Search results for: decision matrix

Authors: Abbas Hani, Maryam Jassasizadeh

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The concentrations of Cd, Pb, and Ni were determined from 40 soil samples collected in surface soils of Khark Island. Geostatistic methods and GIS were used to identify heavy metal sources and their spatial pattern. Principal component analysis coupled with correlation between heavy metals showed that level of mentioned heavy metal was lower than the standard level. Then the data obtained from the soil analyzing were studied for the purposes of normal distribution. The best way of interior finding for cadmium and nickel was ordinary kriging and the best way of interpolation of lead was inverse distance weighted. The result of this study help us to understand heavy metals distribution and make decision for remediation of soil pollution.

Keywords: geostatistics, ordinary kriging, heavy metals, GIS, Khark

Procedia PDF Downloads 161

Authors: Fadi Althoey, Yaghoob Farnam

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Sodium chloride (NaCl) can interact with the tricalcium aluminate (C3A) and its hydrates in concrete matrix. This interaction can result in formation of a harmful chemical phase as the temperature changes. It is thought that this chemical phase is embroiled in the premature concrete deterioration in the cold regions. This work examines the potential formation of the harmful chemical phase in various pastes prepared by using different types of ordinary portland cement (OPC) and supplementary cementitious materials (SCMs). The quantification of the chemical phase was done by using a low temperature differential scanning calorimetry. The results showed that the chemical phase formation can be reduced by using Type V cement (low content of C3A). The use of SCMs showed different behaviors on the formation of the chemical phase. Slag and Class F fly ash can reduce the chemical phase by the dilution of cement whereas silica fume can reduce the amount of the chemical phase by dilution and pozzolanic activates. Interestingly, the use of Class C fly ash has a negative effect on concrete exposed to NaCl through increasing the formation of the chemical phase.

Keywords: concrete, damage, chemcial phase, NaCl, SCMs

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Payal Mazumdar, Sunita Rattan, Monalisa Mukherjee

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Polymer nanocomposites are a special class of materials having unique properties and wide application in diverse areas such as EMI shielding, sensors, photovoltaic cells, membrane separation properties, drug delivery etc. Recently the nanocomposites are being investigated for their use in biomedical fields as biosensors. Though nanocomposites with carbon nanoparticles have received worldwide attention in the past few years, comparatively less work has been done on nanographite although it has in-plane electrical, thermal and mechanical properties comparable to that of carbon nanotubes. The main challenge in the fabrication of these nanocomposites lies in the establishment of homogeneous dispersion of nanographite in polymer matrix. In the present work, attempts have been made to synthesize the nanocomposites of polystyrene and nanographite using click chemistry. The polymer and the nanographite are functionalized prior to the formation of nanocomposites. The polymer, polystyrene, was functionalized with alkyne moeity and nanographite with azide moiety. The fabricating of the nanocomposites was accomplished through click chemistry using Cu (I)-catalyzed Huisgen dipolar cycloaddition. The functionalization of filler and polymer was confirmed by NMR and FTIR. The nanocomposites formed by the click chemistry exhibit better electrical properties and the sensors are evaluated for their application as biosensors.

Keywords: nanocomposites, click chemistry, nanographite, biosensor

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Authors: Ritu Chaturvedi, Mayank Varun, M. S. Paul

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Heavy metal contamination of soil is a growing area of concern since the soil is the matrix that supports flora and impacts humans directly. Phytoremediation of contaminated sites is gaining popularity due to its cost effectiveness and solar driven nature. Some hyperaccumulators have been identified for their potential. Metal-accumulating plants have various mechanisms to cope up with stress and one of them is increasing antioxidative capacity. The aim of this research is to assess the effect of Vesicular arbuscular mycorrhiza (VAM) application on the phytoremedial potential of Solanum melongena (Eggplant) and level of photosynthetic pigments along with antioxidative enzymes. Results showed that VAM application increased shoot length, root proliferation pattern of plants. The level of photosynthetic pigments, proline, SOD, CAT, APX altered significantly in response to heavy metal treatment. In conclusion, VAM increased the uptake of heavy metals which lead to the activation of the defense system in plants for scavenging free radicals.

