Search results for: NARX (Nonlinear Autoregressive Exogenous Model)

Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Giovanni Cuciniello

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The introduction of tilt-rotor aircraft into the existing civilian air transportation system will provide beneficial effects due to tilt-rotor capability to combine the characteristics of a helicopter and a fixed-wing aircraft into one vehicle. The disposability of reliable tilt-rotor simulation models supports the development of such vehicle. Indeed, simulation models are required to design automatic control systems that increase safety, reduce pilot's workload and stress, and ensure the optimal aircraft configuration with respect to flight envelope limits, especially during the most critical flight phases such as conversion from helicopter to aircraft mode and vice versa. This article presents a process to build a simplified tilt-rotor simulation model, derived from the analysis of flight data. The model aims to reproduce the complex dynamics of tilt-rotor during the in-flight conversion phase. It uses a set of scheduled linear transfer functions to relate the autopilot reference inputs to the most relevant rigid body state variables. The model also computes information about the rotor flapping dynamics, which are useful to evaluate the aircraft control margin in terms of rotor collective and cyclic commands. The rotor flapping model is derived through a mixed theoretical-empirical approach, which includes physical analytical equations (applicable to helicopter configuration) and parametric corrective functions. The latter are introduced to best fit the actual rotor behavior and balance the differences existing between helicopter and tilt-rotor during flight. Time-domain system identification from flight data is exploited to optimize the model structure and to estimate the model parameters. The presented model-building process was applied to simulated flight data of the ERICA Tilt-Rotor, generated by using a high fidelity simulation model implemented in FlightLab environment. The validation of the obtained model was very satisfying, confirming the validity of the proposed approach.

Keywords: flapping dynamics, flight dynamics, system identification, tilt-rotor modeling and simulation

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Authors: Drew Fockler

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This research examines how to improve an employee’s experience when transferring between departments within an organization. This research includes a historical review of a Canadian retail organization. Based on this historical review, gaps are identified between current and future visions to show where problems with existing training and development practices need to be resolved to reduce front-line employee turnover within an organization. The strategies within this paper support leaders through the LEAD: Listen, Explore, Act and Develop, Change Management Model. The LEAD Change Management Model supports the change process. This research proposes three possible solutions to improve an employee who is transferring between departments. The best solution to resolve the problem of improving an employee moving between departments experience is creating a Training Manager position within the retail store. A Training Manager position could support both employees and leadership with training and development of staff who are moving between departments. Within this research, an implementation plan using the TransX Model was created. The TransX Model is a hybrid of Leader-Member Exchange Theory and Transformational Leadership Theory to facilitate this organizational change within an organization by creating a common vision. Finally, this research provides the next steps as well as future considerations to enhance the training manager role within an organization.

Keywords: employee transfers, employee engagement, human resources, employee induction, TransX model, lead change management model

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Authors: Alibakhsh Kasaeian, Mohammad Sameti, Zahra Noori, Mona Rastgoo Bahambari

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Nowadays, the parabolic trough solar collector technology has become the most promising large-scale technology among various solar thermal generations. In this paper, a detailed numerical heat transfer model for a parabolic trough collector with nanofluid is presented based on the finite difference approach for which a MATLAB code was developed. The model was used to simulate the performance of a parabolic trough solar collector’s linear receiver, called a heat collector element (HCE). In this model, the heat collector element of the receiver was discretized into several segments in axial directions and energy balances were used for each control volume. All the heat transfer correlations, the thermodynamic equations and the optical properties were considered in details and the set of algebraic equations were solved simultaneously using iterative numerical solutions. The modeling assumptions and limitations are also discussed, along with recommendations for model improvement.

