Search results for: fuzzy linear regression

Authors: Abhishek, Seema Devi, Jyoti Ohri

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A mobile ad hoc network (MANET) represents a system of wireless mobile nodes that can self-organize freely and dynamically into arbitrary and temporary network topology. Unlike a wired network, wireless network interface has limited transmission range. Routing is the task of forwarding data packets from source to a given destination. Ad-hoc On Demand Distance Vector (AODV) routing protocol creates a path for a destination only when it required. This paper describes the implementation of AODV routing protocol using MATLAB-based Truetime simulator. In MANET's node movements are not fixed while they are random in nature. Hence intelligent techniques i.e. fuzzy and ANFIS are used to optimize the transmission range. In this paper, we compared the transmission range of AODV, fuzzy AODV and ANFIS AODV. For soft computing AODV, we have taken transmitted power and received threshold as input and transmission range as output. ANFIS gives better results as compared to fuzzy AODV.

Keywords: ANFIS, AODV, fuzzy, MANET, reactive routing protocol, routing protocol, truetime

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Authors: M. Bassoui, M. Ferfra, M. Chrayagne

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This paper presents a qualitative modeling for the nonlinear B-H curve of the saturable magnetic materials for a transformer with shunts used in the power supply for the magnetron. This power supply is composed of a single phase leakage flux transformer supplying a cell composed of a capacitor and a diode, which double the voltage and stabilize the current, and a single magnetron at the output of the cell. A procedure consisting of a fuzzy clustering method and a rule processing algorithm is then employed for processing the constructed fuzzy modeling rules to extract the qualitative properties of the curve.

Keywords: B(H) curve, fuzzy clustering, magnetron, power supply

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Authors: Azar Khodabakhshi, Elham Bolandnazar

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Agriculture as the most important food manufacturing sector is not only the energy consumer, but also is known as energy supplier. Using energy is considered as a helpful parameter for analyzing and evaluating the agricultural sustainability. In this study, the pattern of energy consumption of strawberry greenhouses of Jiroft in Kerman province of Iran was surveyed. The total input energy required in the strawberries production was calculated as 113314.71 MJ /ha. Electricity with 38.34% contribution of the total energy was considered as the most energy consumer in strawberry production. In this study, Neuro Fuzzy networks was used for function modeling in the production of strawberries. Results showed that the best model for predicting the strawberries function had a correlation coefficient, root mean square error (RMSE) and mean absolute percentage error (MAPE) equal to 0.9849, 0.0154 kg/ha and 0.11% respectively. Regards to these results, it can be said that Neuro Fuzzy method can be well predicted and modeled the strawberry crop function.

Keywords: crop yield, energy, neuro-fuzzy method, strawberry

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Authors: Robert O. Abolade, Olumide O. Ajayi, Zacheaus K. Adeyemo, Solomon A. Adeniran

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Link adaptation is an important strategy for achieving robust wireless multimedia communications based on quality of service (QoS) demand. Scheme switching in multiple-input multiple-output (MIMO) systems is an aspect of link adaptation, and it involves selecting among different MIMO transmission schemes or modes so as to adapt to the varying radio channel conditions for the purpose of achieving QoS delivery. However, finding the most appropriate switching method in MIMO links is still a challenge as existing methods are either computationally complex or not always accurate. This paper presents an intelligent switching method for the MIMO system consisting of two schemes - transmit diversity (TD) and spatial multiplexing (SM) - using fuzzy logic technique. In this method, two channel quality indicators (CQI) namely average received signal-to-noise ratio (RSNR) and received signal strength indicator (RSSI) are measured and are passed as inputs to the fuzzy logic system which then gives a decision – an inference. The switching decision of the fuzzy logic system is fed back to the transmitter to switch between the TD and SM schemes. Simulation results show that the proposed fuzzy logic – based switching technique outperforms conventional static switching technique in terms of bit error rate and spectral efficiency.

Keywords: channel quality indicator, fuzzy logic, link adaptation, MIMO, spatial multiplexing, transmit diversity

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Authors: Afsaneh Mohammadnejad, Marianne Nygaard, Jan Baumbach, Shuxia Li, Weilong Li, Jesper Lund, Jacob v. B. Hjelmborg, Lene Christensen, Qihua Tan

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Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies.

