Search results for: algorithms and data structure

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Masoud Mahdavi

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Today, developed and industrial cities have all kinds of sewage and water transfer canals, subway tunnels, infrastructure facilities, etc., which have caused underground cavities to be created under the buildings. The presence of these cavities causes behavioral changes in the structural behavior that must be fully evaluated. In the present study, using Abaqus finite element software, the effect of cavities with 0.5 and 1.5 meters in diameter at a depth of 2.5 meters from the earth's surface (with a circular cross-section) on the performance of the foundation and the ground (soil) has been evaluated. For this purpose, the Kobe earthquake was applied to the models for 10 seconds. Also, pore water pressure and weight were considered on the models to get complete results. The results showed that by creating and increasing the diameter of circular cavities in the soil, three indicators; 1) von Mises stress, 2) displacement and 3) plastic strain have had oscillating, ascending and ascending processes, respectively, which shows the relationship between increasing the diameter index of underground cavities and structural indicators of structure-foundation-soil.

Keywords: underground excavations, foundation, structural substrates, Abaqus software, Kobe earthquake, time history analysis

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Authors: Md. Maksudur Rahman Khan, M. Rahim Uddin, Hamidah Abdullah, Kaykobad Md. Rezaul Karim, Abu Yousuf, Chin Kui Cheng, Huei Ruey Ong

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A systematic study was conducted to explore the photocatalytic reduction of carbon dioxide (CO₂) into methanol on TiO₂ loaded copper ferrite (CuFe₂O₄) photocatalyst under visible light irradiation. The phases and crystallite size of the photocatalysts were characterized by X-ray diffraction (XRD) and it indicates CuFe₂O₄ as tetragonal phase incorporation with anatase TiO₂ in CuFe₂O₄/TiO₂ hetero-structure. The XRD results confirmed the formation of spinel type tetragonal CuFe₂O₄ phases along with predominantly anatase phase of TiO₂ in the CuFe₂O₄/TiO₂ hetero-structure. UV-Vis absorption spectrum suggested the formation of the hetero-junction with relatively lower band gap than that of TiO₂. Photoluminescence (PL) technique was used to study the electron–hole (e⁻/h⁺) recombination process. PL spectra analysis confirmed the slow-down of the recombination of electron–hole (e⁻/h⁺) pairs in the CuFe₂O₄/TiO₂ hetero-structure. The photocatalytic performance of CuFe₂O₄/TiO₂ was evaluated based on the methanol yield with varying amount of TiO₂ over CuFe₂O₄ (0.5:1, 1:1, and 2:1) and changing light intensity. The mechanism of the photocatalysis was proposed based on the fact that the predominant species of CO₂ in aqueous phase were dissolved CO₂ and HCO₃^- at pH ~5.9. It was evident that the CuFe₂O₄ could harvest the electrons under visible light irradiation, which could further be injected to the conduction band of TiO₂ to increase the life time of the electron and facilitating the reactions of CO₂ to methanol. The developed catalyst showed good recycle ability up to four cycles where the loss of activity was ~25%. Methanol was observed as the main product over CuFe₂O₄, but loading with TiO₂ remarkably increased the methanol yield. Methanol yield over CuFe₂O₄/TiO₂ was found to be about three times higher (651 μmol/g_cat L) than that of CuFe₂O₄ photocatalyst. This occurs because the energy of the band excited electrons lies above the redox potentials of the reaction products CO₂/CH₃OH.

Keywords: photocatalysis, CuFe2O4/TiO2, band-gap energy, methanol

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Authors: Carlos Joel Tchawouo Mbiada

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This paper intends to refrain from debating the different meanings of justice, such as its social or moral meaning, nor to discuss the different theories of justice. This paper focuses on the legal understanding of access to justice to mean access to the court. Using the Labour and Labour Appeal Courts as a case study, this paper investigates whether the composition of the bench, the personnel and state mechanisms to promote access to court offer ideal conditions to access to court. The investigation is benchmarked against the South African new constitutional order underpinned by the concept of social justice to eradicate past injustices. To provide justice to all, the Constitution of the Republic of South Africa 1996 guarantees the right to access to the court. The question that takes centre stage in this paper is whether litigants are denied the right to access the Labour and Labour Appeal Courts. The paper argues that factors such as the status of the Labour and Labour Appeal Courts, the number of judges, and the building structure prevent litigants from accessing these courts. The paper advocates for a legislative overhaul of the Labour and Labour Appeal Courts structure so that litigants may access the courts. Until such time, the paper argues that the right to access the Labour and Labour Appeal Courts would remain far from the reach of many litigants.

