Search results for: gas-lift dual gradient drilling

Authors: Benjamin K. Ghansah, Richard K. Appoh, Iliya Nababa, Eric K. Forkuo

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The Nasia River is an important source of water for domestic and agricultural purposes to the inhabitants of its catchment. Major farming activities takes place within the floodplain of the river and its network of tributaries. The actual inundation extent of the river system is; however, unknown. Reasons for this lack of information include financial constraints and inadequate human resources as flood modelling is becoming increasingly complex by the day. Knowledge of the inundation extent will help in the assessment of risk posed by the annual flooding of the river, and help in the planning of flood recession agricultural activities. This study used a simple terrain based algorithm, Height Above Nearest Drainage (HAND), to delineate the floodplain of the Nasia River and its tributaries. The HAND model is a drainage normalized digital elevation model, which has its height reference based on the local drainage systems rather than the average mean sea level (AMSL). The underlying principle guiding the development of the HAND model is that hillslope flow paths behave differently when the reference gradient is to the local drainage network as compared to the seaward gradient. The new terrain model of the catchment was created using the NASA’s SRTM Digital Elevation Model (DEM) 30m as the only data input. Contours (HAND Contour) were then generated from the normalized DEM. Based on field flood inundation survey, historical information of flooding of the area as well as satellite images, a HAND Contour of 2m was found to best correlates with the flood inundation extent of the river and its tributaries. A percentage accuracy of 75% was obtained when the surface area created by the 2m contour was compared with surface area of the floodplain computed from a satellite image captured during the peak flooding season in September 2016. It was estimated that the flooding of the Nasia River and its tributaries created a floodplain area of 1011 km².

Keywords: digital elevation model, floodplain, HAND contour, inundation extent, Nasia River

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Authors: Talha Ali Khan

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Discovering different energy resources to full fill the world growing demand is now one of the society’s bigger challenge for the next half-century. The main task is to convert the sun radiation into electricity via photovoltaic solar cells which is suddenly decreasing $/watt of delivered solar electricity. Therefore, in this context, the sun trackers are those devices that can be used to ameliorate efficiency. In this paper, a variety of the sun tracking systems are evaluated and their merits and demerits are highlighted. The most adept and proficient sun-tracking devices are polar axis and azimuth-elevation types.

Keywords: dual axis, fixed axis, sun tracker, MPPT

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Authors: Pervaiz Ahmad, Mayeen Uddin Khandaker, Yusoff Mohd Amin

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A unique cone-like morphologies of boron nitride microtubes with larger internal space and thin walls structure are synthesized in a dual zone quartz tube furnace at 1200 ° C with ammonia as a reaction atmosphere. The synthesized microtubes are found to have diameter in the range of 1 to ̴ 2 μm with walls thickness estimated from 10 – 100 nm. XPS survey shows N 1s and B 1s peaks at 398.7 eV and 191 eV that represent h-BN in the sample. Raman spectroscopy indicates a high intensity peak at 1372.53 (cm-1) that corresponds to the E2g mode of h-BN.

Keywords: BNMTs, synthesis, reaction atmosphere, growth

Procedia PDF Downloads 388

Authors: Talha Ali Khan

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Discovering different energy resources to fulfill the world's growing demand is now one of the society’s bigger challenges for the next half-century. The main task is to convert the sun radiation into electricity via photovoltaic solar cells which is suddenly decreasing $/watt of delivered solar electricity. Therefore, in this context the sun trackers are those devices that can be used to ameliorate efficiency. In this paper, a variety of the sun tracking systems are evaluated and their merits and demerits are highlighted. The most adept and proficient sun-tracking devices are polar axis and azimuth-elevation types.

