Search results for: generalized pareto distribution

Authors: Yuanjin Liu

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Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

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Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Aleksandra Tepic-Horecki, Zdravko Sumic

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In this paper, the influence of six different phytochemical content (phenols, carotenoids, chlorophyll a, chlorophyll b, chlorophyll a + b and vitamin C) on antioxidant potential of Murai and Levistro lettuce varieties was evaluated. Variable selection was made by generalized pair correlation method (GPCM) as a novel ranking method. This method is used for the discrimination between two variables that almost equal correlate to a dependent variable. Fisher’s conditional exact and McNemar’s test were carried out. Established multiple linear (MLR) models were statistically evaluated. As the best phytochemicals for the antioxidant potential prediction, chlorophyll a, chlorophyll a + b and total carotenoids content stand out. This was confirmed through both GPCM and MLR, predictive ability of obtained MLR can be used for antioxidant potential estimation for similar lettuce samples. This article is based upon work from the project of the Provincial Secretariat for Science and Technological Development of Vojvodina (No. 114-451-347/2015-02).

Keywords: antioxidant activity, generalized pair correlation method, lettuce, regression analysis

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Authors: C. F. Kumru, C. Kocatepe, O. Arikan

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In this study, electric field distribution analyses for three pylon models are carried out by a Finite Element Method (FEM) based software. Analyses are performed in both stationary and time domains to observe instantaneous values along with the effective ones. Considering the results of the study, different line geometries is considerably affecting the magnitude and distribution of electric field although the line voltages are the same. Furthermore, it is observed that maximum values of instantaneous electric field obtained in time domain analysis are quite higher than the effective ones in stationary mode. In consequence, electric field distribution analyses should be individually made for each different line model and the limit exposure values or distances to residential buildings should be defined according to the results obtained.

Keywords: electric field, energy transmission line, finite element method, pylon

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Authors: Chaitanya Varma, Arpan Mehar

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The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Norazlin Mohamad, Saiful Adli Bukry, Zarina Zahari, Haidzir Manaf, Hanafi Sawalludin

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Background: Deficit in neuromuscular recruitment and decrease force distribution were the common problems among ankle instability patients due to altered joint kinematics that lead to recurrent ankle injuries. Rigid Tape and KT Tape had widely been used as therapeutic and performance enhancement tools in ankle stability. However the difference effect between this two tapes is still controversial. Objective: To investigate the different effect between Rigid Tape and KT Tape on force distribution and muscle activation among ankle instability patients while standing. Study design: Crossover trial. Participants: 27 patients, age between 18 to 30 years old participated in this study. All the subjects were applied with KT Tape & Rigid Tape on their affected ankle with 3 days of interval for each intervention. The subjects were tested with their barefoot (without tape) first to act as a baseline before proceeding with KT Tape, and then with Rigid Tape. Result: There were no significant difference on force distribution at forefoot and back-foot for both tapes while standing. However the mean data shows that Rigid Tape has the highest force distribution at back-foot rather than forefoot when compared with KT Tape that had more force distribution at forefoot while standing. Regarding muscle activation (Peroneus Longus), results showed significant difference between Rigid Tape and KT Tape (p= 0.048). However, there was no significant difference on Tibialis Anterior muscle activation between both tapes while standing. Conclusion: The results indicated that Peroneus longus muscle was more active when applied Rigid Tape rather than KT Tape in ankle instability patients while standing.

Keywords: ankle instability, kinematic, muscle activation, force distribution, Rigid Tape, KT tape

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Authors: Tomoaki Hashimoto

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In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution.

Keywords: optimal control, stochastic systems, discrete time systems, probabilistic constraints

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Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

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Authors: Sima Hassankhali, Ildar Sadeqi

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In the theory of Pareto efficient points, the existence of a bounded base for a cone K of a normed space X is so important. In this article, we study the geometric structure of a nonzero closed convex cone K with a bounded base. For this aim, we study the structure of the polar cone K# of K. Furthermore, we obtain a necessary and sufficient condition for a nonempty closed convex set C so that its recession cone C∞ has a bounded base.