Keywords: cognition, generalized correlation coefficient, GWAS, twins

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Authors: Mesfer Al Duhayyim, Paul Newbury

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This paper proposes an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the chosen subject area. A Fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain, and produce a ranked concept list of learning materials to address weaknesses in the learner’s understanding. This system obtains information on the learner's understanding of concepts by an initial pre-test before the system is used for learning and a post-test after using the learning system. A Fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance learner's performance and understanding. In addition, this research aims to increase participants' overall understanding of their learning level by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.

Keywords: adaptive e-learning system, coloured concept map, fuzzy logic, ranked concept list

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Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean

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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.

Keywords: non-linear wilcoxon, robust estimation, variogram estimation, wilcoxon norm

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Authors: Suman Bala, Sunil Kamboj, Vipin Saini

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Quantitative Structure Activity Relationship (QSAR) analysis has been developed to relate antifungal activity of novel substituted 1,3,4-oxadiazole against Candida albicans and Aspergillus niger using computer assisted multiple regression analysis. The study has shown the better relationship between antifungal activities with respect to various descriptors established by multiple regression analysis. The analysis has shown statistically significant correlation with R² values 0.932 and 0.782 against Candida albicans and Aspergillus niger respectively. These derivatives were further subjected to molecular docking studies to investigate the interactions between the target compounds and amino acid residues present in the active site of glucosamine-6-phosphate synthase. All the synthesized compounds have better docking score as compared to standard fluconazole. Our results could be used for the further design as well as development of optimal and potential antifungal agents.

Keywords: 1, 3, 4-oxadiazole, QSAR, multiple linear regression, docking, glucosamine-6-phosphate synthase

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Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

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Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: artificial neural network, bending angle, fuzzy logic, laser forming

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Authors: K. Kouzi

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In order to have stable and high performance of direct torque and flux control (DTFC) of double star induction motor drive (DSIM), proper on-line adaptation of the stator resistance is very important. This is inevitably due to the variation of the stator resistance during operating conditions, which introduces error in estimated flux position and the magnitude of the stator flux. Error in the estimated stator flux deteriorates the performance of the DTFC drive. Also, the effect of error in estimation is very important especially at low speed. Due to this, our aim is to overcome the sensitivity of the DTFC to the stator resistance variation by proposing on-line fuzzy estimation stator resistance. The fuzzy estimation method is based on an on-line stator resistance correction through the variations of the stator current estimation error and its variations. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of the suggested algorithm control is to avoid the drive instability that may occur in certain situations and ensure the tracking of the actual stator resistance. The validity of the technique and the improvement of the whole system performance are proved by the results.

Keywords: direct torque control, dual stator induction motor, Fuzzy Logic estimation, stator resistance adaptation

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Authors: Abbas Al-Refaie

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This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L₉ array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.

Keywords: fuzzy goal programming, control charts, process capability, tablet optimization

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Authors: Cristina Schreiner, Romeo Ciobanu, Marius Pislaru

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This paper aims to build a system based on fuzzy models that can be implemented in the assessment of ecological systems, to determine appropriate methods of action for reducing adverse effects on environmental and implicit the population. The model proposed provides new perspective for environmental assessment, and it can be used as a practical instrument for decision-making.

Keywords: ecosystem model, environmental security, fuzzy logic, sustainability of habitable regions

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Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

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The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

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Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

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

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

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

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Authors: Mehdi Jafari Matehkolaee

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In this paper, we have obtained the adjoint of an arbitrary operator (linear and nonlinear) in Hilbert space by introducing an n-dimensional Riemannian manifold. This general formalism covers every linear operator (non – differential) in Hilbert space. In fact, our approach shows that instead of using the adjoint definition of an operator directly, it can be obtained directly by relying on a suitable generalized space according to the action of the operator in question. For the case of nonlinear operators, we have to change the definition of the linear operator adjoint. But here, we have obtained an adjoint of these operators with respect to the definition of the derivative of the operator. As a matter of fact, we have shown one of the straight applications of the ''Frechet derivative'' in the algebra of the operators.

Keywords: adjoint operator, non-linear operator, differentiable operator, manifold

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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

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

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: Alireza Gilani

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In this paper, we consider the fuzzification of implicative pseudo-ideal in a pseudo-BCK algebra, and then we investigate some of their properties. We prove that the family of fuzzy implicative pseudo-ideal is completely distributive lattice.

