Search results for: 7A6 series manipulator

Authors: Lia Matchavariani

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Water reservoir construction on the rivers flowing into the sea complicates the coast protection, seashore starts to degrade causing coast erosion and disaster on the backdrop of current climate change. The instruments of the impact of a water reservoir on the climate and coastal areas are its contact surface with the atmosphere and the area irrigated with its water or humidified with infiltrated waters. The Black Sea coastline is characterized by the highest ecological vulnerability. The type and intensity of the water reservoir impact are determined by its morphometry, type of regulation, level regime, and geomorphological and geological characteristics of the adjoining area. Studies showed the impact of the water reservoir on the climate, on its comfort parameters is positive if it is located in the zone of insufficient humidity and vice versa, is negative if the water reservoir is found in the zone with abundant humidity. There are many natural and anthropogenic factors determining the peculiarities of the impact of the water reservoir on the climate, which can be assessed with maximum accuracy by the so-called “long series” method, which operates on the meteorological elements (temperature, wind, precipitations, etc.) with the long series formed with the stationary observation data. This is the time series, which consists of two periods with statistically sufficient duration. The first period covers the observations up to the formation of the water reservoir and another period covers the observations accomplished during its operation. If no such data are available, or their series is statistically short, “an analog” method is used. Such an analog water reservoir is selected based on the similarity of the environmental conditions. It must be located within the zone of the designed water reservoir, under similar environmental conditions, and besides, a sufficient number of observations accomplished in its coastal zone.

Keywords: coast-constituent sediment, eustasy, meteorological parameters, seashore degradation, water reservoirs impact

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Tommaso Picciafuoco, Corrado Corradini

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A considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimate of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships and in determining if climate change is producing effects on extreme event intensities and frequencies. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results indicate that: 1) in the worst conditions, for d=ta, the estimation of a single Hd value can be affected by an underestimation error up to 50%, while the average underestimation error for a series with at least 15-20 Hd values, is less than or equal to 16.7%; 2) the underestimation error values follow an exponential probability density function; 3) each very long time series of Hd contains many underestimated values; 4) relationships between the non-dimensional ratio ta/d and the average underestimate of Hd, derived from continuous rainfall data observed in many stations of Central Italy, may overcome this issue; 5) these equations should allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions.

Keywords: central Italy, extreme events, rainfall data, underestimation errors

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Smita Sonker, Uaday Singh

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Various investigators have determined the degree of approximation of functions belonging to the classes W(L r , ξ(t)), Lip(ξ(t), r), Lip(α, r), and Lipα using different summability methods with monotonocity conditions. Recently, Lal has determined the degree of approximation of the functions belonging to Lipα and W(L r , ξ(t)) classes by using Ces`aro-N¨orlund (C 1 .Np)- summability with non-increasing weights {pn}. In this paper, we shall determine the degree of approximation of 2π - periodic functions f belonging to the function classes Lipα and W(L r , ξ(t)) by C 1 .T - means of Fourier series of f. Our theorems generalize the results of Lal and we also improve these results in the light off. From our results, we also derive some corollaries.

Keywords: Lipschitz classes, product matrix operator, signals, trigonometric Fourier approximation

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Authors: Lucy Jackson, Celyn Walters, Steve Eckersley, Mini Rai, Simon Hadfield

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Space robots will play an integral part in exploring the universe and beyond. A correctly designed space robot will facilitate OOA, satellite servicing and ADR. However, problems arise when trying to design such a system as it is a highly complex multidimensional problem into which there is little research. Current design techniques are slow and specific to terrestrial manipulators. This paper presents a solution to the slow speed of robotic hardware design, and generalizes the technique to free-flying space robots. It presents Ta-DAH Design, an automated design approach that utilises a multi-objective cost function in an iterative and automated pipeline. The design approach leverages prior knowledge and facilitates the faster output of optimal designs. The result is a system that can optimise the size of the base spacecraft, manipulator and some key subsystems for any given task. Presented in this work is the methodology behind Ta-DAH Design and a number optimal space robot designs.

Keywords: space robots, automated design, on-orbit operations, hardware design

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Authors: Serkan Aras, Emrah Gulay

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Combining various forecasting models is an important tool for researchers to attain more accurate forecasts. A great number of papers have shown that selecting single models as dissimilar models, or methods based on different information as possible leads to better forecasting performances. However, there is not a certain rule regarding the number of single models to be used in any combining methods. This study focuses on determining the optimal or near optimal number for single models with the help of statistical tests. An extensive experiment is carried out by utilizing some well-known time series data sets from diverse fields. Furthermore, many rival forecasting methods and some of the commonly used combining methods are employed. The obtained results indicate that some statistically significant performance differences can be found regarding the number of the single models in the combining methods under investigation.