Keywords: solid cones, recession cones, polar cones, bounded base

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Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

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Authors: Hanmei Peng, Yiqun Wang, Mao Tan, Zhuocen Dai, Yongxin Su

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Efficient energy management in multi-microgrid distribution systems holds significant importance for enhancing the economic benefits of regional power grids. To better balance conflicts among various stakeholders, a two-stage game-based collaborative optimization approach is proposed in this paper, effectively addressing the realistic scenario involving both competition and collaboration among stakeholders. The first stage, aimed at maximizing individual benefits, involves constructing a non-cooperative tariff game model for the distribution network and surplus microgrid. In the second stage, considering power flow and physical line capacity constraints we establish a cooperative P2P game model for the multi-microgrid distribution system, and the optimization involves employing the Lagrange method of multipliers to handle complex constraints. Simulation results demonstrate that the proposed approach can effectively improve the system economics while harmonizing individual and collective rationality.

Keywords: cooperative game, collaborative optimization, multi-microgrid distribution system, non-cooperative game

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Authors: Md. Rashidul Hasan, Atikur Rahman Baizid

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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.

Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function

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Authors: David Hasurungan

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This paper objective is to analyze the impact from photovoltaic system integration to power distribution network. The case study in Badak Liquefied Natural Gas (LNG) plant is presented in this paper. Badak LNG electricity network is operated in islanded mode. The total power generation in Badak LNG plant is significantly affected to feed gas supply. Meanwhile, to support the Government regulation, Badak LNG continuously implemented the grid-connected photovoltaic system in existing power distribution network. The impact between train operational mode change in Badak LNG plant and the growth of photovoltaic system is also encompassed in analysis. The analysis and calculation are performed using software Power Factory 15.1.

Keywords: power quality, distribution network, grid-connected photovoltaic system, power management system

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Authors: Jiin-Yuh Jang, Yu-Feng Gan

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In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

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Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins's problem, double-input rule module, fuzzy inference model, obstacle avoidance, single-input rule module

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Authors: Emilienne Lardy, Mariam Lafkihi, Eric Ballot

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Efficient city logistics requires a comprehensive understanding of traffic dynamics, particularly as it pertains to kinetic parameters influencing energy consumption and trip duration estimations. While real-time traffic information is increasingly accessible, current high-precision forecasting services embedded in route planning often function as opaque 'black boxes' for users. These services, typically relying on AI-processed counting data, fall short in accommodating open design parameters essential for management studies, notably within Supply Chain Management. This work revisits the modeling of traffic conditions in the context of city logistics, emphasizing its significance from the user’s point of view, with two focuses. Firstly, the focus is not on the vehicle flow but on the vehicles themselves and the impact of the traffic conditions on their driving behaviour. This means opening the range of studied indicators beyond vehicle speed to describe extensively the kinetic and dynamic aspects of driving behaviour. To achieve this, we leverage the Art. Kinema parameters are designed to characterize driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determines the mentioned driving behaviour. Specifically, we explore it investigates how accurately the kinetic behaviour of a vehicle can be predicted based on a limited set of exogenous factors, such as time, day, road type, orientation, slope, and weather conditions. To answer this question, statistical analysis was conducted on real-world driving data, which includes high-frequency measurements of vehicle speed. A Generalized Linear Model has been established to link kinetic parameters with independent categorical contextual variables. The results include the analysis of the regression’s accuracy, as well as the utilization of the regression’s results in freight distribution scenario elaboration and analysis for Supply Chain Management purposes.

Keywords: driving context, generalized linear model, kinetic behaviour, real world driving data

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Authors: Christian Meinecke, Bernd Scholz-Reiter

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The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics.

Keywords: production and outbound distribution, integrated planning, heuristic, decomposition, integration

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Waddah S. Abdullah, Khaled F. Al-Omari

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Electrokinetic remediation is one of the most influential and effective methods to decontaminate contaminated soils. Electroosmosis and electromigration are the processes of electrochemical extraction of contaminants from soils. The driving force that causes removing contaminants from soils (electroosmosis process or electromigration process) is voltage gradient. Therefore, the electric field distribution throughout the soil domain is extremely important to investigate and to determine the factors that help to establish a uniform electric field distribution in order to make the clean-up process work properly and efficiently. In this study, small-sized passive electrodes (made of graphite) were placed at predetermined locations within the soil specimen, and the voltage drop between these passive electrodes was measured in order to observe the electrical distribution throughout the tested soil specimens. The electrokinetic test was conducted on two types of soils; a sandy soil and a clayey soil. The electrical distribution throughout the soil domain was conducted with different tests properties; and the electrical field distribution was observed in three-dimensional pattern in order to establish the electrical distribution within the soil domain. The effects of density, applied voltages, and degree of saturation on the electrical distribution within the remediated soil were investigated. The distribution of the moisture content, concentration of the sodium ions, and the concentration of the calcium ions were determined and established in three-dimensional scheme. The study has shown that the electrical conductivity within soil domain depends on the moisture content and concentration of electrolytes present in the pore fluid. The distribution of the electrical field in the saturated soil was found not be affected by its density. The study has also shown that high voltage gradient leads to non-uniform electric field distribution within the electroremediated soil. Very importantly, it was found that even when the electric field distribution is uniform globally (i.e. between the passive electrodes), local non-uniformity could be established within the remediated soil mass. Cracks or air gaps formed due to temperature rise (because of electric flow in low conductivity regions) promotes electrical tortuosity. Thus, fracturing or cracking formed in the remediated soil mass causes disconnection of electric current and hence, no removal of contaminant occur within these areas.

