Search results for: speed hump detection

Authors: Carmen E. Zapata, Juan Bautista, Olga Montoya, Claudia Moreno, Marisol Suarez, Alejandra Betancur, Duvan Nanclares, Natalia A. Cano

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This study aims to evaluate the diversity of microorganisms in filters PM2.5 and PM10; and determine the genotoxic and cytotoxic activity of the complex mixture present in PM2.5 filters used in the Aburrá Valley Air Quality Monitoring Network (Colombia). The research results indicate that particulate matter PM2.5 of different monitoring stations are bacteria; however, this study of detection of bacteria and their phylogenetic relationship is not complete evidence to connect the microorganisms with pathogenic or degrading activities of compounds present in the air. Additionally, it was demonstrated the damage induced by the particulate material in the cell membrane, lysosomal and endosomal membrane and in the mitochondrial metabolism; this damage was independent of the PM2.5 concentrations in almost all the cases.

Keywords: cytotoxic, genotoxic, microbiological analysis, PM10, PM2.5

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Authors: Amal Raqib Shameran, Ghanim Aboud Al-Mola

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Respiratory syncytial virus (hRSV) is the major pathogens of respiratory tract infections (RTI) among infants and children in the world. They are classified in family Paramyxoviridae and sub-family Pneumovirinae. The current work aimed to detect the role of RSV in the lower respiratory tract infection (LRTI) in Hilla, Iraq. The samples were collected from 50 children who were admitted to hospital suffering from lower respiratory tract infections (LRTI). 50 nasal and pharyngeal swabs were taken from patients at the period from January 2010 till April 2011, hospitalized in Hilla Maternity and Children Hospital. The results showed that the proportion of children infected with hRSV accounted for 24% 12/50 with lower respiratory tract infections (LRTI) when they tested by polymerase chain reaction (RT-PCR).

Keywords: respiratory syncytial virus, respiratory tract infections, infants, polymerase chain reaction (PCR)

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Authors: Edwin Cartledge, Jack Clark, Mazaher Molaei-Chalchooghi

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The mixing in the crystallization step of active pharmaceutical ingredient manufacturers was studied via advanced modeling tools to enable a successful scale-up. A virtual representation of the vessel was created, and computational fluid dynamics were used to simulate multiphase flow and, thus, the mixing environment within this vessel. The study identified a significant dead zone in the vessel underneath the impeller and found that increasing the impeller speed and power did not improve the mixing. A series of sensitivity analyses found that to improve mixing, the vessel had to be redesigned, and found that optimal mixing could be obtained by adding two extra cylindrical baffles. The same two baffles from the simulated environment were then constructed and added to the process vessel. By identifying these potential issues before starting the manufacture and modifying the vessel to ensure good mixing, this study mitigated a failed crystallization and potential batch disposal, which could have resulted in a significant loss of high-value material.

Keywords: active pharmaceutical ingredient, baffles, computational fluid dynamics, mixing, modelling

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Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

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Authors: Varsha Singh, Shraddha Prajapati, M. B. Kiran

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Texture identification is useful in predicting the functionality of a component. Many of the existing texture identification methods are of contact in nature, which limits its measuring speed. These contact measurement techniques use a diamond stylus and the diamond stylus being sharp going to damage the surface under inspection and hence these techniques can be used in statistical sampling. Though these contact methods are very accurate, they do not give complete information for full characterization of surface. In this context, the presented method assumes special significance. The method uses a relatively low cost vision system for image acquisition. Software is developed based on wavelet transform, for analyzing texture images. Specimens are made using different manufacturing process (shaping, grinding, milling etc.) During experimentation, the specimens are illuminated using proper lighting and texture images a capture using CCD camera connected to the vision system. The software installed in the vision system processes these images and subsequently identify the texture of manufacturing processes.

