Search results for: memory optimization

Authors: Hichem Kmimech, Achref Jabeur Telmoudi, Lotfi Nabli

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The estimation of the initial marking minimum (MIM) is a crucial problem in labeled Petri nets. In the case of multiple choices, the search for the initial marking leads to a problem of optimization of the minimum allocation of resources with two constraints. The first concerns the firing sequence that could be legal on the initial marking with respect to the firing vector. The second deals with the total number of tokens that can be minimal. In this article, the MIM problem is solved by the meta-heuristic particle swarm optimization (PSO). The proposed approach presents the advantages of PSO to satisfy the two previous constraints and find all possible combinations of minimum initial marking with the best computing time. This method, more efficient than conventional ones, has an excellent impact on the resolution of the MIM problem. We prove through a set of definitions, lemmas, and examples, the effectiveness of our approach.

Keywords: marking, production system, labeled Petri nets, particle swarm optimization

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Authors: A. Sri Janani, K. Immanuel Arokia James

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Cognitive radio ad hoc networks different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. Cognitive radio automatically detects available channels in wireless spectrum. This is a form of dynamic spectrum management. Cross-layer optimization is proposed, using this can allow far away secondary users can also involve into channel work. So it can increase the throughput and it will overcome the collision and time delay.

Keywords: cognitive radio, cross layer optimization, CR mesh network, heterogeneous spectrum, mesh topology, random routing optimization technique

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Authors: Abdulbaset Saad, Adel Younis, Zuomin Dong

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For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge.

Keywords: differential evolution, engineering design, expensive computations, meta-modeling, radial basis function, optimization

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Authors: Reza Khani, Ziba Hosseini

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This study was an attempt to investigate the difference between learners of Iranian Kurdish–Persian bilingual language and Persian monolinguals, regarding language strategy use (LLS). The participants of the study were 120 monolingual Persian and 120 bilingual Kurdish studying English as a foreign language (EFL). Data were collected using strategy inventory for language learning SILL. The results show bilingual reported higher use of language learning strategies in all categories of SILL except memory strategies.

Keywords: language learning, memory, monolingual, comparative study

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Authors: Q. Raza

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Getting old is inevitable, tasks which were once simple are now a daily struggle. This paper is a study of how older users interact with web application based upon a series of experiments. The experiments conducted involved 12 participants and the experiments were split into two parts. The first set gives the users a feel of current social networks and the second set take into considerations from the participants and the results of the two are compared. This paper goes in detail on the psychological aspects such as social exclusion, Metacognition memory and Therapeutic memories and how this relates to users becoming isolated from society, social networking can be the roof on a foundation of successful computer interaction. The purpose of this paper is to carry out a study and to propose new ideas to help users to be able to use social networking sites easily and efficiently.

Keywords: cognitive psychology, special memory, social networking and human computer interaction

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Authors: Mitat Uysal, Aynur Uysal

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Computer engineering and mathematics share a deep and symbiotic relationship, with mathematics providing the foundational theories and models for computer engineering advancements. From algorithm development to optimization techniques, mathematics plays a pivotal role in solving complex computational problems. This paper explores key mathematical principles that underpin computer engineering, illustrating their significance through a case study that demonstrates the application of optimization techniques using Python code. The case study addresses the well-known vehicle routing problem (VRP), an extension of the traveling salesman problem (TSP), and solves it using a genetic algorithm.

Keywords: VRP, TSP, genetic algorithm, computer engineering, optimization

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Authors: Ward Bakker, Shadi Alhakimi

