Search results for: crop disease detection

Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim

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Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.

Keywords: building system, time series, diagnosis, outliers, delay, data gap

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Authors: M. A. Sohaly, M. A. Elfouly

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Parkinson's disease (PD) is a heterogeneous movement disorder that often appears in the elderly. PD is induced by a loss of dopamine secretion. Some drugs increase the secretion of dopamine. In this paper, we will simply study the stability of PD models as a nonlinear delay differential equation. After a period of taking drugs, these act as positive feedback and increase the tremors of patients, and then, the differential equation has positive coefficients and the system is unstable under these conditions. We will present a set of suggested modifications to make the system more compatible with the biodynamic system. When giving a set of numerical examples, this research paper is concerned with the mathematical analysis, and no clinical data have been used.

Keywords: Parkinson's disease, stability, simulation, two delay differential equation

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Zhongjie Yu, Hancheng Yu

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In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.

Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC

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Authors: Amin Mehrabi, Mohsen Salesi, Pourya Pasavand

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Context and objective: The purpose of this research is to study the effect of exercise time (morning/evening) on amount of Troponin T in males' plasma suffering from cardiovascular disease. Method: 15 cardiovascular patients selected as the research subjects. At 7 a.m. pretest blood samples taken from the subjects and they did the exercise protocol in presence of a doctor. Immediately after and 3 hours after that blood measurements done. A week later, the subjects did the same steps at 7 p.m. The SPSS v.20 software used to analyze data. Findings: This study proved that circadian rhythm does not have any effect on the response of myocarditis tissue to exercise and cardiovascular patients allowed to exercise in any times of a day.

Keywords: cardiovascular disease, time of exercise, troponin T (cTnT), myocarditis

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Authors: Memoona Ramzan, Bushra Tabassum, Anwar Khan, Muhammad Tariq, Mudassar Fareed Awan, Idrees Ahmad Nasir, Zahida Qamar, Naila Shahid, Tayyab Husnain

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Biological control is a novel approach being used in crop protection nowadays. Bacteria like Bacillus and Pseudomonas are reported for this purpose and few of their products are commercially available too. Rhizosphere and phyllosphere of healthy cotton plants were used as a source to isolate bacteria capable of exhibiting properties worthy for selection as biocontrol agent. For this purpose all isolated strains were screened for the activities like phosphate solubilization, Indole acetic acid (IAA) production and biocontrol against fungi. Two strains S1HL3 and S1HL4 showed phosphate solubilization and IAA production simultaneously while two other JS2HR4 and JS3HR2 were good inhibitors of fungal pathogens. Through biochemical and molecular characterization these bacteria were identified as P. aeruginosa, Burkholderia and Bacillus respectively. In green house trials of these isolates against Cotton leaf curl virus (CLCuV), seven treatments including individual bacterial isolate and consortia were included. Treated plants were healthy as compared to control plants in which upto 74% CLCuV symptomatic plants exist. Maximum inhibition of CLCuV was observed in T7 treated plants where viral load was only 0.4% as compared to control where viral load was upto 74%. This treatment consortium included Bacillus and Pseudomonas isolates; S1HL3, S1HL4, JS2HR4 and JS3HR2. Principal Component Biplot depicted highly significant correlation between percentage viral load and the disease incidence.

Keywords: cotton leaf curl virus, biological control, bacillus, pseudomonas

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Authors: N. Larbi, F. Debbat

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Wireless communication systems have changed dramatically and shown spectacular evolution over the past two decades. These radio technologies are engaged in a quest endless high-speed transmission coupled to a constant need to improve transmission quality. Various radio communication systems being developed use code division multiple access (CDMA) technique. This work analyses a hybrid honey bees mating optimization algorithm (HBMO) applied to multiuser detection (MuD) in CDMA communication systems. The HBMO is a swarm-based optimization algorithm, which simulates the mating process of real honey bees. We apply a hybridization of HBMO with simulated annealing (SA) in order to improve the solution generated by the HBMO. Simulation results show that the detection based on Hybrid HBMO, in term of bit error rate (BER), is viable option when compared with the classic detectors from literature under Rayleigh flat fading channel.