Keywords: diamond stylus, manufacturing process, texture identification, vision system

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Authors: Hao-Hsi Tseng, Hsin-Yun Lee

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The Most Advantageous Tender (MAT) has been criticized for its susceptibility to dictatorial situations and for its processing of same score, same rank issues. This study applies the four criteria from Arrow's Impossibility Theorem to construct a mechanism for revealing illegitimate scores in scoring methods. While commonly be used to improve on problems resulting from extreme scores, ranking methods hide significant defects, adversely affecting selection fairness. To address these shortcomings, this study relies mainly on the overall evaluated score method, using standardized scores plus normal cumulative distribution function conversion to calculate the evaluation of vender preference. This allows for free score evaluations, which reduces the influence of dictatorial behavior and avoiding same score, same rank issues. Large-scale simulations confirm that this method outperforms currently used methods using the Impossibility Theorem.

Keywords: Arrow’s impossibility theorem, cumulative normal distribution function, most advantageous tender, scoring method

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Authors: Makkhan Lal Meena, R. C. Bairwa, G. S. Dangayach, Rahul Jain

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The current study looked at the frequency of chronic illness conditions, accidents, health complaints, and ergonomic issues among 100 conventional and 100 greenhouse farmers. Data related to the health symptoms and ergonomic problems were collected through questionnaires by conducting direct interviews of farmers. According to the findings, symptoms of heat exposure (skin rashes, headache, dizziness, and lack of appetite) were substantially higher among conventional farmers than greenhouse farmers. The greenhouse farmers reported much more pain, numbness, or weakness in wrists/hands, fingers, upper back, hips, and ankles/feet than conventional farmers. The findings of the study suggest that suitable ergonomic knowledge and awareness campaign programs concentrating on safety at work, particularly low back pain, should be implemented in workplaces to allow for earlier detection of symptoms among the greenhouse farmers.

Keywords: accident, conventional farmer, ergonomics, health symptoms, greenhouse farmers, pesticide

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Authors: Naureen Akhtar

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The stability of the protein in detergent-containing solution is the key for its successful crystallisation. Fluorescence-detection size-exclusion chromatography (FSEC) is a potential approach for screening monodispersity as well as the stability of protein in a detergent-containing-solution. In this present study, covalently linked Green Fluorescent Protein (GFP) to bacterial nitrate transporter, NarU from Escherichia coli was studied for pre-crystallisation trials by FSEC. Immobilised metal ion affinity chromatography (IMAC) and gel filtration were employed for their purification. The main objectives of this study were over-expression, detergent screening and crystallisation of nitrate transporter proteins. This study could not produce enough proteins that could realistically be taken forward to achieve the objectives set for this particular research. In future work, different combinations of variables like vectors, tags, creation of mutant proteins, host cells, position of GFP (N- or C-terminal) and/or membrane proteins would be tried to determine the best combination as the principle of technique is still promising.

Keywords: transporters, detergents, over-expression, crystallography

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: J. Jasmee, I. Roslina, A. Mohammed Yaziz & A.H Juazer Rizal

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The need of an effective building information collection method is vital to support a diversity of land development activities. At present, advances in remote sensing such as airborne LiDAR (Light Detection and Ranging) is an established technology for building information collection, location, and elevation of the reflecting laser points towards the construction of 3D building models. In this study, LiDAR datasets and terrestrial photographic images of buildings towards the construction of 3D building models is explored. It is found that, the quantitative accuracy of the constructed 3D building model, namely in the horizontal and vertical components were ± 0.31m (RMSEx,y) and ± 0.145m (RMSEz) respectively. The accuracies were computed based on sixty nine (69) horizontal and twenty (20) vertical surveyed points. As for the qualitative assessment, it is shown that the appearance of the 3D building model is adequate to support the requirements of LOD3 presentation based on the OGC (Open Geospatial Consortium) standard CityGML.

