Search results for: academic speed and accuracy

Authors: Christina Peristeri, Tobias Berg, Domenico Caridi, Paul Hutcheson, Robert Winstanley

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In recent years the demand for rapid innovations in the automotive industry has led to the need for accelerated simulation procedures while retaining a detailed representation of the simulated phenomena. The project’s aim is to create a fast transient workflow for external aerodynamic CFD simulations of road vehicles. The geometry used was the SAE Notchback Closed Cooling DrivAer model, and the simulation results were compared with data from wind tunnel tests. The meshes generated for this study were of two types. One was a mix of polyhedral cells near the surface and hexahedral cells away from the surface. The other was an octree hex mesh with a rapid method of fitting to the surface. Three different grid refinement levels were used for each mesh type, with the biggest total cell count for the octree mesh being close to 1 billion. A series of steady-state solutions were obtained on three different grid levels using a pseudo-transient coupled solver and a k-omega-based RANS turbulence model. A mesh-independent solution was found in all cases with a medium level of refinement with 200 million cells. Stress-Blended Eddy Simulation (SBES) was chosen for the transient simulations, which uses a shielding function to explicitly switch between RANS and LES mode. A converged pseudo-transient steady-state solution was used to initialize the transient SBES run that was set up with the SIMPLEC pressure-velocity coupling scheme to reach the fastest solution (on both CPU & GPU solvers). An important part of this project was the use of FLUENT’s Multi-GPU solver. Tesla A100 GPU has been shown to be 8x faster than an Intel 48-core Sky Lake CPU system, leading to significant simulation speed-up compared to the traditional CPU solver. The current study used 4 Tesla A100 GPUs and 192 CPU cores. The combination of rapid octree meshing and GPU computing shows significant promise in reducing time and hardware costs for industrial strength aerodynamic simulations.

Keywords: CFD, DrivAer, LES, Multi-GPU solver, octree mesh, RANS

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

Procedia PDF Downloads 262

Authors: Yusra Pintaningrum, Ilma Fahira Basyir, Sony Hilal Wicaksono, Vito A. Damay

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Background: Coronary artery calcium (CAC) scores play an important role in improving prognostic accuracy and can be selectively used to guide the allocation of statin therapy for atherosclerotic cardiovascular disease outcomes and potentially associated with the occurrence of MACE (Major Adverse Cardiovascular Event) and MI (Myocardial Infarction). Objective: This systematic review and meta-analysis aim to analyze the findings of a study about CAC Score and statin treatment effect on MI and MACE risk. Methods: Search for published scientific articles using the PRISMA (Preferred Reporting, Items for Systematic Reviews and Meta-Analysis) method conducted on PubMed, Cochrane Library, and Medline databases published in the last 20 years on “coronary artery calcium” AND “statin” AND “cardiovascular disease” Further systematic review and meta-analysis using RevMan version 5.4 were performed based on the included published scientific articles. Results: Based on 11 studies included with a total of 1055 participants, we performed a meta-analysis and found that individuals with CAC score > 0 increased risk ratio of MI 8.48 (RR = 9.48: 95% CI: 6.22 – 14.45) times and MACE 2.48 (RR = 3.48: 95% CI: 2.98 – 4.05) times higher than CAC score 0 individual. Statin compared against non-statin treatment showed a statistically insignificant overall effect on the risk of MI (P = 0.81) and MACE (P = 0.89) in an individual with elevated CAC score 1 – 100 (P = 0.65) and > 100 (P = 0.11). Conclusions: This study found that an elevated CAC scores individual has a higher risk of MI and MACE than a non-elevated CAC score individual. There is no significant effect of statin compared against non-statin treatment to reduce MI and MACE in elevated CAC score individuals of 1 – 100 or > 100.

Keywords: coronary artery calcium, statin, cardiovascular disease, myocardial infarction, MACE

Procedia PDF Downloads 106

Authors: Mariem Saied, Jens Gustedt, Gilles Muller

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We present Dido, a source-to-source auto-generation and optimization framework for multi-dimensional stencil computations. It enables a large programmer community to easily and safely implement stencil codes on distributed-memory parallel architectures with Ordered Read-Write Locks (ORWL) as an execution and communication back-end. ORWL provides inter-task synchronization for data-oriented parallel and distributed computations. It has been proven to guarantee equity, liveness, and efficiency for a wide range of applications, particularly for iterative computations. Dido consists mainly of an implicitly parallel domain-specific language (DSL) implemented as a source-level transformer. It captures domain semantics at a high level of abstraction and generates parallel stencil code that leverages all ORWL features. The generated code is well-structured and lends itself to different possible optimizations. In this paper, we enhance Dido to handle both Jacobi and Gauss-Seidel grid traversals. We integrate temporal blocking to the Dido code generator in order to reduce the communication overhead and minimize data transfers. To increase data locality and improve intra-node data reuse, we coupled the code generation technique with the polyhedral parallelizer Pluto. The accuracy and portability of the generated code are guaranteed thanks to a parametrized solution. The combination of ORWL features, the code generation pattern and the suggested optimizations, make of Dido a powerful code generation framework for stencil computations in general, and for distributed-memory architectures in particular. We present a wide range of experiments over a number of stencil benchmarks.