Keywords: BER, DS-CDMA multiuser detection, genetic algorithm, hybrid HBMO, simulated annealing

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Authors: Apoorva D. R.

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INTRODUCTION: Hemophagocytic lymphohistiocytosis (HLH) is a severe, potentially fatal syndrome in which there is excessive immune activation. The disease is seen in children and people of all ages, but infants from birth to 18 months are most frequently affected. HLH is a sporadic or familial condition that can be triggered by various events that disturb immunological homeostasis. In cases with a genetic predisposition and sporadic occurrences, infection is a frequent trigger. Because of the rarity of this disease, the diverse clinical presentation, and the lack of specificity in the clinical and laboratory results, prompt treatment is essential, but the biggest obstacle to a favorable outcome is frequently a delay in identification. CASE REPORT: Here we report a case of a 6-month-old male infant who presented to the dermatology outpatient with disseminated skin lesions present over the face, abdomen, scalp, and bilateral upper and lower limbs for the past month. The lesions were insidious in onset, initially started over the abdomen, and gradually progressed to involve other body parts. The patient also had a history of fever which was moderate in grade, on and off in nature for 1 month. There were no significant complaints in the past, family, or drug history. There was no history of feeding difficulties in the baby. Parents gave a history of developmental milestones appropriate for age. Examination findings include multiple well-defined monomorphic erythematous papules with a central crater present over bilateral cheeks. Few lichenoid shiny papules present over bilateral arms, legs, and abdomen. Ultrasound of the abdomen and pelvis showed mild degree hepatosplenomegaly, intraabdominal lymphadenopathy, and bilateral inguinal lymphadenopathy. Routine blood investigations showed anemia and lymphopenia. Multiple X-rays of the skull, chest, and bilateral upper and lower limbs were done and were normal. Histopathology features were suggestive of non-Langerhans cell cutaneous histiocytosis. CONCLUSION: HLH is a fatal and rare disease. A high level of suspicion and an interdisciplinary approach among experienced clinicians, pathologists, and microbiologists to define the diagnosis and causative disease are key to diagnosing this case. Early detection and treatment can reduce patient morbidity and mortality.

Keywords: histiocytosis, non langerhans cell, case report, fatal, rare

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Authors: Fernando L. P. Santos, Luiz G. Lyra, Helenice O. Florentino, Daniela R. Cantane

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Dengue is a febrile infectious disease caused by a virus of the family Flaviridae. It is transmitted by the bite of mosquitoes, usually of the genus Aedes aegypti. It occurs in tropical and subtropical areas of the world. This disease has been a major public health problem worldwide, especially in tropical countries such as Brazil, and its incidence has increased in recent years. Dengue is a subject of intense research. Efficient forms of mosquito control must be considered. In this work, the mono-objective optimal control problem was solved for analysing the dengue disease problem. Chemical and biological controls were considered in the mathematical aspect. This model describes the dynamics of mosquitoes in water and winged phases. We applied the genetic algorithms (GA) to obtain optimal strategies for the control of dengue. Numerical simulations have been performed to verify the versatility and the applicability of this algorithm. On the basis of the present results we may recommend the GA to solve optimal control problem with a large region of feasibility.

Keywords: genetic algorithm, dengue, Aedes aegypti, biological control, chemical control

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Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Ana Corberan-Vallet, Karen C. Florez, Ingrid C. Marino, Jose D. Bermudez

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In 2016, the Region of the Americas was declared free of measles, a viral disease that can cause severe health problems. However, since 2017, measles has reemerged in Venezuela and has subsequently reached neighboring countries. In 2018, twelve American countries reported confirmed cases of measles. Governmental and health authorities in Colombia, a country that shares the longest land boundary with Venezuela, are aware of the need for a strong response to restrict the expanse of the epidemic. In this work, we apply a Bayesian hierarchical Poisson model with an underlying cluster structure to describe disease incidence in Colombia. Concretely, the proposed methodology provides relative risk estimates at the department level and identifies clusters of disease, which facilitates the implementation of targeted public health interventions. Socio-demographic factors, such as the percentage of migrants, gross domestic product, and entry routes, are included in the model to better describe the incidence of disease. Since the model does not impose any spatial correlation at any level of the model hierarchy, it avoids the spatial confounding problem and provides a suitable framework to estimate the fixed-effect coefficients associated with spatially-structured covariates.

