Search results for: multiple detection

Authors: Nouf Alharbi

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The electrospray deposition technique became an important method that enables fragile, nonvolatile molecules to be deposited in situ in high vacuum environments. Furthermore, it is considered one of the ways to close the gap between basic surface science and molecular engineering, which represents a gradual change in the range of scientist research. Also, this paper talked about one of the most important techniques that have been developed and aimed for helping to further develop and characterize the electrospray by providing data collected using an image charge detection instrument. Image charge detection mass spectrometry (CDMS) is used to measure speed and charge distributions of the molecular ions. As well as, some data has been included using SIMION simulation to simulate the energies and masses of the molecular ions through the system in order to refine the mass-selection process.

Keywords: charge, deposition, electrospray, image, ions, molecules, SIMION

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Authors: Vinod Ponnagandla, Brenton McLaury, Siamack Shirazi

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Sand production can lead to deposition of particles or erosion. Low production rates resulting in deposition can partially clog systems and cause under deposit corrosion. Commercially available nonintrusive acoustic sand detectors are attractive as they claim to detect sand production. Acoustic sand detectors are used during oil and gas production; however, operators often do not know the threshold detection limits of these devices. It is imperative to know the detection limits to appropriately plan for cleaning of separation equipment or examine risk of erosion. These monitors are based on detecting the acoustic signature of sand as the particles impact the pipe walls. The objective of this work is to determine threshold detection limits for acoustic sand monitors that are commercially available. The minimum threshold sand concentration that can be detected in a pipe are determined as a function of flowing gas and liquid velocities. A large scale flow loop with a 4-inch test section is utilized. Commercially available sand monitors (ClampOn and Roxar) are evaluated for different flow regimes, sand sizes and pipe orientation (vertical and horizontal). The manufacturers’ recommend that the monitors be placed on a bend to maximize the number of particle impacts, so results are shown for monitors placed at 45 and 90 degree positions in a bend. Acoustic sand monitors that clamp to the outside of pipe are passive and listen for solid particle impact noise. The threshold sand rate is calculated by eliminating the background noise created by the flow of gas and liquid in the pipe for various flow regimes that are generated in horizontal and vertical test sections. The average sand sizes examined are 150 and 300 microns. For stratified and bubbly flows the threshold sand rates are much higher than other flow regimes such as slug and annular flow regimes that are investigated. However, the background noise generated by slug flow regime is very high and cause a high uncertainty in detection limits. The threshold sand rates for annular flow and dry gas conditions are the lowest because of high gas velocities. The effects of monitor placement around elbows that are in vertical and horizontal pipes are also examined for 150 micron. The results show that the threshold sand rates that are detected in vertical orientation are generally lower for all various flow regimes that are investigated.

Keywords: acoustic monitor, sand, multiphase flow, threshold

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Yongjian Du, Jinhua Sun, Kim M. Liew, Huahua Xiao

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Optimal allocation of emergency resources before a disaster is of great importance for emergency response. In reality, the pre-protection for a single critical node where accidents may occur is common. In this study, a model is developed to determine location and inventory decisions of multiple emergency resources among a set of candidate stations to minimize the total cost based on the constraints of budgetary and capacity. The total cost includes the economic accident loss which is accorded with probability distribution of time and the warehousing cost of resources which is increasing over time. A ratio is set to measure the degree of a storage station only serving the target node that becomes larger with the decrease of the distance between them. For the application of linear program, it is assumed that the length of travel time to the accident scene of emergency resources has a linear relationship with the economic accident loss. A computational experiment is conducted to illustrate how the proposed model works, and the results indicate its effectiveness and practicability.

Keywords: emergency response, integer linear program, multiple emergency resources, pre-allocation decisions, single potential accident node

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Authors: Muhammad Fezan Afzal, Asad Abbas, Imran Khan, Salma Imtiaz

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Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL.

