Search results for: estimation algorithm

Authors: Roger L. Goodwin

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This paper presents a rank correlation curve. The traditional correlation coefficient is valid for both continuous variables and for integer variables using rank statistics. Since the correlation coefficient has already been established in rank statistics by Spearman, such a calculation can be extended to the correlation curve. This paper presents two survey questions. The survey collected non-continuous variables. We will show weak to moderate correlation. Obviously, one question has a negative effect on the other. A review of the qualitative literature can answer which question and why. The rank correlation curve shows which collection of responses has a positive slope and which collection of responses has a negative slope. Such information is unavailable from the flat, "first-glance" correlation statistics.

Keywords: Bayesian estimation, regression model, rank statistics, correlation, correlation curve

Procedia PDF Downloads 460

Authors: Chalakorn Chitsaart, Suchada Rianmora, Noppawat Vongpiyasatit

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In order to reduce the transportation time and cost for direct interface between customer and manufacturer, the image processing technique has been introduced in this research where designing part and defining manufacturing process can be performed quickly. A3D virtual model is directly generated from a series of multi-view images of an object, and it can be modified, analyzed, and improved the structure, or function for the further implementations, such as computer-aided manufacturing (CAM). To estimate and quote the production cost, the user-friendly platform has been developed in this research where the appropriate manufacturing parameters and process detections have been identified and planned by CAM simulation.

Keywords: image processing technique, feature detections, surface registrations, capturing multi-view images, Production costs and Manufacturing processes

Procedia PDF Downloads 238

Authors: Vlad Andrei Raducanu, Mariana Lucia Angelescu, Ion Cinca, Vasile Danut Cojocaru, Doina Raducanu

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By using smart digital tools, such as generative design (GD) and digital fabrication (DF), problems of high actuality concerning resources optimization (materials, energy, time) can be solved and applications or products of free-form type can be created. In the new digital technology materials are active, designed in response to a set of performance requirements, which impose a total rethinking of old material practices. The article presents the design procedure key steps of a free-form architectural object - a column type one with connections to get an adaptive 3D surface, by using the parametric design methodology and by exploiting the properties of conventional metallic materials. In parametric design the form of the created object or space is shaped by varying the parameters values and relationships between the forms are described by mathematical equations. Digital parametric design is based on specific procedures, as shape grammars, Lindenmayer - systems, cellular automata, genetic algorithms or swarm intelligence, each of these procedures having limitations which make them applicable only in certain cases. In the paper the design process stages and the shape grammar type algorithm are presented. The generative design process relies on two basic principles: the modeling principle and the generative principle. The generative method is based on a form finding process, by creating many 3D spatial forms, using an algorithm conceived in order to apply its generating logic onto different input geometry. Once the algorithm is realized, it can be applied repeatedly to generate the geometry for a number of different input surfaces. The generated configurations are then analyzed through a technical or aesthetic selection criterion and finally the optimal solution is selected. Endless range of generative capacity of codes and algorithms used in digital design offers various conceptual possibilities and optimal solutions for both technical and environmental increasing demands of building industry and architecture. Constructions or spaces generated by parametric design can be specifically tuned, in order to meet certain technical or aesthetical requirements. The proposed approach has direct applicability in sustainable architecture, offering important potential economic advantages, a flexible design (which can be changed until the end of the design process) and unique geometric models of high performance.

Keywords: parametric design, algorithmic procedures, free-form architectural object, sustainable architecture

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

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

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

Procedia PDF Downloads 143

Authors: Mohsen Ehsanpour, Maryam Ehsanpour, ‎Majid Afkhami, Fatemeh Afkhami ‎

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Heavy metals are increasingly being released into natural waters from geological and anthropogenic sources. The distribution of several heavy metals (Cd, Pb) was investigated in muscle, liver in six different fish species seasonally collected in Persian Gulf (autumn 2009-summer 2010). The concentrations of all metals were lower in flesh than those recorded in liver due to their physiological roles. The THQ index for fish was calculated. Estimation of target hazard quotients calculations for the contaminated fish consumption was calculated to evaluate the effect of pollution on health. Total metal THQs values (Pb and Cd) for adults were 0.05 and 0.04 in Bushehr and Bandar-Genaveh, respectively, and for children they were 0.08 and 0.05 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: Persian Gulf, heavy metals, health risks, THQ index

Procedia PDF Downloads 694

Authors: Mahmoud Almasri, Xavier Marjou, Fanny Parzysz

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The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learning-based algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Keywords: drones connectivity, reinforcement learning, handovers optimization, decision distance

