Search results for: Mindlin’s 2nd gradient model

Authors: Adedapo Jepson Olumide, Kurowska Ewa, K. Schoeneich, Ikpokonte A. Enoch

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In this paper, subsurface temperature measured from continuous temperature logs were used to determine the geothermal gradient of NigerDelta sedimentary basin. The measured temperatures were corrected to the true subsurface temperatures by applying the American Association of Petroleum Resources (AAPG) correction factor, borehole temperature correction factor with La Max’s correction factor and Zeta Utilities borehole correction factor. Geothermal gradient in this basin ranges from 1.20C to 7.560C/100m. Six geothermal anomalies centres were observed at depth in the southern parts of the Abakaliki anticlinorium around Onitsha, Ihiala, Umuaha area and named A1 to A6 while two more centre appeared at depth of 3500m and 4000m named A7 and A8 respectively. Anomaly A1 describes the southern end of the Abakaliki anticlinorium and extends southwards, anomaly A2 to A5 were found associated with a NW-SE structural alignment of the Calabar hinge line with structures describing the edge of the Niger Delta basin with the basement block of the Oban massif. Anomaly A6 locates in the south-eastern part of the basin offshore while A7 and A8 are located in the south western part of the basin offshore. At the average exploratory depth of 3500m, the geothermal gradient values for these anomalies A1, A2, A3, A4, A5, A6, A7, and A8 are 6.50C/100m, 1.750C/100m, 7.50C/100m, 1.250C/100m, 6.50C/100m, 5.50C/100m, 60C/100m, and 2.250C/100m respectively. Anomaly A8 area may yield higher thermal value at greater depth than 3500m. These results show that anomalies areas of A1, A3, A5, A6 and A7 are potentially prospective and explorable for geothermal energy using abandoned oil wells in the study area. Anomalies A1, A3.A5, A6 occur at areas where drilled boreholes were not exploitable for oil and gas but for the remaining areas where wells are so exploitable there appears no geothermal anomaly. Geothermal energy is environmentally friendly, clean and reversible.

Keywords: temperature logs, geothermal gradient anomalies, alternative energy, Niger delta basin

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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: Supriya Manikrao Ovhal

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In India, the summer monsoon rainfall occurs owing to large scale convection with reference to continental ITCZ. It was found that convection over tropical ocean increases with SST from 26 to 28 degree C, and when SST is above 29 degree C, it sharply decreases for warm pool areas of Indian and for monsoon areas of West Pacific Ocean. The reduction in convection can be influenced by large scale subsidence forced by nearby or remotely generated deep convection, thus it was observed that under the influence of strong large scale rising motion, convection does not decreases but increases monotonically with SST even if SST value is higher than 29.5 degree C. Since convection is related to SST gradient, that helps to generate low level moisture convergence and upward vertical motion in the atmosphere. Strong wind fields like cross equatorial low level jet stream on equator ward side of the warm pool are produced due to convection initiated by SST gradient. Areas having maximum SST have low SST gradient, and that result in feeble convection. Hence it is imperative to mention that the oceanic role (other than SST) could be prominent in influencing large Scale Atmospheric convection. Since warm oceanic surface somewhere or the other contributes to penetrate the heat radiation to the subsurface of the ocean, and as there is no studies seen related to oceanic subsurface role in large Scale Atmospheric convection, in the present study, we are concentrating on the oceanic subsurface contribution in large Scale Atmospheric convection by considering the SST gradient, mixed layer depth (MLD), thermocline, barrier layer. The present study examines the probable role of subsurface ocean parameters in influencing convection.