Keywords: contaminant removal, electrical tortuousity, electromigration, electroosmosis, voltage distribution

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Sakhon Woothipatanapan, Surasit Prakobkit

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This paper presents a model for analysis the induced voltage of transmission lines (energized) acting on neighboring distribution lines (de-energized). From environmental restrictions, 22 kV distribution lines need to be installed under 115 kV transmission lines. With the installation of the two parallel circuits like this, they make the induced voltage which can cause harm to operators. This work was performed with the ATP-EMTP modeling to analyze such phenomenon before field testing. Simulation results are used to find solutions to prevent danger to operators who are on the pole.

Keywords: transmission system, distribution system, induced voltage, off-line operation

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Authors: Heba Esaily, Rawhia Eledl, Daila Aboelela, Rasha Noreldin

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Objective: Assess serum level of Transforming growth factor beta 2 (TGF-β2) and Matrilin-3 (MATN3) SNP6 polymorphism in osteoarthritic patients Background: Osteoarthritis (OA) is a musculoskeletal disease characterized by pain and joint stiffness. TGF-β 2 is involved in chondrogenesis and osteogenesis, It has found that MATN3 gene and protein expression was correlated with the extent of tissue damage in OA. Findings suggest that regulation of MATN3 expression is essential for maintenance of the cartilage extracellular matrix microenvironment Subjects and Methods: 72 cases of primary OA (56 with knee OA and 16 with generalized OA were compared with that of 18 healthy controls. Radiographs were scored with the Kellgren-Lawrence scale. Serum TGF-β2 was measured by using (ELISA), levels of marker were correlated to radiographic grading of disease and MATN3 SNP6 polymorphism was determined by (PCR-RFLP). Results: MATN3 SNP6 polymorphism and serum level of TGF-β2 were higher in OA compared with controls. Genotype, NN and N allele frequency were higher in patients with OA compared with controls. NN genotype and N allele frequency were higher in knee osteoarthritis than generalized OA. Significant positive correlation between level of TGFβ2 and radiographic grading in group with knee OA, but no correlation between serum level of TGFβ2 and radiographic grading in generalized OA. Conclusion: MATN3 SNP6 polymorphism and TGF-β2 implicated in the pathogenesis of osteoarthritis. Association of N/N genotype with primary osteoarthritis emphasizes on the need for prospective study include larger sample size to confirm the results of the present study.

Keywords: Matrilin-3, transforming growth factor beta 2, primary osteoarthritis, knee osteoarthritis

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Herman Susilo, Rahmat Budiman

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Bali province is one of the most famous tourist destinations in Indonesia. As a central tourist destination, Bali is very concerned about the use of clean energy. Since Bali is an area that does not have natural resources, so all of its energy sources are imported from java island and other islands. As an example, currently, Pertagas is developing the use of LNG for the needs of the retail industry. Right now, LNG is transported from the LNG plant facility in Bontang (Kalimantan Province) using ISO Tanks which are transported by cargo ships and then transported by trucks to the island of Bali. After that, LNG from ISO Tank is breakbulk into LNG Cylinders for distribution to retail customers. The existing distribution scheme is very long and costly since the source of LNG is come from another island (Kalimantan) and is relatively far away. To solve this problem, we plan to build the mini-LNG plant on Java Island since there are lots of gas sources available. There are some small gas reserves (flared or stranded gas) that are not yet monetized and are less valuable (cheaper) because the volume is very small. After liquifying the gas from the gas field, the LNG is transported by the truck using ISO Tank. After that, LNG from ISO Tank is breakbulk into LNG Cylinders for distribution to retail customers. From this new LNG distribution scheme, there are 4-5 USD/MMBTU saving compared to the existing distribution scheme. It is hoped that with these cost savings, the number of retail LNG sales can increase rapidly.

