Search results for: crop disease detection

Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

Procedia PDF Downloads 252

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Background: Avian influenza, more commonly known as Bird flu, is a viral zoonotic respiratory disease stemming from various species of poultry, including pets and migratory birds. Researchers have purported that the accessibility of health information online, in addition to the low-cost data collection methods the internet provides, has revolutionized the methods in which epidemiological and disease surveillance data is utilized. This paper examines the feasibility of using internet data sources, such as Twitter and livestock forums, for the early detection of the avian flu outbreak, through the use of text mining algorithms and social network analysis. Methods: Social media mining was conducted on Twitter between the period of 01/01/2021 to 31/12/2021 via the Twitter API in Python. The results were filtered firstly by hashtags (#avianflu, #birdflu), word occurrences (avian flu, bird flu, H5N1), and then refined further by location to include only those results from within the UK. Analysis was conducted on this text in a time-series manner to determine keyword frequencies and topic modeling to uncover insights in the text prior to a confirmed outbreak. Further analysis was performed by examining clinical signs (e.g., swollen head, blue comb, dullness) within the time series prior to the confirmed avian flu outbreak by the Animal and Plant Health Agency (APHA). Results: The increased search results in Google and avian flu-related tweets showed a correlation in time with the confirmed cases. Topic modeling uncovered clusters of word occurrences relating to livestock biosecurity, disposal of dead birds, and prevention measures. Conclusions: Text mining social media data can prove to be useful in relation to analysing discussed topics for epidemiological surveillance purposes, especially given the lack of applied research in the veterinary domain. The small sample size of tweets for certain weekly time periods makes it difficult to provide statistically plausible results, in addition to a great amount of textual noise in the data.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, avian influenza, social media

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Authors: Farheen Tabassum, Shoab Ahmed Khan

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The brutality of attacks on networks and decisive infrastructures are on the climb over recent years and appears to continue to do so. Distributed Denial of service attack is the most prevalent and easy attack on the availability of a service due to the easy availability of large botnet computers at cheap price and the general lack of protection against these attacks. Application layer DDoS attack is DDoS attack that is targeted on wed server, application server or database server. These types of attacks are much more sophisticated and challenging as they get around most conventional network security devices because attack traffic often impersonate normal traffic and cannot be recognized by network layer anomalies. Conventional techniques of single-hosted security systems are becoming gradually less effective in the face of such complicated and synchronized multi-front attacks. In order to protect from such attacks and intrusion, corporation among all network devices is essential. To overcome this issue, a collaborative intrusion detection system (CIDS) is proposed in which multiple network devices share valuable information to identify attacks, as a single device might not be capable to sense any malevolent action on its own. So it helps us to take decision after analyzing the information collected from different sources. This novel attack detection technique helps to detect seemingly benign packets that target the availability of the critical infrastructure, and the proposed solution methodology shall enable the incident response teams to detect and react to DDoS attacks at the earliest stage to ensure that the uptime of the service remain unaffected. Experimental evaluation shows that the proposed collaborative detection approach is much more effective and efficient than the previous approaches.

Keywords: Distributed Denial-of-Service (DDoS), Collaborative Intrusion Detection System (CIDS), Slowloris, OSSIM (Open Source Security Information Management tool), OSSEC HIDS

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Nemi Bhattarai

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In recent years, there has been a rapid increase in the use of Unmanned Aerial Vehicle (UAVs) in search and rescue (SAR) operations, disaster management, and many more areas where information about the location of human beings are important. This research will primarily focus on the use of optical and thermal camera via UAV platform in real-time detection, localization, and visualization of human beings on GIS. This research will be beneficial in disaster management search of lost humans in wilderness or difficult terrain, detecting abnormal human behaviors in border or security tight areas, studying distribution of people at night, counting people density in crowd, manage people flow during evacuation, planning provisions in areas with high human density and many more.