Keywords: LiDAR datasets, DSM, DTM, 3D building models

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Paulo Cumbe

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This work analyzes the economic and environmental impact of the exploitation of forest resources on populations and their sustainability in the regions where it occurs. There is an intensive and continuous activity of charcoal production, in the Massingir and Mabalane districts, in Gaza, Mozambique, to supply the most used fuel that is used by the population of the capital city, Maputo. Charcoal production is one of the sources of income for several families. However, it causes a negative environmental impact on biodiversity. We have analyzed different studies carried out in these communities that measure the speed, the level, and the impact of deforestation involving different actors, to deepen our understanding of this issue. The results of these studies reveal that the degraded area in five years would need one hundred years to be restored, which is unsustainable from an environmental point of view it is. Populations seek new areas for the same practice to maintain their livelihood, progressing with ecosystem degradation and increasing carbon dioxide emissions into the atmosphere. It is believed that environmental education, creation, and dissemination of new forms of charcoal production that are more profitable and less aggressive to the environment and forest repopulation actions need to be carried out to guarantee the sustainable development of the populations in these regions.

Keywords: deforestation, emissions, sustainability, charcoal

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Authors: Sadia Nasrin Tisha, Mushfika Sharmin Rahman, Javier Orduz

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Machine learning is applied in a variety of fields throughout the world. The healthcare sector has benefited enormously from it. One of the most effective approaches for predicting human heart diseases is to use machine learning applications to classify data and predict the outcome as a classification. However, with the rapid advancement of quantum technology, quantum computing has emerged as a potential game-changer for many applications. Quantum algorithms have the potential to execute substantially faster than their classical equivalents, which can lead to significant improvements in computational performance and efficiency. In this study, we applied quantum machine learning concepts to predict coronary heart diseases from text data. We experimented thrice with three different features; and three feature sets. The data set consisted of 100 data points. We pursue to do a comparative analysis of the two approaches, highlighting the potential benefits of quantum machine learning for predicting heart diseases.

Keywords: quantum machine learning, SVM, QSVM, matrix product state

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Authors: Gaurav Malik, Satyendra Mourya, Jyoti Jaiswal, Ramesh Chandra

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The attractive and multifunctional properties of ZnO make it a promising material for the fabrication of highly sensitive and selective efficient gas sensors at room temperature. This presented article focuses on the development of highly selective and sensitive H₂ gas sensor based on the Pd-Pt decorated ZnO framework and its sensing mechanisms. The gas sensing performance of sputter made Pd-Pt/ZnO electrode on anodized porous silicon (PSi) substrate toward H₂ gas is studied under low detection limit (2–500 ppm) of H₂ in the air. The chemiresistive sensor demonstrated sublimate selectivity, good sensing response, and fast response/recovery time with excellent stability towards H₂ at low temperature operation under ambient environment. The elaborate selective measurement of Pd-Pt/ZnO/PSi structure was performed towards different oxidizing and reducing gases. This structure exhibited advance and reversible response to H₂ gas, which revealed that the acquired architecture with ZnO framework is a promising candidate for H₂ gas sensor.

Keywords: sputtering, porous silicon, ZnO framework, XPS spectra, gas sensor

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Authors: Hassan Mohammad Karimi, Ali Salehzade Nobari, Hosein Shahverdi

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With the advancement of technology and the decreasing weight of aerial structures, there is a growing demand for alternative energy sources. Structural vibrations can now be utilized to power low-power sensors for monitoring structural health and charging small batteries in drones. Research on extracting energy from flutter using piezoelectric has been extensive in recent years. This article specifically examines the use of a single-jointed beam with a free surface attached to its free end and a bimorph piezoelectric patch connected to the joint, providing two degrees of torsional and bending freedom. The study investigates the voltage harvested at various wind speeds and bending and twisting stiffness in a wind tunnel. The results indicate that as flutter speed increases, the output voltage also increases to some extent. However, at high wind speeds, the limited cycle created becomes unstable, negatively impacting the harvester's performance. These findings align with other research published in reputable scientific journals.