Keywords: stencil computations, ordered read-write locks, domain-specific language, polyhedral model, experiments

Procedia PDF Downloads 132

Authors: Chaoge Wang, Xiquan Weng, Yan Meng, Wentao Lin

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Objective: Cold exposure and exercise serve as two physiological stimuli to glycolipid metabolism and insulin sensitivity. So far, it remains to be elucidated whether exercise plus cold exposure can produce an addictive effect on promoting glycolipid metabolism and insulin sensitivity. Methods: 64 SD rats were subjected to high-fat and high-sugar diets for 9-week and sucessfully to establish an obesity model. They were randomly divided into 8 groups: normal control group (NC), normal exercise group (NE), continuous cold control group (CC), continuous cold exercise group (CE), acute clod control group (AC), acute cold exercise group (AE), intermittent cold control group (IC) and intermittent cold exercise group (IE). For continuous cold exposure, the rats stayed in a cold environment all day; for acute cold exposure, the rats were exposed to cold for only 4h before the end of the experiment; for intermittent cold exposure, the rats were exposed to cold for 4h per day. The protocol for treadmill runnings were as follows: 25m/min (speed), 0°C (slope), 30 mins each time, an interval for 10 mins between two runnings, twice/two days, lasting for 5 weeks. Sampling were conducted on the 5th weekend. Blood lipids, free fatty acids, blood glucose (FBG), and serum insulin (FINS) were examined, and the insulin resistance index (HOMA-IR = FBG (mmol/L)×FINS(mIU/L)/22.5) was calculated. SPSS 22.0 was used for statistical analysis of the experimental results, and the ANOVA analysis was performed between groups (p < 0.05 was significant). Results: (1) Compared with the NC group, the FBG of the rats was significantly declined in the NE, CE, AC, AE, and IE groups (p < 0.05), the FINS of the rats was significantly declined in the AE group (p < 0.05), the HOMA-IR of the rats was significantly declined in the NE, CE, AC, AE and IE groups (p < 0.05). Compared with the NE group, the FBG of the rats was significantly declined in the CE, AE, and IE groups (p < 0.05), the FINS and HOMA-IR of the rats were significantly declined in the AE group (p < 0.05). (2) Compared with the NC group, the CHO, TG, LDL-C, and FFA of the rats were significantly declined in CE and IE groups (p < 0.05), the HDL-C of the rats was significantly higher in NE, CC, CE, AE, and IE groups (p < 0.05). Compared with the NE group, the HDL-C of the rats was significantly higher in the CE and IE groups (p < 0.05). Conclusions: Sedentariness or exercise in the acute cold doesn't make sense in the treatment of type 2 diabetes, which led to one-off increases of the body's insulin sensitivity. Exercise in the continuous and intermittent cold can effectively decline the FBG, TC, TG, LDL-C, and FFA levels and increase the HDL-C level and insulin sensitivity in obese rats. These results can impact the prevention and treatment of type 2 diabetes.

Keywords: cold, exercise, insulin sensitivity, obesity

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Authors: Freya Willicks

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The society of the 21st century is characterized by dynamic and complexity, developments that also shape universities and university life. The Bologna reform, for example, has led to restructuring at many European universities. Today's university teachers, therefore, have to meet many expectations: Their tasks include not only teaching but also the general improvement of the quality of teaching, good research, the management of various projects or the development of their own personal skills. This requires a high degree of flexibility and openness to change. The resulting pressure can often lead to exhaustion. Coaching can be a way for university teachers to cope with these pressures because it gives them the opportunity to discuss stressful situations with a coach and self-reflect on them. As a result, more and more universities in Europe offer to coach to their teachers. An analysis of the services provided at universities in Germany, however, quickly reveals an immense disagreement with regard to the understanding of ‘coaching’. A variety of terms is used, such as coaching, counselling or supervision. In addition, each university defines its offer individually, from process-oriented consulting to expert consulting, from group training to individual coaching. The biographic backgrounds of those who coach are also very divergent, both external and internal coaches can be suitable. These findings lead to the following questions: Which structural characteristics for coaching at universities have been proven successful? What competencies should a good coach for university lecturers have? In order to answer these questions, a qualitative study was carried out. In a first step, qualitative semi-structured interviews (N = 14) were conducted, on the one hand with coaches for university teachers and on the other hand with university teachers who have been coached. In a second step, the interviews were transcribed and analyzed using Mayring's qualitative content analysis. The study shows how great the potential of coaching can be for university teachers, who otherwise have little opportunity to talk about their teaching in a private setting. According to the study, the coach should neither be a colleague nor a superior of the coachee but should take an independent perspective, as this is the only way for the coachee to openly reflect on himself/herself. In addition, the coach should be familiar with the university system, i.e., be an academic himself/herself. Otherwise, he/she cannot fully understand the complexity of the teaching situation and the role expectations. However, internal coaches do not necessarily have much coaching experience or explicit coaching competencies. They often come from the university's own didactics department, are experts in didactics, but do not necessarily have a certified coaching education. Therefore, it is important to develop structures and guidelines for internal coaches to support their coaching. In further analysis, such guidelines will be developed on the basis of these interviews.