Keywords: dual axis, fixed axis, sun tracker, sun radiation

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Authors: Reema Tiwari, U. C. Kulshrestha

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As a key regulator of atmospheric oxidative capacity and secondary aerosol formations, the signatures of reactive nitrogen (Nr) emissions are becoming increasingly evident in the cascade of air pollution, acidification, and eutrophication of the ecosystem. However, their accurate estimates in N budget remains limited by the photochemical conversion processes where occurrence of differential atmospheric residence time of gaseous (NOₓ, HNO₃, NH₃) and particulate (NO₃⁻, NH₄⁺) Nr species becomes imperative to their spatio temporal evolution on a synoptic scale. The present study attempts to quantify such interactions under tropical conditions when low anticyclonic winds become favorable to the advections from west during winters. For this purpose, a diurnal sampling was conducted using low volume sampler assembly where ambient concentrations of Nr trace gases along with their ionic fractions in the aerosol samples were determined with UV-spectrophotometer and ion chromatography respectively. The results showed a spatial gradient of the gaseous precursors with a much pronounced inter site variability (p < 0.05) than their particulate fractions. Such observations were confirmed for their limited photochemical conversions where less than 1 ratios of day and night measurements (D/N) for the different Nr fractions suggested an influence of boundary layer dynamics at the background site. These phase conversion processes were further corroborated with the molar ratios of NOₓ/NOᵧ and NH₃/NHₓ where incomplete titrations of NOₓ and NH₃ emissions were observed irrespective of their diurnal phases along the sampling transect. Their calculations with equilibrium based approaches for an NH₃-HNO₃-NH₄NO₃ system, on the other hand, were characterized by delays in equilibrium attainment where plots of their below deliquescence Kₘ and Kₚ values with 1000/T confirmed the role of lower temperature ranges in NH₄NO₃ aerosol formation. These results would help us in not only resolving the changing atmospheric inputs of reduced (NH₃, NH₄⁺) and oxidized (NOₓ, HNO₃, NO₃⁻) Nr estimates but also in understanding the dependence of Nr mixing ratios on their local meteorological conditions.

Keywords: diurnal ratios, gas-aerosol interactions, spatial gradient, thermodynamic equilibrium

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Authors: T. Benyetho, L. El Abdellaoui, J. Terhzaz, H. Bennis, N. Ababssi, A. Tajmouati, A. Tribak, M. Latrach

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This work presents a new planar multiband antenna based on fractal geometry. This structure is optimized and validated into simulation by using CST-MW Studio. To feed this antenna we have used a CPW line which makes it easy to be incorporated with integrated circuits. The simulation results presents a good matching input impedance and radiation pattern in the GSM band at 900 MHz and ISM band at 2.4 GHz. The final structure is a dual band fractal antenna with 70 x 70 mm² as a total area by using an FR4 substrate.

Keywords: Antenna, CPW, fractal, GSM, multiband

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Authors: P. Saha, R. Ganguly, N. K .Singha, A. Pich

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Microgel, a smart class of material, has drawn attention in the past few years due to its response to external stimuli like temperature, pH, and ionic strength of the solution. Among them, one type of polymer becomes soluble, and the other becomes insoluble in water upon heating displaying upper critical solution temperature (UCST) (e.g., polysulfobetaine, PSB) and lower critical solution temperature (LCST) (e.g., poly(N-vinylcaprolactam, PVCL)) respectively. Polyzwitterions, electrically neutral polymers are biocompatible, biodegradable, and non-cytotoxic in nature, and presence of zwitterionic pendant group in the main backbone makes them stable against temperature and pH variations and strong hydration capability in salt solution promotes them to be used as interfacial bio-adhesion resistance material. Majority of zwitterionic microgels have been synthesized in mini- emulsion technique using free radical polymerization approach. Here, a new route to synthesize dual thermo-responsive PVCL microgels decorated with appreciable amount of zwitterionic PSB chains was developed by a purely water-based surfactant-free reversible addition–fragmentation chain transfer (RAFT) precipitation polymerization. PSB macro-RAFTs having different molecular weights were synthesized and utilized for surface-grafting with PVCL microgels varying the macro-RAFT concentration using N,N′-methylenebis(acrylamide) (BIS) as cross-linker. Increasing the PSB concentration in the PVCL microgels resulted in a linear increase in UCST but decrease in hydrodynamic radius due to strong intrachain coulombic attraction forces acting between the opposite charges present in the zwitterionic groups. Anti- fouling effect was observed on addition of BSA protein solution on the microgel-coated membrane surfaces as studied by fluorescence spectrophotoscopy.