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Authors: N. Zarikian

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Comparative analysis of the fauna of two families of butterflies (Lepidoptera: Rhopalocera) – Hesperiidae and Nymphalidae were carried out. In general, 122 species of the families are recorded. among these 33 species belong to Hesperiidae and 89 to Nymphalidae. The numbers by countries are as follows: 72 species are found in Syria (including 24 Hesperiidae and 48 Nymphalidae) and 97 in Armenia (26 and 71 species, respectively). Two species of Hesperiidae are reported for Syrian fauna for the first time and one species is newly recorded for Armenia. From the species above mentioned 38 are common both for Syria and Armenia. For estimation of the similarity of faunas studied were used the Jaccard index. By families the index is rather different, consisting for Hesperiidae 0.5151 and for Nymphalidae 0.337.

Keywords: Armenia, fauna, Hesperiidae, Nymphalidae, (Rhopalocera: Lepidoptera), Syria

Procedia PDF Downloads 243

Authors: M-M. Mohamed Ahmed, Nacer K. M’Sirdi, Aziz Naamane

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In this paper, a longitudinal and lateral control approach based on a nonlinear observer is proposed for a convoy of autonomous vehicles to follow a desired trajectory. To authors best knowledge, this topic has not yet been sufficiently addressed in the literature for the control of multi vehicles. The modeling of the convoy of the vehicles is revisited using a robotic method for simulation purposes and control design. With these models, a sliding mode observer is proposed to estimate the states of each vehicle in the convoy from the available sensors, then a sliding mode control based on this observer is used to control the longitudinal and lateral movement. The validation and performance evaluation are done using the well-known driving simulator Scanner-Studio. The results are presented for different maneuvers of 5 vehicles.

Keywords: autonomous vehicles, convoy, non-linear control, non-linear observer, sliding mode

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Tathagatha Khan, Smruti Sourava Mohapatra

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Median openings are provided in raised median of multilane roads to facilitate U-turn movement. The U-turn movement is a highly complex and risky maneuver because U-turning vehicle (minor stream) makes 180° turns at median openings and merge with the approaching through traffic (major stream). A U-turning vehicle requires a suitable gap in the major stream to merge, and during this process, the possibility of merging conflict develops. Therefore, these median openings are potential hot spot of conflict and posses concern pertaining to safety. The traffic at the median openings could be managed efficiently with enhanced safety when the capacity of a traffic facility has been estimated correctly. The capacity of U-turns at median openings is estimated by Harder’s formula, which requires three basic parameters namely critical gap, follow up time and conflict flow rate. The estimation of conflicting flow rate under mixed traffic condition is very much complicated due to absence of lane discipline and discourteous behavior of the drivers. The understanding of placement of major stream vehicles at median opening is very much important for the estimation of conflicting traffic faced by U-turning movement. The placement data of major stream vehicles at different section in 4-lane and 6-lane divided multilane roads were collected. All the test sections were free from the effect of intersection, bus stop, parked vehicles, curvature, pedestrian movements or any other side friction. For the purpose of analysis, all the vehicles were divided into 6 categories such as motorized 2W, autorickshaw (3-W), small car, big car, light commercial vehicle, and heavy vehicle. For the collection of placement data of major stream vehicles, the entire road width was divided into sections of 25 cm each and these were numbered seriatim from the pavement edge (curbside) to the end of the road. The placement major stream vehicle crossing the reference line was recorded by video graphic technique on various weekdays. The collected data for individual category of vehicles at all the test sections were converted into a frequency table with a class interval of 25 cm each and the placement frequency curve. Separate distribution fittings were tried for 4- lane and 6-lane divided roads. The variation of major stream traffic volume on the placement characteristics of major stream vehicles has also been explored. The findings of this study will be helpful to determine the conflict volume at the median openings. So, the present work holds significance in traffic planning, operation and design to alleviate the bottleneck, prospect of collision and delay at median opening in general and at median opening in developing countries in particular.

Keywords: median opening, U-turn, conflicting traffic, placement, mixed traffic

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

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Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

Procedia PDF Downloads 359

Authors: Khaled Ahmed Kadouh, Kamal Ali Albashiri

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This paper presents a designed algorithm involves improvement of transferring data over Simple Object Access Protocol (SOAP). The aim of this work is to establish whether using SOAP in exchanging XML messages has any added advantages or not. The results showed that XML messages without SOAP take longer time and consume more memory, especially with binary data.