Keywords: coaching, university coaching, university didactics, qualitative interviews

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Authors: María Teresa Baquero, Ester Higueras

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One of the challenges facing 21st century cities, is their adaptation to the phenomenon of an ageing population. International policies have been developed, such as the "Global Network for Age-friendly Cities and Communities". These cities must recognize the diversity of the elderly population, and facilitate an active, healthy, satisfied aging and promote inclusion. In order to promote active and healthy aging, older people should be encouraged to engage in physical activity, sunbathe, socialize and enjoy the public open spaces in the city. Some studies recognize thermal comfort as one of the factors that most influence the use of public open spaces. However, although some studies have shown vulnerability to thermal extremes and environmental conditions in older people, there is little research on thermal comfort for older adults, because it is usually analyzed based on the characteristics of the ¨average young person¨ without considering the physiological, physical and psychological differences that characterize the elderly. This study analyzes the relationship between the microclimate parameters as air temperature, relative humidity, wind speed and sky view factor (SVF) with the personal thermal perception of older adults in three public spaces in Madrid, through a mixed methodology that combines weather measurements with interviews, made during the year 2018. Statistical test like Chi-square, Spearman, and analysis of variance were used to analyze the relationship between preference votes and thermal sensation votes with environmental and personal parameters. The results show that there is a significant correlation between thermal sensation and thermal preference with the measured air temperature, age, level of clothing, the color of clothing, season, time of the day and kind of space while no influence of gender or other environmental variables was detected. These data would contribute to the design of comfortable public spaces that improve the welfare of the elderly contributing to "active and healthy aging" as one of the 21st century challenges cities face.

Keywords: healthy ageing, older adults, outdoor public space, thermal perception

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Authors: Mahmoud Huleihel, George Abu-Aqil, Manal Suleiman, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman

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Urinary tract infections (UTIs) are considered to be the most common bacterial infections worldwide, which are caused mainly by Escherichia (E.) coli (about 80%). Klebsiella pneumoniae (about 10%) and Pseudomonas aeruginosa (about 6%). Although antibiotics are considered as the most effective treatment for bacterial infectious diseases, unfortunately, most of the bacteria already have developed resistance to the majority of the commonly available antibiotics. Therefore, it is crucial to identify the infecting bacteria and to determine its susceptibility to antibiotics for prescribing effective treatment. Classical methods are time consuming, require ~48 hours for determining bacterial susceptibility. Thus, it is highly urgent to develop a new method that can significantly reduce the time required for determining both infecting bacterium at the species level and diagnose its susceptibility to antibiotics. Fourier-Transform Infrared (FTIR) spectroscopy is well known as a sensitive and rapid method, which can detect minor molecular changes in bacterial genome associated with the development of resistance to antibiotics. The main goal of this study is to examine the potential of FTIR spectroscopy, in tandem with machine learning algorithms, to identify the infected bacteria at the species level and to determine E. coli susceptibility to different antibiotics directly from patients' urine in about 30minutes. For this goal, 1600 different E. coli isolates were isolated for different patients' urine sample, measured by FTIR, and analyzed using different machine learning algorithm like Random Forest, XGBoost, and CNN. We achieved 98% success in isolate level identification and 89% accuracy in susceptibility determination.

Keywords: urinary tract infections (UTIs), E. coli, Klebsiella pneumonia, Pseudomonas aeruginosa, bacterial, susceptibility to antibiotics, infrared microscopy, machine learning

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Hamidreza Hoshyarmanesh, Sanju Lama, Garnette R. Sutherland

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In tele-operative robotic surgery, an ideal haptic device should be equipped with an intuitive and smooth end-effector to cover the surgeon’s hand/wrist degrees of freedom (DOF) and translate the hand joint motions to the end-effector of the remote manipulator with low effort and high level of comfort. This research introduces the design and development of a microsurgery-specific end-effector, a gimbal mechanism possessing 4 passive and 1 active DOFs, equipped with a bipolar forceps and haptic feedback. The robust gimbal structure is comprised of three light-weight links/joint, pitch, yaw, and roll, each consisting of low-friction support and a 2-channel accurate optical position sensor. The third link, which provides the tool roll, was specifically designed to grip the tool prongs and accommodate a low mass geared actuator together with a miniaturized capstan-rope mechanism. The actuator is able to generate delicate torques, using a threaded cylindrical capstan, to emulate the sense of pinch/coagulation during conventional microsurgery. While the tool left prong is fixed to the rolling link, the right prong bears a miniaturized drum sector with a large diameter to expand the force scale and resolution. The drum transmits the actuator output torque to the right prong and generates haptic force feedback at the tool level. The tool is also equipped with a hall-effect sensor and magnet bar installed vis-à-vis on the inner side of the two prongs to measure the tooltip distance and provide an analogue signal to the control system. We believe that such a haptic end-effector could significantly increase the accuracy of telerobotic surgery and help avoid high forces that are known to cause bleeding/injury.

Keywords: end-effector, force generation, haptic interface, robotic surgery, surgical tool, tele-operation

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Authors: Shay Kee Tan, Kok Haur Ng, Jennifer So-Kuen Chan

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This paper extends the conditional autoregressive range (CARR) model to multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain in-sample estimates and forecasts of volatilities for these individual and pairwise sum series. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions with the scaled realised open-to-close volatility measure as the proxy for the unobserved true volatility. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba, Engle, Kraft, and Kroner type of generalized autoregressive conditional heteroskedasticity (BEKK-GARCH) models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using Kupiec test to confirm the accuracy of the VaR forecasts.