Keywords: microgels, polyzwitterions, upper critical solution temperature-lower critical solution temperature, UCST-LCST, ionic crosslinking

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Nurjannatul Naim Kamaruddin, Tengku Sifziuzl Tengku Muhammad, Aina Farahiyah Abdul Manan, Habsah Mohamad

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Atherosclerosis which leads to cardiovascular diseases such as myocardial infarction, unstable angina (ischemic heart pain), sudden cardiac death and stroke is the principal cause of death worldwide. It has been a very critical issue as current common drug treatment, statin therapy has left bad side effects like rhabdomyolysis, atrial fibrillation, liver disease, abdominal and chest pain. Interestingly, the discoveries of proprotein convertase subtilisin-kexin type 9 have paved a new way in the treatment of atherosclerosis. This serine protease is believed to involve in the regulation of LDL- uptake by LDL-receptor. Therefore, this study was conducted to evaluate the potential of Acanthaster plancii fractions to reduce the transcriptional activity of the PCSK9 promoter. In this study, the marine organism which is Acanthaster plancii has been used as the source for marine compounds in inhibiting PCSK9. The cytotoxicity activity of ten fractions from the methanol extracts of Acanthaster plancii was investigated on HepG2 cell lines using MTS assay and dual glo luciferase assay was carried out later to analyses the effects of the samples in reducing the transcriptional activity of the PCSK9 promoter. Both assays used fractions with five different concentrations, 3.13µg/mL, 6.25µg/mL, 12.5µg/mL, 25µg/mL, and 50µg/mL. MTS assay indicated that the fractions are non-cytotoxic towards HepG2 cell lines as their IC50 value is greater than 30µg/mL. Whilst, for the dual glo luciferase assay, among all the fractions, Enhance Fraction 2 (EF2) showed the best potential in reducing the transcriptional activity of the PCSK9 promoter. The results indicated that this EF2 gave the lowest PCSK9 promoter expression at low concentration which is 0.2 fold change at 6.25µg/mL. This finding suggested that further analysis should be done to validate the potential of Acanthaster plancii as the source of anti-atherosclerotic agent.

Keywords: Acanthaster plancii, atherosclerosis, luciferase assay, PCSK9

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Authors: Sukru Merey, Caglar Sinayuc

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Gas hydrate deposits which are found in deep ocean sediments and in permafrost regions are supposed to be a fossil fuel reserve for the future. The Black Sea is also considered rich in terms of gas hydrates. It abundantly contains gas hydrates as methane (CH₄~80 to 99.9%) source. In this study, by using the literature, seismic and other data of the Black Sea such as salinity, porosity of the sediments, common gas type, temperature distribution and pressure gradient, the optimum gas production method for the Black Sea gas hydrates was selected as mainly depressurization method. Numerical simulations were run to analyze gas production from gas hydrate deposited in turbidites in the Black Sea by depressurization.

Keywords: CH4 hydrate, Black Sea hydrates, gas hydrate experiments, HydrateResSim

Procedia PDF Downloads 628

Authors: Balasooriya B. M. C. M., Sujeeva N., Thowfeek Z., Siddiqa Omo, Liyanagunawardana J. E., Jayawardana Saiu, Manathunga S. S., Katulanda G. W.