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The amount of people moving towards more privacy focused instant messaging applications has grown significantly. Signal is one of these instant messaging applications, which makes Signal interesting for digital investigators. In this research, we evaluate the artifacts that are generated by the Signal messenger for Android. This evaluation was done by using the features that Signal provides to create artifacts, whereafter, we made an image of the internal storage and the process memory. This image was analysed manually. The manual analysis revealed the content that Signal stores in different locations during its operation. From our research, we were able to identify the artifacts and interpret how they were used. We also examined the source code of Signal. Using our obtain knowledge from the source code, we developed a tool that decrypts some of the artifacts using the key stored in the Android Keystore. In general, we found that most artifacts are encrypted and encoded, even after decrypting some of the artifacts. During data visualization, some artifacts were found, such as that Signal does not use relationships between the data. In this research, two interesting groups of artifacts were identified, those related to the database and those stored in the process memory dump. In the database, we found plaintext private- and group chats, and in the memory dump, we were able to retrieve the plaintext access code to the application. Nevertheless, we conclude that Signal contains a wealth of artifacts that could be very valuable to a digital forensic investigation.

Keywords: forensic, signal, Android, digital

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Authors: Kuen Ming Shu, Ren Kai Ho

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Ultrasonic horn has the functions of amplifying amplitude and reducing resonant impedance in ultrasonic system. Its primary function is to amplify deformation or velocity during vibration and focus ultrasonic energy on the small area. It is a crucial component in design of ultrasonic vibration system. There are five common design methods for ultrasonic horns: analytical method, equivalent circuit method, equal mechanical impedance, transfer matrix method, finite element method. In addition, the general optimization design process is to change the geometric parameters to improve a single performance. Therefore, in the general optimization design process, we couldn't find the relation of parameter and objective. However, a good optimization design must be able to establish the relationship between input parameters and output parameters so that the designer can choose between parameters according to different performance objectives and obtain the results of the optimization design. In this study, an ultrasonic horn provided by Maxwide Ultrasonic co., Ltd. was used as the contrast of optimized ultrasonic horn. The ANSYS finite element analysis (FEA) software was used to simulate the distribution of the horn amplitudes and the natural frequency value. The results showed that the frequency for the simulation values and actual measurement values were similar, verifying the accuracy of the simulation values. The ANSYS DesignXplorer was used to perform Response Surface optimization, which could shows the relation of parameter and objective. Therefore, this method can be used to substitute the traditional experience method or the trial-and-error method for design to reduce material costs and design cycles.

Keywords: horn, natural frequency, response surface optimization, ultrasonic vibration

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Authors: C. Andreu Sabater, D. Drago, C. Key-aberg, W. Moukrim, B. Naccache

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Present study concerns the optimization of a new vertical axis wind turbine system associated to a dynamoelectric motor. The system is composed by three Savonius wind turbines, arranged in an equilateral triangle. The idea is to propose a new concept of wind turbines through a technical approach allowing find a specific power never obtained before and therefore, a significant reduction of installation costs. In this work different wind flows across the system have been simulated, as well as precise definition of parameters and relations established between them. It will allow define the optimal rotor specific power for a given volume. Calculations have been developed with classical Savonius dimensions.

Keywords: VAWT, savonius, specific power, optimization, weibull

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Authors: Rony Tawk, Boutros Ghannam, Maroun Nemer

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Topology optimization technique presents a potential tool for the design and optimization of structures involved in mass and heat transfer. The method starts with an initial intermediate domain and should be able to progressively distribute the solid and the two fluids exchanging heat. The multi-objective function of the problem takes into account minimization of total pressure loss and maximization of heat transfer between solid and fluid subdomains. Existing methods account for the presence of only one fluid, while the actual work extends optimization distribution of solid and two different fluids. This requires to separate the channels of both fluids and to ensure a minimum solid thickness between them. This is done by adding a third objective function to the multi-objective optimization problem. This article uses density approach where each cell holds two local design parameters ranging from 0 to 1, where the combination of their extremums defines the presence of solid, cold fluid or hot fluid in this cell. Finite volume method is used for direct solver coupled with a discrete adjoint approach for sensitivity analysis and method of moving asymptotes for numerical optimization. Several examples are presented to show the ability of the method to find a trade-off between minimization of power dissipation and maximization of heat transfer while ensuring the separation and continuity of the channel of each fluid without crossing or mixing the fluids. The main conclusion is the possibility to find an optimal bi-fluid domain using topology optimization, defining a fluid to fluid heat exchanger device.