Keywords: range-based volatility, correlation, multivariate CARR-return model, value-at-risk, conditional value-at-risk

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Authors: Sara Mosallanejad, Bogdan Z. Dlugogorski, Jeff Gore, Mohammednoor Altarawneh

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Ammonium nitrate (NH­₄NO₃, AN) is commonly used as the main component of AN emulsion and fuel oil (ANFO) explosives, that use extensively in civilian and mining operations for underground development and tunneling applications. The emulsion formulation and wettability of AN prills, which affect the physical stability and detonation of ANFO, highly depend on the surface tension, density, viscosity of the used liquid. Therefore, for engineering applications of this material, the determination of density and surface tension of concentrated aqueous solutions of AN is essential. The molecular dynamics (MD) simulation method have been used to investigate the density and the surface tension of high concentrated ammonium nitrate solutions; up to its solubility limit in water. Non-polarisable models for water and ions have carried out the simulations, and the electronic continuum correction model (ECC) uses a scaling of the charges of the ions to apply the polarisation implicitly into the non-polarisable model. The results of calculated density and the surface tension of the solutions have been compared to available experimental values. Our MD simulations show that the non-polarisable model with full-charge ions overestimates the experimental results while the reduce-charge model for the ions fits very well with the experimental data. Ions in the solutions show repulsion from the interface using the non-polarisable force fields. However, when charges of the ions in the original model are scaled in line with the scaling factor of the ECC model, the ions create a double ionic layer near the interface by the migration of anions toward the interface while cations stay in the bulk of the solutions. Similar ions orientations near the interface were observed when polarisable models were used in simulations. In conclusion, applying the ECC model to the non-polarisable force field yields the density and surface tension of the AN solutions with high accuracy in comparison to the experimental measurements.

Keywords: ammonium nitrate, electronic continuum correction, non-polarisable force field, surface tension

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Christopher B. Obida, George A. Blackburn, James D. Whyatt, Kirk T. Semple

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In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study, a method was developed for delineating river networks using Sentinel 1 imagery. Unsupervised classification was applied to multi-temporal Sentinel 1 data to discriminate water bodies from other land covers then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines, which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available water body products from United States Geological Survey, European Space Agency and OpenStreetMap and a river network derived from a Shuttle Rader Topography Mission Digital Elevation Model. From both raster-based and vector-based accuracy assessments, it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The geometric river network that was constructed permitted a flow routing analysis which is important for a variety of environmental management and planning applications. The extracted network will potentially be applied for modelling dispersion of hydrocarbon pollutants in Ogoniland, a part of the Niger Delta. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries where such data are deficient.

Keywords: Sentinel 1, image processing, river delineation, large scale mapping, data comparison, geometric network

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: Armana Huq, Wahidur Rahman, Sanwar Kader

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Inland waterways in Bangladesh play an important role in providing comfortable and low-cost transportation. However, a maritime accident takes away many lives and creates unwanted hazards every year. This article deals with a comprehensive review of inland waterway accidents in Bangladesh. Additionally, it includes a comparative study between international and local inland research studies based on maritime accidents. Articles from inland waterway areas are analyzed in-depth to make a comprehensive overview of the nature of the academic work, accident and risk management process and different statistical analyses. It is found that empirical analysis based on the available statistical data dominates the research domain. For this study, major maritime accident-related works in the last four decades in Bangladesh (1981-2020) are being analyzed for preparing a bibliometric analysis. A study of maritime accidents of passenger's vessels during (1995-2005) indicates that the predominant causes of accidents in the inland waterways of Bangladesh are collision and adverse weather (77%), out of which collision due to human error alone stands (56%) of all accidents. Another study refers that the major causes of waterway accidents are the collision (60.3%) during 2005-2015. About 92% of this collision occurs due to direct contact with another vessel during this period. Rest 8% of the collision occurs by contact with permanent obstruction on waterway roots. The overall analysis of another study from the last 25 years (1995-2019) shows that one of the main types of accidents is collisions, with about 50.3% of accidents being caused by collisions. The other accident types are cyclone or storm (17%), overload (11.3%), physical failure (10.3%), excessive waves (5.1%), and others (6%). Very few notable works are available in testing or comparing the methods, proposing new methods for risk management, modeling, uncertainty treatment. The purpose of this paper is to provide an overview of the evolution of marine accident-related research domain regarding inland waterway of Bangladesh and attempts to introduce new ideas and methods to abridge the gap between international and national inland maritime-related work domain which can be a catalyst for a safer and sustainable water transportation system in Bangladesh. Another fundamental objective of this paper is to navigate various national maritime authorities and international organizations to implement risk management processes for shipping accident prevention in waterway areas.

Keywords: inland waterways, safety, bibliometric analysis, risk management, accidents

