Search results for: hybrid recommendation systems

Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Luis Andrey Fajardo Fajardo

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We are immersed in complex systems. The human brain, the galaxies, the snowflakes are examples of complex systems. An area of interest in Complex systems is the chaos theory. This revolutionary field of science presents different ways of study than determinism and reductionism. Here is where in junction with the Nonlinear DSP, chaos theory offer valuable techniques that establish a link between time series and complex theory in terms of complex networks, so that, the study of signals can be explored from the graph theory. Recently, some people had purposed a method to transform time series in graphs, but no one had developed a suitable implementation in Python with signals extracted from Chaotic Systems or Complex systems. That’s why the implementation in Python of an existing method to transform one dimensional chaotic signals from time domain to graph domain and some measures that may reveal information not extracted in the time domain is proposed.

Keywords: Python, complex systems, graph theory, dynamical systems

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Authors: Zakariyya Hassan Abdullahi, Zainab Suleiman Abdullahi, Nuhu Alhaji Muhammad

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In this paper, a thorough review of photovoltaic and photovoltaic thermal systems is done on the basis of its performance based on electrical as well as thermal output. Photovoltaic systems are classified according to their use, i.e., electricity production, and thermal, Photovoltaic systems behave in an extraordinary and useful way, they react to light by transforming part of it into electricity useful way and unique, since photovoltaic and thermal applications along with the electricity production. The application of various photovoltaic systems is also discussed in detail. The performance analysis including all aspects, e.g., electrical, thermal, energy, and energy efficiency are also discussed. A case study for PV and PV/T system based on energetic analysis is presented.

Keywords: photovoltaic, renewable, performance, efficiency, energy

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Authors: Mohammed Habchi, Sidi Mohammed Mesli, Rafik Benallal, Mohammed Kotbi

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On the basis of four partial correlation functions and some geometric constraints obtained from neutron scattering experiments, a Reverse Monte Carlo (RMC) simulation has been performed in the study of the aqueous electrolyte LiCl6H2O at the glassy state. The obtained 3-dimensional model allows computing pair radial and orientational distribution functions in order to explore the structural features of the system. Unrealistic features appeared in some coordination peaks. To remedy to this, we use the Hybrid Reverse Monte Carlo (HRMC), incorporating an additional energy constraint in addition to the usual constraints derived from experiments. The energy of the system is calculated using an Environment Dependence Interaction Potential (EDIP). Ions effects is studied by comparing correlations between water molecules in the solution and in pure water at room temperature Our results show a good agreement between experimental and computed partial distribution functions (PDFs) as well as a significant improvement in orientational distribution curves.

Keywords: LiCl6H2O, glassy state, RMC, HRMC

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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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: Haci Mehmet Guzey, Levent Acar

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In this paper, a novel decentralized controller is developed for linear systems with nonlinear unknown interconnections. A model linear decoupled system is assigned for each system. By using the difference actual and model state dynamics, the problem is formulated as inverse problem. Then, the interconnected dynamics are approximated by using Galerkin’s expansion method for inverse problems. Two different sets of orthogonal basis functions are utilized to approximate the interconnected dynamics. Approximated interconnections are utilized in the controller to cancel the interconnections and decouple the systems. Subsequently, the interconnected systems behave as a collection of decoupled systems.

Keywords: decentralized control, inverse problems, large scale systems, nonlinear interconnections, basis functions, system identification

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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: Graziano Chesi

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The topological entropy plays a key role in linear dynamical systems, allowing one to establish the existence of stabilizing feedback controllers for linear systems in the presence of communications constraints. This paper addresses the determination of a robust value of the topological entropy in nonlinear dynamical systems, specifically the largest value of the topological entropy over all linearized models in a region of interest of the state space. It is shown that a sufficient condition for establishing upper bounds of the sought robust value of the topological entropy can be given in terms of a semidefinite program (SDP), which belongs to the class of convex optimization problems.

Keywords: non-linear system, communication constraint, topological entropy

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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: Lucjan Setlak, Emil Ruda

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The subject of this paper is to review, comparative analysis and simulation of selected components of power electronic systems (PES), consistent with the concept of a more electric aircraft (MEA). Comparative analysis and simulation in software environment MATLAB / Simulink were carried out based on a group of representatives of civil aircraft (B-787, A-380) and military (F-22 Raptor, F-35) in the context of multi-pulse converters used in them (6- and 12-pulse, and 18- and 24-pulse), which are key components of high-tech electronics on-board power systems of autonomous power systems (ASE) of modern aircraft (airplanes of the future).

Keywords: converters, electric machines, MEA (more electric aircraft), PES (power electronics systems)

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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: S. P. Koko, K. Kusakana, H. J. Vermaak

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Standalone micro-hydrokinetic river (MHR) system is one of the promising technologies to be used for remote rural electrification. It simply requires the flow of water instead of elevation or head, leading to expensive civil works. This paper demonstrates an economic benefit offered by a standalone MHR system when compared to the commonly used standalone systems such as solar, wind and diesel generator (DG) at the selected study site in Kwazulu Natal. Wind speed and solar radiation data of the selected rural site have been taken from national aeronautics and space administration (NASA) surface meteorology database. The hybrid optimization model for electric renewable (HOMER) software was used to determine the most feasible solution when using MHR, solar, wind or DG system to supply 5 rural houses. MHR system proved to be the best cost-effective option to consider at the study site due to its low cost of energy (COE) and low net present cost (NPC).

Keywords: economic analysis, micro-hydrokinetic, rural-electrification, cost of energy (COE), net present cost (NPC)

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Authors: Basit Aftab, Zhichao Wang, Feng Zhongke

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Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies.