Keywords: heat transfer, nanofluid, numerical analysis, trough

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Authors: Aysun Pınarbaşı, Béla Vizvári

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The countries are the units that procure the vaccines during the COVID-19 pandemic. The delivered quantities are huge. The countries must bear the inventory holding cost according to the variation of stock quantities. This cost depends on the speed of the vaccination in the country. This speed is time-dependent. The vaccinated portion of the population can be approximated by the cumulative distribution function of the Cauchy distribution. A model is provided for determining the minimal-cost inventory policy, and its optimality conditions are provided. The model is solved for 20 countries for different numbers of procurements. The results reveal the individual behavior of each country. We provide an inventory policy for the pandemic period for the countries. This paper presents a deterministic model for vaccines with a demand rate variable over time for the countries. It is aimed to provide an analytical model to deal with the minimization of holding cost and develop inventory policies regarding this aim to be used for a variety of perishable products such as vaccines. The saturation process is introduced, and an approximation of the vaccination curve of the countries has been discussed. According to this aspect, a deterministic model for inventory policy has been developed.

Keywords: covid-19, vaccination, inventory policy, bounded total demand, inventory holding cost, cauchy distribution, sigmoid function

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Authors: Emmanuel Agunloye, Asterios Gavriilidis, Luca Mazzei

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Gold nanoparticles are commonly synthesized by reducing chloroauric acid with sodium citrate. This method, referred to as the citrate method, can produce spherical gold nanoparticles (NPs) in the size range 10-150 nm. Gold NPs of this size are useful in many applications. However, the NPs are usually polydisperse and irreproducible. A better understanding of the synthesis mechanisms is thus required. This work thoroughly investigated the only model that describes the synthesis. This model combines mass and population balance equations, describing the NPs synthesis through a sequence of chemical reactions. Chloroauric acid reacts with sodium citrate to form aurous chloride and dicarboxy acetone. The latter organizes aurous chloride in a nucleation step and concurrently degrades into acetone. The unconsumed precursor then grows the formed nuclei. However, depending on the pH, both the precursor and the reducing agent react differently thus affecting the synthesis. In this work, we investigated the model for different conditions of pH, temperature and initial reactant concentrations. To solve the model, we used Parsival, a commercial numerical code, whilst to test it, we considered various conditions studied experimentally by different researchers, for which results are available in the literature. The model poorly predicted the experimental data. We believe that this is because the model does not account for the acid-base properties of both chloroauric acid and sodium citrate.

Keywords: citrate method, gold nanoparticles, Parsival, population balance equations, Turkevich organizer theory

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Authors: Anastasiia Yu. Timofeeva

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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi

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In this paper a real-time obstacle avoidance approach for both autonomous and non-autonomous dynamical systems (DS) is presented. In this approach the original dynamics of the controller which allow us to determine safety margin can be modulated. Different common types of DS increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle especially when robot moves very fast in changeable complex environments. The method is validated by simulation and influence of different autonomous and non-autonomous DS such as important characteristics of limit cycles and unstable DS. Furthermore, the position of different obstacles in complex environment is explained. Finally, the verification of avoidance trajectories is described through different parameters such as safety factor.

Keywords: limit cycles, nonlinear dynamical system, real time obstacle avoidance, safety margin

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Authors: Muhammad Noor Ul Ane, Dong-Soon Kim, Myron P. Zalucki

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Models can be useful to help understand population dynamics of insects under diverse environmental conditions and in developing strategies to manage pest species better. Adult longevity and fecundity of Helicoverpa armigera (Hübner) were evaluated against a wide range of constant temperatures (15, 20, 25, 30, 35 and 37.5ᵒC). The modified Sharpe and DeMichele model described adult aging rate and was used to estimate adult physiological age. Maximum fecundity of H. armigera was 973 egg/female at 25ᵒC decreasing to 72 eggs/female at 37.5ᵒC. The relationship between adult fecundity and temperature was well described by an extreme value function. Age-specific cumulative oviposition rate and age-specific survival rate were well described by a two-parameter Weibull function and sigmoid function, respectively. An oviposition model was developed using three temperature-dependent components: total fecundity, age-specific oviposition rate, and age-specific survival rate. The oviposition model was validated against independent field data and described the field occurrence pattern of egg population of H. armigera very well. Our model should be a useful component for population modeling of H. armigera and can be independently used for the timing of sprays in management programs of this key pest species.