Keywords: access to justice, access to court, labour court, labour appeal court

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Authors: Mohammad Reza Taherkhani‎

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Sardar Ameer Akram Khan, Shahzad Amin Sheikh

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Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results.

Keywords: blind equalizations, constant modulus algorithm, multi-modulus algorithm, recursive least square algorithm, quadrature amplitude modulation (QAM)

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Authors: Ying Yi Wu

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The study of finite groups is a central topic in algebraic structures, and one of the most fundamental questions in this field is the classification of finite groups up to isomorphism. In this paper, we investigate the cyclic property of groups of prime order, which is a crucial result in the classification of finite abelian groups. We prove the following statement: If p is a prime, then every group G of order p is cyclic. Our proof utilizes the properties of group actions and the class equation, which provide a powerful tool for studying the structure of finite groups. In particular, we first show that any non-identity element of G generates a cyclic subgroup of G. Then, we establish the existence of an element of order p, which implies that G is generated by a single element. Finally, we demonstrate that any two generators of G are conjugate, which shows that G is a cyclic group. Our result has significant implications in the classification of finite groups, as it implies that any group of prime order is isomorphic to the cyclic group of the same order. Moreover, it provides a useful tool for understanding the structure of more complicated finite groups, as any finite abelian group can be decomposed into a direct product of cyclic groups. Our proof technique can also be extended to other areas of group theory, such as the classification of finite p-groups, where p is a prime. Therefore, our work has implications beyond the specific result we prove and can contribute to further research in algebraic structures.

Keywords: group theory, finite groups, cyclic groups, prime order, classification.

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Authors: Innocent C. Ezenwa, Takashi Yoshino

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Knowledge of the transport properties of Fe and its alloys at extreme high pressure (P), temperature (T) conditions are essential for understanding the generation and sustainability of the magnetic field of the rocky planets with a metallic core. Since Pt, an unfilled d-band late transition metal with an electronic structure of Xe4f¹⁴5d⁹6s¹, is paramagnetic and remains close-packed structure at ambient conditions and high P-T, it is expected that its transport properties at these conditions would be similar to those of ε-Fe. We investigated the T-dependent electrical resistivity of solid and liquid Pt up to 8 GPa and found it constant along its melting curve both on the liquid and solid sides in agreement with theoretical prediction and experimental results estimated from thermal conductivity measurements. Our results suggest that the T-dependent resistivity of ε-Fe is linear and would not saturate at high P, T conditions. This, in turn, suggests that the thermal conductivity of liquid Fe at Earth’s core conditions may not be as high as previously suggested by models employing saturation resistivity. Hence, thermal convection could have powered the geodynamo before the birth of the inner core. The electrical resistivity and thermal conductivity on the liquid and solid sides of the inner core boundary of the Earth would be significantly different in values.

Keywords: electrical resistivity, thermal conductivity, transport properties, geodynamo and geomagnetic field

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Authors: F. R. Chen, C. Kisielowski, D. Van Dyck

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In principle, the latest generation aberration-corrected transmission electron microscopes (TEMs) could achieve sub-Å resolution, but there is bottleneck that hinders the final step towards revealing 3D structure. Firstly, in order to achieve a resolution around 1 Å with single atom sensitivity, the electron dose rate needs to be sufficiently large (10⁴-10⁵eÅ⁻² s⁻¹). With such large dose rate, the electron beam can induce surfaces alterations or even bulk modifications, in particular, for electron beam sensitive (soft) materials such as nm size particles, organic materials, proteins or macro-molecules. We will demonstrate methodology of low dose electron holography for observing 3D structure for soft materials such as single Oleic acid molecules at atomic resolution. The main improvement of this new type of electron holography is based on two concepts. Firstly, the total electron dose is distributed over many images obtained at different defocus values from which the electron hologram is then reconstructed. Secondly, in contrast to the current tomographic methods that require projections from several directions, the 3D structural information of the nano-object is then extracted from this one hologram obtained from only one viewing direction.

Keywords: low dose electron microscopy, in-line electron holography, atomic resolution tomography, soft materials

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Cheng Wen, Zhang Jianhua

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This paper aims to probe into the general rules of industry specialization or diversification in a region during its process of economic growth and the specific reasons for the difference of industry specialization development in the eastern, central and western regions of China. It is found in this paper that the changes of regional industry specialization in China, like most of countries in the world, also present the U-shaped curve. Regional industrial structure is diversified in the first place. And when the per capita income exceeds a certain level, distribution of economic resources in this region will be concentrated again. From the perspective of rising total factor productivity and falling of transaction cost in the process of economic development, this paper comes up with a theoretical model to explain the U-shaped curve. Through the empirical test of China's provincial panel data, this paper explains the factors that cause the inequality of the industry specialization development in the eastern, central and western regions of China.