Keywords: Bayesian analysis, cluster identification, disease mapping, risk estimation

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Authors: R. S. Giraddi, B. Thirupam Reddy, D. N. Kambrekar

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Chilli (Capsicum annum L.) an important commercial vegetable crop is ravaged by a number of insect-pests during both vegetative and reproductive phase resulting into significant crop loss.Thrips, Scirtothripsdorsalis, mite, Polyphagotarsonemuslatus and whitefly, Bemisiatabaci are the key sap feeding insects, their infestation leads to leaf curl, stunted growth and yield loss.During flowering and fruit formation stage, gall midge fly, Asphondyliacapparis (Rubsaaman) infesting flower buds and young fruits andHelicoverpaarmigera (Hubner) feeding on matured green fruits are the important insect pests causing significant crop loss.The pest is known to infest both flower buds and young fruits resulting into malformation of flower buds and twisting of fruits.In order to manage these insect-pests a combi product consisting of imidacloprid and betacyfluthrin (Soloman 300 OD) was evaluated for its bio-efficacy, phytotoxicity and effect on predator activity.Imidacloprid, a systemic insecticide belonging to neo-nicotinoid group, is effective against insect pests such as aphids, whiteflies (sap feeders) and other insectsviz., termites and soil insects.Beta-Cyfluthrin is an insecticide of synthetic pyrethroid group which acts by contact action and ingestion. It acts on the insects' nervous system as sodium channel blocker consequently a disorder of the nervous system occurs leading finally to the death. The field experiments were taken up during 2015 and 2016 at the Main Agricultural Research Station of University of Agricultural Sciences, Dharwad, Karnataka, India.The trials were laid out in a Randomized Block Design (RBD) with three replications using popular land race of Byadagi crop variety.Results indicated that the product at 21.6 + 50.4% gai/ha (240 ml/ha) and 27.9 + 65% gai/ha (310 ml/ha) was found quite effective in controlling thrips (0.00 to 0.66 thrips per six leaves) as against the standard check insecticide recommended for thrips by the University of Agricultural Sciences, Dharwad wherein the density of thrips recorded was significantly higher (1.00 to 2.00 Nos./6 leaves). Similarly, the test insecticide was quite effective against other target insects, whiteflies, fruit borer and gall midge fly as indicated by lower insect population observed in the treatments as compared to standard insecticidal control. The predatory beetle activity was found to be normal in all experimental plots. Highest green fruit yield of 5100-5500 kg/ha was recorded in Soloman 300 OD applied crop at 310 ml/ha rate as compared to 4750 to 5050 kg/ha recorded in check. At present 6-8 sprays of insecticides are recommended for management of these insect-pests on the crop. If combi-products are used in pest management programmes, it is possible to reduce insecticide usages in crop ecosystem.

Keywords: Imidacloprid, Betacyfluthrin, gallmidge fly, thrips, chilli

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) learning approach aims to improve knowledge of the farmers through integrated crop management and provide leadership in their decision making process. The study was conducted to assess the impact of FFS on vegetables production before and after FFS intervention in four villages of district Peshawar in cropping season 2012, by interviewing 80 FFS respondents, twenty from each selected village. It was observed from the study results that all the respondents were satisfied from the impact of FFS and they informed an increased in production in vegetables. It was further observed that after the implementation of FFS the sowing seed rate of tomato and cucumber were decreased from 0.185kg/kanal to 0.100 kg/ kanal and 0.120kg/kanal to 0.010kg/kanal where as the production of tomato and cucumber were increased from 8158.75kgs/kanal to 10302. 5kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively. The cost of agriculture inputs per kanal including seed cost, crop management, Farm Yard Manure, and weedicides in case of tomato were reduced by Rs.28, Rs. 3170, Rs.658and Rs 205 whereas in cucumber reduced by Rs.35, Rs.570, Rs 80 and Rs.430 respectively. Only fertilizers cost was increased by Rs. 2200 in case of tomato and Rs 465 in case of cucumber. Overall the cost was reduced to Rs 545 in tomato and Rs 490 in cucumber production.FFS provided a healthy vegetables and also reduced input cost by adopting integrated crop management. Therefore the promotion of FFS is needed to be planned for farmers to reduce cost of production, so that the more farmers should be benefited.