Keywords: topology optimization, density approach, bi-fluid domain, laminar steady state regime, fluid-to-fluid heat exchanger

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Elham Kiyani

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In this paper, we first introduce the concept of Q-efficient solutions in a real linear space not necessarily endowed with a topology, where Q is some nonempty (not necessarily convex) set. We also used the scalarization technique including the Gerstewitz function generated by a nonconvex set to characterize these Q-efficient solutions. The algebraic concepts of interior and closure are useful to study optimization problems without topology. Studying nonconvex vector optimization is valuable since topological interior is equal to algebraic interior for a convex cone. So, we use the algebraic concepts of interior and closure to define Q-weak efficient solutions and Q-Henig proper efficient solutions of set-valued optimization problems, where Q is not a convex cone. Optimization problems with set-valued maps have a wide range of applications, so it is expected that there will be a useful analytical tool in optimization theory for set-valued maps. These kind of optimization problems are closely related to stochastic programming, control theory, and economic theory. The paper focus on nonconvex problems, the results are obtained by assuming generalized non-convexity assumptions on the data of the problem. In convex problems, main mathematical tools are convex separation theorems, alternative theorems, and algebraic counterparts of some usual topological concepts, while in nonconvex problems, we need a nonconvex separation function. Thus, we consider the Gerstewitz function generated by a general set in a real linear space and re-examine its properties in the more general setting. A useful approach for solving a vector problem is to reduce it to a scalar problem. In general, scalarization means the replacement of a vector optimization problem by a suitable scalar problem which tends to be an optimization problem with a real valued objective function. The Gerstewitz function is well known and widely used in optimization as the basis of the scalarization. The essential properties of the Gerstewitz function, which are well known in the topological framework, are studied by using algebraic counterparts rather than the topological concepts of interior and closure. Therefore, properties of the Gerstewitz function, when it takes values just in a real linear space are studied, and we use it to characterize Q-efficient solutions of vector problems whose image space is not endowed with any particular topology. Therefore, we deal with a constrained vector optimization problem in a real linear space without assuming any topology, and also Q-weak efficient and Q-proper efficient solutions in the senses of Henig are defined. Moreover, by means of the Gerstewitz function, we provide some necessary and sufficient optimality conditions for set-valued vector optimization problems.

Keywords: algebraic interior, Gerstewitz function, vector closure, vector optimization

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Authors: Jin-Ho Han, Kyong-Ho Ri, Ju-Yong Hwang, Song-Guk Kim, Sang-Jin Kim

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This study focuses on the design of a sonochemical reactor that has excellent anticorrosion effect and acoustic pressure distribution by optimization of the reaction vessel. Sonochemical reactors using the Barbell horn transducer have advantages, including high efficiency of energy conversion, large amplitude of the transducer and low damping. Meanwhile, we performed COMSOL optimization simulations to minimize the corrosion of the horn and the inner wall of the reaction vessel by cavitation bubbles during the sonochemical reaction. It was experimentally verified that the immersion depth of the horn obtained by simulation and the geometric parameters of the vessel are suitable for optimization purposes. In this way, a sonochemical reactor with good acoustic pressure distribution and suitable for obtaining a purer reaction product can be designed.

Keywords: sonochemical reactor, COMSOL optimization simulation, immersion type, barbell horn

