Search results for: hybrid approach

Authors: Anna Crivellari, Valerio Cozzani

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Even though most of the electricity produced in the entire world still comes from fossil fuels, new policies are being implemented in order to promote a more sustainable use of energy sources. Offshore renewable resources have become increasingly attractive thanks to the huge entity of power potentially obtained. However, the intermittent nature of renewables often limits the capacity of the systems and creates mismatches between supply and demand. Hydrogen is foreseen to be a promising vector to store and transport large amounts of excess renewable power by using existing oil and gas infrastructure. In this work, an offshore hybrid energy system integrating wind energy conversion with hydrogen production was conceptually defined and applied to offshore gas platforms. A techno-economic analysis was performed by considering two different locations for the installation of the innovative power system, i.e., the North Sea and the Adriatic Sea. The water depth, the distance of the platform from the onshore gas grid, the hydrogen selling price and the green financial incentive were some of the main factors taken into account in the comparison. The results indicated that the use of well-defined indicators allows to capture specifically different cost and revenue features of the analyzed systems, as well as to evaluate their competitiveness in the actual and future energy market.

Keywords: cost analysis, energy efficiency assessment, hydrogen production, offshore wind energy

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Authors: Anna Zoli

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In recent years, we have been witnessing a surge of locally-sustained communities committed to promoting new ethical economies while fostering the full participation of socially excluded groups and individuals into the labor market. This article explores the practices of a particular community development model, Transition Towns, as implemented in Monteveglio, Italy. Data were gathered throughout two years long ethnography, using multiple qualitative techniques, namely participant observation, document analysis, and semi-structured interviews. Data were analyzed triangulating from multiple sources of evidence and using hybrid thematic analysis. Major findings show that Transition Town movement works on two main axes, vertical and horizontal. Vertical transition involves interactions with an overreaching political, economic, and social structure which is not transitioning, and therefore poses structural resistances to the transformative social change fostered by the TT. Conversely, horizontal transition involves intragroup dynamics within the communal relational and geographical spaces and therefore poses process resistances between 'self and others' to the interpersonal communication between TT members. The study concludes that a psychosocial approach to community development is essential in order to conflate macro-social dynamics and psychological processes that may obstacle grassroots social movements to thrive. Skills from psychosocial disciplines are a unique set that could facilitate communication and relational processes for community development, and ultimately enabling social change.

Keywords: community development, grassroots social movements, psychosocial approaches, Transition Towns

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Authors: S. Oladoyinbo, A. A. Tijani

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Multilevel inverters (MLI) are used at high power application based on their operation. There are 3 main types of multilevel inverters (MLI); diode clamped, flying capacitor and cascaded MLI. A cascaded MLI requires the least number of components to achieve same number of voltage levels when compared to other types of MLI while the flying capacitor has the minimum harmonic distortion. However, maximizing the advantage of cascaded H-bridge MLI and flying capacitor MLI, an improved MLI can be achieved with fewer components and better performance. In this paper an improved MLI is presented by asymmetrically integrating a flying capacitor to a cascaded H-bridge MLI also integrating an auxiliary transformer to the main transformer to decrease the total harmonics distortion (THD) with increased number of output voltage levels. Furthermore, the system is incorporated with a hybrid time and climate based solar panel cleaner and power selector which intelligently manage the input of the MLI and clean the solar panel weekly ensuring the environmental factor effect on the panel is reduced to minimum.

Keywords: multilevel inverter, total harmonics distortion, cascaded h-bridge inverter, flying capacitor

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Authors: Pascal Roubides

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The presentation proposed herein describes a research case study of a hybrid application of the competency-based education model best exemplified by Western Governor’s University, within the general temporal confines of an accelerated (8-week) term of a College Algebra course at the author’s institution. A competency-based model was applied to an accelerated online College Algebra course, built as an Open Educational Resources (OER) course, seeking quantifiable evidence of any differences in the academic achievement of students enrolled in the competency-based course and the academic achievement of the current delivery of the same course. Competency-based learning has been gaining in support in recent times and the author’s institution has also been involved in its own efforts to design and develop courses based on this approach. However, it is unknown whether there had been any research conducted to quantify evidence of the effect of this approach against traditional approaches prior to the author’s case study. The research question sought to answer in this experimental quantitative study was whether the online College Algebra curriculum at the author’s institution delivered via an OER-based competency-based model can produce statistically significant improvement in retention and success rates against the current delivery of the same course. Results obtained in this study showed that there is no statistical difference in the retention rate of the two groups. However, there was a statistically significant difference found between the rates of successful completion of students in the experimental group versus those in the control group.