Keywords: UAV, human detection, real-time, localization, visualization, haar-like, GIS, thermal sensor

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Shima Nabinejad, Holger Schüttrumpf

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Farmers who are living in flood-prone areas such as coasts are exposed to storm surges increased due to climate change. Crop cultivation is the most important economic activity of farmers, and in the time of flooding, agricultural lands are subject to inundation. Additionally, overflow saline water causes more severe damage outcomes than riverine flooding. Agricultural crops are more vulnerable to salinity than other land uses for which the economic damages may continue for a number of years even after flooding and affect farmers’ decision-making for the following year. Therefore, it is essential to assess what extent the agricultural areas are flooded and how much the associated flood damage to each individual farmer is. To address these questions, we integrated farmers’ decision-making at farm-scale with flood risk management. The integrated model includes identification of hazard scenarios, failure analysis of structural measures, derivation of hydraulic parameters for the inundated areas and analysis of the economic damages experienced by each farmer. The present study has two aims; firstly, it attempts to investigate the flooded cropland and potential crop damages for the whole area. Secondly, it compares them among farmers’ field for three flood scenarios, which differ in breach locations of the flood protection structure. To achieve its goal, the spatial distribution of fields and cultivated crops of farmers were fed into the flood risk model, and a 100-year storm surge hydrograph was selected as the flood event. The study area was Pellworm Island that is located in the German Wadden Sea National Park and surrounded by North Sea. Due to high salt content in seawater of North Sea, crops cultivated in the agricultural areas of Pellworm Island are 100% destroyed by storm surges which were taken into account in developing of depth-damage curve for analysis of consequences. As a result, inundated croplands and economic damages to crops were estimated in the whole Island which was further compared for six selected farmers under three flood scenarios. The results demonstrate the significance and the flexibility of the proposed model in flood risk assessment of flood-prone areas by integrating flood risk management and decision-making.

Keywords: crop damages, flood risk analysis, individual farmer, inundated cropland, Pellworm Island, storm surges

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: James Kaweesa

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The Internet of Things (IoT) has become a critical component of modern technology, enabling the connection of numerous devices to the internet. The interconnected nature of IoT devices, along with their heterogeneous and resource-constrained nature, makes them vulnerable to various types of attacks, such as malware, denial-of-service attacks, and network scanning. Intrusion Detection Systems (IDSs) are a key mechanism for protecting IoT networks and from attacks by identifying and alerting administrators to suspicious activities. In this review, the paper will discuss the different types of IDSs available for IoT systems and evaluate their effectiveness in detecting and preventing attacks. Also, examine the various evaluation methods used to assess the performance of IDSs and the challenges associated with evaluating them in IoT environments. The review will highlight the need for effective and efficient IDSs that can cope with the unique characteristics of IoT networks, including their heterogeneity, dynamic topology, and resource constraints. The paper will conclude by indicating where further research is needed to develop IDSs that can address these challenges and effectively protect IoT systems from cyber threats.

Keywords: cyber-threats, iot, intrusion detection system, networks

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Authors: Desissa Yadeta Muleta

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Purely organic long-persistent luminescence materials (OLPLMs) have been developed as emerging organic materials due to their simple production process, low preparation cost and better biocompatibilities. Notably, OLPLMs with afterglow-time-tunable long-persistent luminescence (LPL) characteristics enable higher-level protection applications and have great prospects in biological applications. The realization of these advanced performances depends on our ability to gradually tune LPL duration under ambient conditions, however, the strategies to achieve this are few due to the lack of unambiguous mechanisms. Here, we propose a two-step strategy to gradually tune LPL duration of OLPLMs over a wide range of seconds in water and air, by using derivatives as the guest and introducing a third-party material into the host-immobilized host–guest doping system. Based on this strategy, we develop an analysis method for deoxyribonucleic acid (DNA) content detection without DNA separation in aqueous samples, which circumvents the influence of the chromophore, fluorophore and other interferents in vivo, enabling a certain degree of in situ detection that is difficult to achieve using today’s methods. This work will expedite the development of afterglow-time-tunable OLPLMs and expand new horizons for their applications in data protection, bio-detection, and bio-sensing

Keywords: deoxyribonucliec acid, long persistent luminescent materials, water, air

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Authors: Gwendy Darras, Mattias Desmet