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Introduction and objectives: Adrenal venous sampling (AVS) is the gold standard to discriminate unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and only performed in a limited number of centers worldwide. To the best of our knowledge, Except for one study conducted in India, no other research studies on this area have been conducted in South Asia. This study aimed to evaluate the effectiveness of AVS in the management of primary aldosteronism. Methods: A total of 32 patients who underwent AVS at the National Hospital of Sri Lanka from April 2021 to April 2023 were enrolled. Demographic, clinical and laboratory data were obtained retrospectively. A procedure was considered successful when adequate cannulation of both adrenal veins was demonstrated. Cortisol gradient across the adrenal vein (AV) and the peripheral vein was used to establish the success of venous cannulation. Lateralization was determined by the aldosterone gradient between the two sides. Continuous and categorical variables were summarized with mean, SD, and proportions, respectively. The mean and standard deviation of the contralateral suppression index (CSI) were estimated with an intercept-only Bayesian inference model. Results: Of the 32 patients, the average age was 52.47 +26.14 and 19 (59.4%) were males. Both AVs were successfully cannulated in 12 (37.5%). Among them, lateralization was demonstrated in 11(91.7%), and one was diagnosed as a bilateral disease. There were no total failures. Right AV cannulation was unsuccessful in 18 (56.25%), of which lateralization was demonstrated in 9 (50%), and others were inconclusive. Left AV cannulation was unsuccessful only in 2 (6.25%); one was lateralized, and the other remained inconclusive. The estimated mean of the CSI was 0.33 (89% credible interval 0.11-0.86). Seven patients underwent unilateral adrenalectomy and demonstrated significant improvement in blood pressure during follow-up. Two patients await surgery. Others were treated medically. Conclusions: Despite failure due to procedural difficulties, AVS remained useful in the management of patients with PA. Moreover, the success of the procedure needs experienced hands and advanced equipment to achieve optimal outcomes in PA.

Keywords: adrenal venous sampling, lateralization, contralateral suppression index, primary aldosteronism

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

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

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

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Authors: Hercules Argyropoulos, Thomas F. Johnson, Nigel B Jackson, Kalliopi Zourna, Daniel G. Bracewell

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Lipid encapsulation has become essential in drug delivery, notably for mRNA vaccines during the COVID-19 pandemic. However, their sterile filtration poses challenges due to the risk of deformation, filter fouling and product loss from adsorption onto the membrane. Choosing the right filtration membrane is crucial to maintain sterility and integrity while minimizing product loss. The objective of this study is to develop a rigorous analytical framework utilizing confocal microscopy and filtration blocking models to elucidate the fouling mechanisms of liposomes as a model system for this class of delivery vehicle during sterile filtration, particularly in response to variations in transmembrane pressure (TMP) during the filtration process. Experiments were conducted using fluorescent Lipoid S100 PC liposomes formulated by micro fluidization and characterized by Multi-Angle Dynamic Light Scattering. Dual-layer PES/PES and PES/PVDF membranes with 0.2 μm pores were used for filtration under constant pressure, cycling from 30 psi to 5 psi and back to 30 psi, with 5, 6, and 5-minute intervals. Cross-sectional membrane samples were prepared by microtome slicing and analyzed with confocal microscopy. Liposome characterization revealed a particle size range of 100-140 nm and an average concentration of 2.93x10¹¹ particles/mL. Goodness-of-fit analysis of flux decline data at varying TMPs identified the intermediate blocking model as most accurate at 30 psi and the cake filtration model at 5 psi. Membrane resistance analysis showed atypical behavior compared to therapeutic proteins, with resistance remaining below 1.38×10¹¹ m⁻¹ at 30 psi, increasing over fourfold at 5 psi, and then decreasing to 1-1.3-fold when pressure was returned to 30 psi. This suggests that increased flow/shear deforms liposomes enabling them to more effectively navigate membrane pores. Confocal microscopy indicated that liposome fouling mainly occurred in the upper parts of the dual-layer membrane.

Keywords: sterile filtration, membrane resistance, microfluidization, confocal microscopy, liposomes, filtration blocking models

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Authors: Zhou Ziyi, Chen Qiuxiao, Wu Shuang

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Population diversification is very common in villages located in the developed coastal areas of China. Based on the analyses of the characteristics of the traditional rural society and its landscape, also in consideration of the diversified landscape demand due to the population diversification of the village, with the dual ideas of heritage and innovation, the ideas and methods of rural landscape design were explored by taking Duxuao Village in Zhejiang Province of China as an example.

Keywords: rural landscape, population diversification, landscape design, architecture

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: Remzi Yildirim, Fatih. V. Çelebi, H. Haldun Göktaş, A. Behzat Şahin

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This study is about a single component cylindrical structured lens with gradient curve which we used for bending laser beams. It operates under atmospheric conditions and bends the laser beam independent of temperature, pressure, polarity, polarization, magnetic field, electric field, radioactivity, and gravity. A single piece cylindrical lens that can bend laser beams is invented. Lenses are made of transparent, tinted or colored glasses and used for undermining or absorbing the energy of the laser beams.