Keywords: sst, d20, olr, wind

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Authors: Jeries J. Abou-Hanna

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Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: fracture mechanics, finite element method, stress intensity factor, stress gradient

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Authors: Vijaya K. Srivastava, Davide Spinello

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This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: constrained integer problems, enumerative search algorithm, Heuristic algorithm, Tunneling algorithm

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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In the present study, the molecular modeling of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives whit significant anticancer activity was carried out. Modelling of studied compounds was performed by CS ChemBioDraw Ultra v12.0 program for drawing 2D molecular structures and CS ChemBio3D Ultra v12.0 for 3D molecular modelling. The obtained 3D structures were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. Full geometry optimization was done by the Austin Model 1 (AM1) until the root mean square (RMS) gradient reached a value smaller than 0.0001 kcal/Åmol using Molecular Orbital Package (MOPAC) program. The obtained physicochemical, lipophilicity and topological descriptors were used for analysis of molecular similarities and dissimilarities applying suitable chemometric methods (principal component analysis and cluster analysis). These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1306.

Keywords: androstane derivatives, anticancer activity, chemometrics, molecular descriptors

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Authors: E. Hovad, J. H. Walther, P. Larsen, J. Thorborg, J. H. Hattel

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In sand casting of metal parts for the automotive industry such as brake disks and engine blocks, the molten metal is poured into a sand mold to get its final shape. The DISAMATIC molding process is a way to construct these sand molds for casting of steel parts and in the present work numerical simulations of this process are presented. During the process green sand is blown into a chamber and subsequently squeezed to finally obtain the sand mould. The sand flow is modelled with the Discrete Element method (DEM) and obtaining the correct material parameters for the simulation is the main goal. Different tests will be used to find or calibrate the DEM parameters needed; Poisson ratio, Young modulus, rolling friction coefficient, sliding friction coefficient and coefficient of restitution (COR). The Young modulus and Poisson ratio are found from compression tests of the bulk material and subsequently used in the DEM model according to the Hertz-Mindlin model. The main focus will be on calibrating the rolling resistance and sliding friction in the DEM model with respect to the behavior of “real” sand piles. More specifically, the surface profile of the “real” sand pile will be compared to the sand pile predicted with the DEM for different values of the rolling and sliding friction coefficients. When the DEM parameters are found for the particle-particle (sand-sand) interaction, the particle-wall interaction parameter values are also found. Here the sliding coefficient will be found from experiments and the rolling resistance is investigated by comparing with observations of how the green sand interacts with the chamber wall during experiments and the DEM simulations will be calibrated accordingly. The coefficient of restitution will be tested with different values in the DEM simulations and compared to video footages of the DISAMATIC process. Energy dissipation will be investigated in these simulations for different particle sizes and coefficient of restitution, where scaling laws will be considered to relate the energy dissipation for these parameters. Finally, the found parameter values are used in the overall discrete element model and compared to the video footage of the DISAMATIC process.

Keywords: discrete element method, physical properties of materials, calibration, granular flow

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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: Mohammadamin Abbasnejad

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The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition.

Keywords: Gaussian Conditional Random Field, ADMM, convergence, gradient descent

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Authors: Rafik Baouche, Rabah Chaouchi

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This work reports the first comprehensive stress field interpretation from the eleven recently drilled wells in the Berkaoui Basin, Algerian Sahara. A cumulative length of 7000+m acoustic image logs from 06 vertical wells were investigated, and a mean NW-SE (128°-145° N) maximum horizontal stress (SHMax) orientation is inferred from the B-D quality wellbore breakouts. The study integrates log-based approach with the downhole measurements to infer pore pressure, in-situ stress magnitudes. Vertical stress (Sv), interpreted from the bulk-density profiles, has an average gradient of 22.36 MPa/km. The Ordovician and Cambrian reservoirs have a pore pressure gradient of 13.47-13.77 MPa/km, which is more than the hydrostatic pressure regime. A 17.2-18.3 MPa/km gradient of minimum horizontal stress (Shmin) is inferred from the fracture closure pressure in the reservoirs. Breakout widths constrained the SHMax magnitude in the 23.8-26.5 MPa/km range. Subsurface stress distribution in the central Saharan Algeria indicates that the present-day stress field in the Berkaoui Basin is principally strike-slip faulting (SHMax > Sv > Shmin). Inferences are drawn on the regional stress pattern and drilling and reservoir development.

