Search results for: nature–inspired algorithm

Authors: Stojan Kravanja, Andrej Ivanič, Tomaž Žula

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This contribution focuses on structural optimization in civil engineering using mixed integer non-linear programming (MINLP). MINLP is characterized as a versatile method that can handle both continuous and discrete optimization variables simultaneously. Continuous variables are used to optimize parameters such as dimensions, stresses, masses, or costs, while discrete variables represent binary decisions to determine the presence or absence of structural elements within a structure while also calculating discrete materials and standard sections. The optimization process is divided into three main steps. First, a mechanical superstructure with a variety of different topology-, material- and dimensional alternatives. Next, a MINLP model is formulated to encapsulate the optimization problem. Finally, an optimal solution is searched in the direction of the defined objective function while respecting the structural constraints. The economic or mass objective function of the material and labor costs of a structure is subjected to the constraints known from structural analysis. These constraints include equations for the calculation of internal forces and deflections, as well as equations for the dimensioning of structural components (in accordance with the Eurocode standards). Given the complex, non-convex and highly non-linear nature of optimization problems in civil engineering, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied. This algorithm alternately solves subproblems of non-linear programming (NLP) and main problems of mixed-integer linear programming (MILP), in this way gradually refines the solution space up to the optimal solution. The NLP corresponds to the continuous optimization of parameters (with fixed topology, discrete materials and standard dimensions, all determined in the previous MILP), while the MILP involves a global approximation to the superstructure of alternatives, where a new topology, materials, standard dimensions are determined. The optimization of a convex problem is stopped when the MILP solution becomes better than the best NLP solution. Otherwise, it is terminated when the NLP solution can no longer be improved. While the OA/ER algorithm, like all other algorithms, does not guarantee global optimality due to the presence of non-convex functions, various modifications, including convexity tests, are implemented in OA/ER to mitigate these difficulties. The effectiveness of the proposed MINLP approach is demonstrated by its application to various structural optimization tasks, such as mass optimization of steel buildings, cost optimization of timber halls, composite floor systems, etc. Special optimization models have been developed for the optimization of these structures. The MINLP optimizations, facilitated by the user-friendly software package MIPSYN, provide insights into a mass or cost-optimal solutions, optimal structural topologies, optimal material and standard cross-section choices, confirming MINLP as a valuable method for the optimization of structures in civil engineering.

Keywords: MINLP, mixed-integer non-linear programming, optimization, structures

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Authors: S. M. Javad Davoodi, Mojtaba Shourian

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Groundwater table close to the ground surface causes major problems in construction and mining operation. One of the methods to control groundwater in such cases is using pumping wells. These pumping wells remove excess water from the site project and lower the water table to a desirable value. Although the efficiency of this method is acceptable, it needs high expenses to apply. It means even small improvement in a design of pumping wells can lead to substantial cost savings. In order to minimize the total cost in the method of pumping wells, a simulation-optimization approach is applied. The proposed model integrates MODFLOW as the simulation model with Firefly as the optimization algorithm. In fact, MODFLOW computes the drawdown due to pumping in an aquifer and the Firefly algorithm defines the optimum value of design parameters which are numbers, pumping rates and layout of the designing wells. The developed Firefly-MODFLOW model is applied to minimize the cost of the dewatering project for the ancient mosque of Kerman city in Iran. Repetitive runs of the Firefly-MODFLOW model indicates that drilling two wells with the total rate of pumping 5503 m3/day is the result of the minimization problem. Results show that implementing the proposed solution leads to at least 1.5 m drawdown in the aquifer beneath mosque region. Also, the subsidence due to groundwater depletion is less than 80 mm. Sensitivity analyses indicate that desirable groundwater depletion has an enormous impact on total cost of the project. Besides, in a hypothetical aquifer decreasing the hydraulic conductivity contributes to decrease in total water extraction for dewatering.