Keywords: competency-based learning, online mathematics, online math education, online courses

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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: A. Derrouazin, N. Mekkakia-M, R. Taleb, M. Helaimi, A. Benbouali

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Nowadays the use of different sources of renewable energy for the production of electricity is the concern of everyone, as, even impersonal domestic use of the electricity in isolated sites or in town. As the conventional sources of energy are shrinking, a need has arisen to look for alternative sources of energy with more emphasis on its optimal use. This paper presents design of a sustainable Hybrid Power System (PV-Wind-Storage) assisted by grid as supplementary sources applied to case study residential house, to meet its entire energy demand. A Fuzzy control system model has been developed to optimize and control flow of power from these sources. This energy requirement is mainly fulfilled from PV and Wind energy stored in batteries module for critical load of a residential house and supplemented by grid for base and peak load. The system has been developed for maximum daily households load energy of 3kWh and can be scaled to any higher value as per requirement of individual /community house ranging from 3kWh/day to 10kWh/day, as per the requirement. The simulation work, using intelligent energy management, has resulted in an optimal yield leading to average reduction in cost of electricity by 50% per day.

Keywords: photovoltaic (PV), wind turbine, battery, microcontroller, fuzzy control (FC), Matlab

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Authors: Nadia Ben Youssef, Aicha Bouzid

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Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach.

Keywords: gradient, edge detection, color image, quaternion

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: A. Sezai Sarac

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Acrylonitrile (AN) copolymers with typical comonomers of vinyl acetate (VAc) or methyl acrylate (MA) exhibit better mechanical behaviors than its homopolymer. To increase processability of conjugated polymer, and to obtain a hybrid nano-structure multi-stepped emulsion polymerization was applied. Such products could be used in, i.e., drug-delivery systems, biosensors, gas-sensors, electronic compounds, etc. Incorporation of a number of flexible comonomers weakens the dipolar interactions among CN and thereby decreases melting point or increases decomposition temperatures of the PAN based copolymers. Hence, it is important to consider the effect of comonomer on the properties of PAN-based copolymers. Acrylonitrile vinylacetate (AN–VAc ) copolymers have the significant effect to their thermal behavior and are also of interest as precursors in the production of high strength carbon fibers. AN is copolymerized with one or two comonomers, particularly with vinyl acetate The copolymer of AN and VAc can be used either as a plastic (VAc > 15 wt %) or as microfibers (VAc < 15 wt %). AN provides the copolymer with good processability, electrochemical and thermal stability; VAc provides the mechanical stability. The free radical copolymerization of AN and VAc copolymer and core Shell structure of polyprrole composites,and nanofibers of poly(m-anthranilic acid)/polyacrylonitrile blends were recently studied. Free radical copolymerization of acrylonitrile (AN) – with different comonomers, i.e. acrylates, and styrene was realized using ammonium persulfate (APS) in the presence of a surfactant and in-situ polymerization of conjugated polymers was performed in this reaction medium to obtain core-shell nano particles. Nanofibers of such nanoparticles were obtained by electrospinning. Morphological properties of nanofibers are investigated by scanning electron microscopy (SEM) and atomic force spectroscopy (AFM). Nanofibers are characterized using Fourier Transform Infrared - Attenuated Total Reflectance spectrometer (FTIR-ATR), Nuclear Magnetic Resonance Spectroscopy (1H-NMR), differential scanning calorimeter (DSC), thermal gravimetric analysis (TGA), and Electrochemical Impedance Spectroscopy. The electrochemical Impedance results of the nanofibers were fitted to an equivalent curcuit by modelling (ECM).