Keywords: laser, bending, lens, light, nonlinear optics

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Authors: Remzi Yildirim, Fatih V. Çelebi, H. Haldun Göktaş, A. Behzat Şahin

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This study is about a single component cylindrical structured lens with gradient curve which we used for bending laser beams. It operates under atmospheric conditions and bends the laser beam independent of temperature, pressure, polarity, polarization, magnetic field, electric field, radioactivity, and gravity. A single piece cylindrical lens that can bend laser beams is invented. Lenses are made of transparent, tinted or colored glasses and used for undermining or absorbing the energy of the laser beams.

Keywords: laser, bending, lens, light, nonlinear optics

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Authors: Tobias Schramm, Günther Prokop

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The average age of passenger cars is rising steadily around the world. Older vehicles are more sensitive to the degradation of chassis components. A higher age and a higher mileage of passenger cars correlate with an increased failure rate of vehicle shock absorbers. The most common degradation mechanism of vehicle shock absorbers is the loss of oil and gas. It is not yet fully understood how the loss of oil and gas in twin-tube shock absorbers affects the lateral dynamics of passenger cars. The aim of this work is to estimate the effect of degraded twin-tube shock absorbers of passenger cars on their safety-critical lateral dynamics. A characteristic curve-based five-mass full vehicle model and a semi-physical phenomenological shock absorber model were set up, parameterized and validated. The shock absorber model is able to reproduce the damping characteristics of vehicle twin-tube shock absorbers with oil and gas loss for various excitations. The full vehicle model was used to simulate stationary cornering and steering wheel angle step maneuvers on road classes A to D. The simulations were carried out in a realistic parameter space in order to demonstrate the influence of various vehicle characteristics on the effect of degraded shock absorbers. As a result, it was shown that degraded shock absorbers have a negative effect on the understeer gradient of vehicles. For stationary lateral dynamics, degraded shock absorbers for high road excitations reduce the maximum lateral accelerations. Degraded rear axle shock absorbers can change the understeer gradient of a vehicle in the direction of oversteer. Degraded shock absorbers also lead to increased rolling angles. Furthermore, degraded shock absorbers have a major impact on driving stability during steering wheel angle steps. Degraded rear axle shock absorbers, in particular, can lead to unstable handling. Especially the tire stiffness, the unsprung mass and the stabilizer stiffness influence the effect of degraded shock absorbers on the lateral dynamics of passenger cars.

Keywords: driving dynamics, numerical simulation, road safety, shock absorber degradation, stationary and nonstationary lateral dynamics.

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Authors: Othmane Bouferroum, Malika Boudraa

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In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature.

Keywords: acoustic invariance, coarticulation, formant transition, locus equation

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Authors: Jenifer Alam, Rima Chatterjee