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Authors: Mohammed Messaoudi

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The higher education sector in Algeria has witnessed in recent years a remarkable transformation, as it witnessed the integration of institutions within the modern technological environment and harnessing all appropriate mechanisms to raise the level of education and the level of training. Observers and those interested that it is necessary for the Algerian university to enter this field, especially with the efforts that seek to employ modern technology in the sector and encourage investment in this field, in addition to the state’s keenness to move towards building a path to benefit from modern technology, and to encourage energies in light of a reality that carries many Aspirations and challenges by achieving openness to the new digital environment and keeping pace with the ranks of international universities. Higher education is one of the engines of development for societies, as it is a vital field for the transfer of knowledge and scientific expertise, and the university is at the top of the comprehensive educational system for various disciplines in light of the achievement of a multi-dimensional educational system, and amid the integration of three basic axes that establish the sound educational process (teaching, research, relevant outputs efficiency), and according to a clear strategy that monitors the advancement of academic work, and works on developing its future directions to achieve development in this field. The Algerian University is considered one of the service institutions that seeks to find the optimal mechanisms to keep pace with the changes of the times, as it has become necessary for the university to enter the technological space and thus ensure the quality of education in it and achieve the required empowerment by dedicating a structure that matches the requirements of the challenges on which the sector is based, amid unremitting efforts to develop the capabilities. He sought to harness the mechanisms of communication and information technology and achieve transformation at the level of the higher education sector with what is called higher education technology. The conceptual framework of information and communication technology at the level of higher education institutions in Algeria is determined through the factors of organization, factors of higher education institutions, characteristics of the professor, characteristics of students, the outcomes of the educational process, and there is a relentless pursuit to achieve a positive interaction between these axes as they are basic components on which the success and achievement of higher education are based for his goals.

Keywords: Information and communication technology, Algerian university, scientific and cognitive development, challenges

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Authors: Pinna Beatrice., Laini Alex, Negro Giovanni, Burgazzi Gemma, Viaroli Pierluigi, Vezza Paolo

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Habitat models rarely consider macroinvertebrates as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the entire community. This research aimed to provide an approach to model the habitat of the macroinvertebrate community. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the meso-habitat scale. Using different datasets gathered from both field data collection and 2D hydrodynamic simulations, the model has been calibrated in the Trebbia river (2019 campaign), and then validated in the Trebbia, Taro, and Enza rivers (2020 campaign). The three rivers are characterized by a braiding morphology, gravel riverbeds, and summer low flows. The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R² coefficient (R²𝒸ᵥ) of the training dataset is 0.71 for the Trebbia River (2019), whereas the R² coefficient for the validation datasets (Trebbia, Taro, and Enza Rivers 2020) is 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and to possible river morphological modifications. Lastly, the proposed approach allows extending the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to flow design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Keywords: ecological flows, macroinvertebrate community, mesohabitat, river habitat modeling

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Authors: Gao Youtao, Zhao Tanran, Jin Bingyu, Xu Bo

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Global navigation satellite system(GNSS) can deliver navigation information for spacecraft orbiting on low-Earth orbits and medium Earth orbits. However, the GNSS cannot navigate the spacecraft on high-Earth orbit or deep space probes effectively. With the deep space exploration becoming a hot spot of aerospace, the demand for a deep space satellite navigation system is becoming increasingly prominent. Many researchers discussed the feasibility and performance of a satellite navigation system on periodic orbits around the Earth-Moon libration points which can be called Lagrangian point satellite navigation system. Autonomous orbit determination (AOD) is an important performance for the Lagrangian point satellite navigation system. With this ability, the Lagrangian point satellite navigation system can reduce the dependency on ground stations. AOD also can greatly reduce total system cost and assure mission continuity. As the elliptical restricted three-body problem can describe the Earth-Moon system more accurately than the circular restricted three-body problem, we study the autonomous orbit determination of Lagrangian navigation constellation using only crosslink range based on elliptical restricted three body problem. Extended Kalman filter is used in the autonomous orbit determination. In order to compare the autonomous orbit determination results based on elliptical restricted three-body problem to the results of autonomous orbit determination based on circular restricted three-body problem, we give the autonomous orbit determination position errors of a navigation constellation include four satellites based on the circular restricted three-body problem. The simulation result shows that the Lagrangian navigation constellation can achieve long-term precise autonomous orbit determination using only crosslink range. In addition, the type of the libration point orbit will influence the autonomous orbit determination accuracy.

Keywords: extended Kalman filter, autonomous orbit determination, quasi-periodic orbit, navigation constellation

Procedia PDF Downloads 286

Authors: Gregor Kosec

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Simple and robust numerical approach for solving flow problems is presented, where involved physical fields are represented through the local approximation functions, i.e., the considered field is approximated over a local support domain. The approximation functions are then used to evaluate the partial differential operators. The type of approximation, the size of support domain, and the type and number of basis function can be general. The solution procedure is formulated completely through local computational operations. Besides local numerical method also the pressure velocity is performed locally with retaining the correct temporal transient. The complete locality of the introduced numerical scheme has several beneficial effects. One of the most attractive is the simplicity since it could be understood as a generalized Finite Differences Method, however, much more powerful. Presented methodology offers many possibilities for treating challenging cases, e.g. nodal adaptivity to address regions with sharp discontinuities or p-adaptivity to treat obscure anomalies in physical field. The stability versus computation complexity and accuracy can be regulated by changing number of support nodes, etc. All these features can be controlled on the fly during the simulation. The presented methodology is relatively simple to understand and implement, which makes it potentially powerful tool for engineering simulations. Besides simplicity and straightforward implementation, there are many opportunities to fully exploit modern computer architectures through different parallel computing strategies. The performance of the method is presented on the lid driven cavity problem, backward facing step problem, de Vahl Davis natural convection test, extended also to low Prandtl fluid and Darcy porous flow. Results are presented in terms of velocity profiles, convergence plots, and stability analyses. Results of all cases are also compared against published data.