Keywords: JAX-WS, SMTP, SOAP, web service, XML

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Authors: Norul Husna A. Kasim, Siva K. Balasundram

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The gap between ‘actual yield’ and ‘potential yield’ has remained a problem in the Malaysian oil palm industry. Ineffective maturity assessment and untimely harvesting have compounded this problem. Typically, the traditional method of palm oil quality and yield assessment is destructive, costly and laborious. Fluorescence-sensing offers a new means of assessing palm oil quality and yield non-destructively. This work describes the estimation of palm oil quality and yield using a multi-parametric fluorescence sensor (Multiplex®) to quantify the concentration of secondary metabolites, such as anthocyanin and flavonoid, in fresh fruit bunches across three different palm ages (6, 9, and 12 years-old). Results show that fluorescence sensing is an effective means of assessing FFB maturity, in terms of palm oil quality and yield quantifications.

Keywords: anthocyanin, flavonoid fluorescence sensor, palm oil yield and quality

Procedia PDF Downloads 789

Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

Procedia PDF Downloads 223

Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: N. Ouslimani, N. Hakimi, H. Aksas

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The reduction of oil reserves in the world makes that many countries are directed towards the study and the use of local and renewable energies. For this purpose, wood energy represents the material of choice. The energy production is primarily thermal and corresponds to a heating of comfort, auxiliary or principal. Wood is generally conditioned in the form of logs, of pellets, even of plates. In Algeria, this way of energy saving could contribute to the safeguarding of the environment, as to the recovery of under wood products (branches, barks and various wastes on the various transformation steps). This work is placed within the framework general of the search for new sources of energy starting from the recovery of the lignocellulosic matter. In this direction, we proposed various sources of products (biomass, under product and by-products) relating to the ‘Eucalyptus species’ being able to be developed, of which we carried out a preliminary physicochemical study, necessary to the development of the densified products with high calorific value.

Keywords: biomass, calorific value, combustion, energy recovery

Procedia PDF Downloads 272

Authors: P. K. Guha Thakurta, Shouvik Roy, Bhawana Raj

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A congestion control scheme in mobile networks is proposed in this paper through a verification based model. The model proposed in this work is represented through performance metric like buffer Occupancy, latency and packet loss rate. Based on pre-defined values, each of the metric is introduced in terms of three different states. A Markov chain based model for the proposed work is introduced to monitor the occurrence of the corresponding state transitions. Thus, the estimation of the network status is obtained in terms of performance metric. In addition, the improved performance of our proposed model over existing works is shown with experimental results.

Keywords: congestion, mobile networks, buffer, delay, call drop, markov chain

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

Procedia PDF Downloads 569

Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: G. L. C. Yap

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Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations.

Keywords: Gaussian process, long-memory, normalization, value-at-risk, volatility, Whittle estimator

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: J. S. Bedi, J. P. S. Gill, R. S. Aulakh, Prabhjit Kaur

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The present study was undertaken to monitor organochlorine, organophosphate and synthetic pyrethroid pesticide residues in butter and ghee samples collected from six different districts of Punjab. The estimation of pesticide residues was done by multiple residue analytical technique using gas chromatography equipped with GC-ECD and GC-FTD. The confirmation of residues was done on gas chromatography mass spectrometry in both SIM and Scan mode. Results indicated the presence of HCH and pp DDE as predominant contaminant in both butter and ghee even after their ban/restriction on usage in India. Residues of HCH were detected in 25.5 and 23.2 % samples of butter and ghee, respectively, while residues of pp DDE were recorded in 29.3 and 25.0 % butter and ghee samples, respectively. More importantly, the presence of endosulfan, cypermethrin, fenvalerate, deltamethrin and chlorpyrifos was observed in few butter and ghee samples indicating the serious concerns. The spatial variation of pesticide residues occurrence indicated the cotton belt of Punjab as most affected.

Keywords: butter, ghee, pesticides residues, Punjab

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Authors: Miguel A. Borrella, Ana P. Fanjul, Suca Munoz, Liliana Herrera

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This paper evaluates the impact of LEADER, a remarkable Community-Led Local Development (CLLD) approach of the European Program for Rural Development applied to rural municipalities of Spain in 2018 and 2019. Using a difference-in-difference estimation strategy and a newly-constructed database, results show that aided municipalities have significantly lower unemployment levels than non-aided municipalities. Results are significant for the decrease in unemployment for both women and people younger than 25 years old, two of the target groups of the policy. Nevertheless, they are larger for male and older workers. Therefore, findings suggest that LEADER 2017-2018 was successful in reducing unemployment in rural areas.