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Upper Assam sedimentary basin is one of the oldest commercially producing basins of India. Being in a tectonically active zone, estimation of tectonic strain and stress magnitudes has vast application in hydrocarbon exploration and exploitation. This East North East –West South West trending shelf-slope basin encompasses the Bramhaputra valley extending from Mikir Hills in the southwest to the Naga foothills in the northeast. Assam Shelf lying between the Main Boundary Thrust (MBT) and Naga Thrust area is comparatively free from thrust tectonics and depicts normal faulting mechanism. The study area is bounded by the MBT and Main Central Thrust in the northwest. The Belt of Schuppen in the southeast, is bordered by Naga and Disang thrust marking the lower limit of the study area. The entire Assam basin shows low-level seismicity compared to other regions of northeast India. Pore pressure (PP), vertical stress magnitude (SV) and horizontal stress magnitudes have been estimated from two wells - N1 and T1 located in Upper Assam. N1 is located in the Assam gap below the Bramhaputra river while T1, lies in the Belt of Schuppen. N1 penetrates geological formations from top Alluvial through Dhekiajuli, Girujan, Tipam, Barail, Kopili, Sylhet and Langpur to the granitic basement while T1 in trusted zone crosses through Girujan Suprathrust, Tipam Suprathrust, Barail Suprathrust to reach Naga Thrust. Normal compaction trend is drawn through shale points through both wells for estimation of PP using the conventional Eaton sonic equation with an exponent of 1.0 which is validated with Modular Dynamic Tester and mud weight. Observed pore pressure gradient ranges from 10.3 MPa/km to 11.1 MPa/km. The SV has a gradient from 22.20 to 23.80 MPa/km. Minimum and maximum horizontal principal stress (Sh and SH) magnitudes under isotropic conditions are determined using poroelastic model. This approach determines biaxial tectonic strain utilizing static Young’s Modulus, Poisson’s Ratio, SV, PP, leak off test (LOT) and SH derived from breakouts using prior information on unconfined compressive strength. Breakout derived SH information is used for obtaining tectonic strain due to lack of measured SH data from minifrac or hydrofracturing. Tectonic strain varies from 0.00055 to 0.00096 along x direction and from -0.0010 to 0.00042 along y direction. After obtaining tectonic strains at each well, the principal horizontal stress magnitudes are calculated from linear poroelastic model. The magnitude of Sh and SH gradient in normal faulting region are 12.5 and 16.0 MPa/km while in thrust faulted region the gradients are 17.4 and 20.2 MPa/km respectively. Model predicted Sh and SH matches well with the LOT data and breakout derived SH data in both wells. It is observed from this study that the stresses SV>SH>Sh prevailing in the shelf region while near the Naga foothills the regime changes to SH≈SV>Sh area corresponds to normal faulting regime. Hence this model is a reliable tool for predicting stress magnitudes from well logs under active tectonic regime in Upper Assam Basin.

Keywords: Eaton, strain, stress, poroelastic model

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Authors: Hamza Rekab Djabri, Salah Daoud

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The present paper deals with the computational of structural and electronic properties of ThC and ThN compounds using density functional theory within generalized-gradient (GGA) apraximation and local density approximation (LDA). We employ the full potential linear muffin-tin orbitals (FP-LMTO) as implemented in the Lmtart code. We have used to examine structure parameter in eight different structures such as in NaCl (B1), CsCl (B2), ZB (B3), NiAs (B8), PbO (B10), Wurtzite (B4) , HCP (A3) βSn (A5) structures . The equilibrium lattice parameter, bulk modulus, and its pressure derivative were presented for all calculated phases. The calculated ground state properties are in good agreement with available experimental and theoretical results.

Keywords: DFT, GGA, LDA, properties structurales, ThC, ThN

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Authors: Issa S. Oweis, Gary Gartenberg, Luma J. Oweis

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The site for a residential apartment building overlies an abandoned iron mine in granitic gneiss in northern New Jersey. The mine stope is about 137 m (450 long) and dipping over 344m (800 feet) at 450 to 500. As the building footprint straddles, the mine site needed remediation. The remediation scheme consisted of compaction grouting a minimum 10 m (30 ft.) depth of the mine stope in rock to establish a buttress for the hanging wall and allow support of the building foundation. The rock strength parameters (friction and cohesion) were established based on Hoek Geologic Strength Index (GSI). The derived strength parameters were used in the wedge analysis to simulate rock cave-in. It was concluded that a cave-in would be unlikely. Verification holes confirmed the effectiveness of grouting. Although post grouting micro gravity survey depicted a few anomalies, no anomalies were found to exist by further drilling and excavation.

Keywords: grout, stope, rock, properties

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Authors: Ikah Ning Prasetiowati Permanasari, Gunawan Handayani, Lilik Hendrajaya

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Landslide occurence at Mukapayung, Cililin West Java with material movement downward slope as far as 500m and hit residential areas of the village Nagrog cause eighteen people died and ten homes were destroyed and twenty-three heads of families evacuated. In order to test the hypothesis that soil at the landslides area is prone to landslides, we do drilling and the following tests were taken: particle size distribution, atterberg limits, shear strength, density, shringkage limits and triaxial unconsolidated and consolidated undrained test. Factor of safety was calculated to find out the possibility of subsequent landslides. The value of FOS of three layers is 1,05 which means that the soil in a critical condition and would be imminent to slide if there is disruption from the outside.