Keywords: LNG, LNG retail, mini LNG, small scale LNG

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Authors: Lina Wu, Ye Li

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An equivalent relation between a harmonic form and a closed co-closed form is established on a complete non-compact manifold. This equivalence has been generalized for a differential k-form ω from Lq spaces to non-Lq spaces when q=2 in the context of p-balanced growth where p=2. Especially for a simple differential k-form on a complete non-compact manifold, the equivalent relation has been verified with the extended scope of q for from finite q-energy in Lq spaces to infinite q-energy in non-Lq spaces when with 2-balanced growth. Generalized Hadamard Theorem, Cauchy-Schwarz Inequality, and Calculus skills including Integration by Parts as well as Convergent Series have been applied as estimation techniques to evaluate growth rates for a differential form. In particular, energy growth rates as indicated by an appropriate power range in a selected test function lead to a balance between a harmonic differential form and a closed co-closed differential form. Research ideas and computational methods in this paper could provide an innovative way in the study of broadening Lq spaces to non-Lq spaces with a wide variety of infinite energy growth for a differential form.

Keywords: closed forms, co-closed forms, harmonic forms, L^q spaces, p-balanced growth, simple differential k-forms

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Authors: Jaime Martín-de-Nicolás, David Mata-Moya, Nerea del-Rey-Maestre, Pedro Gómez-del-Hoyo, María-Pilar Jarabo-Amores

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Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable.

Keywords: SAR, generalized gamma distribution, detection curves, radar detection

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Authors: A. D. Zikiryahodjaev, L. V. Bolotina, A. S. Sukhotko

Abstract:

Purpose. To evaluate the expediency and timeliness of performance of surgical treatment as a component of multi-therapy treatment of patients with stage IV breast cancers. Materials and Methods. This investigation comparatively analyzed the results of complex treatment with or without surgery in patients with metastatic breast cancer. We analyzed retrospectively treatment experience of 196 patients with generalized breast cancer in the department of oncology and breast reconstructive surgery of P.A. Herzen Moscow Cancer Research Institute from 2000 to 2012. The average age was (58±1,1) years. Invasive ductul carcinoma was verified in128 patients (65,3%), invasive lobular carcinoma-33 (16,8%), complex form - 19 (9,7%). Complex palliative care involving drug and radiation therapies was performed in two patient groups. The first group includes 124 patients who underwent surgical intervention as complex treatment, the second group includes 72 patients with only medical therapy. Standard systemic therapy was given to all patients. Results. Overall, 3-and 5-year survival in fist group was 43,8 and 21%, in second - 15,1 and 9,3% respectively [p=0,00002 log-rank]. Median survival in patients with surgical treatment composed 32 months, in patients with only systemic therapy-21. The factors having influencing an influence on the prognosis and the quality of life outcomes for of patients with generalized breast cancer were are also studied: hormone-dependent tumor, Her2/neu hyper-expression, reproductive function status (age, menopause existence). Conclusion.Removing primary breast tumor in patients with generalized breast cancer improve long-term outcomes. Three- and five-year survival increased by 28,7 and 16,3% respectively, and median survival–for 11 months. These patients may benefit from resection of the breast tumor. One explanation for the effect of this resection is that reducing the tumor load influences metastatic growth.

Keywords: breast cancer, combination therapy, factors of prognosis, primary tumor

Procedia PDF Downloads 390

Authors: Ansari Mohammad Reza, Hiroko Ono

Abstract:

In this study, we investigated how much of the urban facilities are fairly distributing in the city of Kabul based on the factor of population. To find the answer to this question we simulated a fair model for the distribution of investigated facilities in the city which is proposed based on the consideration of two factors; the number of users for each facility and the average distance of reach of each facility. Then the model was evaluated to make sure about its efficiency. And finally, the two—the existing pattern and the simulation model—were compared to find the degree of bias in the existing pattern of distribution of facilities in the city. The result of the study clearly clarified that the facilities are not fairly distributed in Kabul city based on the factor of population. Our analysis also revealed that the education services and the parks are the most and the worst fair distributed facilities in this regard.

Keywords: Afghanistan, ArcGIS Software, Kabul City, fair distribution, urban facilities

Procedia PDF Downloads 141