Keywords: groundwater dewatering, pumping wells, simulation-optimization, MODFLOW, firefly algorithm

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Authors: Jiawen Wang, Qijun Chen

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The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

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Authors: Paolo Lenzuni

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Cost-effectiveness of noise abatement treatments at the workplace has not yet received adequate consideration. Furthermore, most of the published work is focused on productivity, despite the poor correlation of this quantity with noise levels. There is currently no tool to estimate the social benefit associated to a specific noise abatement treatment, and no comparison among different options is accordingly possible. In this paper, we present an algorithm which has been developed to predict the cost-effectiveness of any planned noise control treatment in a workplace. This algorithm is based the estimates of hearing threshold shifts included in ISO 1999, and on compensations that workers are entitled to once their work-related hearing impairments have been certified. The benefits of a noise abatement treatment are estimated by means of the lower compensation costs which are paid to the impaired workers. Although such benefits have no real meaning in strictly monetary terms, they allow a reliable comparison between different treatments, since actual social costs can be assumed to be proportional to compensation costs. The existing European legislation on occupational exposure to noise it mandates that the noise exposure level be reduced below the upper action limit (85 dBA). There is accordingly little or no motivation for employers to sustain the extra costs required to lower the noise exposure below the lower action limit (80 dBA). In order to make this goal more appealing for employers, the algorithm proposed in this work also includes an ad-hoc element that promotes actions which bring the noise exposure down below 80 dBA. The algorithm has a twofold potential: 1) it can be used as a quality index to promote cost-effective practices; 2) it can be added to the existing criteria used by workers’ compensation authorities to evaluate the cost-effectiveness of technical actions, and support dedicated employers.

Keywords: cost-effectiveness, noise, occupational exposure, treatment

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Authors: A. Casale, J. Alessi, C. N. Bianchi, G. Bozzini, M. Brunoldi, V. Cappanera, P. Corvisiero, G. Fanciulli, D. Grosso, N. Magnoli, A. Mandich, C. Melchiorre, C. Morri, P. Povero, N. Stasi, M. Taiuti, G. Viano, M. Wurtz

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This work describes a real-time algorithm for detecting, tracking and classifying single motorboats, developed using the acoustic data recorded by a hydrophone array within the framework of EU LIFE + project ARION (LIFE09NAT/IT/000190). The project aims to improve the conservation status of bottlenose dolphins through a real-time simultaneous monitoring of their population and surface ship traffic. A Passive Acoustic Monitoring (PAM) system is installed on two autonomous permanent marine buoys, located close to the boundaries of the Marine Protected Area (MPA) of Portofino (Ligurian Sea- Italy). Detecting surface ships is also a necessity in many other sensible areas, such as wind farms, oil platforms, and harbours. A PAM system could be an effective alternative to the usual monitoring systems, as radar or active sonar, for localizing unauthorized ship presence or illegal activities, with the advantage of not revealing its presence. Each ARION buoy consists of a particular type of structure, named meda elastica (elastic beacon) composed of a main pole, about 30-meter length, emerging for 7 meters, anchored to a mooring of 30 tons at 90 m depth by an anti-twist steel wire. Each buoy is equipped with a floating element and a hydrophone tetrahedron array, whose raw data are send via a Wi-Fi bridge to a ground station where real-time analysis is performed. Bottlenose dolphin detection algorithm and ship monitoring algorithm are operating in parallel and in real time. Three modules were developed and commissioned for ship monitoring. The first is the detection algorithm, based on Time Difference Of Arrival (TDOA) measurements, i.e., the evaluation of angular direction of the target respect to each buoy and the triangulation for obtaining the target position. The second is the tracking algorithm, based on a Kalman filter, i.e., the estimate of the real course and speed of the target through a predictor filter. At last, the classification algorithm is based on the DEMON method, i.e., the extraction of the acoustic signature of single vessels. The following results were obtained; the detection algorithm succeeded in evaluating the bearing angle with respect to each buoy and the position of the target, with an uncertainty of 2 degrees and a maximum range of 2.5 km. The tracking algorithm succeeded in reconstructing the real vessel courses and estimating the speed with an accuracy of 20% respect to the Automatic Identification System (AIS) signals. The classification algorithm succeeded in isolating the acoustic signature of single vessels, demonstrating its temporal stability and the consistency of both buoys results. As reference, the results were compared with the Hilbert transform of single channel signals. The algorithm for tracking multiple targets is ready to be developed, thanks to the modularity of the single ship algorithm: the classification module will enumerate and identify all targets present in the study area; for each of them, the detection module and the tracking module will be applied to monitor their course.