Keywords: fluid flow, meshless, low Pr problem, natural convection

Procedia PDF Downloads 237

Authors: Alena Skalon, Jennifer L. Beaudry

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Eyewitness misidentifications can sometimes lead to wrongful convictions of innocent people. This occurs in part because jurors tend to believe confident eyewitnesses even when the identification took place under suggestive conditions. Empirical research demonstrated that jurors are often unaware of the factors that can influence the reliability of eyewitness identification. Most common legal safeguards that are designed to educate jurors about eyewitness evidence are judicial instructions and expert testimony. To date, very few studies assessed the effectiveness of judicial instructions and most of them found that judicial instructions make jurors more skeptical of eyewitness evidence or do not have any effect on jurors’ judgments. Similar results were obtained for expert testimony. However, none of the previous studies focused on the ability of legal safeguards to improve jurors’ assessment of evidence obtained from suggestive identification procedures—this is one of the gaps addressed by this paper. Furthermore, only three studies investigated whether legal safeguards improve the ultimate accuracy of jurors’ judgments—that is, whether after listening to judicial instructions or expert testimony jurors can differentiate between accurate and inaccurate eyewitnesses. This presentation includes two studies. Both studies used genuine eyewitnesses (i.e., eyewitnesses who watched the crime) and manipulated the suggestiveness of identification procedures. The first study manipulated the presence of judicial instructions; the second study manipulated the presence of one of two types of expert testimony: a traditional, verbal expert testimony or expert testimony accompanied by visual aids. All participant watched a video-recording of an identification procedure and of an eyewitness testimony. The results indicated that neither judicial instructions nor expert testimony affected jurors’ judgments. However, consistent with the previous findings, when the identification procedure was non-suggestive, jurors believed accurate eyewitnesses more often than inaccurate eyewitnesses. When the procedure was suggestive, jurors believed accurate and inaccurate eyewitnesses at the same rate. The paper will discuss the implications of these studies and directions for future research.

Keywords: expert testimony, eyewitness evidence, judicial instructions, jurors’ decision making, legal safeguards

Procedia PDF Downloads 182

Authors: M. Hossain, A. Abdkader, C. Cherif, A. Berger, M. Sparing, R. Hühne, L. Schultz, K. Nielsch

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Due to the best quality of yarn and the flexibility of the machine, the ring spinning process is the most widely used spinning method for short staple yarn production. However, the productivity of these machines is still much lower in comparison to other spinning systems such as rotor or air-jet spinning process. The main reason for this limitation lies on the twisting mechanism of the ring spinning process. In the ring/traveler twisting system, each rotation of the traveler along with the ring inserts twist in the yarn. The rotation of the traveler at higher speed includes strong frictional forces, which in turn generates heat. Different ring/traveler systems concerning with its geometries, material combinations and coatings have already been implemented to solve the frictional problem. However, such developments can neither completely solve the frictional problem nor increase the productivity. The friction free superconducting magnetic bearing (SMB) system can be a right alternative replacing the existing ring/traveler system. The unique concept of SMB bearings is that they possess a self-stabilizing behavior, i.e. they remain fully passive without any necessity for expensive position sensing and control. Within the framework of a research project funded by German research foundation (DFG), suitable concepts of the SMB-system have been designed, developed, and integrated as a twisting device of ring spinning replacing the existing ring/traveler system. With the help of the developed mathematical model and experimental investigation, the physical limitations of this innovative twisting device in the spinning process have been determined. The interaction among the parameters of the spinning process and the superconducting twisting element has been further evaluated, which derives the concrete information regarding the new spinning process. Moreover, the influence of the implemented SMB twisting system on the yarn quality has been analyzed with respect to different process parameters. The presented work reveals the enormous potential of the innovative twisting mechanism, so that the productivity of the ring spinning process especially in case of thermoplastic materials can be at least doubled for the first time in a hundred years. The SMB ring spinning tester has also been presented in the international fair “International Textile Machinery Association (ITMA) 2015”.

Keywords: ring spinning, superconducting magnetic bearing, yarn properties, productivity

Procedia PDF Downloads 240

Authors: Alberto Jiménez-Vargas, Manuel de Jesús Palacios-Gallegos, Miguel Ángel Hernández-López, Rafael Campos-Amezcua, Julio Cesar Solís-Sanchez

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An optimal wind turbine blade design must be able of capturing as much energy as possible from the wind source available at the area of interest. Many times, an optimal design means the use of large quantities of material and complicated processes that make the wind turbine more expensive, and therefore, less cost-effective. For the construction and installation of a wind turbine, the blades may cost up to 20% of the outline pricing, and become more important due to they are part of the rotor system that is in charge of transmitting the energy from the wind to the power train, and where the static and dynamic design loads for the whole wind turbine are produced. The aim of this work is the develop of a blade fluid-structure interaction (FSI) simulation that allows the identification of the major damage zones during the normal production situation, and thus better decisions for design and optimization can be taken. The simulation is a dynamic case, since we have a time-history wind velocity as inlet condition instead of a constant wind velocity. The process begins with the free-use software NuMAD (NREL), to model the blade and assign material properties to the blade, then the 3D model is exported to ANSYS Workbench platform where before setting the FSI system, a modal analysis is made for identification of natural frequencies and modal shapes. FSI analysis is carried out with the two-way technic which begins with a CFD simulation to obtain the pressure distribution on the blade surface, then these results are used as boundary condition for the FEA simulation to obtain the deformation levels for the first time-step. For the second time-step, CFD simulation is reconfigured automatically with the next time-step inlet wind velocity and the deformation results from the previous time-step. The analysis continues the iterative cycle solving time-step by time-step until the entire load case is completed. This work is part of a set of projects that are managed by a national consortium called “CEMIE-Eólico” (Mexican Center in Wind Energy Research), created for strengthen technological and scientific capacities, the promotion of creation of specialized human resources, and to link the academic with private sector in national territory. The analysis belongs to the design of a rotor system for a 5 kW wind turbine design thought to be installed at the Isthmus of Tehuantepec, Oaxaca, Mexico.