Keywords: software product line, feature model, variability management, multi-SPLs

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Authors: Y. de Smet, J. Hubinont

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PROMETHEE and GAIA are well-known Multiple Criteria Decision Aid methods. Given an evaluation table and preference parameters they allow to rank the alternatives, to visualize the problem, to perform sensitivity and robustness analysis, etc. Unfortunately, it is often hard for the Decision Maker (DM) to estimate the precise values of these parameters. Therefore an alternative option is to give ranges of potential values in order to apply Stochastic Multicriteria Acceptability Analysis. This has been recently studied in the context of the SMAA-PROMETHEE method. The aim of this contribution is to propose an SMAA extension of GAIA. We show how this tool can be useful and provide complementary information to SMAA-PROMETHEE. This is illustrated on a pedagogical example.

Keywords: multiple criteria decision making, PROMETHEE, GAIA, SMAA

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Authors: N. Das, N. Samanta, L. Pandey, C. Roy Chaudhuri

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Early diagnosis of infection like Hep-B virus in blood is important for low cost medical treatment. For this purpose, it is desirable to develop a point of care device which should be able to detect trace quantities of the target molecule in blood. In this paper, we report a nanoporous silicon oxide sensor which is capable of detecting down to 1fM concentration of Hep-B surface antigen in blood without the requirement of any centrifuge or pre-concentration. This has been made possible by the presence of resonant peak in the sensitivity characteristics. This peak is observed to be dependent only on the concentration of the specific antigen and not on the interfering species in blood serum. The occurrence of opposite impedance change within the pores and at the bottom of the pore is responsible for this effect. An electronic interface has also been designed to provide a display of the virus concentration.

Keywords: impedance spectroscopy, ultrasensitive detection in blood, peak frequency, electronic interface

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Authors: Benazzouz Djamel, Termeche Adel, Touati Youcef, Alem Said, Ouziala Mahdi

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This paper presents an approach which detect and isolate efficiently a fault in a system. This approach avoids false alarms, non-detections and delays in detecting faults. A study case have been proposed to show the importance of taking into consideration the uncertainties in the decision-making procedure and their effect on the degradation diagnostic performance and advantage of using Bond Graph (BG) for such degradation. The use of BG in the Linear Fractional Transformation (LFT) form allows generating robust Analytical Redundancy Relations (ARR’s), where the uncertain part of ARR’s is used to generate the residuals adaptive thresholds. The study case concerns an electromechanical system composed of a motor, a reducer and an external load. The aim of this application is to show the effectiveness of the BG-LFT approach to robust fault detection.

Keywords: bond graph, LFT, uncertainties, detection and faults isolation, ARR

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Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

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Authors: Priyal Chikhaliwala, Sudeshna Chandra

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Liver cancer is one of the most common malignant tumors with poor prognosis. This is because liver cancer does not exhibit any symptoms in early stage of disease. Increased serum level of AFP is clinically considered as a diagnostic marker for liver malignancy. The present diagnostic modalities include various types of immunoassays, radiological studies, and biopsy. However, these tests undergo slow response times, require significant sample volumes, achieve limited sensitivity and ultimately become expensive and burdensome to patients. Considering all these aspects, electrochemical biosensors based on dendrimer-magnetic nanoparticles (MNPs) was designed. Dendrimers are novel nano-sized, three-dimensional molecules with monodispersed structures. Poly-amidoamine (PAMAM) dendrimers with eight –NH₂ groups using ethylenediamine as a core molecule were synthesized using Michael addition reaction. Dendrimers provide added the advantage of not only stabilizing Fe₃O₄ NPs but also displays capability of performing multiple electron redox events and binding multiple biological ligands to its dendritic end-surface. Fe₃O₄ NPs due to its superparamagnetic behavior can be exploited for magneto-separation process. Fe₃O₄ NPs were stabilized with PAMAM dendrimer by in situ co-precipitation method. The surface coating was examined by FT-IR, XRD, VSM, and TGA analysis. Electrochemical behavior and kinetic studies were evaluated using CV which revealed that the dendrimer-Fe₃O₄ NPs can be looked upon as electrochemically active materials. Electrochemical immunosensor was designed by immobilizing anti-AFP onto dendrimer-MNPs by gluteraldehyde conjugation reaction. The bioconjugates were then incubated with AFP antigen. The immunosensor was characterized electrochemically indicating successful immuno-binding events. The binding events were also further studied using magnetic particle imaging (MPI) which is a novel imaging modality in which Fe₃O₄ NPs are used as tracer molecules with positive contrast. Multicolor MPI was able to clearly localize AFP antigen and antibody and its binding successfully. Results demonstrate immense potential in terms of biosensing and enabling MPI of AFP in clinical diagnosis.