Keywords: combined forecast, forecasting, M-competition, time series

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Authors: F. Messaoudi, O. Haddad, R. Bouras, S. Kaci

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The development of self-compacting concrete (SCC) marks a huge step towards improved efficiency and working conditions on construction sites and in the precast industry. SCC flows easily into more complex shapes and through reinforcement bars, reduces the manpower required for the placement; no vibration is required to ensure correct compaction of concrete. This concrete contains a high volume of binder which is controlled by their rheological behavior. The paste consists of binders (Portland cement with or without supplementary cementitious materials), water, chemical admixtures and fillers. In this study, two series of tests were performed on self-compacting cement pastes made with marble waste additions as the mineral addition. The first series of this investigation was to determine the flow time of paste using Marsh cone, the second series was to determine the rheological parameters of the same paste namely yield stress and plastic viscosity using the rheometer Haake RheoStress 1. The results of this investigation allowed us to study the evolution of the yield stress, viscosity and the flow time Marsh cone paste as a function of the composition of the paste. A correlation between the results obtained on the flow test Marsh cone and those of the plastic viscosity on the mottled different cement pastes is proposed.

Keywords: adjuvant, rheological parameter, self-compacting cement pastes, waste marble

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Authors: Himanshu Chandrakar, Krishnapriya S., Rama Komaragiri, Suja K. J.

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Micro coils are significant components for micro magnetic sensors and actuators especially in biomedical devices. Non-spiral planar microcoils of square, hexagonal and octagonal shapes are introduced for the first time in this paper. Comparison between different planar spiral and non-spiral coils are also discussed. The fabrication advantages and low power dissipation of non-spiral structures make them a strong alternative for conventional spiral planar coils. Series resistance of non-spiral coil is lesser than that of spiral coils though magnetic field is slightly lesser for non-spiral coils. Comparison of different planar microcoils shows that the proposed square non-spiral coil gives better performance than other structures.

Keywords: non-spiral planar microcoil, power dissipation, series resistance, spiral

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Authors: Wieslaw Marszalek, Zdzislaw Trzaska

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Interesting properties of various one-period loops of singularly perturbed memristive circuits with mixed-mode oscillations (MMOs) are analyzed in this paper. The analysis is mixed, both analytical and numerical and focused on the properties of pinched hysteresis of the memristive element and other one-period loops formed by pairs of time-series solutions for various circuits' variables. The memristive element is the only nonlinear element in the two circuits. A theorem on periods of mixed-mode oscillations of the circuits is formulated and proved. Replacements of memristors by parallel G-C or series R-L circuits for a MMO response with equivalent RMS values is also discussed.

Keywords: mixed-mode oscillations, memristive circuits, pinched hysteresis, one-period loops, singularly perturbed circuits

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Authors: Smita Pareek, Ratna Dahiya

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The power generated by solar photovoltaic (PV) module depends on surrounding irradiance, temperature, shading conditions, and shading pattern. This paper presents a simulation of photovoltaic module using Matlab/Simulink. PV Array is also simulated by series and parallel connections of modules and their characteristics curves are given. Further PV module topology/configuration are proposed for 5.5kW inverter available in the literature. Shading of a PV array either complete or partial can have a significant impact on its power output and energy yield; therefore, the simulated model characteristics curves (I-V and P-V) are drawn for uniform shading conditions (USC) and then output power, voltage and current are calculated for variation in insolation for shading conditions. Additionally the characteristics curves are also given for a predetermined shadowing condition.

Keywords: array, series, parallel, photovoltaic, partial shading

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Authors: Daumantas Zidanavicius

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In order to investigate the bond between concrete and steel in the circular steel tube column filled with concrete, the 7 series of specimens were tested with the same geometrical parameters but different concrete properties. Two types of specimens were chosen. For the first type, the expansive additives to the concrete mixture were taken to increase internal forces. And for the second type, mechanical components were used. All 7 series of the short columns were modeled by FEM and tested experimentally. In the work, big attention was taken to the bond-slip models between steel and concrete. Results show that additives to concrete let increase the bond strength up to two times and the mechanical anchorage –up to 6 times compared to control specimens without additives and anchorage.