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Previous research showed that psychological distress has a negative impact on the disease course of viral infections. For COVID-19, the same association was observed in small samples of specific segments of the population (e.g. health care workers). The present study presents a more refined analysis of this association, measuring a broader spectrum of psychological distress in a large sample (n=3084) of the general Flemish population. Several types of psychological distress (state, trait and health anxiety, depression, intra-, and interpersonal stress) are registered throughout three periods: one year before the contamination, one week before the contamination, and during the contamination. In doing so, validated scales such as DASS-21, IIP-32, and FCV-19S are used. Furthermore, the course of COVID-19 is registered in several ways: number of symptoms, number of days sick leave due to COVID-19, and number of days the symptoms have lasted. Also, different control variables such as vaccination status, medical and psychological history are taken into account. Statistical analysis shows that all types of psychological distress are positively correlated with the severity of the COVID-19 disease course. Anxiety during the contamination shows the strongest correlation, but psychological distress one year before the onset of COVID-19 was still significantly associated with the worsening of the disease course. As the assessment of the latter type of distress happened before the onset of the COVID-19 disease course, retrospective bias resulting in artificial associations between self-reported stress and COVID-19 severity is unlikely to have impacted the observations. In view of possible future pandemics, it is important to focus on general stress and anxiety reduction in the general population as soon as possible. It is also advisable to minimize the use of stress-inducing messages to encourage the population to adhere to the measures issued during a pandemic.

Keywords: anxiety, COVID-19, depression, psychoneuroimmunology, psychological distress, stress

Procedia PDF Downloads 83

Authors: Abhaydeep Pandey, Shweta Shukla, Saptamita Goswami, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Sudhanshu Vrati, Arup Banerjee

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Dengue virus infection is now considered as one of the most important mosquito-borne infection in human. The virus is known to promote vascular permeability, cerebral edema leading to Dengue hemorrhagic fever (DHF) or Dengue shock syndrome (DSS). Dengue infection has known to be endemic in India for over two centuries as a benign and self-limited disease. In the last couple of years, the disease symptoms have changed, manifesting severe secondary complication. So far, Delhi has experienced 12 outbreaks of dengue virus infection since 1997 with the last reported in 2014-15. Without specific antivirals, the case management of high-risk dengue patients entirely relies on supportive care, involving constant monitoring and timely fluid support to prevent hypovolemic shock. Nonetheless, the diverse clinical spectrum of dengue disease, as well as its initial similarity to other viral febrile illnesses, presents a challenge in the early identification of this high-risk group. WHO recommends the use of warning signs to identify high-risk patients, but warning signs generally appear during, or just one day before the development of severe illness, thus, providing only a narrow window for clinical intervention. The ability to predict which patient may develop DHF and DSS may improve the triage and treatment. With the recent discovery of high throughput RNA sequencing allows us to understand the disease progression at the genomic level. Here, we will collate the results of RNA-Sequencing data obtained recently from PBMC of different categories of dengue patients from India and will discuss the possible role of deregulated genes and long non-coding RNAs NEAT1 for development of disease progression.

Keywords: long non-coding RNA (lncRNA), dengue, peripheral blood mononuclear cell (PBMC), nuclear enriched abundant transcript 1 (NEAT1), dengue hemorrhagic fever (DHF), dengue shock syndrome (DSS)

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 188

Authors: Eun Hui Bae, Sang Yeob Lim, Bongseong Kim, Tae Ryom Oh, Su Hyun Song, Sang Heon Suh, Hong Sang Choi, Eun Mi Yang, Chang Seong Kim, Seong Kwon Ma, Kyung-Do Han, Soo Wan Kim

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Background: Recent hypertension guidelines have recommended lower blood pressure (BP) targets in high-risk patients. However, there are no specific guidelines based on age or systolic and diastolic blood pressure (SBP and DBP, respectively). We aimed to assess the effects of age-related BP on the development of end-stage renal disease (ESRD) in patients with diabetes. Methods: A total of 2,563,870 patients with DM aged >20 years were selected from the Korean National Health Screening Program from 2009 to 2012 and followed up until the end of 2019. Participants were categorized into age and BP groups, and the hazard ratios (HRs) for ESRD were calculated. Results: During a median follow-up of 7.15 years, the incidence rates of ESRD increased with increasing SBP and DBP. The HR for ESRD was the highest in patients younger than 40 years of age with DBP ≥ 100 mmHg. The effect of SBP and DBP on ESRD development was attenuated with age (interaction p-value was <0.0001 for age and SBP and 0.0022 for age and DBP). The subgroup analysis for sex, anti-hypertension medication, and history of chronic kidney disease (CKD) showed higher HRs for ESRD among males younger than 40 years, not taking anti-hypertension medications and CKD compared to those among females older than 40 years, anti-hypertension medication and non-CKD groups. Conclusions: Higher SBP and DBP increase the risk of developing ESRD in patients with diabetes, and in particular, younger individuals face greater risk. Therefore, intensive BP management is warranted in younger patients to prevent ESRD.