Keywords: core shell nanoparticles, nanofibers, ascrylonitile copolymers, hybrid nanostructures

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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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: Minsung Kim, Jinkoo Kim

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In order to reduce or eliminate seismic damage in structures, many researchers have investigated various energy dissipation devices. In this study, the seismic capacity and cost of a slit-viscoelastic seismic retrofit system composed of a steel slit plate and viscoelastic dampers connected in parallel are evaluated. The combination of the two different damping mechanisms is expected to produce enhanced seismic performance of the building. The analysis model of the system is first derived using various link elements in the nonlinear dynamic analysis software Perform 3D, and fragility curves of the structure retrofitted with the dampers are obtained using incremental dynamic analyses. The analysis results show that the displacement of the structure equipped with the hybrid dampers is smaller than that of the structure with slit dampers due to the enhanced self-centering capability of the system. It is also observed that the initial cost of hybrid system required for the seismic retrofit is smaller than that of the structure with viscoelastic dampers. Acknowledgement: This research was financially supported by the Ministry of Trade, Industry and Energy(MOTIE) and Korea Institute for Advancement of Technology(KIAT) through the International Cooperative R&D program(N043100016_Development of low-cost high-performance seismic energy dissipation devices using viscoelastic material).

Keywords: damped cable systems, seismic retrofit, viscous dampers, self-centering

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Siddhant Srivastav, Shilpa Singh, Sumanta Kumar Meher

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Innovative renewable energy sources for energy storage/conversion is the demand of the current scenario in electrochemical machinery. In this context, choosing suitable organic precipitants for tuning the crystal characteristics and microstructures is a challenge. On the same note, herein we report broccoli flower-like porous Mn3O4/NiSe2−MnSe2 composite synthesized using a simple two step hydrothermal synthesis procedure assisted by sluggish precipitating agent and an effective cappant followed by intermediated anion exchange. The as-synthesized material was exposed to physical and chemical measurements depicting poly-crystallinity, stronger bonding and broccoli flower-like porous arrangement. The material was assessed electrochemically by cyclic voltammetry (CV), chronopotentiometry (CP) and electrochemical impedance spectroscopy (EIS) measurements. The Electrochemical studies reveal redox behavior, supercapacitive charge-discharge shape and extremely low charge transfer resistance. Further, the fabricated Mn3O4/NiSe2−MnSe2 composite based solid-state hybrid supercapacitor (Mn3O4/NiSe2−MnSe2 ||N-rGO) delivers excellent rate specific capacity, very low internal resistance, with energy density (~34 W h kg–1) of a typical rechargeable battery and power density (11995 W kg–1) of an ultra-supercapacitor. Consequently, it can be a favorable contender for supercapacitor applications for high performance energy storage utilizations. A definitive exhibition of the supercapacitor device is credited to electrolyte-ion buffering reservior alike behavior of broccoli flower like Mn3O4/NiSe2−MnSe2, enhanced by upgraded electronic and ionic conductivities of N- doped rGO (negative electrode) and PVA/KOH gel (electrolyte separator), respectively

Keywords: electrolyte-ion buffering reservoir, intermediated-anion exchange, solid-state hybrid supercapacitor, supercapacitive charge-dischargesupercapacitive charge-discharge

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Authors: Ebtehal Qulisy, Geoffrey Dougherty, Kholoud Hothan, Mylene Dandavino