Keywords: impact, farmer field schools, vegetable production, Peshawar Khyber Pakhtunkhwa

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Authors: Gaokai Liu

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Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

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Authors: Septiawan A. Saputro, Purnami Widyaningsih, Sutanto Sastraredja

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Measles is a disease which can spread caused by a virus and has been a priority’s Ministry of Health in Indonesia to be solved. Each infected person can be recovered and get immunity so that the spread of the disease can be constructed with susceptible infected recovered (SIR). To prevent the spread of measles transmission, the Ministry of Health holds vaccinations program. The aims of the research are to derive susceptible vaccinated infected recovered (SVIR) model, to determine the patterns of disease spread with SVIR model, and also to apply the SVIR model on the spread of measles in Indonesia. Based on the article, it can be concluded that the spread model of measles with vaccinations, that is SVIR model. It is a first-order differential equation system. The patterns of disease spread is determined by solution of the model. Based on that model Indonesia will be a measles-free nation in 2186 with the average of vaccinations scope about 88% and the average score of vaccinations failure about 4.9%. If it is simulated as Ministry of Health new programs with the average of vaccinations scope about 95% and the average score of vaccinations failure about 3%, then Indonesia will be a measles-free nation in 2184. Even with the average of vaccinations scope about 100% and no failure of vaccinations, Indonesia will be a measles-free nation in 2183. Indonesia’s target as a measles-free nation in 2020 has not been reached.

Keywords: measles, vaccination, susceptible infected recovered (SIR), susceptible vaccinated infected recovered (SVIR)

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: M. Van 'T Hof, R. V. Pasma, J. T. Bailly, H. W. Hoek, W. A. Ester

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Background: The transition into primary school can be challenging for children with an autism spectrum disorder (ASD). Due to the new demands that are made to children in this period, their limitations in social functioning and school achievements may manifest and appear faster. Detection of possible ASD signals mainly takes place by parents, teachers and during obligatory municipal maternal and child health centre visits. Physicians of municipal maternal and child health centres have limited education and instruments to detect ASD. Further education on detecting ASD is needed to optimally equip these doctors for this task. Most research aims to increase the early detection of ASD in children aged 0-3 years and shows positive results. However, there is a lack of research on educational interventions to detect ASD in children aged 4-6 years by municipal maternal and child health physicians. Aim: The aim of this study is to explore the effect of the online educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health physicians. This educational intervention is developed within The Reach-Aut Academic Centre for Autism; Transitions in education, and will be available throughout The Netherlands. Methods: Ninety-two participants will follow the educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health centre physicians. The educational intervention consists of three, one and a half hour sessions, which are offered through an online interactive classroom. The focus and content of the course has been developed in collaboration with three groups of stakeholders; autism scientists, clinical practitioners (municipal maternal and child health doctors and ASD experts) and parents of children with ASD. The primary outcome measure is knowledge about ASD: signals, early detection, communication with parents and referrals. The secondary outcome measures are the number of ASD related referrals, the attitude towards the mentally ill (CAMI), perceived competency about ASD knowledge and detection skills, and satisfaction about the educational intervention. Results and Conclusion: The study started in January 2016 and data collection will end mid 2017.