Keywords: energy harvesting, piezoelectric, flutter, wind tunnel

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Authors: Aaron J. Small, Craig A. Fletcher

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This study investigates the effect of closed circuit television (CCTV) image patch layout on performance of a simulated train-platform departure task. The within-subjects experimental design measures target detection rate and response latency during a CCTV visual search task conducted as part of the procedure for safe train dispatch. Three interface designs were developed by manipulating CCTV image patch layout. Eye movements, perceived workload and system usability were measured across experimental conditions. Task performance was compared to identify significant differences between conditions. The results of this study have not been determined.

Keywords: rail human factors, workload, closed circuit television, platform departure, attention, information processing, interface design

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Authors: Jihad S. Daba, J. P. Dubois

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Understanding the statistics of non-isotropic scattering multipath channels that fade randomly with respect to time, frequency, and space in a mobile environment is very crucial for the accurate detection of received signals in wireless and cellular communication systems. In this paper, we derive stochastic models for the probability density function (PDF) of the shift in the carrier frequency caused by the Doppler Effect on the received illuminating signal in the presence of a dominant line of sight. Our derivation is based on a generalized Clarke’s and a two-wave partially developed scattering models, where the statistical distribution of the frequency shift is shown to be consistent with the power spectral density of the Doppler shifted signal.

Keywords: Doppler shift, filtered Poisson process, generalized Clark’s model, non-isotropic scattering, partially developed scattering, Rician distribution

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Bing Li, Tong Lu, Lei Qiang

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Stoneley waves are interface waves that propagate at the interface between two solid media. In this study, the dispersion characteristics and wave structures of Stoneley waves in elastic multilayered plates are displayed and investigated. With a perspective of bulk wave, a reasonable assumption of the potential function forms of the expansion wave and shear wave in nth layer medium is adopted, and the characteristic equation of Stoneley waves in a three-layered plate is given in a determinant form. The dispersion curves and wave structures are solved and presented in both numerical and simulation results. It is observed that two Stoneley wave modes exist in a three-layered plate, that conspicuous dispersion occurs on low frequency band, that the velocity of each Stoneley wave mode approaches the corresponding Stoneley wave velocity at interface between two half infinite spaces. The wave structures reveal that the in-plane displacement of Stoneley waves are relatively high at interfaces, which shows great potential for interface defects detection.

Keywords: characteristic equation, interface waves, potential function, Stoneley waves, wave structure

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Authors: Abderrhamane Jarou, Dominique Sauter, Christophe Aubrun

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Over the past decade, the growing demand for energy efficiency in buildings has attracted the attention of the control community. Failures in HVAC (heating, ventilation and air conditioning) systems in buildings can have a significant impact on the desired and expected energy performance of buildings and on the user's comfort as well. FTC is a recent technology area that studies the adaptation of control algorithms to faulty operating conditions of a system. The application of Fault-Tolerant Control (FTC) in HVAC systems has gained attention in the last two decades. The objective is to maintain the variations in system performance due to faults within an acceptable range with respect to the desired nominal behavior. This paper considers the so-called active approach, which is based on fault and identification scheme combined with a control reconfiguration algorithm that consists in determining a new set of control parameters so that the reconfigured performance is "as close as possible, "in some sense, to the nominal performance. Thermal models of buildings and their HVAC systems are described by non-linear (usually bi-linear) equations. Most of the works carried out so far in FDI (fault diagnosis and isolation) or FTC consider a linearized model of the studied system. However, this model is only valid in a reduced range of variation. This study presents a new fault diagnosis (FD) algorithm based on a bilinear observer for the detection and accurate estimation of the magnitude of the HVAC system failure. The main contribution of the proposed FD algorithm is that instead of using specific linearized models, the algorithm inherits the structure of the actual bilinear model of the building thermal dynamics. As an immediate consequence, the algorithm is applicable to a wide range of unpredictable operating conditions, i.e., weather dynamics, outdoor air temperature, zone occupancy profile. A bilinear fault detection observer is proposed for a bilinear system with unknown inputs. The residual vector in the observer design is decoupled from the unknown inputs and, under certain conditions, is made sensitive to all faults. Sufficient conditions are given for the existence of the observer and results are given for the explicit computation of observer design matrices. Dedicated observer schemes (DOS) are considered for sensor FDI while unknown input bilinear observers are considered for actuator or system components FDI. The proposed strategy for FTC works as follows: At a first level, FDI algorithms are implemented, making it also possible to estimate the magnitude of the fault. Once the fault is detected, the fault estimation is then used to feed the second level and reconfigure the control low so that that expected performances are recovered. This paper is organized as follows. A general structure for fault-tolerant control of buildings is first presented and the building model under consideration is introduced. Then, the observer-based design for Fault Diagnosis of bilinear systems is studied. The FTC approach is developed in Section IV. Finally, a simulation example is given in Section V to illustrate the proposed method.