Keywords: heavy metal, phytoextraction, phytostabilization, reactive oxygen species

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Authors: Anatoly B. Rinkevich, Eugeny A. Kuznetsov, Yuri I. Ryabkov

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Transmission of electromagnetic waves through a plate of metamaterial based on permalloy flakes and reflection from the plate is investigated. The metamaterial is prepared of permalloy flakes sized from few to 50μ placed into epoxy-amine matrix. Two series of metamaterial samples are under study with the volume portion of permalloy particles 15% and 30%. There is no direct electrical contact between permalloy particles. Microwave measurements have been carried out at frequencies of 12 to 30 GHz in magnetic fields up to 12 kOe. Sharp decrease of transmitted wave is observed under ferromagnetic resonance condition caused by absorption. Under magnetic antiresonance condition, in opposite, maximum of reflection coefficient is observed at frequencies exceeding 30 GHz. For example, for metamaterial sample with the volume portion of permalloy of 30%, the variation of reflection coefficient in magnetic field reaches 300%. These high variations are of interest to develop magnetic field driven microwave devices. Magnetic field variations of refractive index are also estimated.

Keywords: ferromagnetic resonance, magnetic antiresonance, microwave metamaterials, permalloy flakes, transmission and reflection coefficients

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Authors: Prawal Sinha, Peeyush Singh, Pravir Dutt

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Problems in elastohydrodynamic lubrication have attracted a lot of attention in the last few decades. Solving a two-dimensional problem has always been a big challenge. In this paper, a new discontinuous finite volume method (DVM) for two-dimensional point contact Elastohydrodynamic Lubrication (EHL) problem has been developed and analyzed. A complete algorithm has been presented for solving such a problem. The method presented is robust and easily parallelized in MPI architecture. GMRES technique is implemented to solve the matrix obtained after the formulation. A new approach is followed in which discontinuous piecewise polynomials are used for the trail functions. It is natural to assume that the advantages of using discontinuous functions in finite element methods should also apply to finite volume methods. The nature of the discontinuity of the trail function is such that the elements in the corresponding dual partition have the smallest support as compared with the Classical finite volume methods. Film thickness calculation is done using singular quadrature approach. Results obtained have been presented graphically and discussed. This method is well suited for solving EHL point contact problem and can probably be used as commercial software.

Keywords: elastohydrodynamic, lubrication, discontinuous finite volume method, GMRES technique

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Authors: Małgorzata Cichy

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Afghanistan is a source of conflicts that affect security on a global scale. The role of ethnic and national determinants in the peacebuilding process in this country remains an extremely important factor in this respect. Research methods include literature and data analysis (scientific literature, documents of governmental and non-governmental organizations, statistical data and media reports), institutional and legal analysis, as well as decision-making method. The main objective of the research is a comprehensive answer to the question of how ethnic and national factors affect the process of building peace in Afghanistan after 2021 and what impact it has on international security.

Keywords: Afghanistan, pashtuns, peace, taliban

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Zahra Bakhtiari

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This article explores the development and evolution of the concept of environmental justice, which has shifted from being dominated by white and middle-class individuals to a civil struggle by marginalized communities against environmental injustices. Environmental justice aims to achieve equity in decision-making and policy-making related to the environment. The concept of justice in this context includes four fundamental aspects: distribution, procedure, recognition, and capabilities. Recent scholars have attempted to broaden the concept of justice to include dimensions of participation, recognition, and capabilities. Focusing on all four dimensions of environmental justice is crucial for effective planning and policy-making to address environmental issues. Ignoring any of these aspects can lead to the failure of efforts and the waste of resources.