Keywords: acoustic-noise, bottlenose-dolphin, hydrophone, motorboat

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Authors: Ramin Mansouri

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Due to the contrast of rivers discharge regime with water demands, one of the best ways to use water resources is to regulate the natural flow of the rivers and supplying water needs to construct dams. Optimal utilization of reservoirs, consideration of multiple important goals together at the same is of very high importance. To study about analyzing this method, statistical data of Bakhtiari and Roudbar dam over 46 years (1955 until 2001) is used. Initially an appropriate objective function was specified and using DE algorithm, the rule curve was developed. In continue, operation policy using rule curves was compared to standard comparative operation policy. The proposed method distributed the lack to the whole year and lowest damage was inflicted to the system. The standard deviation of monthly shortfall of each year with the proposed algorithm was less deviated than the other two methods. The Results show that median values for the coefficients of F and Cr provide the optimum situation and cause DE algorithm not to be trapped in local optimum. The most optimal answer for coefficients are 0.6 and 0.5 for F and Cr coefficients, respectively. After finding the best combination of coefficients values F and CR, algorithms for solving the independent populations were examined. For this purpose, the population of 4, 25, 50, 100, 500 and 1000 members were studied in two generations (G=50 and 100). result indicates that the generation number 200 is suitable for optimizing. The increase in time per the number of population has almost a linear trend, which indicates the effect of population in the runtime algorithm. Hence specifying suitable population to obtain an optimal results is very important. Standard operation policy had better reversibility percentage, but inflicts severe vulnerability to the system. The results obtained in years of low rainfall had very good results compared to other comparative methods.

Keywords: reservoirs, differential evolution, dam, Optimal operation

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Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda

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This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the different methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear.

Keywords: particle swarm optimization, identification, hyperelastic, model

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Authors: Muhammad Umar Kiani, Muhammad Shahbaz

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Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.

Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Elaosi Vhurumuku

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During the past twenty years, there has been an increased interest by science educators in researching and developing teachers’ pedagogical content knowledge for teaching the nature of science (PCKNOS). While there has been this surge in interest in the idea of PCKNOS, there has not been a common understanding among NOS researchers as to how exactly the PCKNOS concept should be construed. In this paper, we analyse and evaluate published accredited journal articles on PCKNOS research. We also draw from our teaching experiences. The major points of foci are the researchers’ presentations of SMKNOS and their centres of attention regarding the elements of PCKNOS. Our content, cluster analysis, and evaluation of the studies on PCKNOS reveal that most researchers have presented SMKNOS in the form of a heuristic or a set of heuristics (targeted NOS ideas) to be mastered by teachers or learners. Furthermore, we found that most of the researchers’ attention has been on developing and recommending teacher pedagogical practices for teaching NOS. From this, we synthesize and propose a subject knowledge content structure and a pedagogical approach that we believe is relevant and appropriate for secondary school and science teacher education if the goal of science education for scientific literacy is to be achieved. The justification of our arguments is rooted in tracing and unpacking the origins and meaning of pedagogical content knowledge (PCK). From our analysis, synthesis, and evaluation, as well as teaching experiences, we distil and construct a meaning for the PCKNOS construct.