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Authors: Nurcihan Kiris

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Restricted sleep is common in young adults and adolescents. The results of a few objective studies of sleep deprivation on cognitive performance were not clarified. In particular, the effect of sleep deprivation on the cognitive functions associated with frontal lobe such as attention, executive functions, working memory is not well known. The aim of this study is to investigate the effect of partial sleep deprivation experimentally in adolescents on the cognitive tasks of frontal lobe including working memory, strategic thinking, simple attention, continuous attention, executive functions, and cognitive flexibility. Subjects of the study were recruited from voluntary students of Cukurova University. Eighteen adolescents underwent four consecutive nights of monitored sleep restriction (6–6.5 hr/night) and four nights of sleep extension (10–10.5 hr/night), in counterbalanced order, and separated by a washout period. Following each sleep period, cognitive performance was assessed, at a fixed morning time, using a computerized neuropsychological battery based on frontal lobe functions task, a timed test providing both accuracy and reaction time outcome measures. Only the spatial working memory performance of cognitive tasks was found to be statistically lower in a restricted sleep condition than the extended sleep condition. On the other hand, there was no significant difference in the performance of cognitive tasks evaluating simple attention, constant attention, executive functions, and cognitive flexibility. It is thought that especially the spatial working memory and strategic thinking skills of adolescents may be susceptible to sleep deprivation. On the other hand, adolescents are predicted to be optimally successful in ideal sleep conditions, especially in the circumstances requiring for the short term storage of visual information, processing of stored information, and strategic thinking. The findings of this study may also be associated with possible negative functional effects on the processing of academic social and emotional inputs in adolescents for partial sleep deprivation. Acknowledgment: This research was supported by Cukurova University Scientific Research Projects Unit.

Keywords: attention, cognitive functions, sleep deprivation, working memory

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Authors: Wlodzimierz Ogryczak, Michal Przyluski, Tomasz Sliwinski

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In problems of portfolio selection, the reward-risk ratio criterion is optimized to search for a risky portfolio with the maximum increase of the mean return in proportion to the risk measure increase when compared to the risk-free investments. In the classical model, following Markowitz, the risk is measured by the variance thus representing the Sharpe ratio optimization and leading to the quadratic optimization problems. Several Linear Programming (LP) computable risk measures have been introduced and applied in portfolio optimization. In particular, the Mean Absolute Deviation (MAD) measure has been widely recognized. The reward-risk ratio optimization with the MAD measure can be transformed into the LP formulation with the number of constraints proportional to the number of scenarios and the number of variables proportional to the total of the number of scenarios and the number of instruments. This may lead to the LP models with huge number of variables and constraints in the case of real-life financial decisions based on several thousands scenarios, thus decreasing their computational efficiency and making them hardly solvable by general LP tools. We show that the computational efficiency can be then dramatically improved by an alternative model based on the inverse risk-reward ratio minimization and by taking advantages of the LP duality. In the introduced LP model the number of structural constraints is proportional to the number of instruments thus not affecting seriously the simplex method efficiency by the number of scenarios and therefore guaranteeing easy solvability. Moreover, we show that under natural restriction on the target value the MAD risk-reward ratio optimization is consistent with the second order stochastic dominance rules.

Keywords: portfolio optimization, reward-risk ratio, mean absolute deviation, linear programming

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Authors: Israel B. Bitton

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“Memory” is associated with various phenomena, from physical to mental, personal to collective and historical to cultural. As part of a broader exploration of memory studies in philosophy and science (slated for academic publication October 2021), this specific study employs analytical methods of cognitive psychology and philosophy of memory to theorize that A) the primary human will (drive) is to significance, in that every human action and expression can be rooted in a most primal desire to be cosmically significant (however that is individually perceived); and B) that the will to significance manifests as the will to memorability, an innate desire to be remembered by others after death. In support of these broad claims, a review of various popular culture “touchpoints”—historic and contemporary records spanning literature, film and television, traditional news media, and social media—is presented to demonstrate how this very theory is repeatedly and commonly expressed (and has been for a long time) by many popular public figures as well as “everyday people.” Though developed before COVID, the crisis only increased the theory’s relevance: so many people were forced to die alone, leaving them and their loved ones to face even greater existential angst than what ordinarily accompanies death since the usual expectations for one’s “final moments” were shattered. To underscore this issue of, and response to, what can be considered a sociocultural “memory gap,” this study concludes with a summary of several projects launched by journalists at the height of the pandemic to document the memorable human stories behind COVID’s tragic warped speed death toll that, when analyzed through the lens of Viktor E. Frankl’s psychoanalytical perspective on “existential meaning,” shows how countless individuals were robbed of the last wills and testaments to their self-significance and memorability typically afforded to the dying and the aggrieved. The resulting insight ought to inform how government and public health officials determine what is truly “non-essential” to human health, physical and mental, at times of crisis.