Keywords: atterberg limits, particle size distribution, shear strength parameters, slope geometry, factor of safety

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Haniyeh Sameie

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In the process of globalization, geographical boundaries gradually lose their importance, and ethnic, local, and regional cultures are integrated with each other and even marginalized. Globalization has many manifestations and aspects, including economic, political, social, etc., but this paper has focused on the cultural aspect of globalization. From this point of view, one of the important issues that have always been raised is the assimilation of diverse and plural cultures, which are gradually disappearing and destroyed in the onslaught of global culture and are dissolved in global culture. In the postmodern paradigm, the tools of the globalized world can be used to preserve and strengthen cultural diversity. For example, the Internet, as a globalization tool, can play an important role in preserving and recognizing local cultures. In today's world, the world nations and ethnic groups are trying to revive their specific and native cultures in different ways in opposition to the rising cultural assimilation and challenge the globalization of culture. One of the manifestations of these actions is addressing the issue of tourism and, specifically, eco-tourism, which is being developed in Iran as well as in other parts of the world, relying on the powerful tool of globalization, the Internet. Considering the significant growth of the ecotourism industry in Iran in recent years, this paper focuses on the role of the Internet in the development of ecotourism in Iran as one of the manifestations of tourism in recent decades and how to preserve and survive diverse local cultures and strengthen local communities against global culture through it. One of the major challenges in the development of communication technology in Iran in the last decade has been the debate over the necessity or non-necessity of access to high-speed Internet in the villages of Iran. Some believe that accessing the broadband internet in the villages may lead to the disappearance of local cultures and can facilitate the spread of western culture among villagers. On the other hand, the speed of expansion of ecotourism in Iran in the last ten years is owed to the development of the Internet in villages. In this regard, we pay attention to the dual role of the Internet in cultural assimilation and, at the same time, the platform that the online space has created for the growth and development of ecotourism accommodations as a source of stable income for local communities, focusing on the Iranian experience in the recent decade.

Keywords: tourism, globalization, internet, ecotourism in Iran, cultural assimilation

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Authors: Shoukat Ali, Amjad Hussain, Latif-ur-Rehman, Sehrish Inam

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Radiotherapy plays an important role in the management of cancer patients. Approximately 50% of the cancer patients receive radiotherapy at one point or another during the course of treatment. The entire radiotherapy treatment of curative intent is divided into different phases, depending on the histology of the tumor. The established protocols are useful in deciding the total dose, fraction size, and numbers of phases. The objective of this study was to evaluate the dosimetric differences between the conventional treatment protocols and the three-dimensional conformal simultaneously integrated boost (SIB) plans for three different tumors sites (i.e. bladder, breast, and brain). A total of 30 patients with brain, breast and bladder cancers were selected in this retrospective study. All the patients were CT simulated initially. The primary physician contoured PTV1 and PTV2 in the axial slices. The conventional doses prescribed for brain and breast is 60Gy/30 fractions, and 64.8Gy/36 fractions for bladder treatment. For the SIB plans biological effective doses (BED) were calculated for 25 fractions. The two conventional (Phase I and Phase II) and a single SIB plan for each patient were generated on Eclipse™ treatment planning system. Treatment plans were compared and analyzed for coverage index, conformity index, homogeneity index, dose gradient and organs at risk doses.In both plans 95% of PTV volume received a minimum of 95% of the prescribe dose. Dose deviation in the optic chiasm was found to be less than 0.5%. There is no significant difference in lung V20 and heart V30 in the breast plans. In the rectum plans V75%, V50% and V25% were found to be less than 1.2% different. Deviation in the tumor coverage, conformity and homogeneity indices were found to be less than 1%. SIB plans with three dimensional conformal radiotherapy technique reduce the overall treatment time without compromising the target coverage and without increasing dose to the organs at risk. The higher dose per fraction may increase the late effects to some extent. Further studies are required to evaluate the late effects with the intention of standardizing the SIB technique for practical implementation.

Keywords: coverage index, conformity index, dose gradient, homogeneity index, simultaneously integrated boost

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Authors: Jai Prakash Verma, Khushboo Verma

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In the present paper, a pair of Mond-Weir type non-differentiable multiobjective second-order programming problems, involving two kernel functions, where each of the objective functions contains support function, is formulated. We prove weak, strong and converse duality theorem for the second-order symmetric dual programs under η-pseudoinvexity conditions.