Keywords: BCK-algebra, pseudo-BCK algebra, pseudo-ideal, implicative pseudo-ideal

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Authors: Omid Heydarnia, Akbar Allahverdizadeh, Behnam Dadashzadeh, M. R. Sayyed Noorani

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Underactuated biped robots control is one of the interesting topics in robotics. The main difficulties are its highly nonlinear dynamics, open-loop instability, and discrete event at the end of the gait. One of the methods to control underactuated systems is the partial feedback linearization, but it is not robust against uncertainties and disturbances that restrict its performance to control biped walking and running. In this paper, fuzzy partial feedback linearization is presented to overcome its drawback. Numerical simulations verify the effectiveness of the proposed method to generate stable and robust biped walking and running gaits.

Keywords: underactuated system, biped robot, fuzzy control, partial feedback linearization

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Authors: Mishu Gupta, Rama Gupta

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It has been elucidated here that non- autonomous discrete non-linear Schrödinger equation is associated with saturable non-linearity through photo-refractive media. We have investigated the localized solution of non-autonomous saturable discrete non-linear Schrödinger equations. The similarity transformation has been involved in converting non-autonomous saturable discrete non-linear Schrödinger equation to constant-coefficient saturable discrete non-linear Schrödinger equation (SDNLSE), whose exact solution is already known. By back substitution, the solution of the non-autonomous version has been obtained. We have analysed our solution for the hyperbolic and periodic form of gain/loss term, and interesting results have been obtained. The most important characteristic role is that it helps us to analyse the propagation of electromagnetic waves in glass fibres and other optical wave mediums. Also, the usage of SDNLSE has been seen in tight binding for Bose-Einstein condensates in optical mediums. Even the solutions are interrelated, and its properties are prominently used in various physical aspects like optical waveguides, Bose-Einstein (B-E) condensates in optical mediums, Non-linear optics in photonic crystals, and non-linear kerr–type non-linearity effect and photo refracting medium.

Keywords: B-E-Bose-Einstein, DNLSE-Discrete non linear schrodinger equation, NLSE-non linear schrodinger equation, SDNLSE - saturable discrete non linear Schrodinger equation

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Authors: Rabia Khan, Nargis Munir, Suriya Gharib, Syeda Roshana Ali

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In this paper linear equations are discussed in detail along with elimination method. Gaussian elimination and Gauss Jordan schemes are carried out to solve the linear system of equation. This paper comprises of matrix introduction, and the direct methods for linear equations. The goal of this research was to analyze different elimination techniques of linear equations and measure the performance of Gaussian elimination and Gauss Jordan method, in order to find their relative importance and advantage in the field of symbolic and numeric computation. The purpose of this research is to revise an introductory concept of linear equations, matrix theory and forms of Gaussian elimination through which the performance of Gauss Jordan and Gaussian elimination can be measured.

Keywords: direct, indirect, backward stage, forward stage

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Authors: Qasim M. Kriri

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Few Saudi Arabia production companies face financial profit issues until this moment. This work presents a linear integer programming model that solves a production problem of a Saudi Food Company in Saudi Arabia. An optimal solution to the above-mentioned problem is a Linear Programming solution. In this regard, the main purpose of this project is to maximize profit. Linear Programming Technique has been used to derive the maximum profit from production of natural juice at Saudi Food Co. The operations of production of the company were formulated and optimal results are found out by using Lindo Software that employed Sensitivity Analysis and Parametric linear programming in order develop Linear Programming. In addition, the parameter values are increased, then the values of the objective function will be increased.

Keywords: parameter linear programming, objective function, sensitivity analysis, optimize profit

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Authors: Bouharati Lokman, Bouharati Imen, Bouharati Khaoula, Bouharati Oussama, Bouharati Saddek

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Ionizing radiation exposure is an established cancer risk factor. Compared to other common environmental carcinogens, it is relatively easy to determine organ-specific radiation dose and, as a result, radiation dose-response relationships tend to be highly quantified. Nevertheless, there can be considerable uncertainty about questions of radiation-related cancer risk as they apply to risk protection and public policy, and the interpretations of interested parties can differ from one person to another. Examples of tools used in the analysis of the risk of developing cancer due to radiation are characterized by uncertainty. These uncertainties are related to the history of exposure and different assumptions involved in the calculation. We believe that the results of statistical calculations are characterized by uncertainty and imprecision. Having regard to the physiological variation from one person to another. In this study, we develop a tool based on fuzzy logic inference. As fuzzy logic deals with imprecise and uncertain, its application in this area is adequate. We propose a fuzzy system with three input variables (age, sex and body attainable cancer). The output variable expresses the risk of infringement rate of each organ. A base rule is established from recorded actual data. After successful simulation, this will instantly predict the risk of infringement rate of each body following chronic exposure to 0.1 Gy.