Keywords: hypertension, young adult, end-stage renal disease, diabetes mellitus, chronic kidney disease, blood pressure

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Authors: Harukuni Tokuda, Xu FengHao, Nobutaka Suzuki

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As part of an ongoing our projects to investigate the anti-tumor promoring properties (chemopreventive potency) of Gromwell seed, dry powder materials and its active compounds were carried out through useful test systems. Gromwell seed (Coix lachryma-jobi seed) (GS) is a grass crop that has long been used and played a role in traditional medicine as a nourishing food, and for the treatment of various aliments, paticularly cancer. The application of a new screening procedure which utilizes the synergistic effect of short-chain fatty acids and phorbol esters in enable rapid and easy detection of naturally occurring substances(anti-tumor promoters chemo-preventive agents) with inhibition of Epstein-Barr virus(EBV) activation, using human lymphblastoid cells. In addition, we have now extended these investigations to a new tumorigenesis model in which we initiated the tumors with DMBA intiation and promoted with 1.7 nmol of TPA in two-stage mouse skin test and other models. these results provide a basis for further development of these botanical supplements for human cancer chemoprevention and observations seem that this materials more extensively as one of the trials for the purpose of complementary and alternative medicine.

Keywords: chemoprevention, medicinal plant, mouse, carcinogenesis systems

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: H. Abu-Ali, A. Nabok, T. Smith

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Aptamers are single-strand of DNA or RNA nucleotides sequence which is designed in vitro using selection process known as SELEX (systematic evolution of ligands by exponential enrichment) were developed for the selective detection of many toxic materials. In this work, we have developed an electrochemical biosensor for highly selective and sensitive detection of Hg2+ and Pb2+ using a specific aptamer probe (SAP) labelled with ferrocene (or methylene blue) in (5′) end and the thiol group at its (3′) termini, respectively. The SAP has a specific coil structure that matching with G-G for Pb2+ and T-T for Hg2+ interaction binding nucleotides ions, respectively. Aptamers were immobilized onto surface of screen-printed gold electrodes via SH groups; then the cyclic voltammograms were recorded in binding buffer with the addition of the above metal salts in different concentrations. The resulted values of anode current increase upon binding heavy metal ions to aptamers and analyte due to the presence of electrochemically active probe, i.e. ferrocene or methylene blue group. The correlation between the anodic current values and the concentrations of Hg2+ and Pb2+ ions has been established in this work. To the best of our knowledge, this is the first example of using a specific DNA aptamers for electrochemical detection of heavy metals. Each increase in concentration of 0.1 μM results in an increase in the anode current value by simple DC electrochemical test i.e (Cyclic Voltammetry), thus providing an easy way of determining Hg2+ and Pb2+concentration.

Keywords: aptamer, based, biosensor, DNA, electrochemical, highly, specific

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Authors: McClain Thiel

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Object detection and localization has historically required two or more sensors due to the loss of information from 3D to 2D space, however, most surveillance systems currently in use in the real world only have one sensor per location. Generally, this consists of a single low-resolution camera positioned above the area under observation (mall, jewelry store, traffic camera). This is not sufficient for robust 3D tracking for applications such as security or more recent relevance, contract tracing. This paper proposes a lightweight system for 3D person tracking that requires no additional hardware, based on compressed object detection convolutional-nets, facial landmark detection, and projective geometry. This approach involves classifying the target into a demographic category and then making assumptions about the relative locations of facial landmarks from the demographic information, and from there using simple projective geometry and known constants to find the target's location in 3D space. Preliminary testing, although severely lacking, suggests reasonable success in 3D tracking under ideal conditions.