Keywords: cognitive psychology, covid, neuroscience, philosophy of memory

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Authors: Rahimah Akbar, Abdullah Al-Hashemi, Hanan Taqi, Taiba Sadeq

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As technology continues to develop, teaching curriculums in both schools and universities have begun adopting a more computer/digital based approach to the transmission of knowledge and information, as opposed to the more old-fashioned use of textbooks. This gives rise to the question: Are there any differences in learning from a digital source over learning from a printed source, as in from a textbook? More specifically, which medium of information results in better long-term retention? A review of the confounding factors implicated in understanding the relationship between learning from the two different mediums was done. Alongside this, a 4-week cohort study involving 76 1st year English Language female students was performed, whereby the participants were divided into 2 groups. Group A studied material from a paper source (referred to as the Print Medium), and Group B studied material from a digital source (Digital Medium). The dependent variables were grading of memory recall indexed by a 4 point grading system, and total frequency of item repetition. The study was facilitated by advanced computer software called Super Memo. Results showed that, contrary to prevailing evidence, the Digital Medium group showed no statistically significant differences in terms of the shift from Remember (Episodic) to Know (Semantic) when all confounding factors were accounted for. The shift from Random Guess and Familiar to Remember occurred faster in the Digital Medium than it did in the Print Medium.

Keywords: digital medium, print medium, long-term memory recall, episodic memory, semantic memory, super memo, forgetting index, frequency of repetitions, total time spent

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Authors: Jason Digalakis, John Cotronis

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Memetic algorithms (MAs) are useful for solving optimization problems. It is quite difficult to search the search space of the optimization problem with large dimensions. There is a challenge to use all the cores of the system. In this study, a sequential implementation of the memetic algorithm is converted into a concurrent version, which is executed on the cores of both CPU and GPU. For this reason, CUDA and OpenMP libraries are operated on the parallel algorithm to make a concurrent execution on CPU and GPU, respectively. The aim of this study is to compare CPU and GPU implementation of the memetic algorithm. For this purpose, fourteen benchmark functions are selected as test problems. The obtained results indicate that our approach leads to speedups up to five thousand times higher compared to one CPU thread while maintaining a reasonable results quality. This clearly shows that GPUs have the potential to acceleration of MAs and allow them to solve much more complex tasks.

Keywords: memetic algorithm, CUDA, GPU-based memetic algorithm, open multi processing, multimodal functions, unimodal functions, non-linear optimization problems

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Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

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Authors: Xinhuang Wu, Yousef Sardahi

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A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

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Authors: Rema Vazhappilly, Tapas Das

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Oleic acid is a cis unsaturated fatty acid and is known to be a partially essential fatty acid due to its limited endogenous synthesis during pregnancy and lactation. Previous studies have demonstrated the role of oleic acid in neuronal differentiation and brain phospholipid synthesis. These evidences indicate a major role for oleic acid in learning and memory. Interestingly, oleic acid has been shown to enhance hippocampal long term potentiation (LTP), the physiological correlate of long term synaptic plasticity. However the effect of oleic acid on short term synaptic plasticity has not been investigated. Short term potentiation (STP) is the physiological correlate of short term synaptic plasticity which is the key underlying molecular mechanism of short term memory and neuronal information processing. STP in the hippocampal CA1 region has been known to require the activation of N-methyl-D-aspartate receptors (NMDARs). The NMDAR dependent hippocampal STP as a potential mechanism for short term memory has been a subject of intense interest for the past few years. Therefore in the present study the effect of oleic acid on NMDAR dependent hippocampal STP was determined in mouse hippocampal slices (in vitro) using Multi-electrode array system. STP was induced by weak tetanic Stimulation (one train of 100 Hz stimulations for 0.1s) of the Schaffer collaterals of CA1 region of the hippocampus in slices treated with different concentrations of oleic acid in presence or absence of NMDAR antagonist D-AP5 (30 µM) . Oleic acid at 20 (mean increase in fEPSP amplitude = ~135 % Vs. Control = 100%; P<0.001) and 30 µM (mean increase in fEPSP amplitude = ~ 280% Vs. Control = 100%); P<0.001) significantly enhanced the STP following weak tetanic stimulation. Lower oleic acid concentrations at 10 µM did not modify the hippocampal STP induced by weak tetanic stimulation. The hippocampal STP induced by weak tetanic stimulation was completely blocked by the NMDA receptor antagonist D-AP5 (30µM) in both oleic acid and control treated hippocampal slices. This lead to the conclusion that the hippocampal STP elicited by weak tetanic stimulation and enhanced by oleic acid was NMDAR dependent. Together these findings suggest that oleic acid may enhance the short term memory and neuronal information processing through the modulation of NMDAR dependent hippocampal short-term synaptic plasticity. In conclusion this study suggests the possible role of oleic acid to prevent the short term memory loss and impaired neuronal function throughout development.