Keywords: environmental justice, distribution, procedure, recognition, capabilities

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Authors: Viriyavudh Sim, Jung Kyu Choi, Yong Ju Kwak, Oh Hyeon Jeon, Woo Young Jung

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In this study, we investigated the buckling performance of basalt fiber reinforced polymer (BFRP) sandwich infill panels. Fiber Reinforced Polymer (FRP) is a major evolution for energy dissipation when used as infill material of frame structure, a basic Polymer Matrix Composite (PMC) infill wall system consists of two FRP laminates surrounding an infill of foam core. Furthermore, this type of component is for retrofitting and strengthening frame structure to withstand the seismic disaster. In-plane compression was considered in the numerical analysis with ABAQUS platform to determine the buckling failure load of BFRP infill panel system. The present result shows that the sandwich BFRP infill panel system has higher resistance to buckling failure than those of glass fiber reinforced polymer (GFRP) infill panel system, i.e. 16% increase in buckling resistance capacity.

Keywords: Basalt Fiber Reinforced Polymer (BFRP), buckling performance, FEM analysis, sandwich infill panel

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Authors: Alejandra Cardenas, Ona Flores, Fabiola Gretzinger

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Since the 1990s, legal strategies for the promotion and protection of maternal health rights have achieved significant gains. Successful litigation in courts around the world have shown that these rights can be judicially enforceable. Governments and international organizations have acknowledged the importance of a human rights-based approach to maternal mortality and morbidity, and obstetric violence has been recognized as a human rights issue. Despite the progress made, maternal mortality has worsened in some regions of the world, while progress has stagnated elsewhere, and mistreatment in maternal care is reported almost universally. In this context, issues of maternal autonomy and decision-making during pregnancy, labor, and delivery as a critical barrier to access quality maternal health have been largely overlooked. Indeed, despite the principles of autonomy and informed consent in medical interventions being well-established in international and regional norms, how they are applied particularly during childbirth and pregnancy remains underdeveloped. National and global legal standards and decisions related to maternal health were reviewed and analyzed to determine how maternal autonomy and decision-making during pregnancy, labor, and delivery have been protected (or not) by international and national courts. The results of this legal research and analysis lead to the conclusion that a few standards have been set by courts regarding pregnant people’s rights to make choices during pregnancy and birth; however, most undermine the agency of pregnant people. These decisions recognize obstetric violence and gender-based discrimination, but fail to protect pregnant people’s autonomy, privacy, and their right to informed consent. As current human rights standards stand today, maternal health is the only field in medicine and law in which informed consent can be overridden, and patients can be forced to submit to treatments against their will. Unconsented treatment and loss of agency during pregnancy and childbirth can have long-term physical and mental impacts, reduce satisfaction and trust in health systems, and may deter future health-seeking behaviors. This research proposes a path forward that focuses on the pregnant person as an independent agent, relying on the doctrine of self-determination during pregnancy and childbirth, which includes access to the necessary conditions to enable autonomy and choice throughout pregnancy and childbirth as a critical step towards our approaches to reduce maternal mortality, morbidity, and mistreatment, and realize the promise of access to quality maternal health as a human right.

Keywords: autonomy in childbirth and pregnancy, choice, informed consent, jurisprudential analysis