Keywords: monocular distancing, computer vision, facial analysis, 3D localization

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Authors: Ali Bayram, Remzi Yiğiter

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Objective: Alzheimer's disease (AD), the most common cause of dementia, is a progressive neurodegenerative disease. At present, diagnosis of AD is rather late in the disease. Therefore, we attempted to find peripheral biomarkers for the early diagnosis of AD. Herein, we conducted a study to investigate the unc-51 like autophagy activating kinase-1 (ULK1) mRNA expression levels in human peripheral blood mononuclear cells from patients with Alzheimer's disease. Method: To determine whether ULK1 gene expression are altered in AD patients, we measured their gene expression in human peripheral blood cell in 50 patients with AD and 50 age and gender matched healthy controls by quantitative real-time PCR technique. Results: We found that both ULK1 gene expression in peripheral blood cell were significantly decreased in patients with AD as compared with controls (p <0.05). Lower levels of ULK1 gene expression were significantly associated with the increased risk for AD. Conclusions: Serine/threonine-protein kinase involved in autophagy in response to starvation. Acts upstream of phosphatidylinositol 3-kinase PIK3C3 to regulate the formation of autophagophores, the precursors of autophagosomes. Part of regulatory feedback loops in autophagy: acts both as a downstream effector and negative regulator of mammalian target of rapamycin complex 1 (mTORC1) via interaction with RPTOR. Activated via phosphorylation by AMPK and also acts as a regulator of AMPK by mediating phosphorylation of AMPK subunits PRKAA1, PRKAB2, and PRKAG1, leading to negatively regulate AMPK activity. May phosphorylate ATG13/KIAA0652 and RPTOR; however such data need additional evidences. Plays a role early in neuronal differentiation and is required for granule cell axon formation. Alzheimer is the most common neurodegenerative disease. Our results provide useful information that the ULK1 gene expression is decreased in the neurodegeneration and AD patients with, indicating their possible systemic involvement in AD.

Keywords: Alzheimer’s sisease, ULK1, mRNA expression, RT-PCR

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Authors: Huafeng Wei

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Failures of developing new drugs primarily based on the amyloid pathology hypothesis after decades of efforts internationally lead to changes of focus targeting alternative pathways of pathology in Alzheimer’s disease (AD). Disruption of intracellular Ca2+ homeostasis, especially the pathological and excessive Ca2+ release from the endoplasmic reticulum (ER) via ryanodine receptor (RyRs) Ca2+ channels, has been considered an upstream pathology resulting in major AD pathologies, such as amyloid and Tau pathology, mitochondria damage and inflammation, etc. Therefore, dantrolene, an inhibitor of RyRs that reduces the pathological Ca2+ release from ER and a clinically available drug for the treatment of malignant hyperthermia and muscle spasm, is expected to ameliorate AD multiple pathologies synapse and cognitive dysfunction. Our own studies indicated that dantrolene ameliorated impairment of neurogenesis and synaptogenesis in neurons developed from induced pluripotent stem cells (iPSCs) originated from skin fibroblasts of either familiar (FAD) or sporadic (SAD) AD by restoring intracellular Ca2+ homeostasis. Intranasal administration of dantrolene significantly increased its passage across the blood-brain barrier (BBB) and, therefore its brain concentrations and durations. This can render dantrolene a more effective therapeutic drug with fewer side effects for chronic AD treatment. This review summarizes the potential therapeutic and side effects of dantrolene and repurposes intranasal dantrolene as a disease-modifying drug for future AD treatment.

Keywords: Alzheimer's disease, calcium, drug development, dementia, neurodegeneration, neurogenesis

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Authors: Francesca La Rosa , Marina Saresella, Mario Clerici, Michael Heneka

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Neuroinflammation plays a key role in the modulation of the pathogenesis of neurodegenerative disorder such as Alzheimer's Disease (AD). Microglia, the main immune effector of the brain, are able to migrate to sites of Amyloid-beta (Aβ) deposition to eliminate Aβ phagocytosis upon activation by multiple receptors: Toll like receptors and scavenger receptors. The issue of whether microglia are able to eliminate pathological lesions such as neurofibrillary tangles or senile plaques from AD brain still remains the matter of controversy. Recent data suggest that the Nod Like Receptor 3 (NLRP3), multiprotein inflammasome complexes, plays a role in AD, as its activation in the microglia by Aβ triggers. IL-1β is produced as a biologically inactive pro-form and requires caspase-1 for activation and secretion. Caspase-1 activity is controlled by inflammasomes. We investigate about the importance of inflammasomes complex in the Aβ phagocytosis and its degradation. The preliminary results of phagocytosis assay and immunofluorescent experiment on primary Microglia cells to lipopolysaccharide (LPS) an Aβ exposure show that a previous treatment with LPS reduce Aβ phagocytosis. Different results were obtained in Primary Microglia wild type, NLRP3 and ASC Knockout suggesting a real inflammasomes involvement in Alzheimer's pathology. Inflammasomes inactivation reduces the production of inflammatory cytokines prolonging the protective activity of microglia and Aβ clearance, featuring a typical microglia phenotype of the early stage of AD disease.