Keywords: blade, dynamic, fsi, wind turbine

Procedia PDF Downloads 486

Authors: Masanori Kimura

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Due to the decline of the 18-year-old population, Japanese universities have a tendency to convert their form of employment from tenured positions to fixed-term positions for newly hired teachers. The advantage of this is that universities can be more flexible in their employment plans in case they fail to fill the enrollment of quotas of prospective students or they need to supplement teachers who can engage in other academic fields or research areas where new demand is expected. The most serious disadvantage of this, however, is that if secure positions cannot be provided to faculty members, there is the possibility that coherence of education and continuity of research supported by the university cannot be achieved. Therefore, the question of this presentation is as follows: Are universities aiming to give first priority to management, or are they trying to prioritize educational and research rather than management? To answer this question, the author examined the number of job offerings for college foreign language teachers posted on the JREC-IN (Japan Research Career Information Network, which is run by Japan Science and Technology Agency) website from April 2012 to October 2015. The results show that there were 1,002 and 1,056 job offerings for tenured positions and fixed-term contracts respectively, suggesting that, overall, today’s Japanese universities show a tendency to give first priority to management. More detailed examinations of the data, however, show that the tendency slightly varies depending on the types of universities. National universities which are supported by the central government and state universities which are supported by local governments posted more job offerings for tenured positions than for fixed-term contracts: national universities posted 285 and 257 job offerings for tenured positions and fixed-term contracts respectively, and state universities posted 106 and 86 job offerings for tenured positions and fixed-term contracts respectively. Yet the difference in number between the two types of employment status at national and state universities is marginal. As for private universities, they posted 713 job offerings for fixed-term contracts and 616 offerings for tenured positions. Moreover, 73% of the fixed-term contracts were offered for low rank positions including associate professors, lectures, and so forth. Generally speaking, those positions are offered to younger teachers. Therefore, this result indicates that private universities attempt to cut their budgets yet expect the same educational effect by hiring younger teachers. Although the results have shown that there are some differences in personal strategies among the three types of universities, the author argues that all three types of universities may lose important human resources that will take a pivotal role at their universities in the future unless they urgently review their employment strategies.

Keywords: higher education, management, employment status, foreign language education

Procedia PDF Downloads 136

Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

Procedia PDF Downloads 61

Authors: O’Day Luke

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Purpose: To validate virtual non-contrast reconstructions generated from dual-layer spectral computed tomography (DL-CT) data as an alternative for the acquisition of a dedicated true non-contrast dataset during multiphase contrast studies. Material and methods: Thirty-three patients underwent a routine multiphase clinical CT examination, using Dual-Layer Spectral CT, from March to August 2021. True non-contrast (TNC) and virtual non-contrast (VNC) datasets, generated from both portal venous and arterial phase imaging were evaluated. For every patient in both true and virtual non-contrast datasets, a region-of-interest (ROI) was defined in aorta, liver, fluid (i.e. gallbladder, urinary bladder), kidney, muscle, fat and spongious bone, resulting in 693 ROIs. Differences in attenuation for VNC and TNV images were compared, both separately and combined. Consistency between VNC reconstructions obtained from the arterial and portal venous phase was evaluated. Results: Comparison of CT density (HU) on the VNC and TNC images showed a high correlation. The mean difference between TNC and VNC images (excluding bone results) was 5.5 ± 9.1 HU and > 90% of all comparisons showed a difference of less than 15 HU. For all tissues but spongious bone, the mean absolute difference between TNC and VNC images was below 10 HU. VNC images derived from the arterial and the portal venous phase showed a good correlation in most tissue types. The aortic attenuation was somewhat dependent however on which dataset was used for reconstruction. Bone evaluation with VNC datasets continues to be a problem, as spectral CT algorithms are currently poor in differentiating bone and iodine. Conclusion: Given the increasing availability of DL-CT and proven accuracy of virtual non-contrast processing, VNC is a promising tool for generating additional data during routine contrast-enhanced studies. This study shows the utility of virtual non-contrast scans as an alternative for true non-contrast studies during multiphase CT, with potential for dose reduction, without loss of diagnostic information.