Keywords: bilinear systems, fault diagnosis, fault-tolerant control, multi-zones building

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Authors: Francesco Morgan Bono, Simone Cinquemani

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This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: A. Chateauneuf, M. Mostoufi, D. Vyncke

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Monte Carlo (MC) simulation is a technique that provides approximate solutions to a broad range of mathematical problems. A drawback of the method is its high computational cost, especially in a high-dimensional setting, such as estimating the Tail Value-at-Risk for large portfolios or pricing basket options and Asian options. For these types of problems, one can construct an upper bound in the convex order by replacing the copula by the comonotonic copula. This comonotonic upper bound can be computed very quickly, but it gives only a rough approximation. In this paper we introduce the Comonotonic Monte Carlo (CoMC) simulation, by using the comonotonic approximation as a control variate. The CoMC is of broad applicability and numerical results show a remarkable speed improvement. We illustrate the method for estimating Tail Value-at-Risk and pricing basket options and Asian options when the logreturns follow a Black-Scholes model or a variance gamma model.

Keywords: control variate Monte Carlo, comonotonicity, option pricing, scientific computing

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Authors: Sang Hun Park, Chul Gyu Song

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Artery and vein occlusion changes observed in patients and experimental animals are unexplainable symptoms. As the fat accumulated in cardiovascular ruptures, it causes vascular blocking. Likewise, early detection of cardiovascular disease can be useful for treatment. In this study, we used the mouse femoral occlusion model to observe the arterial and venous occlusion changes without darkroom. We observed the femoral arterial flow pattern changes by proposed fluorescent imaging system using an animal model of thrombosis. We adjusted the near-infrared light source current in order to control the intensity of the fluorescent substance light. We got the clear fluorescent images and femoral artery flow pattern were measured by a 5-minute interval. The result showed that the fluorescent substance flowing in the femoral arteries were accumulated in thrombus as time passed, and the fluorescence of other vessels gradually decreased.

Keywords: thrombus, fluorescence, femoral, arteries

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Authors: Vipan Kumar Sohpal

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Temperature effect on the adsorption of crystal violet dye (CVD) was investigated using a castor leaf powder (CLP) that was prepared from the mature leaves of castor trees, through chemical reaction. The optimum values of pH (8), adsorbent dose (10g/L), initial dye concentration (10g/L), time (2hrs), and stirrer speed (120 rpm) were fixed to investigate the influence of temperature on adsorption capacity, percentage of removal of dye and free energy. A central composite design (CCD) was successfully employed for experimental design and analysis of the results. The combined effect of temperature, absorbance, and concentration on the dye adsorption was studied and optimized using response surface methodology. The optimum values of adsorption capacity, percentage of removal of dye and free energy were found to be 0.965(mg/g), 93.38 %, -8202.7(J/mol) at temperature 55.97 °C having desirability > 90% for removal of crystal violet dye respectively. The experimental values were in good agreement with predicted values.