Keywords: stress, imagery, breakouts, sahara

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Authors: Zhijun Ren, Zhang Lin, Zhao Ye, Zuo Xiangyu, Mei Dongxing

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As a pretreatment process of ballast water treatment, the performance of high gradient magnetic separation (HGMS) technology for the removal of particulates and microorganisms was studied. The results showed that HGMS process could effectively remove suspended particles larger than 5 µm and had ability to resist impact load. Microorganism could also be effectively removed by HGMS process, and the removal effect increased with increasing magnetic field strength. The maximum removal rates for Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) were 4016.1% and 9675.3% higher, respectively, than without the magnetic field. In addition, the superoxide dismutase (SOD) activity of the microbes decreased by 32.2% when the magnetic field strength was 15.4 mT for 72 min. The microstructure of the stainless steel wool was investigated, and the results showed that particle removal by HGMS has common function by the magnetic force of the high-strength, high-gradient magnetic field on weakly magnetic particles in the water, and on the stainless steel wool.

Keywords: HGMS, particulates, superoxide dismutase (SOD) activity, steel wool magnetic medium

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Stella C. Okenu, Stephen O. Adikwu, Martins E. Okoro

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The geothermal energy resource potential of the Lower Benue Trough (LBT) in Nigeria was evaluated in this study using spectral analysis of high-resolution aeromagnetic (HRAM) data. The reduced to the equator aeromagnetic data was divided into sixteen (16) overlapping blocks, and each of the blocks was analyzed to obtain the radial averaged power spectrum which enabled the computation of the top and centroid depths to magnetic sources. The values were then used to assess the Curie Point Depth (CPD), geothermal gradients, and heat flow variations in the study area. Results showed that CPD varies from 7.03 to 18.23 km, with an average of 12.26 km; geothermal gradient values vary between 31.82 and 82.50°C/km, with an average of 51.21°C/km, while heat flow variations range from 79.54 to 206.26 mW/m², with an average of 128.02 mW/m². Shallow CPD zones that run from the eastern through the western and southwestern parts of the study area correspond to zones of high geothermal gradient values and high subsurface heat flow distributions. These areas signify zones associated with anomalous subsurface thermal conditions and are therefore recommended for detailed geothermal energy exploration studies.

Keywords: geothermal energy, curie-point depth, geothermal gradient, heat flow, aeromagnetic data, LBT

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Authors: Kuniyoshi Abe

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Bi-conjugate gradient (Bi-CG) is a well-known method for solving linear equations Ax = b, for x, where A is a given n-by-n matrix, and b is a given n-vector. Typically, the dimension of the linear equation is high and the matrix is sparse. A number of hybrid Bi-CG methods such as conjugate gradient squared (CGS), Bi-CG stabilized (Bi-CGSTAB), BiCGStab2, and BiCGstab(l) have been developed to improve the convergence of Bi-CG. Bi-CGSTAB has been most often used for efficiently solving the linear equation, but we have seen the convergence behavior with a long stagnation phase. In such cases, it is important to have Bi-CG coefficients that are as accurate as possible, and the stabilization strategy, which stabilizes the computation of the Bi-CG coefficients, has been proposed. It may avoid stagnation and lead to faster computation. Motivated by a large number of processors in present petascale high-performance computing hardware, the scalability of Krylov subspace methods on parallel computers has recently become increasingly prominent. The main bottleneck for efficient parallelization is the inner products which require a global reduction. The resulting global synchronization phases cause communication overhead on parallel computers. The parallel variants of Krylov subspace methods reducing the number of global communication phases and hiding the communication latency have been proposed. However, the numerical stability, specifically, the convergence speed of the parallel variants of Bi-CGSTAB may become worse than that of the standard Bi-CGSTAB. In this paper, therefore, we compare the convergence speed between the standard Bi-CGSTAB and the parallel variants by numerical experiments and show that the convergence speed of the standard Bi-CGSTAB is faster than the parallel variants. Moreover, we propose the stabilization strategy for the parallel variants.