Keywords: concrete filled steel tube, push-out test, bond slip relationship, bond stress distribution

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Harold Vásquez, Cesar Hernández, Ingrid Páez

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This paper approach cognitive radio technic and applied pure proactive handoff Model to decrease interference between PU and SU and comparing it with reactive handoff model. Through the study and analysis of multivariate models SAW and TOPSIS join to 3 dynamic prediction techniques AR, MA ,and ARMA. To evaluate the best model is taken four metrics: number failed handoff, number handoff, number predictions, and number interference. The result presented the advantages using this type of pure proactive models to predict changes in the PU according to the selected channel and reduce interference. The model showed better performance was TOPSIS-MA, although TOPSIS-AR had a higher predictive ability this was not reflected in the interference reduction.

Keywords: cognitive radio, spectrum handoff, decision making, time series, wireless networks

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Authors: Harish Rajak, Preeti Patel

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HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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Authors: Rahul Arora, S. S. Dhami

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This paper implements the design structure of industrial robot along with the different transmission components like gear assembly and analysis of complete industrial robot. In this paper, it gives the overview on the most efficient types of modeling and different analysis results that can be obtained for an industrial robot. The investigation is executed in regards to two classifications i.e. the deformation and the stress tests. SolidWorks is utilized to design and review the 3D drawing plan while ANSYS Workbench is utilized to execute the FEA on an industrial robot and the designed component. The CAD evaluation was conducted on a disentangled model of an industrial robot. The study includes design and drafting its transmission system. In CAE study static, modal and dynamic analysis are presented. Every one of the outcomes is divided in regard with the impact of the static and dynamic analysis on the situating exactness of the robot. It gives critical data with respect to parts of the industrial robot that are inclined to harm under higher high force applications. Therefore, the mechanical structure under different operating conditions can help in optimizing the manipulator geometry and in selecting the right material for the same. The FEA analysis is conducted for four different materials on the same industrial robot and gear assembly.

Keywords: CAD, CAE, FEA, robot, static, dynamic, modal, gear assembly

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Seung-Seok Bak, Chang-Mu Jung

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An enough amount supply of industrial complexes is an important national policy in South Korea, which is highly dependent on foreign trade. A development process of the industrial complex can distinguish between the planning stage and the construction stage. The planning stage consists of the process of consulting with many stakeholders on the contents of the development of industrial complex, feasibility study, compliance with the Regional policies, and so on. The industrial complex planning stage, including licensing procedure, usually takes about three years in South Korea. The government determined that the appropriate supply of industrial complexes have been delayed, due to the long licensing period and drafted a law to shorten the license period in 2008. The law was expected to shorten the period of licensing, which was about three years, to six months. This paper attempts to show that the shortening of the licensing period does not positively affect the appropriate supply of industrial complexes. To do this, we used Interrupted Time Series Designs. As a result, it was found that the supply of industrial complexes was influenced more by other factors such as actual industrial complex demand of private sector and macro-level economic variables. In addition, the specific provisions of the law conflict with local policy and cause some problems such as damage to nature and agricultural land, traffic congestion.

Keywords: development of industrial complexes, industrial complexes, interrupted time series designs, simplification of licensing procedures for industrial complexes, time series regression

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Ajay Kumar Sinha, Sharad Singh, Anukriti Sinha

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Passive energy dissipation method for earthquake protection of structures is undergoing developments for improved performance. Combined use of different types of damping mechanisms has shown positive results in the near past. Different supplemental damping methods like viscous damping, frictional damping and metallic damping are being combined together for optimum performance. The conventional method of connecting passive dampers to structures is a parallel connection between the damper unit and structural member. Researchers are investigating coupling effect of different types of dampers. The most popular choice among the research community is coupling of viscous dampers and frictional dampers. The series and parallel coupling of these damping units are being studied for relative performance of the coupled system on response control of structures against earthquake. In this paper an attempt has been made to couple Fluid Viscous Dampers and Frictional Dampers in series and parallel to form a single unit of damping system. The relative performance of the coupled units has been studied on three dimensional reinforced concrete framed structure. The current theories of structural dynamics in practice for viscous damping and frictional damping have been incorporated in this study. The time history analysis of the structural system with coupled damper units, uncoupled damper units as well as of structural system without any supplemental damping has been performed in this study. The investigations reported in this study show significant improved performance of coupled system. A higher natural frequency of the system outside the forcing frequency has been obtained for structural systems with coupled damper units as against the other cases. The structural response of the structure in terms of storey displacement and storey drift show significant improvement for the case with coupled damper units as against the cases with uncoupled units or without any supplemental damping. The results are promising in terms of improved response of the structure with coupled damper units. Further investigations in this regard for a comparative performance of the series and parallel coupled systems will be carried out to study the optimum behavior of these coupled systems for enhanced response control of structural systems.