Keywords: radiation exposure, cancer, modeling, fuzzy logic

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Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: Danny Fraser, James Zhang

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Aim of the study: Our study aims to identify any statistically significant evidence as it relates to PROMs for mastectomy and implant-based reconstruction to guide future surgical management. Method: The demographic, pre and post-operative treatment and implant characteristics were collected of all patients at Basildon hospital who underwent breast reconstruction from 2017-2023. We used the Breast-Q psychosocial well-being, physical well-being, and satisfaction with breasts scales. An Independent t-test was conducted for each group, and linear regression of age and implant size. Results: 69 patients were contacted, and 39 PROMs returned. The mean age of patients was 57.6. 40% had smoked before, and 40.8% had BMI>30. 29 had pre-pectoral placement, and 40 had subpectoral placement. 17 had smooth implants, and 52 textured. Sub pectoral placement was associated with higher (75.7 vs. 61.9 p=0.046) psychosocial scores than pre pectoral, and textured implants were associated with a lower physical score than the smooth surface (34.7 VS 50.2 P=0.046). On linear regression, age was positively associated (p=0.007) with psychosocial score. Conclusion: We present a large cohort of patients who underwent breast reconstruction. Understanding the PROMs of these procedures can guide clinicians, patients and policy makers to be more informed of the course of rehabilitation of these operations. Significance: We have found that from a patient perspective subpectoral implant placement was associated with a statistically significant improvement in psychosocial scores.

Keywords: breast surgery, mastectomy, breast implants, oncology

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Authors: Z. B. Ibrahim, N. Ismail, K. I. Othman

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Fuzzy Differential Equations (FDEs) play an important role in modelling many real life phenomena. The FDEs are used to model the behaviour of the problems that are subjected to uncertainty, vague or imprecise information that constantly arise in mathematical models in various branches of science and engineering. These uncertainties have to be taken into account in order to obtain a more realistic model and many of these models are often difficult and sometimes impossible to obtain the analytic solutions. Thus, many authors have attempted to extend or modified the existing numerical methods developed for solving Ordinary Differential Equations (ODEs) into fuzzy version in order to suit for solving the FDEs. Therefore, in this paper, we proposed the development of a fuzzy version of three-point block method based on Block Backward Differentiation Formulas (FBBDF) for the numerical solution of first order FDEs. The three-point block FBBDF method are implemented in uniform step size produces three new approximations simultaneously at each integration step using the same back values. Newton iteration of the FBBDF is formulated and the implementation is based on the predictor and corrector formulas in the PECE mode. For greater efficiency of the block method, the coefficients of the FBBDF are stored at the start of the program. The proposed FBBDF is validated through numerical results on some standard problems found in the literature and comparisons are made with the existing fuzzy version of the Modified Simpson and Euler methods in terms of the accuracy of the approximated solutions. The numerical results show that the FBBDF method performs better in terms of accuracy when compared to the Euler method when solving the FDEs.

Keywords: block, backward differentiation formulas, first order, fuzzy differential equations

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Jinsiang Shaw, Shih-Chieh Tseng

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Active vibration control is good for low frequency excitation, with advantages of light weight and adaptability. This paper use a macro-fiber composite (MFC) actuator for vibration suppression in a cantilevered beam due to its higher output force to suppress the disturbance. A fuzzy sliding mode controller is developed and applied to this system. Experimental results illustrate that the controller and MFC actuator are very effective in attenuating the structural vibration near the first resonant freuqency. Furthermore, this controller is shown to outperform the traditional skyhook controller, with nearly 90% of the vibration suppressed at the first resonant frequency of the structure.

Keywords: Fuzzy sliding mode controller, macro-fiber-composite actuator, skyhook controller, vibration suppression

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