Keywords: pedagogical content knowledge, teaching, nature of science, construct, subject matter knowledge

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Authors: Chloe Richards, Adrian Delgado Ollero, Yan Delaure, Fiona Regan

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Growing concern about the natural environment has accelerated the search for non-toxic, but at the same time, economically reasonable, antifouling materials. Bioinspired surfaces, due to their nano and micro topographical antifouling capabilities, provide a hopeful approach to the design of novel antifouling surfaces. Biological organisms are known to have highly evolved and complex topographies, demonstrating antifouling potential, i.e. shark skin. Previous studies have examined the antifouling ability of topographic patterns, textures and roughness scales found on natural organisms. One of the mechanisms used to explain the adhesion of cells to a substrate is called attachment point theory. Here, the fouling organism experiences increased attachment where there are multiple attachment points and reduced attachment, where the number of attachment points are decreased. In this study, an attempt to characterize the microtopography of the common brill fish, Scophthalmus rhombus, was undertaken. Scophthalmus rhombus is a small flatfish of the family Scophthalmidae, inhabiting regions from Norway to the Mediterranean and the Black Sea. They reside in shallow sandy and muddy coastal areas at depths of around 70 – 80 meters. Six engineered surfaces (inspired by the Brill fish scale) produced by a 2-photon polymerization (2PP) process were evaluated for their potential as an antifouling solution for incorporation onto tidal energy blades. The micro-textures were analyzed for their AF potential under both static and dynamic laboratory conditions using two laboratory grown diatom species, Amphora coffeaeformis and Nitzschia ovalis. The incorporation of a surface topography was observed to cause a disruption in the growth of A. coffeaeformis and N. ovalis cells on the surface in comparison to control surfaces. This work has demonstrated the importance of understanding cell-surface interaction, in particular, topography for the design of novel antifouling technology. The study concluded that biofouling can be controlled by physical modification, and has contributed significant knowledge to the use of a successful novel bioinspired AF technology, based on Brill, for the first time.

Keywords: attachment point theory, biofouling, Scophthalmus rhombus, topography

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Authors: Manas Shah

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Black Scholes option pricing model is one of the most important concepts in modern world of computational finance. However, its practical use can be challenging as one of the input parameters must be estimated; implied volatility of the underlying security. The more precisely these values are estimated, the more accurate their corresponding estimates of theoretical option prices would be. Here, we present a novel model based on Cuckoo Search Optimization (CS) which finds more precise estimates of implied volatility than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Keywords: black scholes model, cuckoo search optimization, particle swarm optimization, genetic algorithm

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Authors: Ajish K. Abraham

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Hearing aid is the most fundamental technology employed towards rehabilitation of persons with sensory neural hearing impairment. Hearing in noise is still a matter of major concern for many hearing aid users and thus continues to be a challenging issue for the hearing aid designers. Several techniques are being currently used to enhance the speech at the hearing aid output. Most of these techniques, when implemented, result in reduction of intelligibility of the speech signal. Thus the dissatisfaction of the hearing aid user towards comprehending the desired speech amidst noise is prevailing. Multichannel Wiener Filter is widely implemented in binaural hearing aid technology for noise reduction. In this study, Wiener filter based noise reduction approach is experimented for a single microphone based hearing aid set up. This method checks the status of the input speech signal in each frequency band and then selects the relevant noise reduction procedure. Results showed that the Wiener filter based algorithm is capable of enhancing speech even when the input acoustic signal has a very low Signal to Noise Ratio (SNR). Performance of the algorithm was compared with other similar algorithms on the basis of improvement in intelligibility and SNR of the output, at different SNR levels of the input speech. Wiener filter based algorithm provided significant improvement in SNR and intelligibility compared to other techniques.

Keywords: hearing aid output speech, noise reduction, SNR improvement, Wiener filter, speech enhancement

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Authors: Ana Serafimovic, Karthik Devarajan

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Non-negative matrix factorization approximates a high dimensional non-negative matrix V as the product of two non-negative matrices, W and H, and allows only additive linear combinations of data, enabling it to learn parts with representations in reality. It has been successfully applied in the analysis and interpretation of high dimensional data arising in neuroscience, computational biology, and natural language processing, to name a few. The objective of this paper is to assess a hybrid algorithm for non-negative matrix factorization with multiplicative updates. The method aims to minimize the symmetric version of Kullback-Leibler divergence known as intrinsic information and assumes that the noise is signal-dependent and that it originates from an arbitrary distribution from the exponential family. It is a generalization of currently available algorithms for Gaussian, Poisson, gamma and inverse Gaussian noise. We demonstrate the potential usefulness of the new generalized algorithm by comparing its performance to the baseline methods which also aim to minimize symmetric divergence measures.