Keywords: frictional damping, parallel coupling, response control, series coupling, supplemental damping, viscous damping

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Authors: Nazima Ellahi, Danish Ramzan

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There is a growing trend among development economists regarding the importance of financial sector for economic development and growth activities. The development thus introduced, helps to promote welfare effects and poverty alleviation. This study is an attempt to find the nature of link between Islamic banking financing and development of output growth for Pakistan. Time series data set has been utilized for a time period ranging from 1990 to 2010. Following the Phillip Perron (PP) and Augmented Dicky Fuller (ADF) test of unit root this study applied Ordinary Least Squares (OLS) method of estimation and found encouraging results in favor of promoting the Islamic banking practices in Pakistan.

Keywords: Islamic finance, poverty alleviation, economic growth, finance, commerce

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Authors: Ghaleb Y. Abbasi, Israa Abu Rumman

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This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking.

Keywords: ARIMA models, sales demand forecasting, time series, R code

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Authors: Sandra Baßler, Peter Dünow, Mathias Marquardt

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A Fourier series based learning control (FSBLC) algorithm for tracking trajectories of mechanical systems with unknown nonlinearities is presented. Two processes are introduced to which the FSBLC with PD controller is applied. One is a simplified service robot capable of climbing stairs due to special wheels and the other is a propeller driven pendulum with nearly the same requirements on control. Additionally to the investigation of learning the feed forward for the desired trajectories some considerations on the implementation of such an algorithm on low cost microcontroller hardware are made. Simulations of the service robot as well as practical experiments on the pendulum show the capability of the used FSBLC algorithm to perform the task of improving control behavior for repetitive task of such mechanical systems.

Keywords: climbing stairs, FSBLC, ILC, service robot

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Authors: D. Prangchumpol

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This research presents a forecasting method of requisition document demands for Military units by using Exponential Smoothing methods to analyze data. The data used in the forecast is an actual data requisition document of The Adjutant General Department. The results of the forecasting model to forecast the requisition of the document found that Holt–Winters’ trend and seasonality method of α=0.1, β=0, γ=0 is appropriate and matches for requisition of documents. In addition, the researcher has developed a requisition online system to improve the performance of requisition documents of The Adjutant General Department, and also ensuring that the operation can be checked.

Keywords: requisition, holt–winters, time series, royal thai army

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Authors: Adam J. Carinci

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Background: The opioid epidemic is the most pressing healthcare crisis of our time. There is increasing recognition that opioids have limited long-term efficacy and are associated with hyperalgesia, addiction, and increased morbidity and mortality. Therefore, alternative strategies to combat chronic pain are paramount. We initiated a multicenter retrospective case series to review the efficacy of DRG stimulation in facilitating opioid tapering, opioid discontinuation and as a viable alternative to chronic opioid therapy. Purpose: The dorsal root ganglion (DRG) plays a key role in the development and maintenance of pain. Recent innovations in neuromodulation, specifically, dorsal root ganglion stimulation, offers an effective alternative to opioids in the treatment of chronic pain. This retrospective case series demonstrates preliminary evidence that DRG stimulation facilitates opioid tapering, opioid discontinuation and presents a viable alternative to chronic opioid therapy. Procedure: This small multicenter retrospective case series provides preliminary evidence that DRG stimulation facilitates opioid weaning, opioid tapering and is a viable option to opioid therapy in the treatment of chronic pain. A retrospective analysis was completed. Visual analog scale pain scores and pain medication usage were collected at the baseline visit and after four weeks, 3 months and 6 months of treatment. Ten consecutive patients across two study centers were included. The pain was rated 7.38 at baseline and decreased to 1.50 at the 4-week follow-up, a reduction of 79.5%. All patients significantly decreased their opioid pain medication use with an average > 30% reduction in morphine equivalents and four were able to discontinue their medications entirely. Conclusion: This Retrospective case series demonstrates preliminary evidence that DRG stimulation facilitates opioid tapering, opioid discontinuation and presents a viable alternative to chronic opioid therapy.