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Background: For more than 200 years, journal clubs (JCs) have been used to teach the fundamentals of critical appraisal and evidence-based practice (EBP). However, JCs curricula face important challenges, including poor sustainability, insufficient time to prepare for and conduct the activities, and lack of trainee skills and self-efficacy with critical appraisal. Andragogy principles and modern technology could help EBP be taught in more relevant, modern, and interactive ways. Method: We propose a fresh educational activity to teach EBP. Educational sessions are designed to encourage collaborative and experiential learning and do not require advanced preparation by the participants. Each session lasts 60 minutes and is adaptable to in-person, virtual, or hybrid contexts. Sessions are structured around a worksheet and include three educational objectives: “1. Identify a Clinical Conundrum”, “2. Compare and Contrast Current Guidelines”, and “3. Choose a Recent Journal Article”. Sessions begin with a short presentation by a facilitator of a clinical scenario highlighting a “grey-zone” in pediatrics. Trainees are placed in groups of two to four (based on the participants’ number) of varied training levels. The first task requires the identification of a clinical conundrum (a situation where there is no clear answer but only a reasonable solution) related to the scenario. For the second task, trainees must identify two or three clinical guidelines. The last task requires trainees to find a journal article published in the last year that reports an update regarding the scenario’s topic. Participants are allowed to use their electronic devices throughout the session. Our university provides full-text access to major journals, which facilitated this exercise. Results: Participants were a convenience sample of trainees in the inpatient services at the Montréal Children’s Hospital, McGill University. Sessions were conducted as a part of an existing weekly academic activity and facilitated by pediatricians with experience in critical appraisal. There were 28 participants in 4 sessions held during Spring 2022. Time was allocated at the end of each session to collect participants’ feedback via a self-administered online survey. There were 22 responses, were 41%(n=9) pediatric residents, 22.7%(n=5) family medicine residents, 31.8%(n=7) medical students, and 4.5%(n=1) nurse practitioner. Four respondents participated in more than one session. The “Satisfied” rates were 94.7% for session format, 100% for topic selection, 89.5% for time allocation, and 84.3% for worksheet structure. 60% of participants felt that including the sessions during the clinical ward rotation was “Feasible.” As per self-efficacy, participants reported being “Confident” for the tasks as follows: 89.5% for the ability to identify a relevant conundrum, 94.8% for the compare and contrast task, and 84.2% for the identification of a published update. The perceived effectiveness to learn EBP was reported as “Agreed” by all participants. All participants would recommend this session for further teaching. Conclusion: We developed a modern approach to teach EBP, enjoyed by all levels of participants, who also felt it was a useful learning experience. Our approach addresses known JCs challenges by being relevant to clinical care, fostering active engagement but not requiring any preparation, using available technology, and being adaptable to hybrid contexts.

Keywords: medical education, journal clubs, post-graduate teaching, andragogy, experiential learning, evidence-based practice

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Authors: Min Sang Lee, Hee Jae Shin, In Pyo Cha, Hyun Kyung Yoon, Seong Woo Hong, Min Jae Yu, Hong Gun Kim, Lee Ku Kwac

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In this paper, the electromagnetic shielding characteristics of an up-to-date typical carbon filler material, carbon fiber used with a metal mesh were investigated. Carbon fiber 12k-prepregs, where carbon fibers were impregnated with epoxy, were laminated with wire meshes, vacuum bag-molded and hardened to manufacture hybrid-type specimens, with which an electromagnetic shield test was performed in accordance with ASTM D4935-10, through which was known as the most excellent reproducibility is obtainable among electromagnetic shield tests. In addition, glass fiber prepress whose electromagnetic shielding effect were known as insignificant were laminated and formed with wire meshes to verify the validity of the electromagnetic shield effect of wire meshes in order to confirm the electromagnetic shielding effect of metal meshes corresponding existing carbon fiber 12k-prepregs. By grafting carbon fibers, on which studies are being actively underway in the environmental aspects and electromagnetic shielding effect, with hybrid-type wire meshes that were analyzed through the tests, in this study, the applicability and possibility are proposed.

Keywords: Carbon Fiber Reinforced Plastic(CFRP), Glass Fiber Reinforced Plastic(GFRP), stainless wire mesh, electromagnetic shielding

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Authors: Saima Nasreen, Uzaira Rafique, Shery Ehrman, Muhammad Aqeel Ashraf