Keywords: non-negative matrix factorization, dimension reduction, clustering, intrinsic information, symmetric information divergence, signal-dependent noise, exponential family, generalized Kullback-Leibler divergence, dual divergence

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Authors: Carmen García Sánchez

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The UN's 17 Sustainable Development Goals – specifically the nº 3 and nº 11- urgently call for new architectural design solutions at different design scales to increase human contact with nature in the health and wellbeing promotion of primarily urban communities. The discipline of Interior Design offers an important alternative to large-scale nature-inclusive actions which are not always possible due to space limitations. These circumstances provide an immense opportunity to integrate biophilic design, a complex emerging and under-developed approach that pursues sustainable design strategies for increasing the human-nature connection through the experience of the built environment. Biophilic design explores the diverse ways humans are inherently inclined to affiliate with nature, attach meaning to and derive benefit from the natural world. It represents a biological understanding of architecture which categorization is still in progress. The internationally renowned Danish domestic architecture built in the 1950´s and early 1960´s - a golden age of Danish modern architecture - left a leading legacy that has greatly influenced the domestic sphere and has further led the world in terms of good design and welfare. This study examines how four existing post-war domestic buildings establish a dialogue with nature and her variations over time. The case-studies unveil both memorable and unique biophilic resources through sophisticated and original design expressions, where transformative processes connect the users to the natural setting and reflect fundamental ways in which they attach meaning to the place. In addition, fascinating analogies in terms of this nature interaction with particular traditional Japanese architecture inform the research. They embody prevailing lessons for our time today. The research methodology is based on a thorough literature review combined with a phenomenological analysis into how these case-studies contribute to the connection between humans and nature, after conducting fieldwork throughout varying seasons to document understanding in nature transformations multi-sensory perception (via sight, touch, sound, smell, time and movement) as a core research strategy. The cases´ most outstanding features have been studied attending the following key parameters: 1. Space: 1.1. Relationships (itineraries); 1.2. Measures/scale; 2. Context: Context: Landscape reading in different weather/seasonal conditions; 3. Tectonic: 3.1. Constructive joints, elements assembly; 3.2. Structural order; 4. Materiality: 4.1. Finishes, 4.2. Colors; 4.3. Tactile qualities; 5. Daylight interplay. Departing from an artistic-scientific exploration this groundbreaking study provides sustainable practical design strategies, perspectives, and inspiration to boost humans´ contact with nature through the experience of the interior built environment. Some strategies are associated with access to outdoor space or require ample space, while others can thrive in a dense urban context without direct access to the natural environment. The objective is not only to produce knowledge, but to phase in biophilic design in the built environment, expanding its theory and practice into a new dimension. Its long-term vision is to efficiently enhance the health and well-being of urban communities through daily interaction with Nature.

Keywords: sustainability, biophilic design, architectural design, interior design, nature, Danish architecture, Japanese architecture

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Authors: Farshad Ghassemi Toosi, Nikola S. Nikolov

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We present a visualization technique for radial drawing of trees consisting of two slightly different algorithms. Both of them make use of node-link diagrams for visual encoding. This visualization creates clear drawings without edge crossing. One of the algorithms is suitable for real-time visualization of large trees, as it requires minimal recalculation of the layout if leaves are inserted or removed from the tree; while the other algorithm makes better utilization of the drawing space. The algorithms are very similar and follow almost the same procedure but with different parameters. Both algorithms assign angular coordinates for all nodes which are then converted into 2D Cartesian coordinates for visualization. We present both algorithms and discuss how they compare to each other.