Keywords: land use land cover, Markov chain model, multi-layer perceptron, random forest, sustainable land, remote sensing.

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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: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: Gulshad Azatova, Aleksandr Kapitonov, Natig Aminov

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Multiagent systems have gained significant attention in various fields, such as robotics, autonomous vehicles, and distributed computing, where multiple agents cooperate and communicate to achieve complex tasks. Efficient communication among agents is a crucial aspect of these systems, as it directly impacts their overall performance and scalability. This scholarly work provides an exploration of essential communication elements and conducts a comparative assessment of diverse protocols utilized in multiagent systems. The emphasis lies in scrutinizing the strengths, weaknesses, and applicability of these protocols across various scenarios. The research also sheds light on emerging trends within communication protocols for multiagent systems, including the incorporation of machine learning methods and the adoption of blockchain-based solutions to ensure secure communication. These trends provide valuable insights into the evolving landscape of multiagent systems and their communication protocols.

Keywords: communication, multi-agent systems, protocols, consensus

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water 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: 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: Achim Kampker, Peter Burggräf, Moritz Krunke, Hanno Voet

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As a reaction to current challenges in factory planning, many companies think about introducing factory standards to lower planning times and decrease planning costs. If these factory standards are set-up with a high level of modularity, they are defined as modular factory systems. This paper deals with the main current problems in the application of modular factory systems in practice and presents a solution approach with its basic models. The methodology is based on methods from factory planning but also uses the tools of other disciplines like product development or technology management to deal with the high complexity, which the development of modular factory systems implies. The four basic models that such a methodology has to contain are introduced and pointed out.

Keywords: factory planning, modular factory systems, factory standards, cost-benefit analysis

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Authors: Hailah Alballaa, Azeddine Chikh

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Several challenges impede the adoption of Recommender Systems for Technology Enhanced Learning (TEL): to collect and identify possible datasets; to select between different recommender approaches; to evaluate their performances. The aim is of this paper is twofold: First, it aims to introduce a survey on the most significant work in this area. Second, it aims at identifying possible research directions.

Keywords: datasets, content-based filtering, recommender systems, TEL

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Authors: G. Ghamrawi, Y. Abunnasr, M. Fawaz, S. Yazigi

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Coastal cities are constantly exposed to environmental degradation and economic regression fueled by rapid and uncontrolled urban growth as well as continuous resource depletion. This is the case of the City of Mina in Tripoli (Lebanon), where lack of awareness to preserve social, ecological, and historical assets, coupled with the increasing development pressures, are threatening the socioeconomic status of the city residents, the quality of life and accessibility to the coast. To address these challenges, a holistic coastal urban design and planning approach was developed to analyze the environmental, political, legal, and socioeconomic context of the city. This approach aims to investigate the potential of balancing urban development with the protection and enhancement of cultural, ecological, and environmental assets under an integrated coastal zone management approach (ICZM). The analysis of Mina's different sectors adopted several tools that include direct field observation, interviews with stakeholders, analysis of available data, historical maps, and previously proposed projects. The findings from the analysis were mapped and graphically represented, allowing the recognition of character zones that become the design intervention units. Consequently, the thesis proposes an urban, city-scale intervention that identifies 6 different character zones (the historical fishing port, Abdul Wahab island, the abandoned Port Said, Hammam el Makloub, the sand beach, and the new developable area) and proposes context-specific design interventions that capitalize on the main characteristics of each zone. Moreover, the intervention builds on the institutional framework of ICZM as well as other studies previously conducted for the coast and adopts nature-based solutions with hybrid systems for providing better environmental design solutions for developing the coast. This enables the realization of an all-inclusive, well-connected shoreline with easy and free access towards the sea; a developed shoreline with an active local economy, and an improved urban environment.

Keywords: blue green infrastructure, coastal cities, hybrid solutions, integrated coastal zone management, sustainable development, urban planning

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Authors: Sairoel Amertet, Girma Gebresenbet

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Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made in smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, and as a result, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from the smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules and various networks applied in agriculture processing were examined. Finally, we explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strongly related keywords for different journals. With the virtually limited use of sophisticated mechatronics in the agricultural industry and, at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics systems in agricultural sectors would be taken into consideration in order to overcome these issues.

Keywords: mechatronics, robotic, robotic system, automation, agriculture mechanism

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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: Takele Nemomsa, Girma Mamo, Tesfaye Balemi

Abstract:

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

Procedia PDF Downloads 375

Authors: Slah Alsaleh

Abstract:

In the recent years and with popularity of internet and technology, e-learning became a major part of majority of education systems. One of the advantages the e-learning systems provide is the large amount of information available about the students' behavior while communicating with the e-learning system. Such information is very rich and it can be used to improve the capability and efficiency of e-learning systems. This paper discusses how e-learning can benefit from implicit data in different ways including; creating homogeneous groups of student, evaluating students' learning, creating behavior profiles for students and identifying the students through their behaviors.

Keywords: e-learning, implicit data, user behavior, data mining

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Authors: Muhammad Badamasi Abdullahi

Abstract:

Teacher education, both pre- and in-service programs, is offered in many countries of the world by different teacher education institutions as declared in the Policies on Education of the countries. However, differences exist from one country to another as a result of some factors peculiar to them. Notwithstanding, there also exist similarities among them in regard to teacher education. This paper is expected to dig into teacher education programs in Nigeria and India so that areas of similarities and differences would be highlighted as well as provide a venue for possible recommendation of both countries to learn from one another. All this is directed towards providing a no -border approach in enhancing effective teaching and learning.

Keywords: teacher education, teaching and learning, pre-service, in-service

Procedia PDF Downloads 378