Keywords: bi-conjugate gradient stabilized method, convergence speed, Krylov subspace methods, linear equations, parallel variant

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Authors: Tu-Chieh Hung, Wei-Mon Yan, Xiao-Dong Wang, Yu-Xian Huang

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This work employs a combined optimization procedure including a simplified conjugate-gradient method and a three-dimensional fluid flow and heat transfer model to study the optimal geometric parameter design of double-layered microchannel heat sinks. The overall thermal resistance RT is the objective function to be minimized with number of channels, N, the channel width ratio, β, the bottom channel aspect ratio, αb, and upper channel aspect ratio, αu, as the search variables. It is shown that, for the given bottom area (10 mm×10 mm) and heat flux (100 W cm-2), the optimal (minimum) thermal resistance of double-layered microchannel heat sinks is about RT=0.12 ℃/m2W with the corresponding optimal geometric parameters N=73, β=0.50, αb=3.52, and, αu= 7.21 under a constant pumping power of 0.05 W. The optimization process produces a maximum reduction by 52.8% in the overall thermal resistance compared with an initial guess (N=112, β=0.37, αb=10.32 and, αu=10.93). The results also show that the optimal thermal resistance decreases rapidly with the pumping power and tends to be a saturated value afterward. The corresponding optimal values of parameters N, αb, and αu increase while that of β decrease as the pumping power increases. However, further increasing pumping power is not always cost-effective for the application of heat sink designs.

Keywords: optimization, double-layered microchannel heat sink, simplified conjugate-gradient method, thermal resistance

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Authors: Saikat Datta

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Incompressible laminar time-varying flow is investigated over a rounded backward-facing step for a triangular piston motion at the inlet of a straight channel with very high acceleration, followed by a slow deceleration experimentally and through numerical simulation. The backward-facing step is an important test-case as it embodies important flow characteristics such as separation point, reattachment length, and recirculation of flow. A sliding piston imparts two successive triangular velocities at the inlet, constant acceleration from rest, 0≤t≤t0, and constant deceleration to rest, t0≤tKeywords: laminar boundary layer separation, rounded backward facing step, separation bubble, unsteady separation, unsteady vortex flows

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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

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Authors: Manjula Weerasekera, Chris Sissons, Lisa Wong, Sally Anderson, Ann Holmes, Richard Cannon

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The aim of this study was to characterize bacterial population and yeasts in saliva by Polymerase chain reaction followed by denaturing gradient gel electrophoresis (PCR-DGGE) and measure yeast levels by culture. PCR-DGGE was performed to identify oral bacteria and yeasts in 24 saliva samples. DNA was extracted and used to generate DNA amplicons of the V2–V3 hypervariable region of the bacterial 16S rDNA gene using PCR. Further universal primers targeting the large subunit rDNA gene (25S-28S) of fungi were used to amplify yeasts present in human saliva. Resulting PCR products were subjected to denaturing gradient gel electrophoresis using Universal mutation detection system. DGGE bands were extracted and sequenced using Sanger method. A potential relationship was evaluated between groups of bacteria identified by cluster analysis of DGGE fingerprints with the yeast levels and with their diversity. Significant interpersonal variation of salivary microbiome was observed. Cluster and principal component analysis of the bacterial DGGE patterns yielded three significant major clusters, and outliers. Seventeen of the 24 (71%) saliva samples were yeast positive going up to 10³ cfu/mL. Predominately, C. albicans, and six other species of yeast were detected. The presence, amount and species of yeast showed no clear relationship to the bacterial clusters. Microbial community in saliva showed a significant variation between individuals. A lack of association between yeasts and the bacterial fingerprints in saliva suggests the significant ecological person-specific independence in highly complex oral biofilm systems under normal oral conditions.