Keywords: alpha-fetoprotein, dendrimers, electrochemical biosensors, magnetic nanoparticles

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Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

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Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

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This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

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Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks

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Authors: Sabiu Bala Muhammad, Rosli Saad

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Multiple linear regression (MLR) models for fast estimation of true subsurface resistivity from apparent resistivity field measurements are developed and assessed in this study. The parameters investigated were apparent resistivity (ρₐ), horizontal location (X) and depth (Z) of measurement as the independent variables; and true resistivity (ρₜ) as the dependent variable. To achieve linearity in both resistivity variables, datasets were first transformed into logarithmic domain following diagnostic checks of normality of the dependent variable and heteroscedasticity to ensure accurate models. Four MLR models were developed based on hierarchical combination of the independent variables. The generated MLR coefficients were applied to another data set to estimate ρₜ values for validation. Contours of the estimated ρₜ values were plotted and compared to the observed data plots at the colour scale and blanking for visual assessment. The accuracy of the models was assessed using coefficient of determination (R²), standard error (SE) and weighted mean absolute percentage error (wMAPE). It is concluded that the MLR models can estimate ρₜ for with high level of accuracy.

Keywords: apparent resistivity, depth, horizontal location, multiple linear regression, true resistivity

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Siwimol Phoomniyom, Pathom Karaipoom, Rossaphorn Kittyaowaman

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Background: C. trachomatis is the most common bacterial sexually transmitted infections. Although infection with C. trachomatis can be treated with antibiotic, it is frequently asymptomatic, especially in extragenital sites. Hence, if screening tests are not performed, undetected and untreated is a crucial problem for C. trachomatis infection, especially in Thailand, which is less well studied. We sought to assess the prevalence of C. trachomatis infection in multiple anatomical sites among patients attending Bangrak STIs Center. Methods: We examined laboratory results of all patients at baseline visit from 3 January 2018 to 27 December 2019. These results were tested by a validated in-house real time PCR specify for the cryptic plasmid gene of C. trachomatis. The prevalence of C. trachomatis was analyzed by anatomical sites, sexes, and ages. Urogenital samples were obtained from urethral swab of men and cervical swab of women. The median ages of the patients were 32 years (range 13-89 years). Chi-square test by IBM SPSS statistic version 20 was used to assess difference in the distribution of variables between groups. Results: Among 3,789 patients, the prevalence for C. trachomatis infection was the highest in rectal (16.1%), followed by urogenital (11.2%) and pharyngeal (3.5%) sites. Rectal and urogenital infection in men was higher than in women, with the highest prevalence of 16.6% in rectal site. Both rectal and urogenital sites also showed statistically significant differences between sexes (P<0.001). Meanwhile, pharyngeal C. trachomatis infection rate was higher in women than men. Interestingly, the chlamydia prevalence was the highest in age 13-19 years of all three sites (18.5%, urogenital; 17.7%, rectal; 6.5%, pharyngeal), with statistically significant difference between age groups (P<0.001). Total of 45 C. trachomatis infections, 20.0%, 51.1%, and 6.7% were isolated from urogenital, rectal, and pharyngeal sites. In total, 75.6%, 26.7%, and 80.0% of chlamydia infections would have been missed, if only urogenital, rectal, or pharyngeal screening was performed. Conclusions: The highest source of C. trachomatis infection was the rectal site. While, the highest prevalence in men was at rectal site, that in women was at urogenital site. The highest chlamydia prevalence was found in adolescent age group, indicating that the pediatric population was a high-risk group. This finding also elucidated that a high proportion of C. trachomatis infection would be missed, if only single anatomical site screening was performed, especially in extragenital sites. Hence, extragenital screening is also required for the extensive C. trachomatis detection.