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Authors: Mirjana Dokmanovic

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In recent years, the disadvantaged and marginalised position of rural women in the Republic of Serbia has been recognised in a number of national strategies and policy papers. A number of measures have been adopted by the government aimed at economic empowerment of rural women and eliminating barriers to accessing decision making and economic and social opportunities. However, their implementation pace is still slow. The aim of the paper is to indicate the necessity of a comprehensive policy approach to eliminating discrimination against rural women that would include policy and financial commitments for enhancing agricultural and rural development as a whole, instead of taking fragmented measures targeting consequences instead of causes. The paper introduces main findings of the study of challenges, constraints, and opportunities of rural women in Serbia to enjoy their economic and social rights. The research methodology included the desk research and the qualitative analysis of the available data, statistics, policy papers, studies, and reports produced by the government, ministries and other governmental bodies, independent human rights bodies, and civil society organizations (CSOs). The findings of the study reveal that rural women are at great risk of poverty, particularly in remote areas, and when getting old or widowed. Young rural women working in agriculture are also in unfavorable position, as they do not have opportunities to enjoy their rights during pregnancy and maternity leave, childcare leave and leave due to the special care of a child. The study indicates that the main causes of their unfavorable position are related to the prevalent patriarchal surrounding and economic and social underdevelopment of rural areas in Serbia. Gender inequalities have been particularly present in accessing land and property rights, inheritance, education, social protection, healthcare, and decision making. Women living in the rural areas are exposed at high risk of discrimination in all spheres of public and private life that undermine their enjoyment of basic economic, social and cultural rights. The vulnerability of rural women to discrimination increases in cases of the intersectionality of other grounds of discrimination, such as disability, ethnicity, age, health condition and sexual discrimination. If they are victims of domestic violence, their experience lack of access to shelters and protection services. Despite the State’s recognition of the marginalized position of rural women, there is still a lack of a comprehensive policy approach to improving the economic and social position of rural women.

Keywords: agricultural and rural development, care economy, discrimination against women, economic and social rights, feminization of poverty, Republic of Serbia, rural women

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Authors: Zouhair Gassim

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The reconfiguration of representations of women's bodies as a "chest of pregnancies", the growing commitment of their bodies to the decision of pregnancies previously monopolized by men, the emergence of new practices of the bodies of men and women suggest that the borders between the masculine and the feminine in Morocco are moving. However, the persistence of sexual violence against girls/women indicates that these changes did not contribute to the lifting of the men's control over the bodies of women. This paper aims to analyze the lessons learned about sex education as a discourse related to the fabric of bodies in relation to sexuality in school in order to understand to what extent this institution contributes to the (re) production of gender inequalities. As a result, the educational discourse on sexuality still remains one of the spaces of resistance against gender equality and thus contributes to the (re) production of gender inequalities.

Keywords: gender, sexual education, Morocco, educational system

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Authors: Hebert Montegranario, Mauricio Londoño

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Solutions of Helmholtz equation are essential in seismic imaging methods like full wave inversion, which needs to solve many times the wave equation. Traditional methods like Finite Element Method (FEM) or Finite Differences (FD) have sparse matrices but may suffer the so called pollution effect in the numerical solutions of Helmholtz equation for large values of the wave number. On the other side, global radial basis functions have a better accuracy but produce full matrices that become unstable. In this research we combine the virtues of both approaches to find numerical solutions of Helmholtz equation, by applying a meshless method that produce sparse matrices by local radial basis functions. We solve the equation with absorbing boundary conditions of the kind Clayton-Enquist and PML (Perfect Matched Layers) and compared with results in standard literature, showing a promising performance by tackling both the pollution effect and matrix instability.

Keywords: Helmholtz equation, meshless methods, seismic imaging, wavefield inversion

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Authors: Tamara Trček Pečak, Andrej Mohar, Denis Trček

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This article investigates the latest technological means, which deploy smart structures that are based on (advanced) wireless sensors technologies and ubiquitous computing in general in order to support the above mentioned decision making. Based on two years of in-field research experiences it gives their analysis for these kinds of purposes and provides appropriate architectures and architectural solutions. Moreover, the directions for future research are stated, because these technologies are currently the most promising ones to enable cost-effective preservation of cultural heritage not only in uncontrolled places, but also in general.

Keywords: smart structures, wireless sensors, sensors networks, green computing, cultural heritage preservation, monitoring, cost effectiveness

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Authors: R. H. Fattepur, Sameer R. Fattepur, D. K. Sreekantha

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Dairy Farming is one of the key industries in India. India is the leading producer and also the consumer of milk, milk-based products in the world. In this paper, we have attempted to the replace the human expert system and to develop an artificial expert system prototype to increase the speed and accuracy of decision making dairy farming credit risk evaluation. Fuzzy logic is used for dealing with uncertainty, vague and acquired knowledge, fuzzy rule base method is used for representing this knowledge for building an effective expert system.