Keywords: ASD, child, detection, educational intervention, physicians

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Authors: Elmongi Elbellili, Ben Lauwens, Daan Huybrechs

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The increasing complexity of modern engineering systems presents a challenge to the digital simulation of these systems which usually can be represented by differential equations. The Linearly Implicit Quantized State System (LIQSS) offers an alternative approach to traditional numerical integration techniques for solving Ordinary Differential Equations (ODEs). This method proved effective for handling discontinuous and large stiff systems. However, the inherent discrete nature of LIQSS may introduce oscillations that result in unnecessary computational steps. The current oscillation detection mechanism relies on a condition that checks the significance of the derivatives, but it could be further improved. This paper describes a different cycle detection mechanism and presents the outcomes using LIQSS order one in simulating the Advection Diffusion problem. The efficiency of this new cycle detection mechanism is verified by comparing the performance of the current solver against the new version as well as a reference solution using a Runge-Kutta method of order14.

Keywords: numerical integration, quantized state systems, ordinary differential equations, stiffness, cycle detection, simulation

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Authors: Kunal Das, Priya Sengupta, Abhishek K. Singh

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Pin assignment, scheduling, routing and error detection for arbitrary biochemical protocols in Digital Microfluidic Biochip have been reported in this paper. The research work is concentrating on pin assignment for 2 or 3 droplets routing in the arbitrary biochemical protocol, scheduling and routing in m × n biochip. The volumetric error arises due to droplet split in the biochip. The volumetric error detection is also addressed using biochip AND logic gate which is known as microfluidic AND or mAND gate. The algorithm for pin assignment for m × n biochip required m+n-1 numbers of pins. The basic principle of this algorithm is that no same pin will be allowed to be placed in the same column, same row and diagonal and adjacent cells. The same pin should be placed a distance apart such that interference becomes less. A case study also reported in this paper.

Keywords: digital microfludic biochip, cross-contamination, pin assignment, microfluidic AND gate

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Authors: Chun-Wei Huang, Jyh-Cherng Gu, Ming-Ta Yang

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Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method.

Keywords: ferroresonance, wavelet transform, intelligent electronic device, transformer

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Authors: Eyup Ozkan, Ozkan U. Nalbantoglu, Aycan Gundogdu, Mehmet Hora, A. Emre Onuk

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The human microbiome has been associated with cardiological conditions and this relationship is becoming to be defined beyond the gastrointestinal track. In this study, we investigate the alteration in circulatory microbiota in the context of Coronary Artery Disease (CAD). We received circulatory blood samples from suspected CAD patients and maintain 16S ribosomal RNA sequencing to identify each patient’s microbiome. It was found that Corynebacterium and Methanobacteria genera show statistically significant differences between healthy and CAD patients. The overall biodiversities between the groups were observed to be different revealed by machine learning classification models. We also achieve and demonstrate the performance of a diagnostic method using circulatory blood microbiome-based estimation.

Keywords: coronary artery disease, blood microbiome, machine learning, angiography, next-generation sequencing

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Authors: H. C. Chinwenyi, H. D. Ibrahim, J. O. Adekunle

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The incubation period is defined as the time from infection with a microorganism to development of symptoms. In this research, two disease models: one with incubation period and another without incubation period were studied. The study involves the use of a  mathematical model with a single incubation period. The test for the existence and stability of the disease free and the endemic equilibrium states for both models were carried out. The fourth order Runge-Kutta method was used to solve both models numerically. Finally, a computer program in MATLAB was developed to run the numerical experiments. From the results, we are able to show that the endemic equilibrium state of the model with incubation period is locally asymptotically stable whereas the endemic equilibrium state of the model without incubation period is unstable under certain conditions on the given model parameters. It was also established that the disease free equilibrium states of the model with and without incubation period are locally asymptotically stable. Furthermore, results from numerical experiments using empirical data obtained from Nigeria Centre for Disease Control (NCDC) showed that the overall population of the infected people for the model with incubation period is higher than that without incubation period. We also established from the results obtained that as the transmission rate from susceptible to infected population increases, the peak values of the infected population for the model with incubation period decrease and are always less than those for the model without incubation period.