Keywords: Radial drawing, Visualization, Algorithm, Use of node-link diagrams

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Authors: E. R. Swart, S. J. Gismondi, N. R. Swart, C. E. Bell

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We present an heuristic algorithm that decides graph non-Hamiltonicity. All graphs are directed, each undirected edge regarded as a pair of counter directed arcs. Each of the n! Hamilton cycles in a complete graph on n+1 vertices is mapped to an n-permutation matrix P where p(u,i)=1 if and only if the ith arc in a cycle enters vertex u, starting and ending at vertex n+1. We first create exclusion set E by noting all arcs (u, v) not in G, sufficient to code precisely all cycles excluded from G i.e. cycles not in G use at least one arc not in G. Members are pairs of components of P, {p(u,i),p(v,i+1)}, i=1, n-1. A doubly stochastic-like relaxed LP formulation of the Hamilton cycle decision problem is constructed. Each {p(u,i),p(v,i+1)} in E is coded as variable q(u,i,v,i+1)=0 i.e. shrinks the feasible region. We then implement the Weak Closure Algorithm (WCA) that tests necessary conditions of a matching, together with Boolean closure to decide 0/1 variable assignments. Each {p(u,i),p(v,j)} not in E is tested for membership in E, and if possible, added to E (q(u,i,v,j)=0) to iteratively maximize |E|. If the WCA constructs E to be maximal, the set of all {p(u,i),p(v,j)}, then G is decided non-Hamiltonian. Only non-Hamiltonian G share this maximal property. Ten non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian G, the complement of E covers a matching, perhaps useful in searching for cycles. We also present an example where the WCA fails.

Keywords: Hamilton cycle decision problem, computational complexity theory, graph theory, theoretical computer science

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Authors: Jonathan H. Westover, Maureen S. Andrade, Angela Schill, Jeff Peterson, Samuel Choi

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The purpose of this research is to understand employee perspectives on their work characteristics and conditions, particularly related to the COVID-19 pandemic and the future of work. Working conditions impact job satisfaction. People tend to measure job satisfaction by comparing aspects of the job they have with those they want. Job satisfaction is related to the value that one places on specific aspects of a job, such as autonomy, pay and benefits, challenge, growth, or meaningful work, and the degree to which such elements are present. The value one places on these various job characteristics may differ based on gender, age, personality, occupation, context, or other factors. This study will examine various job characteristics and working conditions with an emphasis on COVID-19 to determine how managers and leaders and better support and develop their employees.

Keywords: COVID-19, employee perspectives nature of work, future of work

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Authors: Akhilesh G. Naik, Dipankar Pal

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In today’s scenario, the complexity of digital signal processing (DSP) applications and various microcontroller architectures have been increasing to such an extent that the traditional approaches to multiplier design in most processors are becoming outdated for being comparatively slow. Modern processing applications require suitable pipelined approaches, and therefore, algorithms that are friendlier with pipelined architectures. Traditional algorithms like Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda architectures have been proven to be comparatively slow for pipelined architectures. These architectures, therefore, need to be optimized or combined with other architectures amongst them to enhance its performances and to be made suitable for pipelined hardware/architectures. Recently, Vedic algorithm mathematically has proven to be efficient by appearing to be less complex and with fewer steps for its output establishment and have assumed renewed importance. This paper describes and shows how the Vedic algorithm can be better suited for pipelined architectures and also can be combined with traditional architectures and algorithms for enhancing its ability even further. In this paper, we also established that for complex applications on DSP and other microcontroller architectures, using Vedic approach for multiplication proves to be the best available and efficient option.

Keywords: Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda, Vedic, Single-Stage Karatsuba (SSK), Looped Karatsuba (LK)

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Authors: Art Tsang

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In educational research, the scope of investigations has almost always been determined by researchers. As learners are at the forefront of education, it is essential to balance researchers’ and learners’ voices in educational studies. In this paper, a data collection method that helps partly address the dearth of learners’ voices in research design is introduced. Inspired by the repertory grid approach (RGA), the group RGA approach, created by the author and his doctoral student, was successfully piloted with learners in Hong Kong. This method will very likely be of interest and use to many researchers, teachers, and postgraduate students in the field of education and beyond.