Keywords: Alzheimer disease, innate immunity, neuroinflammation, NLRP3

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Authors: M. A. Alavianmehr, A. Tashk, A. Sodagaran

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Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method.

Keywords: image processing, background models, video surveillance, foreground detection, Gaussian mixture model

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Authors: Luigi Pari, Alessandro Suardi, Enrico Santangelo

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In the last decade Jatropha curcas L. (an oleaginous crop native to Central America and part of South America) has raised particular interest owing to of its properties and uses. Its capsules may contain up to 40% in oil and can be used as feedstock for biodiesel production. The harvesting phase is made difficult by the physiological traits of the specie, because fruits are in bunches and do not ripen simultaneously. Three harvesting methodologies are currently diffused and differ for the level of mechanization applied: manual picking, semi-mechanical harvesting, and mechanical harvesting. The manual picking is the most common in the developing countries but it is also the most time consuming and inefficient. Mechanical harvesting carried out with modified grape harvesters has the higher productivity, but it is very costly as initial investment and requires appropriate schemes of cultivation. The semi-mechanical harvesting method is achieved with shaker tools employed to facilitate the fruit detachment. This system resulted much cheaper than the fully mechanized one and quite flexible for small and medium scale applications, but it still requires adjustments for improving the productive performance. CRA-ING, within the European project Jatromed (http://www.jatromed.aua.gr) has carried out preliminary studies on the applicability of such approach, adapting an olive shaker to harvest Jatropha fruits. The work is a survey of the harvesting methods currently available for Jatropha, show the pros and cons of each system, and highlighting the criteria to be considered for choosing one respect another. The harvesting of Jatropha curcas L. remains a big constrains for the spread of the species as energy crop. The approach pursued by CRA-ING can be considered a good compromise between the fully mechanized harvesters and the exclusive manual intervention. It is an attempt to promote a sustainable mechanization suited to the social context of developing countries by encouraging the concrete involvement of local populations.

Keywords: jatropha curcas, energy crop, harvesting, central america, south america

Procedia PDF Downloads 388

Authors: Ming-Hwi Yao, Su-Szu Yang

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Typhoons are one of the major types of disasters that affect Taiwan each year and that cause severe damage to agriculture. Indeed, the damage exacted during a typical typhoon season can be up to $1 billion, and is responsible for nearly 75% of yearly agricultural losses. However, there is no consensus on how to reduce the damage caused by the strong winds and heavy precipitation engendered by typhoons. One suggestion is the use of windbreak nets, which are a low-cost and easy-to-use disaster mitigation strategy for crop production. In the present study, we conducted an evaluation to determine the optimal conditions of a windbreak net by using a computational fluid dynamics (CFD) model. This model may be used as a reference for crop protection. The results showed that CFD simulation validated windbreak nets of different mesh sizes and heights in the experimental area; thus, CFD is an efficient tool for evaluating the effectiveness of windbreak nets. Specifically, the effective wind protection length and height were found to be 6 and 1.3 times the length and height of the windbreak net, respectively. During a real typhoon, maximum wind gusts of 18 m s-1 can be reduced to 4 m s-1 by using a windbreak net that has a 70% blocking rate. In short, windbreak nets are significantly effective in protecting typhoon-affected areas.