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The release of heavy metals into the environment is a potential threat to water and soil quality as well as to plant, animal and human health. In current research work, organically functionalized mesoporous silicates (MSU-H) were prepared by the co-condensation between sodium silicate and oregano alkoxysilanes in the presence of the nonionic surfactant triblock copolymer P104. The surfactant was used as a template for improving the porosity of the hybrid gels. Synthesized materials were characterized by TEM, FT-IR, SEM/EDX, TG, surface area analysis. The surface morphology and textural properties of such materials varied with various kinds of groups in the channels. In this study, removal of some heavy metals ions from aqueous solution by adsorption process was investigated. Batch adsorption studies show that the adsorption capacity of metal ions on the functionalized silicates is more than that on pure MSU-H. Data shows adsorption on synthesized materials is a time efficient process, suggesting adsorption on external surface as well as the mesoporous process. Adsorption models of Langmuir, Freundlich, and Temkin depicted equal goodness for all adsorbents, whereas pseudo 2nd order kinetics is in best agreement with experimental data.

Keywords: heavy metals, mesoporous silica, hybrid, adsorption, freundlich, langmuir, temkin

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: (Ted) Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup

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This research details a coverage measure for evaluating the effectiveness of observer node placements in a spatial bipartite network. This coverage measure can be used to optimize the configuration of stationary or mobile spatially oriented observer nodes, or a hybrid of the two, over time in order to fully utilize their capabilities. To demonstrate the practical application of this approach, we construct a SpatioTemporal Bipartite Network (STBN) using real-time crime center (RTCC) camera nodes and NOPD calls for service (CFS) event nodes from New Orleans, La (NOLA). We use the coverage measure to identify optimal placements for moving mobile RTCC camera vans to improve coverage of vulnerable areas based on temporal patterns.

Keywords: coverage measure, mobile node dynamics, Monte Carlo simulation, observer nodes, observable nodes, spatiotemporal bipartite knowledge graph, temporal spatial analysis

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Authors: Jihad S. Daba, J. P. Dubois, Yvette Antar

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With the exponential growth of cellular users, a new generation of cellular networks is needed to enhance the required peak data rates. The co-channel interference between neighboring base stations inhibits peak data rate increase. To overcome this interference, multi-cell cooperation known as coordinated multipoint transmission is proposed. Such a solution makes use of multiple-input-multiple-output (MIMO) systems under two different structures: Micro- and macro-diversity. In this paper, we study the capacity and bit error rate in cellular networks using MIMO technology. We analyse both micro- and macro-diversity schemes and develop a hybrid model that switches between macro- and micro-diversity in the case of hard handoff based on a cut-off range of signal-to-noise ratio values. We conclude that our hybrid switched micro-macro MIMO system outperforms classical MIMO systems at the cost of increased hardware and software complexity.

Keywords: cooperative multipoint transmission, ergodic capacity, hard handoff, macro-diversity, micro-diversity, multiple-input-multiple output systems, orthogonal frequency division multiplexing

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Authors: Boumesbah Asma, Chergui Mohamed El-amine

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Since it has been shown that the multi-objective minimum spanning tree problem (MOST) is NP-hard even with two criteria, we propose in this study a hybrid NSGA-II algorithm with an exact mutation operator, which is only used with low probability, to find an approximation to the Pareto front of the problem. In a connected graph G, a spanning tree T of G being a connected and cycle-free graph, if k edges of G\T are added to T, we obtain a partial graph H of G inducing a reduced size multi-objective spanning tree problem compared to the initial one. With a weak probability for the mutation operator, an exact method for solving the reduced MOST problem considering the graph H is then used to give birth to several mutated solutions from a spanning tree T. Then, the selection operator of NSGA-II is activated to obtain the Pareto front approximation. Finally, an adaptation of the VNS metaheuristic is called for further improvements on this front. It allows finding good individuals to counterbalance the diversification and the intensification during the optimization search process. Experimental comparison studies with an exact method show promising results and indicate that the proposed algorithm is efficient.