Keywords: chlamydia trachomatis, anatomical sites, sexes, ages

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Authors: O. Hassoon, M. Tarfoui, A. El Malk

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Fiber Bragg optic sensor embedded in composite material to detect and monitor the damage which is occur in composite structure. In this paper we deal with the mode-Ι delamination to determine the resistance of material to crack propagation, and use the coupling mode theory and T-matrix method to simulating the FBGs spectrum for both uniform and non-uniform strain distribution. The double cantilever beam test which is modeling in FEM to determine the Longitudinal strain, there are two models which are used, the first is the global half model, and the second the sub-model to represent the FBGs with refine mesh. This method can simulate the damage in the composite structure and converting the strain to wavelength shifting of the FBG spectrum.

Keywords: fiber bragg grating, delamination detection, DCB, FBG spectrum, structure health monitoring

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Authors: Chang Ting, Wu Chun Kuan

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Wireless sensing technology is increasingly developed, in order to avoid caregiver neglect children in poor physiological condition, so there are more and more products into the wireless sensor-related technologies, in order to reduce the risk of infants. In view of this, the study will focus on Somatosensory detection wristbands Applied Research of Baby, and to explore through observation and literature, to find design criteria which conform baby products, as well as the advantages and disadvantages of existing products. This study will focus on 0-2 years of infant research and product design, to provide 2-3 new design concepts and products to identify weaknesses through the use of the actual product, further provide future baby wristbands design reference.

Keywords: infants, observation, design criteria, wireless sensing

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Authors: Shahram Jamali, Samira Hamed

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One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

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Authors: Hua Cao, Laurent Peyrodie, Olivier Agnani, Cecile Donze

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Multiple sclerosis (MS) is a disease, which affects the central nervous system, and causes balance problem. In clinical, this disorder is usually evaluated using static posturography. Some linear or nonlinear measures, extracted from the posturographic data (i.e. center of pressure, COP) recorded during a balance test, has been used to analyze postural control of MS patients. In this study, the trend (TREND) and the sample entropy (SampEn), two nonlinear parameters were chosen to investigate their relationships with the expanded disability status scale (EDSS) score. Forty volunteers with different EDSS scores participated in our experiments with eyes open (EO) and closed (EC). TREND and two types of SampEn (SampEn1 and SampEn2) were calculated for each combined COP’s position signal. The results have shown that TREND had a weak negative correlation to EDSS while SampEn2 had a strong positive correlation to EDSS. Compared to TREND and SampEn1, SampEn2 showed a better significant correlation to EDSS and an ability to discriminate the MS patients in the EC case. In addition, the outcome of the study suggests that the multi-dimensional nonlinear analysis could provide some information about the impact of disability progression in MS on dynamics of the COP data.

Keywords: balance, multiple sclerosis, nonlinear analysis, postural sway

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Authors: Byung-Ik Kim, Hong-Koo Kang, Tae-Jin Lee, Hae-Ryong Park

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Cyber terrors against specific enterprises or countries have been increasing recently. Such attacks against specific targets are called advanced persistent threat (APT), and they are giving rise to serious social problems. The malicious behaviors of APT attacks mostly affect websites and penetrate enterprise networks to perform malevolent acts. Although many enterprises invest heavily in security to defend against such APT threats, they recognize the APT attacks only after the latter are already in action. This paper discusses the characteristics of APT attacks at each step as well as the strengths and weaknesses of existing malicious code detection technologies to check their suitability for detecting APT attacks. It then proposes a network-based malicious behavior detection algorithm to protect the enterprise or national networks.

Keywords: Advanced Persistent Threat (APT), malware, network security, network packet, exploit kits

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Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

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Authors: Majid Pourahmadi

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The performance of time-reversal MUSIC algorithm will be dramatically degrades in presence of strong noise and multiple scattering (i.e. when scatterers are close to each other). This is due to error in determining the number of scatterers. The present paper provides a new approach to alleviate such a problem using an information theoretic criterion referred as minimum description length (MDL). The merits of the novel approach are confirmed by the numerical examples. The results indicate the time-reversal MUSIC yields accurate estimate of the target locations with considerable noise and multiple scattering in the received signals.