Keywords: cotton bollworm, life table, temperature-dependent adult development, temperature-dependent fecundity

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Jozef Mindas, Jaroslav Skvarenina, Jana Skvareninova

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Study of the effects of climate change on Norway Spruce (Picea abies) forests has mainly focused on the diversity of tree species diversity of tree species as a result of the ability of species to tolerate temperature and moisture changes as well as some effects of disturbance regime changes. The tree species’ diversity changes in spruce forests due to climate change have been analyzed via gap model. Forest gap model is a dynamic model for calculation basic characteristics of individual forest trees. Input ecological data for model calculations have been taken from the permanent research plots located in primeval forests in mountainous regions in Slovakia. The results of regional scenarios of the climatic change for the territory of Slovakia have been used, from which the values are according to the CGCM3.1 (global) model, KNMI and MPI (regional) models. Model results for conditions of the climate change scenarios suggest a shift of the upper forest limit to the region of the present subalpine zone, in supramontane zone. N. spruce representation will decrease at the expense of beech and precious broadleaved species (Acer sp., Sorbus sp., Fraxinus sp.). The most significant tree species diversity changes have been identified for the upper tree line and current belt of dwarf pine (Pinus mugo) occurrence. The results have been also discussed in relation to most important disturbances (wind storms, snow and ice storms) and phenological changes which consequences are little known. Special discussion is focused on biomass production changes in relation to carbon storage diversity in different carbon pools.

Keywords: biodiversity, climate change, Norway spruce forests, gap model

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Authors: Leonard Kabeya Mukeba Yakasham

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The theoretical machinery from singularity theory introduced by Glolubitsky, Stewart, and Schaeffer, to study equivariant bifurcation problem is completed and expanded wile generalized to the multiparameter context. In this setting the finite deterinancy theorem or normal forms, the stability of equivariant bifurcation problem, and the structural stability of universal unfolding are discussed. With Yakam Matrix the solutions are limited for some partial differential equations stochastic nonlinear of the open questions in singularity artificial intelligence for future.

Keywords: equivariant bifurcation, symmetry singularity, equivariant jets and transversality, normal forms, universal unfolding instability, structural stability

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Authors: Misagh Faezipour

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System dynamics is a systems engineering approach that can help address the complex challenges in different systems. Little is known about how the brain represents people to predict behavior. This work is based on how the brain simulates different personal behavior and responds to them in the case of young children ages one to five. As we know, children’s minds/brains are just as clean as a crystal, and throughout time, in their surroundings, families, and education center, they grow to develop and have different kinds of behavior towards the world and the society they live in. Hence, this work aims to identify how young children respond to various personality behavior and observes their reactions towards them from a system dynamics perspective. We will be exploring the Big Five personality traits in young children. A causal model is developed in support of the system dynamics approach. These models graphically present the factors and factor relationships that contribute to the big five personality traits and provide a better understanding of the entire behavior model. A simulator will be developed that includes a set of causal model factors and factor relationships. The simulator models the behavior of different factors related to personality traits and their impacts and can help make more informed decisions in a risk-free environment.

Keywords: personality traits, systems engineering, system dynamics, causal model, behavior model

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Authors: Shubham Dwivedi, Raghavender Medishetti, Rita Rani, Aarti Sevilimedu, Pushkar Kulkarni, Yogeeswari Perumal

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder of early onset, characterized by impaired sociability, cognitive function and stereotypies. There is a significant urge to develop and establish new animal models with ASD-like characteristics for better understanding of underlying mechanisms. The aim of the present study was to develop a cost and time effective zebrafish model with quantifiable parameters to facilitate mechanistic studies as well as high-throughput screening of new molecules for autism. Zebrafish embryos were treated with valproic acid and a battery of behavioral tests (anxiety, inattentive behavior, irritability and social impairment) was performed on larvae at 7th day post fertilization, followed by study of molecular markers of autism. This model shows a significant behavioural impairment in valproic acid treated larvae in comparison to control which was again supported by alteration in few marker genes and proteins of autism. The model also shows a rescue of behavioural despair with positive control drugs. The model shows robust parameters to study behavior, molecular mechanism and drug screening approach in a single frame. Thus we postulate that our 7 days zebrafish larval model for autism can help in high throughput screening of new molecules on autism.