Keywords: asymptotic stability, Hartman-Grobman stability criterion, incubation period, Routh-Hurwitz criterion, Runge-Kutta method

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Authors: Gholam Reza Mirzaei

Abstract:

This research aimed to investigate the effectiveness of acceptance and commitment therapy (ACT) in reducing corona disease anxiety in the staff working at Shahid Beheshti Hospital of Shiraz. The current research was a quasi-experimental study having pre-test and post-test with two experimental and control groups. The statistical population of the research included all the staff of Shahid Beheshti Hospital of Shiraz in 2021. From among the statistical population, 30 participants (N =15 in the experimental group and N =15 in the control group) were selected by available sampling. The materials used in the study comprised the Cognitive Emotion Regulation Questionnaire (CERQ) and Corona Disease Anxiety Scale (CDAS). Following data collection, the participants’ scores were analyzed using SPSS 20 at both descriptive (mean and standard deviation) and inferential (analysis of covariance) levels. The results of the analysis of covariance (ANCOVA) showed that acceptance and commitment therapy (ACT) is effective in reducing Corona disease anxiety (mental and physical symptoms) in the staff working at Shahid Beheshti Hospital of Shiraz. The effectiveness of acceptance and commitment therapy (ACT) on reducing mental symptoms was 25.5% and on physical symptoms was 13.8%. The mean scores of the experimental group in the sub-scales of Corona disease anxiety (mental and physical symptoms) in the post-test were lower than the mean scores of the control group.

Keywords: acceptance and commitment therapy, corona disease anxiety, hospital staff, Shiraz

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Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

Abstract:

The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

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Authors: Thanh Nga Thai

Abstract:

A major issue in air transportation is in flight safety. Recent developments in control engineering have an attractive potential for resolving new issues related to guidance, navigation, and control of flying vehicles. Many future atmospheric missions will require increased on board autonomy including fault diagnosis and the subsequent control and guidance recovery actions. To improve designing system diagnostic, an efficient FDI- fault detection and identification- methodology is necessary to achieve. Contribute to characteristic of different faults in sensor and actuator in the view of mathematics brings a lot of profit in some condition changes in the system. This research finds some profit to reduce a trade-off to achieve between fault detection and performance of the closed loop system and cost and calculated in simulation.

Keywords: fault detection and identification, sensor faults, actuator faults, flight control system

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

Abstract:

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level, as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, speech emotion recognition, longitudinal biomarker, machine learning

Procedia PDF Downloads 113

Authors: Diana C. El Assal, Fatima Monteiro, Caroline May, Peter Barbuti, Silvia Bolognin, Averina Nicolae, Hulda Haraldsdottir, Lemmer R. P. El Assal, Swagatika Sahoo, Longfei Mao, Jens Schwamborn, Rejko Kruger, Ines Thiele, Kathrin Marcus, Ronan M. T. Fleming

Abstract:

Degeneration of substantia nigra pars compacta dopaminergic neurons is one of the hallmarks of Parkinson's disease. These neurons have a highly complex axonal arborisation and a high energy demand, so any reduction in ATP synthesis could lead to an imbalance between supply and demand, thereby impeding normal neuronal bioenergetic requirements. Synaptic mitochondria exhibit increased vulnerability to dysfunction in Parkinson's disease. After biogenesis in and transport from the cell body, synaptic mitochondria become highly dependent upon oxidative phosphorylation. We applied a systems biochemistry approach to identify the metabolic pathways used by neuronal mitochondria for energy generation. The mitochondrial component of an existing manual reconstruction of human metabolism was extended with manual curation of the biochemical literature and specialised using omics data from Parkinson's disease patients and controls, to generate reconstructions of synaptic and somal mitochondrial metabolism. These reconstructions were converted into stoichiometrically- and fluxconsistent constraint-based computational models. These models predict that Parkinson's disease is accompanied by an increase in the rate of glycolysis and a decrease in the rate of oxidative phosphorylation within synaptic mitochondria. This is consistent with independent experimental reports of a compensatory switching of bioenergetic pathways in the putamen of post-mortem Parkinson's disease patients. Ongoing work, in the context of the SysMedPD project is aimed at computational prediction of mitochondrial drug targets to slow the progression of neurodegeneration in the subset of Parkinson's disease patients with overt mitochondrial dysfunction.

Keywords: bioenergetics, mitochondria, Parkinson's disease, systems biochemistry

Procedia PDF Downloads 294