Keywords: oleic acid, short-term potentiation, memory, field excitatory post synaptic potentials, NMDA receptor

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Authors: Gulcan Ozerim, Gunay Anlas

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In shape memory alloys, upon loading, stress increases around crack tip and a martensitic phase transformation occurs in early stages. In many studies the stress distribution in the vicinity of the crack tip is represented by using linear elastic fracture mechanics (LEFM) although the pseudo-elastic behavior results in a nonlinear stress-strain relation. In this study, the HRR singularity (Hutchinson, Rice and Rosengren), that uses Rice’s path independent J-integral, is tried to formulate the stress distribution around the crack tip. In HRR approach, the Ramberg-Osgood model for the stress-strain relation of power-law hardening materials is used to represent the elastic-plastic behavior. Although it is recoverable, the inelastic portion of the deformation in martensitic transformation (up to the end of transformation) resembles to that of plastic deformation. To determine the constants of the Ramberg-Osgood equation, the material’s response is simulated in ABAQUS using a UMAT based on ZM (Zaki-Moumni) thermo-mechanically coupled model, and the stress-strain curve of the material is plotted. An edge cracked shape memory alloy (Nitinol) plate is loaded quasi-statically under mode I and modeled using ABAQUS; the opening stress values ahead of the cracked tip are calculated. The stresses are also evaluated using the asymptotic equations of both LEFM and HRR. The results show that in the transformation zone around the crack tip, the stress values are much better represented when the HRR singularity is used although the J-integral does not show path independent behavior. For the nodes very close to the crack tip, the HRR singularity is not valid due to the non-proportional loading effect and high-stress values that go beyond the transformation finish stress.

Keywords: crack, HRR singularity, shape memory alloys, stress distribution

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Authors: Mahsa Mousavi, Hamid Reza Pourshaghaghi, Mohammad Tahghighi, Henk Corporaal