Keywords: autism, zebrafish, valproic acid, neurodevelopment, behavioral assay

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Authors: Hengki Eko Putra, Dennish Ari Putro, Tri Wahyu Hadi, Edi Riawan, Junnaedhi Dewa Gede, Aditia Rojali, Fariza Dian Prasetyo, Yudhistira Satya Pribadi, Dita Fatria Andarini, Mila Khaerunisa, Raditya Hanung Prakoswa

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Catastrophe risk management can only be done if we are able to calculate the exposed risks. Jakarta is an important city economically, socially, and politically and in the same time exposed to severe floods. On the other hand, flood risk calculation is still very limited in the area. This study has calculated the risk of flooding for Jakarta using 2-Dimensional Model ANUGA. 2-Dimensional model ANUGA and 1-Dimensional Model HEC-RAS are used to calculate the risk of flooding from 13 major rivers in Jakarta. ANUGA can simulate physical and dynamical processes between the streamflow against river geometry and land cover to produce a 1-meter resolution inundation map. The value of streamflow as an input for the model obtained from hydrological analysis on rainfall data using hydrologic model HEC-HMS. The probabilistic streamflow derived from probabilistic rainfall using statistical distribution Log-Pearson III, Normal and Gumbel, through compatibility test using Chi Square and Smirnov-Kolmogorov. Flood event on 2007 is used as a comparison to evaluate the accuracy of model output. Property damage estimations were calculated based on flood depth for 1, 5, 10, 25, 50, and 100 years return period against housing value data from the BPS-Statistics Indonesia, Centre for Research and Development of Housing and Settlements, Ministry of Public Work Indonesia. The vulnerability factor was derived from flood insurance claim. Jakarta's flood loss estimation for the return period of 1, 5, 10, 25, 50, and 100 years, respectively are Rp 1.30 t; Rp 16.18 t; Rp 16.85 t; Rp 21.21 t; Rp 24.32 t; and Rp 24.67 t of the total value of building Rp 434.43 t.

Keywords: 2D hydrodynamic model, ANUGA, flood, flood modeling

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Authors: Huda Al Shuaily, Karen Renaud

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Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: pattern, SQL, learning, model

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Authors: C. Surmei

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Autism Spectrum Disorder (ASD) is a complex developmental disorder that can affect an individual’s (but is not limited to) cognitive development, emotional development, language acquisition and the capability to relate to others. Ecological Systems Theory is a sociocultural theory that focuses on environmental systems with which an individual interacts. The SCERTS Model is an educational approach and multidisciplinary framework that addresses the challenges confronted by individuals on the autism spectrum and other developmental disabilities. To aid the understanding of ASD and educational philosophies for families, educators, and the global community alike, a Comparative Analysis was undertaken to examine key variables (the child, society, education, nurture/care, relationships, communication). The results indicated that the Ecological Systems Theory and the SCERTS Model were comparable in focus, motivation, and application, attaining to a viable and notable relationship between both theories. This paper unpacks two child development philosophies and their relationship to each other.

Keywords: autism spectrum disorder, ecological systems theory, education, SCERTS model

Procedia PDF Downloads 592

Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Carlos Riascos, Peter Thomson

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Real-time hybrid simulation (RTHS) is a modern cyber-physical technique used for the experimental evaluation of complex systems, that treats the system components with predictable behavior as a numerical substructure and the components that are difficult to model as an experimental substructure. Therefore it is an attractive method for evaluation of the response of civil structures under earthquake, wind and anthropic loads. Another practical application of RTHS is the evaluation of control systems, as these devices are often nonlinear and their characterization is an important step in the design of controllers with the desired performance. In this paper, the response of three-story shear frame controlled by a tuned liquid column damper (TLCD) and subject to base excitation is considered. Both passive and semi-active control strategies were implemented and are compared. While the passive TLCD achieved a reduction of 50% in the acceleration response of the main structure in comparison with the structure without control, the semi-active TLCD achieved a reduction of 70%, and was robust to variations in the dynamic properties of the main structure. In addition, a RTHS was implemented with the main structure modeled as a linear, time-invariant (LTI) system through a state space representation and the TLCD, with both control strategies, was evaluated on a shake table that reproduced the displacement of the virtual structure. Current assessment measures for RTHS were used to quantify the performance with parameters such as generalized amplitude, equivalent time delay between the target and measured displacement of the shake table, and energy error using the measured force, and prove that the RTHS described in this paper is an accurate method for the experimental evaluation of structural control systems.