Keywords: community-led local development, ex-post evaluation, LEADER, rural development

Procedia PDF Downloads 326

Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim, Rashidah Ghazali, Noramli Abdul Razak

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of prediction models has facilitated the estimation process in recent years. In this study, 110 crude palm oil (CPO) samples were used to build a free fatty acid (FFA) prediction model. 60% of the collected data were used for training purposes and the remaining 40% used for testing. The visible peaks on the NIR spectrum were at 1725 nm and 1760 nm, indicating the existence of the first overtone of C-H bands. Principal component regression (PCR) was applied to the data in order to build this mathematical prediction model. The optimal number of principal components was 10. The results showed R2=0.7147 for the training set and R2=0.6404 for the testing set.

Keywords: palm oil, fatty acid, NIRS, regression

Procedia PDF Downloads 496

Authors: Mahmoud Salari

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Corruption and gender inequality are the main topics of interest for both economists and policymakers. This study develops various static and dynamic estimation models to examine the impact of gender inequality in politics and the labor market on corruption using data of 170 countries from 1998 to 2014. This study uses two most reliable corruption indexes, including Corruption Perceptions Index (CPI) and Corruption Control (CC), to evaluate corruption levels across countries. The results indicate that gender inequality in politics has a strong impact on corruption level, and those countries that have larger/smaller gender inequality in their parliaments are faced with higher/lower corruption, respectively. Meanwhile, there is no enough evidence that supports the relationship between gender inequality in the labor market and corruption, and the results indicate that gender inequality in the labor market is not directly linked to the corruption level.

Keywords: corruption, female labor force participation, politics, gender inequality

Procedia PDF Downloads 175

Authors: Fatemeh Babaeian, Nemai Chandra Karmakar

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Chipless RFID system is a wireless system for tracking and identification which use passive tags for encoding data. The advantage of using chipless RFID tag is having a planar tag which is printable on different low-cost materials like paper and plastic. The printed tag can be attached to different items in the labelling level. Since the price of chipless RFID tag can be as low as a fraction of a cent, this technology has the potential to compete with the conventional optical barcode labels. However, due to the passive structure of the tag, data processing of the reflection signal is a crucial challenge. The captured reflected signal from a tag attached to an item consists of different components which are the reflection from the reader antenna, the reflection from the item, the tag structural mode RCS component and the antenna mode RCS of the tag. All these components are summed up in both time and frequency domains. The effect of reflection from the item and the structural mode RCS component can distort/saturate the frequency domain signal and cause difficulties in extracting the desired component which is the antenna mode RCS. Therefore, it is required to study the reflection of the tag in both time and frequency domains to have a better understanding of the nature of the captured chipless RFID signal. The other benefits of this study can be to find an optimised encoding technique in tag design level and to find the best processing algorithm the chipless RFID signal in decoding level. In this paper, the reflection from a typical backscattered chipless RFID tag with six resonances is analysed, and different components of the signal are separated in both time and frequency domains. Moreover, the time domain signal corresponding to each resonator of the tag is studied. The data for this processing was captured from simulation in CST Microwave Studio 2017. The outcome of this study is understanding different components of a measured signal in a chipless RFID system and a discovering a research gap which is a need to find an optimum detection algorithm for tag ID extraction.

Keywords: antenna mode RCS, chipless RFID tag, resonance, structural mode RCS

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

Abstract:

Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

Procedia PDF Downloads 178

Authors: M. A. Ahmed, Sidra A. Shaikh, M. W. Akhtar

Abstract:

Global and Diffuse Solar radiation on horizontal surface over two cities of Sindh, namely Jacobabad and Rohri were carried out using sunshine hour data of the area to assess the feasibility of solar energy utilization in Sindh province. The result obtained shows a high variation in direct and diffuse component of solar radiation in summer and winter months (80% direct and 20% diffuse). The contribution of diffuse solar radiation is low even in monsoon months i.e. July and August. The appearance of cloud is rare even in monsoon months. The estimated value indicates that this part of Sindh has higher solar potential and solar panels can be used for power generation. The solar energy can be utilized throughout the year in this part of Sindh, Pakistan.

Keywords: solar potential over Sindh, global and diffuse solar radiation, radiation over two cities of Sindh, environmental engineering

Procedia PDF Downloads 424