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Recently, Statistic Random-Access Memory-based (SRAM-based) Field-Programmable Gate Arrays (FPGAs) are widely used in aeronautics and space systems where high dependability is demanded and considered as a mandatory requirement. Since design’s circuit is stored in configuration memory in SRAM-based FPGAs; they are very sensitive to Single Event Upsets (SEUs). In addition, the adverse effects of SEUs on the electronics used in space are much higher than in the Earth. Thus, developing fault tolerant techniques play crucial roles for the use of SRAM-based FPGAs in space. However, fault tolerance techniques introduce additional penalties in system parameters, e.g., area, power, performance and design time. In this paper, an accurate estimation of configuration memory vulnerability to SEUs is proposed for approximate-tolerant applications. This vulnerability estimation is highly required for compromising between the overhead introduced by fault tolerance techniques and system robustness. In this paper, we study applications in which the exact final output value is not necessarily always a concern meaning that some of the SEU-induced changes in output values are negligible. We therefore define and propose Approximate-based Configuration Memory Vulnerability Factor (ACMVF) estimation to avoid overestimating configuration memory vulnerability to SEUs. In this paper, we assess the vulnerability of configuration memory by injecting SEUs in configuration memory bits and comparing the output values of a given circuit in presence of SEUs with expected correct output. In spite of conventional vulnerability factor calculation methods, which accounts any deviations from the expected value as failures, in our proposed method a threshold margin is considered depending on user-case applications. Given the proposed threshold margin in our model, a failure occurs only when the difference between the erroneous output value and the expected output value is more than this margin. The ACMVF is subsequently calculated by acquiring the ratio of failures with respect to the total number of SEU injections. In our paper, a test-bench for emulating SEUs and calculating ACMVF is implemented on Zynq-7000 FPGA platform. This system makes use of the Single Event Mitigation (SEM) IP core to inject SEUs into configuration memory bits of the target design implemented in Zynq-7000 FPGA. Experimental results for 32-bit adder show that, when 1% to 10% deviation from correct output is considered, the counted failures number is reduced 41% to 59% compared with the failures number counted by conventional vulnerability factor calculation. It means that estimation accuracy of the configuration memory vulnerability to SEUs is improved up to 58% in the case that 10% deviation is acceptable in output results. Note that less than 10% deviation in addition result is reasonably tolerable for many applications in approximate computing domain such as Convolutional Neural Network (CNN).

Keywords: fault tolerance, FPGA, single event upset, approximate computing

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Authors: Omar Bouhassoun, Cristian Garrido, César Hueso

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The radiation shielding design is an optimization problem with multiple -constrained- objective functions (radiation dose, weight, price, etc.) that depend on several parameters (material, thickness, position, etc.). The classical approach for shielding design consists of a brute force trial-and-error process subject to previous designer experience. Therefore, the result is an empirical solution but not optimal, which can degrade the overall performance of the shielding. In order to automate the shielding design procedure, the IDOM Shielding Optimization Tool (ISOT) has been developed. This software combines optimization algorithms with the capabilities to read/write input files, run calculations, as well as parse output files for different radiation transport codes. In the first stage, the software was established to adjust the input files for two well-known Monte Carlo codes (MCNP and Serpent) and optimize the result (weight, volume, price, dose rate) using multi-objective genetic algorithms. Nevertheless, its modular implementation easily allows the inclusion of more radiation transport codes and optimization algorithms. The work related to the development of ISOT and its verification on a simple 3D multi-layer shielding problem using both MCNP and Serpent will be presented. ISOT looks very promising for achieving an optimal solution to complex shielding problems.

Keywords: optimization, shielding, nuclear, genetic algorithm

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

Abstract:

Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Zair Firdaous, Fourka Mohamed, Elfelsoufi Zoubir

Abstract:

The arrival of e-commerce has changed the supply chain management on the operational level as well as on the organization and strategic and even tactical decisions of the companies. The optimization of resources is an issue that is needed on the tactical and operational strategic plan. This work considers the allocation of resources in the case of pure players that have launched online sales. The aim is to improve the level of customer satisfaction and maintaining the benefits of e-retailer and of its cooperators and reducing costs and risks. We first modeled the B2C chain with all operations that integrates and possible scenarios since online retailers offer a wide selection of personalized service. The personalized services that online shopping companies offer to the clients can be embodied in many aspects, such as the customizations of payment, the distribution methods, and after-sales service choices. Every aspect of customized service has several modes. At that time, we analyzed the optimization problems of supply chain resource in customized online shopping service mode. Then, we realized an optimization model and algorithm for the development based on the analysis of the of the B2C supply chain resources. It is a multi-objective optimization that considers the collaboration of resources in operations, time and costs but also the risks and the quality of services as well as dynamic and uncertain characters related to the request.

Keywords: supply chain resource, e-commerce, pure-players, optimization

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Authors: Geetanjali Panda, Suvrakanti Chakraborty

Abstract:

In this paper, Newton-like descent methods are proposed for unconstrained optimization problems, which use q-derivatives of the gradient of an objective function. First, a local scheme is developed with alternative sufficient optimality condition, and then the method is extended to a global scheme. Moreover, a variant of practical Newton scheme is also developed introducing a real sequence. Global convergence of these schemes is proved under some mild conditions. Numerical experiments and graphical illustrations are provided. Finally, the performance profiles on a test set show that the proposed schemes are competitive to the existing first-order schemes for optimization problems.