Keywords: structural control, hybrid simulation, tuned liquid column damper, semi-active sontrol strategy

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Authors: Sarra Haouala, Issam Doghri

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In this work, a multiscale computational strategy is proposed for the analysis of structures, which are described at a refined level both in space and in time. The proposal is applied to two-phase viscoelastic-viscoplastic (VE-VP) reinforced thermoplastics subjected to large numbers of cycles. The main aim is to predict the effective long time response while reducing the computational cost considerably. The proposed computational framework is a combination of the mean-field space homogenization based on the generalized incrementally affine formulation for VE-VP composites, and the asymptotic time homogenization approach for coupled isotropic VE-VP homogeneous solids under large numbers of cycles. The time homogenization method is based on the definition of micro and macro-chronological time scales, and on asymptotic expansions of the unknown variables. First, the original anisotropic VE-VP initial-boundary value problem of the composite material is decomposed into coupled micro-chronological (fast time scale) and macro-chronological (slow time-scale) problems. The former is purely VE, and solved once for each macro time step, whereas the latter problem is nonlinear and solved iteratively using fully implicit time integration. Second, mean-field space homogenization is used for both micro and macro-chronological problems to determine the micro and macro-chronological effective behavior of the composite material. The response of the matrix material is VE-VP with J2 flow theory assuming small strains. The formulation exploits the return-mapping algorithm for the J2 model, with its two steps: viscoelastic predictor and plastic corrections. The proposal is implemented for an extended Mori-Tanaka scheme, and verified against finite element simulations of representative volume elements, for a number of polymer composite materials subjected to large numbers of cycles.

Keywords: asymptotic expansions, cyclic loadings, inclusion-reinforced thermoplastics, mean-field homogenization, time homogenization

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: S. K. Thind, Aparjot Kaur

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Heat stress is one of the major environmental factors drastically reducing wheat production. Crop heat tolerance can be enhanced by preconditioning of plants by exogenous application of osmoprotectants. Presently, the effect of trehalose pretreatment (at 1 mM, and 1.5 nM) under heat stress of 35±2˚C (moderate) and 40±2˚ (severe) for four and eight hour was conducted in wheat (Tricticum aestivum L.) genotypes viz. HD2967, PBW 175, PBW 343, PBW 621, and PBW 590. Heat stress affects wide spectrum of physiological processes within plants that are irreversibly damaged by stress. Membrane thermal stability (MTS) and cell viability was significantly decreased under heat stress for eight hours. Pretreatment with trehalose improved MTS and cell viability under stress and this effect was more promotory with higher concentration. Thermal stability of photosynthetic apparatus differed markedly between genotypes and Hill reaction activity was recorded more in PBW621 followed by C306 as compared with others. In all genotypes photolysis of water showed decline with increase in temperature stress. Trehalose pretreatment helped in sustaining Hill reaction activity probably by stabilizing the photosynthetic apparatus against heat-induced photo inhibition. Both plant growth and development were affected by temperature in both shoot and root under heat stress. The reduction was compensated partially by trehalose (1.5 mM) application. Adaption to heat stress is associated with the metabolic adjustment which led to accumulation of soluble sugars including non-reducing and reducing for their role in adaptive mechanism. Higher acid invertase activity in shoot of tolerant genotypes appeared to be a characteristic for stress tolerance. As sucrose synthase play central role in sink strength and in studied wheat genotype was positively related to dry matter accumulation. The duration of heat stress for eight hours had more severe effect on these parameters and trehalose application at 1.5 mM ameliorated it to certain extent.