Keywords: minimum spanning tree, multiple objective linear optimization, combinatorial optimization, non-sorting genetic algorithm, variable neighborhood search

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Authors: Armin Solemanifar, Arthur Wilkinson, Kinjalkumar Patel

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Carbon fibre (CF) - polymer laminate composites have very low densities (approximately 40% lower than aluminium), high strength and high stiffness but in terms of toughness properties they often require modifications. For example, adding rubbers or thermoplastics toughening agents are common ways of improving the interlaminar fracture toughness of initially brittle thermoset composite matrices. The main aim of this project was to toughen CF-epoxy resin laminate composites using hybrid CF-fabrics incorporating Innegra™ a commercial highly-oriented polypropylene (PP) fibre, in which more than 90% of its crystal orientation is parallel to the fibre axis. In this study, the damage tolerance of hybrid (carbon-Innegra, CI) composites was investigated. Laminate composites were produced by resin-infusion using: pure CF fabric; fabrics with different ratios of commingled CI, and two different types of pure Innegra fabrics (Innegra 1 and Innegra 2). Dynamic mechanical thermal analysis (DMTA) was used to measure the glass transition temperature (Tg) of the composite matrix and values of flexural storage modulus versus temperature. Mechanical testing included drop-weight impact, compression-after-impact (CAI), and interlaminar (short-beam) shear strength (ILSS). Ultrasonic C-Scan imaging was used to determine the impact damage area and scanning electron microscopy (SEM) to observe the fracture mechanisms that occur during failure of the composites. For all composites, 8 layers of fabrics were used with a quasi-isotropic sequence of [-45°, 0°, +45°, 90°]s. DMTA showed the Tg of all composites to be approximately same (123 ±3°C) and that flexural storage modulus (before the onset of Tg) was the highest for the pure CF composite while the lowest were for the Innegra 1 and 2 composites. Short-beam shear strength of the commingled composites was higher than other composites, while for Innegra 1 and 2 composites only inelastic deformation failure was observed during the short-beam test. During impact, the Innegra 1 composite withstood up to 40 J without any perforation while for the CF perforation occurred at 10 J. The rate of reduction in compression strength upon increasing the impact energy was lowest for the Innegra 1 and 2 composites, while CF showed the highest rate. On the other hand, the compressive strength of the CF composite was highest of all the composites at all impacted energy levels. The predominant failure modes for Innegra composites observed in cross-sections of fractured specimens were fibre pull-out, micro-buckling, and fibre plastic deformation; while fibre breakage and matrix delamination were a major failure observed in the commingled composites due to the more brittle behaviour of CF. Thus, Innegra fibres toughened the CF composites but only at the expense of reducing compressive strength.

Keywords: hybrid composite, thermoplastic fibre, compression strength, damage tolerance

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Authors: Gopashis Biswas G. Son

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This paper will attempt to explore the notions that the two Caribbean poets- Derek Walcott and Edward Kamau Brathwaite endorse on Caribbean history in their poems. Though both of these poets hold almost the same notion regarding history but their approach is totally different from one another. Coming from a 'hybrid' race, Walcott is aware of the history and acknowledges it and writes in 'mulatto of style'; whereas Brathwaite is enraged by it and attempts to sublimate it to erect a history of the new world. It is Walcott’s view to rise above the delusion and hatred and engulf the world of literature with creativity. On the other hand, Brathwaite holds the grudge which helps him not to forget and forgive the past experience but to transform that very experience into something positive which may help the Caribbean to transform their frustration into something creative and to help the Caribbean to overcome the present struggle against the legacy of colonization. Following discourse analysis, this paper seeks to identify if it is possible to rewrite and re-‘right’ the Caribbean history which has been lost in the route and analyze Walcott and Brathwaite’s attitude towards that very history which has been implemented through their poetry.

Keywords: Caribbean history, colonialism, mulatto of style, Walcott vis-à-vis Brathwaite

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Authors: Takele Nemomsa, Girma Mamo, Tesfaye Balemi

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Climate change refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or variance of its properties and that persists for an extended period, typically decades or longer. In Ethiopia; Maize production in relation to climate change at regional and sub- regional scales have not been studied in detail. Thus, this study was aimed to analyse the impact of climate change on maize yield in Ambo Districts, Central Ethiopia. To this effect, weather data, soil data and maize experimental data for Arganne hybrid were used. APSIM software was used to investigate the response of maize (Zea mays) yield to different agronomic management practices using current and future (2020s–2080s) climate data. The climate change projections data which were downscaled using SDSM were used as input of climate data for the impact analysis. Compared to agronomic practices the impact of climate change on Arganne in Central Ethiopia is minute. However, within 2020s-2080s in Ambo area; the yield of Arganne hybrid is projected to reduce by 1.06% to 2.02%, and in 2050s it is projected to reduce by 1.56 While in 2080s; it is projected to increase by 1.03% to 2.07%. Thus, to adapt to the changing climate; farmers should consider increasing plant density and fertilizer rate per hectare.