Keywords: Descent algorithm, line search method, q calculus, Quasi Newton method

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Authors: Sofiane Bououden, Ilyes Boulkaibet

Abstract:

In terms of security of the information signal, the meta-heuristic Penguins Search Optimization Algorithm (PeSOA) is applied to synchronize chaotic encryption communications in the case of sensitive dependence on initial conditions in chaotic generator oscillator. The objective of this paper is the use of the PeSOA algorithm to exploring search space with random and iterative processes for synchronization of symmetric keys in both transmission and reception. Simulation results show the effectiveness of the PeSOA algorithm in generating symmetric keys of the encryption process and synchronizing.

Keywords: meta-heuristic, PeSOA, chaotic systems, encryption, synchronization optimization

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Authors: Ahmed Emara, Maged Ghoneima, Mohamed Dessouky

Abstract:

Demand for low power fast memories is increasing with the increase in IC’s complexity, in this paper we introduce a proposal for a compact SRAM based on memristor devices. The compact size of the proposed cell (1T2M compared to 6T of traditional SRAMs) allows denser memories on the same area. In this paper, we will discuss the proposed memristor memory cell for single/multi bit data storing configurations along with the writing and reading operations. Stored data stability across successive read operation will be illustrated, operational simulation results and a comparison of our proposed design with previously conventional SRAM and previously proposed memristor cells will be provided.

Keywords: memristor, multi-bit, single-bit, circuits, systems

Procedia PDF Downloads 376

Authors: Cory B. Lord

Abstract:

Falls among the elderly population has become an area of concern in healthcare today. The negative impacts of falls lead to increased morbidity, mortality, and financial burdens for both patients and healthcare systems. Falls in the United States is reported at an annual rate of 36 million in those aged 65 and older. Each year, one out of four people in this age group will suffer a fall, with 20% of these falls causing injury. The setting for this Doctor of Nursing Practice (DNP) project was a memory care unit in an assisted living community, as these facilities house cognitively impaired older adults. These communities lack fall prevention programs; therefore, the need exists to add to the body of knowledge to positively impact this population. The objective of this project was to reduce fall rates through the implementation of the Center for Disease Control and Prevention (CDC) STEADI (stopping elderly accidents, deaths, and injuries) program. The DNP project performed was a quality improvement pilot study with a pre and post-test design. This program was implemented in the memory care setting over 12 weeks. The project included an educational session for staff and a fall risk assessment with appropriate resident referrals. The three aims of the DNP project were to reduce fall rates among the elderly aged 65 and older who reside in the memory care unit, increase staff knowledge of STEADI fall prevention measures after an educational session, and assess the willingness of memory care unit staff to adopt an evidence-based a fall prevention program. The Donabedian model was used as a guiding conceptual framework for this quality improvement pilot study. The fall rate data for 12 months before the intervention was evaluated and compared to post-intervention fall rates. The educational session comprised of a pre and post-test to assess staff knowledge of the fall prevention program and the willingness of staff to adopt the fall prevention program. The overarching goal was to reduce falls in the elderly population who live in memory care units. The results of the study showed, on average that the fall rate during the implementation period of STEADI (μ=6.79) was significantly lower when compared to the prior 12 months (μ= 9.50) (p=0.02, α = 0.05). The mean staff knowledge scores improved from pretest (μ=77.74%) to post-test (μ=87.42%) (p=0.00, α= 0.05) after the education session. The results of the willingness to adopt a fall prevention program were scored at 100%. In summation, implementing the STEADI fall prevention program can assist in reducing fall rates for residents aged 65 and older who reside in a memory care setting.

Keywords: dementia, elderly, falls, STEADI

Procedia PDF Downloads 137