 working in negative Multi Reacting Monitoring Mode (MRM). Typically mass detection limits were determined to be near the 1-pg level. The APPI-MS2 technology applied to the detection of PCDD/Fs allows fast and reliable atmospheric analysis, minimizing considerably operational times and costs, with respect other technologies available. In addition, the limit of detection can be easily improved using a more sensitive mass spectrometer since the background in the analysis channel is very low. The APPI developed by SEADM allows polar and non-polar compounds ionization with high efficiency and repeatability.

Keywords: atmospheric pressure photoionization-mass spectrometry (APPI-MS), dioxin, furan, incinerator

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Authors: Jamal R. Elbergali

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Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero-Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol (x ̃_(N_T )), then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.

Keywords: SNR, BER, BPSK, MIMO, modulation, zero forcing (ZF), OSIC, ZF-IC, spatial multiplexing (SM)

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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A simple adaptive voice activity detector (VAD) is implemented using Gabor and gammatone atomic decomposition of speech for high Gaussian noise environments. Matching pursuit is used for atomic decomposition, and is shown to achieve optimal speech detection capability at high data compression rates for low signal to noise ratios. The most active dictionary elements found by matching pursuit are used for the signal reconstruction so that the algorithm adapts to the individual speakers dominant time-frequency characteristics. Speech has a high peak to average ratio enabling matching pursuit greedy heuristic of highest inner products to isolate high energy speech components in high noise environments. Gabor and gammatone atoms are both investigated with identical logarithmically spaced center frequencies, and similar bandwidths. The algorithm performs equally well for both Gabor and gammatone atoms with no significant statistical differences. The algorithm achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR and 98% accuracy at a 20dB SNR using 30dB SNR as a reference for voice activity.

Keywords: atomic decomposition, gabor, gammatone, matching pursuit, voice activity detection

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Authors: Evgeny E. Tereshatov

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Astatine-211 is considered one of the most promising radionuclides for Targeted Alpha Therapy. In order to develop reliable procedures to label biomolecules and utilize efficient delivery vehicle principles, one should understand the main chemical characteristics of astatine. The short half-life of 211At (~7.2 h) and absence of any stable isotopes of this element are limiting factors towards studying the behavior of astatine. Our team has developed a procedure for rapid and efficient isolation of astatine from irradiated bismuth material in nitric acid media based on 3-octanone and 1-octanol extraction chromatography resins. This process has been automated and it takes 20 min from the beginning of the target dissolution to the At-211 fraction elution. Our next step is to consider commercially available chromatography resins and their applicability in astatine purification in the same media. Results obtained along with the corresponding sorption mechanisms will be discussed.

Keywords: astatine-211, chromatography, automation, mechanism, radiopharmaceuticals

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Authors: P. Behera, K. K. Singh, D. K. Saini, M. De

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Implementation of 2D materials in the detection of bio analytes is highly advantageous in the field of sensing because of its high surface to volume ratio. We have designed our sensor array with different cationic two-dimensional MoS₂, where surface modification was achieved by cationic thiol ligands with different functionality. Green fluorescent protein (GFP) was chosen as signal transducers for its biocompatibility and anionic nature, which can bind to the cationic MoS₂ surface easily, followed by fluorescence quenching. The addition of bio-analyte to the sensor can decomplex the cationic MoS₂ and GFP conjugates, followed by the regeneration of GFP fluorescence. The fluorescence response pattern belongs to various analytes collected and transformed to linear discriminant analysis (LDA) for classification. At first, 15 different proteins having wide range of molecular weight and isoelectric points were successfully discriminated at 50 nM with detection limit of 1 nM. The sensor system was also executed in biofluids such as serum, where 10 different proteins at 2.5 μM were well separated. After successful discrimination of protein analytes, the sensor array was implemented for bacteria sensing. Six different bacteria were successfully classified at OD = 0.05 with a detection limit corresponding to OD = 0.005. The optimized sensor array was able to classify uropathogens from non-uropathogens in urine medium. Further, the technique was applied for discrimination of bacteria possessing resistance to different types and amounts of drugs. We found out the mechanism of sensing through optical and electrodynamic studies, which indicates the interaction between bacteria with the sensor system was mainly due to electrostatic force of interactions, but the separation of native bacteria from their drug resistant variant was due to Van der Waals forces. There are two ways bacteria can be detected, i.e., through bacterial cells and lysates. The bacterial lysates contain intracellular information and also safe to analysis as it does not contain live cells. Lysates of different drug resistant bacteria were patterned effectively from the native strain. From unknown sample analysis, we found that discrimination of bacterial cells is more sensitive than that of lysates. But the analyst can prefer bacterial lysates over live cells for safer analysis.