Keywords: dual-layer spectral computed tomography, virtual non-contrast, true non-contrast, clinical comparison

Procedia PDF Downloads 145

Authors: Muhammad Nouman, Fahad Tiwana, Muhammad Irfan, Mohsin Tiwana

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Traffic congestion has been increasing significantly in major metropolitan areas as a result of increased motorization, urbanization, population growth and changes in the urban density. Traffic congestion compromises efficiency of transport infrastructure and causes multiple traffic concerns; including but not limited to increase of travel time, safety hazards, air pollution, and fuel consumption. Traffic management has become a serious challenge for federal and provincial governments, as well as exasperated commuters. Effective, flexible, efficient and user-friendly traffic information/database management systems characterize traffic conditions by making use of traffic counts for storage, processing, and visualization. While, the emerging data collection technologies continue to proliferate, its accuracy can be guaranteed through the comparison of observed data with the manual handheld counters. This paper presents the design of tablet based manual traffic counting application and framework for development of traffic database management system for Pakistan. The database management system comprises of three components including traffic counting android application; establishing online database and its visualization using Google maps. Oracle relational database was chosen to develop the data structure whereas structured query language (SQL) was adopted to program the system architecture. The GIS application links the data from the database and projects it onto a dynamic map for traffic conditions visualization. The traffic counting device and example of a database application in the real-world problem provided a creative outlet to visualize the uses and advantages of a database management system in real time. Also, traffic data counts by means of handheld tablet/ mobile application can be used for transportation planning and forecasting.

Keywords: manual count, emerging data sources, traffic information quality, traffic surveillance, traffic counting device, android; data visualization, traffic management

Procedia PDF Downloads 201

Authors: Banu Baybars Hawks

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To date, academic attention in social sciences remains inadequate with regard to research and analysis of public opinion in Turkey. Most of the existing research has assessed the public opinion during political election periods. Therefore, it is of great interest to find out what the public thinks about current issues in Turkey, and how to interpret the results to be able to reveal whether they may have any reflections on social, political, and cultural structure of the country. Accordingly, the current study seeks to fill the gap in the social sciences literature in English regarding Turkey’s social and political stand which may be perceived to be very different by other nations. Without timely feedback from public surveys, various programs for improving different services and institutions functioning in the country might not achieve their expected goal, nor can decisions about which programs to implement be made rationally. Additionally, the information gathered may not only yield important insights into public’s opinion regarding current agenda in Turkey, but also into the correlates shaping public policies. Agenda-setting studies including agenda-building, agenda melding, reversed agenda-setting and information diffusion studies will be used to explain the roles of factors and actors in the formation of public opinion in Turkey. Knowing the importance of public agenda in the agenda setting and building process, this paper aims to reveal the social and political tendencies of the Turkish public. For that purpose, a survey will be carried out in December of 2014 to determine the social and political trends in Turkey for that same year. The subjects for the study, which utilize a questionairre in one-on-one interviews, will include 1,000 individuals aged 18 years and older from 26 cities representing general population. A stratified random sampling frame will be used. The topics covered by the survey include: The most important current problem in Turkey; the Economy; Terror; Approaches to the Kurdish Issue; Evaluations of the Government and Opposition Parties; Evaluations of Institutional Efficiency; Foreign Policy; the Judicial System/Constitution; Democracy and the Media; and, Social Relations/Life in Turkey. Since the beginning of the 21st century, Turkey has been undergoing a rapid transformation. The reflections of the changes can be seen in all areas from economics to politics. It is my hope that findings of this study may shed light on the important aspects of institutions, variables setting the agenda, and formation process of public opinion in Turkey.

Keywords: public opinion, media, agenda setting, information diffusion, government, freedom, Turkey

Procedia PDF Downloads 470

Authors: S. Paszkiewicz, A. Zubkiewicz, A. Szymczyk, D. Pawlikowska, I. Irska, E. Piesowicz, A. Linares, T. A. Ezquerra

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Over the last century, the world has become increasingly dependent on oil as its main source of chemicals and energy. Driven largely by the strong economic growth of India and China, demand for oil is expected to increase significantly in the coming years. This growth in demand, combined with diminishing reserves, will require the development of new, sustainable sources for fuels and bulk chemicals. Biomass is an attractive alternative feedstock, as it is widely available carbon source apart from oil and coal. Nowadays, academic and industrial research in the field of polymer materials is strongly oriented towards bio-based alternatives to petroleum-derived plastics with enhanced properties for advanced applications. In this context, 2,5-furandicarboxylic acid (FDCA), a biomass-based chemical product derived from lignocellulose, is one of the most high-potential biobased building blocks for polymers and the first candidate to replace the petro-derived terephthalic acid. FDCA has been identified as one of the top 12 chemicals in the future, which may be used as a platform chemical for the synthesis of biomass-based polyester. The aim of this study is to synthesize and characterize the multiblock copolymers containing rigid segments of poly(trimethylene 2,5-furanoate) (PTF) and soft segments of poly(tetramethylene oxide) (PTMO) with excellent elastic properties or aliphatic polyesters of polycaprolactone (PCL). Two series of PTF based copolymers, i.e., PTF-block-PTMO-T and PTF-block-PCL-T, with different content of flexible segments were synthesized by means of a two-step melt polycondensation process and characterized by various methods. The rigid segments of PTF, as well as the flexible PTMO/or PCL ones, were randomly distributed along the chain. On the basis of 1H NMR, SAXS and WAXS, DSC an DMTA results, one can conclude that both phases were thermodynamically immiscible and the values of phase transition temperatures varied with the composition of the copolymer. The copolymers containing 25, 35 and 45wt.% of flexible segments (PTMO) exhibited elastomeric property characteristics. Moreover, with respect to the flexible segments content, the temperatures corresponding to 5%, 25%, 50% and 90% mass loss as well as the values of tensile modulus decrease with the increasing content of aliphatic polyether or aliphatic polyester in the composition.

Keywords: furan based polymers, multiblock copolymers, supramolecular structure, functional properties

Procedia PDF Downloads 133