Keywords: education, learners, repertory grids, research methods

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Authors: Ana Staneva, Vessela Stoimenova

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A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R.

Keywords: Bayesian, branching processes, EM algorithm, Gibbs sampler, Monte Carlo methods, statistical estimation

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Authors: Siti Ruhliah Lizarose Samion, Mohamed Sukri Mat Ali, Con Doolan

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The present work is an experimental investigation of adapting a passive treatment leading edge serrations over a rod-airfoil flow-induced noise generation. The leading edge serrations are bio-inspired from a barn-owl silent flight. The rod-airfoil configuration is a benchmark configuration taken to investigate airfoil-turbulence interaction noise (ATIN). Location of serrations placed and the wideness of serrations are the two parameters taken in this study. The ATIN is reduced up to 3.5 dB for a wide leading serrations case. A correlation is found between the wideness of serrations and the noise reduction mechanism of the airfoil.

Keywords: aerodynamic noise, leading edge serrations, rod-airfoil, experiment

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Authors: Z. Mazrouei-Sebdani, A. Khoddami, H. Hadadzadeh, M. Zarrebini

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Silica aerogels are well-known meso-porous materials with high specific surface area (500–1000 m2/g), high porosity (80–99.8%), and low density (0.003–0.8 g/cm3). However, the silica aerogels generally are highly brittle due to their nanoporous nature. Physical and mechanical properties of the silica aerogels can be enhanced by compounding with the fibers. Although some reports presented incorporation of the fibers into the sol, followed by further modification and drying stages, no information regarding the aerogel powders as filler in the polymeric fibers is available. In this research, waterglass based aerogel powder was prepared in the following steps: sol–gel process to prepare a gel, followed by subsequent washing with propan-2-ol, n-Hexane, and TMCS, then ambient pressure drying, and ball milling. Inspired by limited dust releasing, aerogel powder was introduced to the PET electrospinning solution in an attempt to create required bulk and surface structure for the nano fibers to improve their hydrophobic and insulation properties. The samples evaluation was carried out by measuring density, porosity, contact angle, sliding angle, heat transfer, FTIR, BET and SEM. According to the results, porous silica aerogel powder was fabricated with mean pore diameter of 24 nm and contact angle of 145.9º. The results indicated the usefulness of the aerogel powder confined into nano fibers to control surface roughness for manipulating superhydrophobic nanowebs with sliding angle of 5˚ and water contact angle of 147º. It can be due to a multi-scale surface roughness which was created by nanowebs structure itself and nano fibers surface irregularity in presence of the aerogels while a laye of fluorocarbon created low surface energy. The wettability of a solid substrate is an important property that is controlled by both the chemical composition and geometry of the surface. Also, a decreasing trend in the heat transfer was observed from 22% for the nano fibers without any aerogel powder to 8% for the nano fibers with 4% aerogel powder. The development of thermal insulating materials has become increasingly more important than ever in view of the fossil energy depletion and global warming that call for more demanding energy-saving practices.

Keywords: Superhydrophobicity, Insulation, Sol-gel, Surface energy, Roughness.

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Authors: Usama Abdulwahab, Mohammed Wahab

Abstract:

This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

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Authors: N. Gayathri Devi, K. Batri

Abstract:

The advent of technology has increased the traffic hazards and the road accidents take place. Collision detection system in automobile aims at reducing or mitigating the severity of an accident. This project aims at avoiding Vehicle head on collision by means of collision detection algorithm. This collision detection algorithm predicts the collision and the avoidance or minimization have to be done within few seconds on confirmation. Under critical situation collision minimization is made possible by turning the vehicle to the desired turn radius so that collision impact can be reduced. In order to avoid the collision completely, the turning of the vehicle should be achieved at reduced speed in order to maintain the stability.

Keywords: collision avoidance system, time to collision, time to turn, turn radius

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Serena Gomez, Raeesa Tanseen, Netra Shaligram, Nithin Francis, Sandesh B. J.