Keywords: array-based sensing, drug resistant bacteria, linear discriminant analysis, two-dimensional MoS₂

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Authors: Shi-Yu Xu

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The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Carolina Gouveia, José Vieira, Pedro Pinho

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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.

Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR

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Authors: Reshmi Vijayan

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Alkaline protease is one of the most important enzymes in the biological world. Microbial production of alkaline protease is getting more attention from researchers due to its unique properties and substantial activity. Microorganisms are the most common sources of commercial enzymes due to their physiological and biochemical properties. The study was conducted on Ayiramthenghu mangrove sediments to isolate protease producing bacteria. All the isolates were screened for proteolytic activity on a skim milk agar plate at 37˚C for 48hrs. Protease activities were determined by the formation of a clear zone around the colonies on Skim milk agar medium. The activity of the enzyme was measured by the tyrosine standard curve, and it was found to be 0.186285 U/ml/min.

Keywords: protease, protease assay, skim milk agar medium, mangrove ecosystem

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Loïc Warscotte, Jehan Boreux

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The scientific literature of changepoint detection is vast. Today, a lot of methods are available to detect abrupt changes or slight drift in a signal, based on CUSUM or EWMA charts, for example. However, these methods rely on strong assumptions, such as the stationarity of the stochastic underlying process, or even the independence and Gaussian distributed noise at each time. Recently, the breakthrough research on locally stationary processes widens the class of studied stochastic processes with almost no assumptions on the signals and the nature of the changepoint. Despite the accurate description of the mathematical aspects, this methodology quickly suffers from impractical time and space complexity concerning the signals with high-rate data collection, if the characteristics of the process are completely unknown. In this paper, we then addressed the problem of making this theory usable to our purpose, which is monitoring a high-speed weigh-in-motion system (HS-WIM) towards direct enforcement without supervision. To this end, we first compute bounded approximations of the initial detection theory. Secondly, these approximating bounds are empirically validated by generating many independent long-run stochastic processes. The abrupt changes and the drift are both tested. Finally, this relaxed methodology is tested on real signals coming from a HS-WIM device in Belgium, collected over several months.

Keywords: changepoint, weigh-in-motion, process, non-parametric

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Amit Bhunia, Mohit Kumar Singh, Maryam Al Huwayz, Mohamed Henini, Shouvik Datta

Abstract:

We report [https://arxiv.org/abs/2107.13518] experimental detection and control of Schrodinger’s Cat like macroscopically large, quantum coherent state of a two-component Bose-Einstein condensate of spatially indirect electron-hole pairs or excitons using a resonant tunneling diode of III-V Semiconductors. This provides access to millions of excitons as qubits to allow efficient, fault-tolerant quantum computation. In this work, we measure phase-coherent periodic oscillations in photo-generated capacitance as a function of an applied voltage bias and light intensity over a macroscopically large area. Periodic presence and absence of splitting of excitonic peaks in the optical spectra measured by photocapacitance point towards tunneling induced variations in capacitive coupling between the quantum well and quantum dots. Observation of negative ‘quantum capacitance’ due to a screening of charge carriers by the quantum well indicates Coulomb correlations of interacting excitons in the plane of the sample. We also establish that coherent resonant tunneling in this well-dot heterostructure restricts the available momentum space of the charge carriers within this quantum well. Consequently, the electric polarization vector of the associated indirect excitons collective orients along the direction of applied bias and these excitons undergo Bose-Einstein condensation below ~100 K. Generation of interference beats in photocapacitance oscillation even with incoherent white light further confirm the presence of stable, long-range spatial correlation among these indirect excitons. We finally demonstrate collective Rabi oscillations of these macroscopically large, ‘multipartite’, two-level, coupled and uncoupled quantum states of excitonic condensate as qubits. Therefore, our study not only brings the physics and technology of Bose-Einstein condensation within the reaches of semiconductor chips but also opens up experimental investigations of the fundamentals of quantum physics using similar techniques. Operational temperatures of such two-component excitonic BEC can be raised further with a more densely packed, ordered array of QDs and/or using materials having larger excitonic binding energies. However, fabrications of single crystals of 0D-2D heterostructures using 2D materials (e.g. transition metal di-chalcogenides, oxides, perovskites etc.) having higher excitonic binding energies are still an open challenge for semiconductor optoelectronics. As of now, these 0D-2D heterostructures can already be scaled up for mass production of miniaturized, portable quantum optoelectronic devices using the existing III-V and/or Nitride based semiconductor fabrication technologies.

Keywords: exciton, Bose-Einstein condensation, quantum computation, heterostructures, semiconductor Physics, quantum fluids, Schrodinger's Cat

Procedia PDF Downloads 180