Keywords: expert system, fuzzy logic, knowledge base, dairy farming, credit risk

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Authors: Jianzheng Wei, Duo Zhen, Zhihan Yang, Huifeng Tan

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The deployable descent technology is a lightweight entry method using an inflatable heat shield. The heatshield consists of a pressurized core which is covered by different layers of thermal insulation and flexible ablative materials in order to protect against the thermal loads. In this paper, both aluminum and silicon-aluminum aerogels were prepared by freeze-drying method. The latter material has bigger specific surface area and nano-scale pores. Mullite fibers are used as the reinforcing fibers to prepare the aerogel matrix to improve composite flexibility. The flexible composite materials were performed as an insulation layer to an underlying aramid fabric by a thermal shock test at a heat flux density of 120 kW/m² and uniaxial tensile test. These results show that the aramid fabric with untreated mullite fibers as the thermal protective layer is completely carbonized at the heat of about 60 s. The aramid fabric as a thermal resistance layer of the composite material still has good mechanical properties at the same heat condition.

Keywords: aerogel, aramid fabric, flexibility, thermal resistance

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Authors: A. Sivakumar, K. Srinivasan

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The main objective of the study is focused in producing slag based geopolymer concrete obtained with the addition of alkali activator. Test results indicated that the reaction of silicates in slag is based on the reaction potential of sodium hydroxide and the formation of alumino-silicates. The study also comprises on the evaluation of the efficiency of polymer reaction in terms of the strength gain properties for different geopolymer mixtures. Geopolymer mixture proportions were designed for different binder to total aggregate ratio (0.3 & 0.45) and fine to coarse aggregate ratio (0.4 & 0.8). Geopolymer concrete specimens casted with normal curing conditions reported a maximum 28 days compressive strength of 54.75 MPa. The addition of glued steel fibres at 1.0% Vf in geopolymer concrete showed reasonable improvements on the compressive strength, split tensile strength and flexural properties of different geopolymer mixtures. Further, comparative assessment was made for different geopolymer mixtures and the reinforcing effects of steel fibres were investigated in different concrete matrix.

Keywords: accelerators, alkali activators, geopolymer, hot air oven curing, polypropylene fibres, slag, steam curing, steel fibres

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: Emmanuel Barrantes Chaves, Ernesto Rivera Alvarado

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This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI.

Keywords: AI, MCTS, RHEA, Terraria, VGDL, YOLOv5

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Authors: Salvatore Brischetto, Domenico Cesare

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Major improvements in future aircraft and spacecraft could be those dependent on an increasing use of conventional and unconventional multilayered structures embedding composite materials, functionally graded materials, piezoelectric or piezomagnetic materials, and soft foam or honeycomb cores. Layers made of such materials can be combined in different ways to obtain structures that are able to fulfill several structural requirements. The next generation of aircraft and spacecraft will be manufactured as multilayered structures under the action of a combination of two or more physical fields. In multifield problems for multilayered structures, several physical fields (thermal, hygroscopic, electric and magnetic ones) interact each other with different levels of influence and importance. An exact 3D shell model is here proposed for these types of analyses. This model is based on a coupled system including 3D equilibrium equations, 3D Fourier heat conduction equation, 3D Fick diffusion equation and electric and magnetic divergence equations. The set of partial differential equations of second order in z is written using a mixed curvilinear orthogonal reference system valid for spherical and cylindrical shell panels, cylinders and plates. The order of partial differential equations is reduced to the first one thanks to the redoubling of the number of variables. The solution in the thickness z direction is obtained by means of the exponential matrix method and the correct imposition of interlaminar continuity conditions in terms of displacements, transverse stresses, electric and magnetic potentials, temperature, moisture content and transverse normal multifield fluxes. The investigated structures have simply supported sides in order to obtain a closed form solution in the in-plane directions. Moreover, a layerwise approach is proposed which allows a 3D correct description of multilayered anisotropic structures subjected to field loads. Several results will be proposed in tabular and graphical formto evaluate displacements, stresses and strains when mechanical loads, temperature gradients, moisture content gradients, electric potentials and magnetic potentials are applied at the external surfaces of the structures in steady-state conditions. In the case of inclusions of piezoelectric and piezomagnetic layers in the multilayered structures, so called smart structures are obtained. In this case, a free vibration analysis in open and closed circuit configurations and a static analysis for sensor and actuator applications will be proposed. The proposed results will be useful to better understand the physical and structural behaviour of multilayered advanced composite structures in the case of multifield interactions. Moreover, these analytical results could be used as reference solutions for those scientists interested in the development of 3D and 2D numerical shell/plate models based, for example, on the finite element approach or on the differential quadrature methodology. The correct impositions of boundary geometrical and load conditions, interlaminar continuity conditions and the zigzag behaviour description due to transverse anisotropy will be also discussed and verified.