Keywords: computational fluid dynamics, disaster, typhoon, windbreak net

Procedia PDF Downloads 191

Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

Procedia PDF Downloads 130

Authors: H. C. Chen, S. W. C. Chan, K. Cheng, A. Vathsala, H. K. Sran, H. He

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Background: End-stage renal disease (ESRD) is the last stage of chronic kidney disease. The numbers of patients with ESRD have increased worldwide due to the growing number of aging, diabetes and hypertension populations. Patients with ESRD suffer from physical illness and psychological distress due to complex treatment regimens, which often affect the patients’ social and psychological functioning. As a result, the patients may fail to perform daily self-care and self-management, and consequently experience worsening conditions. Aims: The study aims to examine the effectiveness of a self-efficacy psychoeducational programme on primary outcome (self-efficacy) and secondary outcomes (psychological wellbeing, treatment adherence, and quality of life) in patients with ESRD and haemodialysis in Singapore. Methodology: A randomised controlled, two-group pretest and repeated posttests design will be carried out. A total of 154 participants (n=154) will be recruited. The participants in the control group will receive a routine treatment. The participants in the intervention group will receive a self-efficacy psychoeducational programme in addition to the routine treatment. The programme is a two-session of educational intervention in a week. A booklet, two consecutive sessions of face-to-face individual education, and an abdominal breathing exercise are adopted in the programme. Outcome measurements include Dialysis Specific Self-efficacy Scale, Kidney Disease Quality of Life- 36 Hospital Anxiety and Depression Scale, Renal Adherence Attitudes Questionnaire and Renal Adherence Behaviour Questionnaire. The questionnaires will be used to measure at baseline, 1- and 3- and 6-month follow-up periods. Process evaluation will be conducted with a semi-structured face to face interview. Quantitative data will be analysed using SPSS21.0 software. Qualitative data will be analysed by content analysis. Significance of the study: This study will identify a clinically useful and potentially effective approach to help patients with end-stage renal disease and haemodialysis by enhancing their self-efficacy in self-care behaviour, and therefore improving their psychological well-being, treatment adherence and quality of life. This study will provide information to develop clinical guidelines to improve patients’ disease self-management and to enhance health-related outcomes and it will help reducing disease burden.

Keywords: end-stage renal disease (ESRD), haemodialysis, psychoeducation, self-efficacy

Procedia PDF Downloads 320

Authors: Zeynep R. Ege, Aydin Akan, Faik N. Oktar, Betul Karademir, Oguzhan Gunduz

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Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.

Keywords: chitosan, coaxial electrospinning, controlled releasing, drug delivery, indocyanine green, polyethylene oxide

Procedia PDF Downloads 169

Authors: Comsun Thongchai, Vorapoj Promasatayaprot

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Background: Child development center is a day care center that gathers large numbers of children in the same areas. As a result, it provides high opportunity for infection, especially gastrointestinal and respiratory infections. Ubon Ratchathani has been a province with an increasing number of cases of Hand foot and mouth disease each year reported between 2014 and 2016. Accorded to a recent investigation reported, HFMD occurred in the Child Development Center and kindergartens, this was a place where HFMD spreads. This research was aimed to investigate the knowledge, attitude and behavior about hand foot and mouth disease preventing of child caregivers in child development centers, Ubon Ratchathani Province. Method: Descriptive study was conducted between April and July, 2017. The study instruments used questionnaires and in-depth interviews on their practices of prevention and environment management of HFMD. The samples of survey questionnaires were caregivers who are working in 160 child development centers of the 160 parishes in Ubon Ratchathani province. The data was analyzed by percentages, means and standard deviations and Pearson Product Moment Correlation Coefficient. Result: The results showed that the majority were female (96.3%), average age 41 years (68.3%), marital status were couples (85.7%) and studied in undergraduate (75.2%). with a period of performance as teachers in child development centers range from 10 to 14 years were percentage 58.7 and 71.8 percent of them had been trained by health worker about the control HFMD. The knowledge for preventive in hand foot mouth disease on child caregivers was at high level. The mean score was 2.76 (S.D. = 0.114). The attitude of child caregivers was at a moderate level. Its mean score was 2.28 (S.D. = 0.247). On the other hand, the level of environmental management to prevent HFMD was low. The mean score was 1.34 (S.D. = 0.215). The factor of personal characteristics as gender, age, educational level, duration at work, knowledge and attitude of preventive HFMD was associated with Preventive of Behaviors to a statistically significant level (p<0.05 respectively). Conclusion: These results should be concerned to develop knowledge and improving practice for preventive hand foot mouth disease of child caregivers in child development centers by training. Preparation of media education, Surveillance of hand foot mouth disease and health behaviors promotion with community participation need to be supported continuously.

Keywords: preventive behavior, child development center, hand foot mouth disease, Thailand

Procedia PDF Downloads 196

Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

Procedia PDF Downloads 125