Keywords: bacteria, denaturing gradient gel electrophoresis, oral biofilm, yeasts

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Authors: J. A. Louw Coetzee, Josua P. Meyer

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The thermodynamic properties of different refrigerants in combination with the variation in geometrical properties (hydraulic diameter, aspect ratio, and inclination angle) of a rectangular microchannel determine the two-phase frictional pressure gradient. The effect of aspect ratio on frictional pressure drop had not been investigated enough during adiabatic two-phase flow and condensation in rectangular microchannels. This experimental study was concerned with measurement of the frictional pressure gradient in a rectangular microchannel, with hydraulic diameter of 900 μm. The aspect ratio of this microchannel was varied over a range that stretched from 0.3 to 3 in order to capture the effect of aspect ratio variation. A commonly used refrigerant, R134a, was used in the tests that spanned over a mass flux range of 100 to 1000 kg m-2 s-1 as well as the whole vapour quality range. This study formed part of a refrigerant condensation experiment and was therefore conducted at a saturation temperature of 40 °C. The study found that there was little influence of the aspect ratio on the frictional pressure drop at the test conditions. The data was compared to some of the well known micro- and macro-channel two-phase pressure drop correlations. Most of the separated flow correlations predicted the pressure drop data well at mass fluxes larger than 400 kg m-2 s-1 and vapour qualities above 0.2.

Keywords: aspect ratio, microchannel, two-phase, pressure gradient

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Authors: Jiahui Song

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The use of very high electric fields (~ 100kV/cm or higher) with pulse durations in the nanosecond range has been a recent development. The electric pulses have been used as tools to generate electroporation which has many biomedical applications. Most of the studies of electroporation have ignored possible thermal effects because of the small duration of the applied voltage pulses. However, it has been predicted membrane temperature gradients ranging from 0.2×109 to 109 K/m. This research focuses on thermal gradients that drives for electroporative enhancements, even though the actual temperature values might not have changed appreciably from their equilibrium levels. The dynamics of pore formation with the application of an externally applied electric field is studied on the basis of molecular dynamics (MD) simulations using the GROMACS package. Different temperatures are assigned to various regions to simulate the appropriate temperature gradients. The GROMACS provides the force fields for the lipid membranes, which is taken to comprise of dipalmitoyl-phosphatidyl-choline (DPPC) molecules. The water model mimicks the aqueous environment surrounding the membrane. Velocities of water and membrane molecules are generated randomly at each simulation run according to a Maxwellian distribution. For statistical significance, a total of eight MD simulations are carried out with different starting molecular velocities for each simulation. MD simulation shows no pore is formed in a 10-ns snapshot for a DPPC membrane set at a uniform temperature of 295 K after a 0.4 V/nm electric field is applied. A nano-sized pore is clearly seen in a 10-ns snapshot on the same geometry but with the top and bottom membrane surfaces kept at temperatures of 300 and 295 K, respectively. For the same applied electric field, the formation of nanopores is clearly demonstrated, but only in the presence of a temperature gradient. MD simulation results show enhanced electroporative effects arising from thermal gradients. The study suggests the temperature gradient is a secondary driver, with the electric field being the primary cause for electroporation.

Keywords: nanosecond, electroporation, thermal effects, molecular dynamics

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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: Katherine Carl Payne, Moshe Dror

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The problem of minimum cost routing has been extensively explored in a variety of contexts. While there is a prevalence of routing applications based on least distance, time, and related attributes, exertion-based routing has remained relatively unexplored. In particular, the network structures traditionally used to construct minimum cost paths are not suited to representing exertion or finding paths of least exertion based on road gradient. In this paper, we introduce a topographical network or “topograph” that enables minimum cost routing based on the exertion metric on each arc in a given road network as it is related to changes in road gradient. We describe an algorithm for topograph construction and present the implementation of the topograph on a road network of the state of California with ~22 million nodes.