Keywords: heat stress, Triticum aestivum, trehalose, membrane thermal stability, triphenyl tetrazolium chloride, reduction test, growth, sugar metabolism

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Authors: Irfan Ali, Srikant Gupta, Aquil Ahmed

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Every country needs a sustainable development (SD) for its economic growth by forming suitable policies and initiative programs for the development of different sectors of the country. This paper is comprised of modeling and optimization of different sectors of India that form a multi-criterion model. In this paper, we developed a fractional goal programming (FGP) model that helps in providing the efficient allocation of resources simultaneously by achieving the sustainable goals in gross domestic product (GDP), electricity consumption (EC) and greenhouse gasses (GHG) emission by the year 2030. Also, a weighted model of FGP is presented to obtain varying solution according to the priorities set by the policy maker for achieving future goals of GDP growth, EC, and GHG emission. The presented models provide a useful insight to the decision makers for implementing strategies in a different sector.

Keywords: sustainable and economic development, multi-objective fractional programming, fuzzy goal programming, weighted fuzzy goal programming

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Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Tanzeel Rashid, Burhan Ali, Juntakan Taweekun

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Currently, the energy crisis is taking serious attention. Globally, industries and building are major share takers of energy. 72% of total global energy is consumed by residential houses, markets, and commercial building. Additionally, in appliances air conditioners are major consumer of electricity; about 60% energy is used for cooling purpose in houses due to HVAC units. Energy demand will aid in determining what changes will be needed whether it is the estimation of the required energy for households or instituting conservation measures. Bottom-up model is one of the most famous methods for forecasting. In current research bottom-up model of air conditioners' energy consumption in all income classes in comparison with seasonal variation and hourly consumption is calculated. By comparison of energy consumption of all income classes by usage of air conditioners, total consumption of actual demand and current availability can be seen.

Keywords: air conditioning, bottom up model, income classes, energy demand

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Manouchehr Amiri

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This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

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Authors: Elsheikh Asser

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Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: genetic algorithms, time-cost trade-off, multi-objective model, project scheduling

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Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

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Concurrent planning of the resource constraint project scheduling and material ordering problems have received significant attention within the last decades. Hence, the issue has been investigated here with the aim to minimize total project costs. Furthermore, the presented model considers different discount options in order to approach the real world conditions. The incorporated alternatives consist of all-unit and incremental discount strategies. On the other hand, a modified version of the genetic algorithm is applied in order to solve the model for larger sizes, in particular. Finally, the applicability and efficiency of the given model is tested by different numerical instances.

Keywords: genetic algorithm, material ordering, project management, project scheduling

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Damien Kunjal, Faeezah Peerbhai

Abstract:

Despite the increasing popularity of exchange-traded funds (ETFs), ETF investment choices may not always be rational. Excess trading volume, misevaluations of securities, and excess return volatility present in financial markets can be attributed to the influence of the overconfidence bias. Whilst previous research has explored the overconfidence bias in stock markets; this study focuses on trading in ETF markets. Therefore, the objective of this study is to investigate the presence of investor overconfidence in the South African ETF market. Using vector autoregressive models, the lead-lag relationship between market turnover and the market return is examined for the market of South African ETFs tracking domestic benchmarks and for the market of South African ETFs tracking international benchmarks over the period November 2000 till August 2019. Consistent with the overconfidence hypothesis, a positive relationship between current market turnover and lagged market return is found for both markets, even after controlling for market volatility and cross-sectional dispersion. This relationship holds for both market and individual ETF turnover suggesting that investors are overconfident when trading in South African ETFs tracking domestic benchmarks and South African ETFs tracking international benchmarks since trading activity depends on past market returns. Additionally, using the global recession as a structural break, this study finds that investor overconfidence is more pronounced after the global recession suggesting that investors perceive ETFs as risk-reducing assets due to their diversification benefits. Overall, the results of this study indicate that the overconfidence bias has a significant influence on ETF investment choices, therefore, suggesting that the South African ETF market is inefficient since investors’ decisions are based on their biases. As a result, the effect of investor overconfidence can account for the difference between the fair value of ETFs and its current market price. This finding has implications for policymakers whose responsibility is to promote the efficiency of the South African ETF market as well as ETF investors and traders who trade in the South African ETF market.

Keywords: exchange-traded fund, market return, market turnover, overconfidence, trading activity

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