Keywords: dorsal root ganglion, neuromodulation, opioid sparing, stimulation

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Authors: Chokri Slim

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The relative failure of simultaneous equation models in the seventies has led researchers to turn to other approaches that take into account the dynamics of economic and financial systems. In this paper, we use an approach based on vector autoregressive model that is widely used in recent years. Their popularity is due to their flexible nature and ease of use to produce models with useful descriptive characteristics. It is also easy to use them to test economic hypotheses. The standard econometric techniques assume that the series studied are stable over time (stationary hypothesis). Most economic series do not verify this hypothesis, which assumes, when one wishes to study the relationships that bind them to implement specific techniques. This is cointegration which characterizes non-stationary series (integrated) with a linear combination is stationary, will also be presented in this paper. Since the work of Johansen, this approach is generally presented as part of a multivariate analysis and to specify long-term stable relationships while at the same time analyzing the short-term dynamics of the variables considered. In the empirical part, we have applied these concepts to study the dynamics of of the exchange rate in Tunisia, which is one of the most important economic policy of a country open to the outside. According to the results of the empirical study by the cointegration method, there is a cointegration relationship between the exchange rate and its determinants. This relationship shows that the variables have a significant influence in determining the exchange rate in Tunisia.

Keywords: stationarity, cointegration, dynamic models, causality, VECM models

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Authors: Ahmad Awajan, Sadam Al Wadi

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In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

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Authors: Alexander Winkler, Jozef Suchý

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This paper investigates simple implicit force control algorithms realizable with industrial robots. A lot of approaches already published are difficult to implement in commercial robot controllers, because the access to the robot joint torques is necessary or the complete dynamic model of the manipulator is used. In the past we already deal with explicit force control of a position controlled robot. Well known schemes of implicit force control are stiffness control, damping control and impedance control. Using such algorithms the contact force cannot be set directly. It is further the result of controller impedance, environment impedance and the commanded robot motion/position. The relationships of these properties are worked out in this paper in detail for the chosen implicit approaches. They have been adapted to be implementable on a position controlled robot. The behaviors of stiffness control and damping control are verified by practical experiments. For this purpose a suitable test bed was configured. Using the full mechanical impedance within the controller structure will not be practical in the case when the robot is in physical contact with the environment. This fact will be verified by simulation.

Keywords: robot force control, stiffness control, damping control, impedance control, stability

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Authors: Walaa Obaidallah Alqarafi, Wafaa Mohammed Fakieh, Alaa Abdallah Altassan

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Algebraic graph theory is defined as a bridge between algebraic structures and graphs. It has several uses in many fields, including chemistry, physics, and computer science. The prime graph is a type of graph associated with a ring R, where the vertex set is the whole ring R, and two vertices x and y are adjacent if either xRy=0 or yRx=0. However, the investigation of the prime graph over rings remains relatively limited. The behavior of this graph in extended rings, like R[x] and R[[x]], where R is a non-commutative ring, deserves more attention because of the wider applicability in algebra and other mathematical fields. To study the prime graphs over polynomials and power series rings, we used a combination of ring-theoretic and graph-theoretic techniques. This paper focuses on two invariants: the diameter and the girth of these graphs. Furthermore, the work discusses how the graph structures change when passing from R to R[x] and R[[x]]. In our study, we found that the set of strong zero-divisors of ring R represents the set of vertices in prime graphs. Based on this discovery, we redefined the vertices of prime graphs using the definition of strong zero divisors. Additionally, our results show that although the prime graphs of R[x] and R[[x]] are comparable to the graph of R, they have different combinatorial characteristics since these extensions contain new strong zero-divisors. In particular, we find conditions in which the diameter and girth of the graphs, as they expand from R to R[x] and R[[x]], do not change or do change. In conclusion, this study shows how extending a non-commutative ring R to R[x] and R[[x]] affects the structure of their prime graphs, particularly in terms of diameter and girth. These findings enhance the understanding of the relationship between ring extensions and graph properties.

Keywords: prime graph, diameter, girth, polynomial ring, power series ring

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Authors: Jazim Sohail, Filipe Teixeira-Dias

Abstract:

Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Ahmed M. Mansour, Ali M. El-Sawy, Ahmed T. Sayed

Abstract:

Logarithms reduce products to sums and powers to products; they play an important role in signal processing, communication and information theory. They are primarily used for hardware calculations, handling multiplications, divisions, powers, and roots effectively. There are three commonly used bases for logarithms; the logarithm with base-10 is called the common logarithm, the natural logarithm with base-e and the binary logarithm with base-2. This paper demonstrates different methods of calculation for log2 showing the complexity of each and finds out the most accurate and efficient besides giving in- sights to their hardware design. We present a new method called Floor Shift for fast calculation of log2, and then we combine this algorithm with Taylor series to improve the accuracy of the output, we illustrate that by using two examples. We finally compare the algorithms and conclude with our remarks.

Keywords: logarithms, log2, floor, iterative, CORDIC, Taylor series

Procedia PDF Downloads 532