Keywords: microwave imaging, time reversal, MUSIC algorithm, minimum description length (MDL)

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Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Abolfazl Hosseinkhani, Sepehr Sanaye

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Different approaches have been used to predict the performance of the vertical axis wind turbines (VAWT), such as experimental, computational fluid dynamics (CFD), and analytical methods. Analytical methods, such as momentum models that use streamtubes, have low computational cost and sufficient accuracy. The double multiple streamtube (DMST) is one of the most commonly used of momentum models, which divide the rotor plane of VAWT into upwind and downwind. In fact, results from the DMST method have shown some discrepancy compared with experiment results; that is because the Darrieus turbine is a complex and aerodynamically unsteady configuration. In this study, analytical-experimental-based corrections, including dynamic stall, streamtube expansion, and finite blade length correction are used to improve the DMST method. Results indicated that using these corrections for a SANDIA 17-m VAWT will lead to improving the results of DMST.

Keywords: vertical axis wind turbine, analytical, double multiple streamtube, streamtube expansion model, dynamic stall model, finite blade length correction

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Y. T. Gebreslassie, Abrha Tadesse, R. C. Saini, Rishi Pal

Abstract:

Caffeine, known chemically as 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione, is a naturally occurring alkaloid classified as an N-methyl derivative of xanthine. Given its widespread use in coffee and other caffeine-containing products, it is the most commonly consumed psychoactive substance in everyday human life. This research aimed to develop a cost-effective, sensitive, and easily manufacturable sensor for the detection of caffeine. Antraquinone-modified carbon paste electrode (AQMCPE) was fabricated, and the electrochemical behavior of caffeine on this electrode was investigated using cyclic voltammetry (CV) and square wave voltammetry (SWV) in a solution of 0.1M perchloric acid at pH 0.56. The modified electrode displayed enhanced electrocatalytic activity towards caffeine oxidation, exhibiting a two-fold increase in peak current and an 82 mV shift of the peak potential in the negative direction compared to an unmodified carbon paste electrode (UMCPE). Exploiting the electrocatalytic properties of the modified electrode, SWV was employed for the quantitative determination of caffeine. Under optimized experimental conditions, a linear relationship between peak current and concentration was observed within the range of 2.0 x 10⁻⁶ to 1.0× 10⁻⁴ M, with a correlation coefficient of 0.998 and a detection limit of 1.47× 10⁻⁷ M (signal-to-noise ratio = 3). Finally, the proposed method was successfully applied to the quantitative analysis of caffeine in pharmaceutical formulations, yielding recovery percentages ranging from 95.27% to 106.75%.

Keywords: antraquinone-modified carbon paste electrode, caffeine, detection, electrochemical sensor, quantitative analysis

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Sina Mahdavi

Abstract:

Background and Objective: Multiple sclerosis (MS) is the most common inflammatory autoimmune disease of the central nervous system (CNS) that affects the myelination process in the CNS. Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially Epstein-Barr virus (EBV) and MS, is one potential cause that is not well understood. In this study, we aim to summarize the available data on EBV infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis," "Epstein-Barr virus," and "central nervous system" in the databases PubMed, Google Scholar, Sid, and MagIran between 2016 and 2022 were searched, and 14 articles were chosen, studied, and analyzed. Results: Demyelinated lesions isolated from MS patients contain EBNAs from EBV proteins. The EBNA1 domain contains a pentapeptide fragment identical to B-crystallin, a heat shock peptide, that is increased in peripheral B cells in response to B-crystallin infection, resulting in myelin-directed autoimmunity mediated by proinflammatory T cells. EBNA2, which is involved in the regulation of viral transcription, may enhance transcription from MS risk loci. A 7-fold increase in the risk of MS has been observed in EBV infection with HLA-DR15 synergy. Conclusion: EBV infection along with a variety of specific genetic risk alleles, cause inflammatory cascades in the CNS by infected B cells. There is a high expression of EBV during the course of MS, which indicates the relationship between EBV and MS, that this virus can play a role in the development of MS by creating an inflammatory state. Therefore, measures to modulate the expression of EBV may be effective in reducing inflammatory processes in demyelinated areas of MS patients.

Keywords: multiple sclerosis, Epstein-Barr virus, central nervous system, EBNAs

Procedia PDF Downloads 94