Abstract:

The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations.

Keywords: microbes, metabolic modeling, flux balance analysis, artificial bee colony

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Authors: Shidvash Vakilipour, Scott Ormiston, Masoud Mohammadi, Rouzbeh Riazi, Kimia Amiri, Sahar Barati

Abstract:

Two-phase and multi-phase flows are common flow types in fluid mechanics engineering. Among the basic and applied problems of these flow types, two-phase parallel flow is the one that two immiscible fluids flow in the vicinity of each other. In this type of flow, fluid properties (e.g. density, viscosity, and temperature) are different at the two sides of the interface of the two fluids. The most challenging part of the numerical simulation of two-phase flow is to determine the location of interface accurately. In the present work, a coupled interface tracking algorithm is developed based on Arbitrary Lagrangian-Eulerian (ALE) approach using a cell-centered, pressure-based, coupled solver. To validate this algorithm, an analytical solution for fully developed two-phase flow in presence of gravity is derived, and then, the results of the numerical simulation of this flow are compared with analytical solution at various flow conditions. The results of the simulations show good accuracy of the algorithm despite using a nearly coarse and uniform grid. Temporal variations of interface profile toward the steady-state solution show that a greater difference between fluids properties (especially dynamic viscosity) will result in larger traveling waves. Gravity effect studies also show that favorable gravity will result in a reduction of heavier fluid thickness and adverse gravity leads to increasing it with respect to the zero gravity condition. However, the magnitude of variation in favorable gravity is much more than adverse gravity.

Keywords: coupled solver, gravitational force, interface tracking, Reynolds number to Froude number, two-phase flow

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Authors: M. Orefice, V. Di Vito

Abstract:

This paper provides a description of a Collision Avoidance algorithm that has been developed starting from the mathematical modeling of the flight of insects, in terms of spirals and conchospirals geometric paths. It is able to calculate a proper avoidance manoeuver aimed to prevent the infringement of a predefined distance threshold between ownship and the considered intruder, while minimizing the ownship trajectory deviation from the original path and in compliance with the aircraft performance limitations and dynamic constraints. The algorithm is designed in order to be suitable for real-time applications, so that it can be considered for the implementation in the most recent airborne automatic collision avoidance systems using the traffic data received through an ADS-B IN device. The presented approach is able to take into account the rules-of-the-air, due to the possibility to select, through specifically designed decision making logic based on the consideration of the encounter geometry, the direction of the calculated collision avoidance manoeuver that allows complying with the rules-of-the-air, as for instance the fundamental right of way rule. In the paper, the proposed collision avoidance algorithm is presented and its preliminary design and software implementation is described. The applicability of this method has been proved through preliminary simulation tests performed in a 2D environment considering single intruder encounter geometries, as reported and discussed in the paper.

Keywords: ADS-B Based Application, Collision Avoidance, RPAS, Spiral Geometry.

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Authors: Eldho Paul, Brijesh Paul

Abstract:

This work proposes an auction mechanism based solution methodology for the optimum scheduling of trucks in a cross-docking centre. The cross-docking centre is an important element of lean supply chain. It reduces the amount of storage and transportation costs in the distribution system compared to an ordinary warehouse. Better scheduling of trucks in a cross-docking center is the best way to reduce storage and transportation costs. Auction mechanism is commonly used for allocation of limited resources in different real-life applications. Here, we try to schedule inbound trucks by integrating auction mechanism with the functioning of a cross-docking centre. A mathematical model is developed for the optimal scheduling of inbound trucks based on the auction methodology. The determination of exact solution for problems involving large number of trucks was found to be computationally difficult, and hence a genetic algorithm based heuristic methodology is proposed in this work. A comparative study of exact and heuristic solutions is done using five classes of data sets. It is observed from the study that the auction-based mechanism is capable of providing good solutions to scheduling problem in cross-docking centres.

Keywords: auction mechanism, cross-docking centre, genetic algorithm, scheduling of trucks

Procedia PDF Downloads 392