Keywords: topograph, RPE, routing, GIS

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: Iddy Iddy, Qin Jiang, Changkuan Zhang

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This paper addresses the hydraulic performance of curtain wall breakwaters as a coastal structure protection based on the particles method modelling. The hydraulic functions of curtain wall as wave barriers by reflecting large parts of incident waves through the vertical wall, a part transmitted and a particular part was dissipating the wave energies through the eddy flows formed beneath the lower end of the plate. As a Lagrangian particle, the Moving Particle Semi-implicit (MPS) method which has a robust capability for numerical representation has proven useful for design of structures application that concern free-surface hydrodynamic flow, such as wave breaking and overtopping. In this study, a vertical two-dimensional numerical model for the simulation of violent flow associated with the interaction between the curtain-wall breakwaters and progressive water waves is developed by MPS method in which a higher precision pressure gradient model and free surface particle recognition model were proposed. The wave transmission, reflection, and energy dissipation of the vertical wall were experimentally and theoretically examined. With the numerical wave flume by particle method, very detailed velocity and pressure fields around the curtain-walls under the action of waves can be computed in each calculation steps, and the effect of different wave and structural parameters on the hydrodynamic characteristics was investigated. Also, the simulated results of temporal profiles and distributions of velocity and pressure in the vicinity of curtain-wall breakwaters are compared with the experimental data. Herein, the numerical investigation of hydraulic performance of curtain wall breakwaters indicated that the incident wave is largely reflected from the structure, while the large eddies or turbulent flows occur beneath the curtain-wall resulting in big energy losses. The improved MPS method shows a good agreement between numerical results and analytical/experimental data which are compared to related researches. It is thus verified that the improved pressure gradient model and free surface particle recognition methods are useful for enhancement of stability and accuracy of MPS model for water waves and marine structures. Therefore, it is possible for particle method (MPS method) to achieve an appropriate level of correctness to be applied in engineering fields through further study.

Keywords: curtain wall breakwaters, free surface flow, hydraulic performance, improved MPS method

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Authors: Stella Sten, Peter Hedström, Joakim Odqvist, Susanne Norgren

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Two functionally graded cemented carbide samples have been produced by local addition of Titanium carbide (TiC) to a pressed Tungsten carbide and Cobalt, WC-10 wt% Co, green body prior to sintering, with the aim of creating a gradient in both composition and grain size in the as-sintered component. The two samples differ only by the in-going WC particle size, where one sub-micron and one coarse WC particle size have been chosen for comparison. The produced sintered samples had a gradient, thus a non-homogenous structure. The Titanium (Ti), Cobalt (Co), and Carbon (C) concentration profiles have been investigated using SEM-EDS and WDS; in addition, the Vickers hardness profile has been measured. Moreover, the Ti concentration profile has been simulated using DICTRA software and compared with experimental results. The concentration and hardness profiles show a similar trend for both samples. Ti and C levels decrease, as expected from the area of TiC application, whereas Co increases towards the edge of the samples. The non-homogenous composition affects the number of stable phases and WC grain size evolution. The sample with finer in-going WC grain size shows a shorter gamma (γ) phase zone and a larger difference in WC grain size compared to the coarse-grained sample. Both samples show, independent of the composition, the presence of abnormally large grains.

Keywords: cemented carbide, functional gradient material, grain growth, sintering

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

Abstract:

This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Qingqing Zhao, Junhong Bai

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To investigate the effects of salinity on dynamics of soil carbon and nitrogen contents and stocks, soil samples were collected at a depth of 30 cm at four sampling sites (Sites B, T, S and P) along a salinity gradient in a drained coastal wetland, the Yellow River Delta, China. The salinity of these four sites ranked in the order: B (8.68±4.25 ms/cm) > T (5.89±3.17 ms/cm) > S (3.19±1.01 ms/cm) > P (2.26±0.39 ms/cm). Soil total carbon (TC), soil organic carbon (SOC), soil microbial biomass carbon (MBC), soil total nitrogen (TC) and soil microbial biomass carbon (MBC) were measured. Based on these data, soil organic carbon density (SOCD), soil microbial biomass carbon density (MBCD), soil nitrogen density (TCD) and soil microbial biomass nitrogen density (MBND) were calculated at four sites. The results showed that the mean concentrations of TC, SOC, MBC, TN and MBN showed a general deceasing tendency with increasing salinities in the top 30 cm of soils. The values of SOCD, MBCD, TND and MBND exhibited similar tendency along the salinity gradient. As for profile distribution pattern, The C/N ratios ranged from 8.28 to 56. 51. Higher C/N ratios were found in samples with high salinity. Correlation analysis showed that the concentrations of TC, SOC and MBC at four sampling sites were significantly negatively correlated with salinity (P < 0.01 or P < 0.05), indicating that salinity could inhibit soil carbon accumulation. However, no significant relationship was observed between TN, MBN and salinity (P > 0.05).