Keywords: composite structures, 3D shell model, stress analysis, multifield loads, exponential matrix method, layer wise approach

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Authors: Mohamed Eldessouki, Ebraheem Shady, Yasser Gowayed

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Carbon nanotubes (CNTs) are known for having high elastic properties with high surface area that promote them as good candidates for reinforcing polymeric matrices. In composite materials, CNTs lack chemical bonding with the surrounding matrix which decreases the possibility of better stress transfer between the components. In this work, a chemical treatment for activating the surface of the multi-wall carbon nanotubes (MWCNT) was applied and the effect of this functionalization on the elastic properties of the epoxy nanocomposites was studied. Functional amino-groups were added to the surface of the CNTs and it was evaluated to be about 34% of the total weight of the CNTs. Elastic modulus was found to increase by about 40% of the neat epoxy resin at CNTs’ weight fraction of 0.5%. The elastic modulus was found to decrease after reaching a certain concentration of CNTs which was found to be 1% wt. The scanning electron microscopic pictures showed the effect of the CNTs on the crack propagation through the sample by forming stress concentrated spots at the nanocomposite samples.

Keywords: carbon nanotubes functionalization, crack propagation, elastic modulus, epoxy nanocomposites

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Authors: Rama Debbarma

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The present study deals with the performance of Linear base isolation system to mitigate seismic response of structures characterized by random system parameters. This involves optimization of the tuning ratio and damping properties of the base isolation system considering uncertain system parameters. However, the efficiency of base isolator may reduce if it is not tuned to the vibrating mode it is designed to suppress due to unavoidable presence of system parameters uncertainty. With the aid of matrix perturbation theory and first order Taylor series expansion, the total probability concept is used to evaluate the unconditional response of the primary structures considering random system parameters. For this, the conditional second order information of the response quantities are obtained in random vibration framework using state space formulation. Subsequently, the maximum unconditional root mean square displacement of the primary structures is used as the objective function to obtain optimum damping parameters Numerical study is performed to elucidate the effect of parameters uncertainties on the optimization of parameters of linear base isolator and system performance.

Keywords: linear base isolator, earthquake, optimization, uncertain parameters

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Authors: Razieh Khalafi

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The brain is the information processing centre of the human body. Stimuli in the form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research, we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modelling of EEG signal in case external stimuli but it can be used for modelling of brain response in case of internal stimuli.

Keywords: brain, stimuli, partial differential equation, response, EEG signal

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Luciano Oliveira, Elsa Vásquez-Alvarez

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The increase in rail freight transport in Brazil in recent years requires new railway lines and the maintenance of existing ones, which generates high costs for concessionaires. It is in this context that this work is inserted, whose objective is to propose a method that uses Binary Linear Programming for the choice of sleeper and rail fastening, from various options, including the way to apply these materials, with focus to minimize costs. Unit value information, the life cycle each of material type, and service expenses are considered. The model was implemented in commercial software using real data for its validation. The formulated model can be replicated to support decision-making for other railway projects in the choice of sleepers and rail fastening with lowest cost.

Keywords: linear programming, rail fastening, rail sleeper, railway

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 90