Keywords: non-differentiable multiobjective programming, second-order symmetric duality, efficiency, support function, eta-pseudoinvexity

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Authors: Najmeh Hoseini

Abstract:

Strokerecovery happens through neuroplasticity, which is highly influenced by the environment, including neuro-rehabilitation. Transcranial direct current stimulation (tDCS) may enhance recovery by modulating neuroplasticity. With tDCS, weak direct currents are applied noninvasively to modify excitability in the cortical areas under its electrodes. Combined with functional activities, this may facilitate motor recovery in neurologic disorders such as stroke. The purpose of this case study was to examine the effect of tDCS combined with 30 minutes of traditional physical therapy (PT)on arm function following a stroke. A 29-year-old male with chronic stroke involving the left middle cerebral artery territory went through the treatment protocol. Design The design included 5 weeks of treatment: 1 week of traditional PT, 2 weeks of sham tDCS combined with traditional PT, and 2 weeks of tDCS combined with traditional PT. PT included functional electrical stimulation (FES) of wrist extensors followed by task-specific functional training. Dual hemispheric tDCS with 1 mA intensity was applied on the sensorimotor cortices for the first 20 min of the treatment combined with FES. Assessments before and after each treatment block included Modified Ashworth Scale, ChedokeMcmaster Arm and Hand inventory, Action Research Arm Test (ARAT), and the Box and Blocks Test. Results showed reduced spasticity in elbow and wrist flexors only after tDCS combination weeks (+1 to 0). The patient demonstrated clinically meaningful improvements in gross motor and fine motor control over the duration of the study; however, components of the ARAT that require fine motor control improved the greatest during the experimental block. Average time improvement compared to baseline was26.29 s for tDCS combination weeks, 18.48 s for sham tDCS, and 6.83 for PT standard of care weeks. Combining dual hemispheric tDCS with the standard of care PT demonstrated improvements in hand dexterity greater than PT alone in this patient case.

Keywords: tDCS, stroke, case study, physical therapy

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Authors: Petr Slobodian, Pavel Riha, Jiri Matyas, Robert Olejnik, Nuri Karakurt

Abstract:

Thermoelectric devices generate an electrical current when there is a temperature gradient between the hot and cold junctions of two dissimilar conductive materials typically n-type and p-type semiconductors. Consequently, also the polymeric semiconductors composed of polymeric matrix filled by different forms of carbon nanotubes with proper structural hierarchy can have thermoelectric properties which temperature difference transfer into electricity. In spite of lower thermoelectric efficiency of polymeric thermoelectrics in terms of the figure of merit, the properties as stretchability, flexibility, lightweight, low thermal conductivity, easy processing, and low manufacturing cost are advantages in many technological and ecological applications. Polyethylene-octene copolymer based highly elastic composites filled with multi-walled carbon nanotubes (MWCTs) were prepared by sonication of nanotube dispersion in a copolymer solution followed by their precipitation pouring into non-solvent. The electronic properties of MWCNTs were moderated by different treatment techniques such as chemical oxidation, decoration by Ag clusters or addition of low molecular dopants. In this concept, for example, the amounts of oxygenated functional groups attached on MWCNT surface by HNO₃ oxidation increase p-type charge carriers. p-type of charge carriers can be further increased by doping with molecules of triphenylphosphine. For partial altering p-type MWCNTs into less p-type ones, Ag nanoparticles were deposited on MWCNT surface and then doped with 7,7,8,8-tetracyanoquino-dimethane. Both types of MWCNTs with the highest difference in generated thermoelectric power were combined to manufacture polymeric based thermoelectric module generating thermoelectric voltage when the temperature difference is applied between hot and cold ends of the module. Moreover, it was found that the generated voltage by the thermoelectric module at constant temperature gradient was significantly affected when exposed to vapors of different volatile organic compounds representing then a self-powered thermoelectric sensor for chemical vapor detection.

Keywords: carbon nanotubes, polymer composites, thermoelectric materials, self-powered gas sensor

Procedia PDF Downloads 158