Keywords: crystal violet dye, CVD, castor leaf powder, CLP, response surface methodology, temperature, optimization

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Authors: Utkan Caliskan

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Two types of numerical codes based on finite volume method are developed in order to solve compressible Euler equations to simulate the flow through forward facing step channel. Both algorithms have AUSM+- up (Advection Upstream Splitting Method) scheme for flux splitting and two-stage Runge-Kutta scheme for time stepping. In this study, the flux calculations differentiate between the algorithm based on OpenFOAM mesh format which is called 'face-based' algorithm and the basic algorithm which is called 'element-based' algorithm. The face-based algorithm avoids redundant flux computations and also is more flexible with hybrid grids. Moreover, some of OpenFOAM’s preprocessing utilities can be used on the mesh. Parallelization of the face based algorithm for which atomic operations are needed due to the shared memory model, is also presented. For several mesh sizes, 2.13x speed up is obtained with face-based approach over the element-based approach.

Keywords: cell centered finite volume method, compressible Euler equations, OpenFOAM mesh format, OpenMP

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Authors: Abdulkadir Magaji

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This study analyzes and compares the actual measured and simulated performance of a 3.2 kwP grid-connected photovoltaic system. The system is located at the Outdoor Facility of Government Day secondary School Katsina State, which lies approximately between coordinate of 12°15′N 7°30′E. The system consists of 14 Mono crystalline silicon modules connected in two strings of 7 series-connected modules, each facing north at a fixed tilt of 340. The data presented in this study were measured in the year 2015, where the system supplied a total of 4628 kWh to the local electric utility grid. The performance of the system was simulated using PVsyst software using measured and Meteonorm derived climate data sets (solar radiation, ambient temperature and wind speed). The comparison between measured and simulated energy yield are discussed. Although, both simulation results were similar, better comparison between measured and predicted monthly energy yield is observed with simulation performed using measured weather data at the site. The measured performance ratio in the present study shows 58.4% is higher than those reported elsewhere as compared in the study.

Keywords: performance, evaluation, grid connection, photovoltaic system

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Authors: Ivana Loncarevic, Biljana Pajin, Ivana Vasiljevic, Milana Lazovic, Danica Mrkajic, Aleksandar Fises, Strahinja Kovacevic

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Biscuits belong to a group of flour-confectionery products that are considerably consumed worldwide. The basic raw material for their production is wheat flour or integral flour as a nutritionally highly valuable component. However, this raw material is also a potential source of contamination since it may contain the residues of biochemical compounds originating from plant and soil protection agents. Therefore, it is necessary to examine the health safety of both raw materials and final products. The aim of this research was to examine the content of undesirable residues of pesticides (mostly organochlorine pesticides, organophosphorus pesticides, carbamate pesticides, triazine pesticides, and pyrethroid pesticides) in 30 different biscuit samples of domestic origin present on the Vojvodina market using Gas Chromatograph Thermo ISQ/Trace 1300. The results showed that all tested samples had the limit of detection of pesticide content below 0.01 mg/kg, indicating that this type of confectionary products is not contaminated with pesticides.

Keywords: biscuits, pesticides, contamination, quality

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Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

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Authors: Xiaohui Wang, Zhichuan Guan, Roman Shor, Chuanbin Xu

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Early monitoring and real-time quantitative description of gas intrusion under the premise of ensuring the integrity of the drilling fluid circulation system will greatly improve the accuracy and effectiveness of deepwater gas-kick monitoring. Therefore, in order to study the propagation characteristics of ultrasonic waves in the gas-liquid two-phase flow within the marine riser, in this paper, a numerical simulation method of ultrasonic propagation in the annulus of the riser was established, and the credibility of the numerical analysis was verified by the experimental results of the established gas intrusion monitoring simulation experimental device. The numerical simulation can solve the sound field in the gas-liquid two-phase flow according to different physical models, and it is easier to realize the single factor control. The influence of each parameter on the received signal can be quantitatively investigated, and the law with practical guiding significance can be obtained.

Keywords: gas-kick detection, ultrasonic, void fraction, coda wave velocity

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