Keywords: APSIM, downscaling, response, SDSM

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Authors: Eunice Tan

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The purpose of this presentation is to look at an alternative to the approach and methodologies of working with a child with special needs. The strength-based approach to education embodies a paradigm shift. It is a strategy to move away from a deficit-based methodology which inadvertently may lead to an extensive list of things that the child cannot do or is unable to do. Today, many parents of individuals with special needs are focused on the individual’s deficits rather than on his or her strengths. Even when parents recognise and identify their child’s savant strengths to be valuable and wish to develop their abilities, they face the challenge that there are insufficient programs committed to supporting the development and improvement of such abilities. What is a strength-based approach in education? A strength-based approach in education focuses on students' positive qualities and contributions to class instead of the skills and abilities they may not have. Many schools are focused on the child’s special educational needs rather than the whole child. Parents interviewed have said that they have to engage external tutors to help hone in on their child’s interests and strengths. The strength-based approach to writing statements encourages educators to find out: • What a child can do • What a child can do when he or she is given educational support • Learning more about children with special needs and their strengths and talents will broaden our understanding of how we can help them with language acquisition, social skills, as well as self-help and independence skills.

Keywords: special needs, strengths, and talents, alternative educational approach, strength based approach

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Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Gamze Dogan Birer

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Women’s shelters provide service to women who had been subjected to physical, psychological, economical, and sexual violence. It is proposed that adopting a trauma-informed approach in these shelters would contribute to the ‘woman-defined’ success of the service. This includes reshaping the physical qualities of the shelter, contacts, and interventions that women face during their stay in a way that accepts and addresses their traumatic experiences. It is stated in this paper that the trauma-informed approach has commonalities with the basic psychological needs that are proposed by self-determination theory. Therefore, it is proposed that self-determination theory can be used as a theoretical background for trauma-informed approach

Keywords: self determination theory, trauma informed approach, violence against women, women's shelters

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Authors: Surita Maini, Sanjay Dhanka

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Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: David Nantes, George Gilbert

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There are a lot of risks that might crop up as a tunneling project develops, and it's crucial to be aware of them. Due to the unexpected nature of tunneling projects and the interconnectedness of risk occurrences, the risk assessment approach presents a significant challenge. The purpose of this study is to provide a hybrid FDEMATEL-ANP model to help prioritize risks during tunnel construction projects. The ambiguity in expert judgments and the relative severity of interdependencies across risk occurrences are both taken into consideration by this model, thanks to the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). The Analytic Network Process (ANP) method is used to rank priorities and assess project risks. The authors provide a case study of a subway tunneling construction project to back up the validity of their methodology. The results showed that the proposed method successfully isolated key risk factors and elucidated their interplay in the case study. The proposed method has the potential to become a helpful resource for evaluating dangers associated with tunnel construction projects.

Keywords: risk, prioritization, FDEMATEL, ANP, tunneling construction projects

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Saeed Delara, Kendra MacKay

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Varying methodologies are considered for pile design for both Russian and Western approaches. Although both approaches rely on toe and side frictional resistances, different calculation methods are proposed to estimate pile capacity. The Western approach relies on compactness (internal friction angle) of soil for cohesionless soils and undrained shear strength for cohesive soils. The Russian approach relies on grain size for cohesionless soils and liquidity index for cohesive soils. Though most recommended methods in the Western approaches are relatively simple methods to predict pile settlement, the Russian approach provides a detailed method to estimate single pile and pile group settlement. Details to calculate pile axial capacity and settlement using the Russian and Western approaches are discussed and compared against field test results.

Keywords: pile capacity, pile settlement, Russian approach, western approach

Procedia PDF Downloads 167