Keywords: carbon content and stock, nitrogen content and stock, salinity, coastal wetland

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Authors: Mamyrbek A. Beisenbi, Nurgul M. Kissikova, Saltanat E. Beisembina, Salamat T. Suleimenova, Samal A. Kaliyeva

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The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system.

Keywords: gradient-velocity method of Lyapunov vector-functions, hyperbolic umbilic, self-organizing control system, stability

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Authors: Pierce Radecki, Pulkit Malik, Bharath Ramaswamy, Ben Shapiro

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The ability to effectively design and test magnetic nanoparticles for controlled movement has been an elusive goal in the design of these particles. Magnetic nanoparticles of various characteristics have been created for use towards therapeutic effects, however the challenge of designing for controlled movement remains unmet. A step towards design in this aspect is a first principles model that captures and predicts the behaviors of particles in a magnetic field. The model is governed by four forces acting on the particles, the magnetic gradient, the dipole-dipole forces, the steric forces, and the viscous drag force. The particles are multi-core or single core, and incorporate a preferred magnetization axis. Particles exhibit behaviors, such as chaining, in simulations that are similar to those witnessed through experimentation. Currently, experimental results are being compared to the modeling results for verification of the model, through the analysis of chaining behaviors. This modeling system will be used in designing magnetic nanoparticles for specific chaining and movement behaviors.

Keywords: controlled movement, modeling, magnetic nanoparticles, nanoparticle design

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Authors: Mansur Abdulrasheed, Hussein I. Ibrahim, Ahmed F. Umar

Abstract:

Nitrification, the microbial oxidation of ammonia to nitrate (NO3-) via nitrite (NO2-) is a vital process in the biogeochemical nitrogen cycle and is performed by two distinct functional groups; ammonia oxidisers (comprised of ammonia oxidising bacteria (AOB) and ammonia oxidising archaea (AOA)) and nitrite oxidising bacteria. Autotrophic nitrification is said to occur in acidic soils, even though most laboratory cultures of isolated ammonia and nitrite oxidising bacteria fail to grow below neutral pH. Published studies revealed that soil pH is a major driver for determining the distribution and abundance of AOB and AOA. To determine whether distinct populations of nitrite oxidising bacteria within the lineages of Nitrospira and Nitrobacter are adapted to a particular range of pH as observed in ammonia oxidising organisms, the community structure of Nitrospira-like and Nitrobacter-like NOB were examined across a pH gradient (4.5–7.5) by amplifying nitrite oxido-reductase (nxrA) and 16S rRNA genes followed by denaturing gradient gel electrophoresis (DGGE). The community structure of both Nitrospira and Nitrobacter changed with soil pH, with distinct populations observed in acidic and neutral soils. The abundance of Nitrospira-like 16S rRNA and Nitrobacter-like nxrA gene copies contrasted across the pH gradient. Nitrobacter-like nxrA gene abundance decreased with increasing soil pH, whereas Nitrospira-like 16S rRNA gene abundance increased with increasing pH. Findings indicated that abundance and distributions of soil NOB is influence by soil pH.

Keywords: nitrospira, nitrobacter, nitrite-oxidizing bacteria, nitrification, pH, soil

Procedia PDF Downloads 268