Keywords: u-shaped curve, regional industrial specialization, technological progress, transaction costs

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Authors: Sukaesi Marianti

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This study aims to develop the Relational Mobility Scale for the Indonesian population and to investigate its psychometric properties. New items of the scale were created taking into account the Indonesian population which consists of two parallel forms (A and A’). This study uses 30 newly orchestrated items while keeping in mind the characteristics of the targeted population. The scale was administered to 433 public high school students in Malang, Indonesia. Construct validity of its factor structure was demonstrated using exploratory factor analysis and confirmatory factor analysis. The result exhibits that he model fits the data, and that the delayed alternate form method shows acceptable result. Results yielded that 21 items of the three-dimensional Relational Mobility Scale is suitable for measuring relational mobility in high school students of Indonesian population.

Keywords: confirmatory factor analysis, delayed alternate form, Indonesian population, relational mobility scale

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Authors: Abdullah Al-Nassri, Ahmed Al-Alawi

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Seaweeds are classified into three groups: red, green, and brown. Each group of seaweeds consists of several types that have differences in composition. Even at the species level, there are differences in some ingredients, although in general composition, they are the same. Environmental conditions, availability of nutrients, and maturity stage are the main reasons for composition differences. In this study, two red seaweed species, Melanothamnus somalensis & Gelidium omanense, were collected in September 2021 from Sadh (Dhofar governorate, Oman). Five organic solvents were used sequentially to achieve extraction. The solvents were applied in the following order: hexane, dichloromethane, ethyl acetate, acetone, and methanol. Preparative HPLC (PrepLC) was performed to fraction the extracts. The chemical composition was measured; also, total phenols, flavonoids, and tannins were investigated. The structure of the extracts was analyzed by Fourier-transform infrared spectroscopy (FTIR). Seaweeds demonstrated high differences in terms of chemical composition, total phenolic content (TPC), total flavonoid content (TFC), and total tannin content (TTC). Gelidium omanense showed high moisture content, lipid content and carbohydrates (9.8 ± 0.15 %, 2.29 ± 0.09 % and 70.15 ± 0.42 %, respectively) compared to Melanothamnus somalensis (6.85 ± 0.01 %, 2.05 ± 0.12 % and 52.7 ± 0.36 % respectively). However, Melanothamnus somalensis showed high ash content and protein (27.68 ± 0.40 % and 52.7 ± 0.36 % respectively) compared to Gelidium omanense (8.07 ± 0.39 % and 9.70 ± 0.22 % respectively). Melanothamnus somalensis showed higher elements and minerals content, especially sodium and potassium. This is attributed to the jelly-like structure of Melanothamnus somalensis, which allows storage of more solutes compared to the leafy-like structure of Gelidium omanense. Furthermore, Melanothamnus somalensis had higher TPC in all fractions except the hexane fraction than Gelidium omanense. Except with hexane, TFC in the other solvents’ extracts was significantly different between Gelidium omanense and Melanothamnus somalensis. In all fractions, except dichloromethane and ethyl acetate fractions, there were no significant differences in TTC between Gelidium omanense and Melanothamnus somalensis. FTIR spectra showed variation between fractions, which is an indication of different functional groups.

Keywords: chemical composition, organic extract, Omani seaweeds, biological activity, FTIR

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Authors: Mohammad I. Hossain, Omar F. Hamim

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This paper presents a case study on a pile integrity test for assessing the integrity of piles as well as a physical dimension (e.g., cross-sectional area, length), continuity, and consistency of the pile materials. The recent boom in the socio-economic condition of Bangladesh has given rise to the building of high-rise commercial and residential infrastructures. The advantage of the pile integrity test lies in the fact that it is possible to get an approximate indication regarding the quality of the sub-structure before commencing the construction of the super-structure. This paper aims at providing a classification of cast-in-situ piles based on characteristic reflectograms obtained using the Sonic Integrity Testing program for the sub-soil condition of Narayanganj, Bangladesh. The piles have been classified as 'Pile Type-1', 'Pile Type-2', 'Pile Type-3', 'Pile type-4', 'Pile Type-5' or 'Pile Type-6' from the visual observations of reflections from the generated stress waves by striking the pile head with a handheld hammer. With respect to construction quality and integrity, piles have been further classified into three distinct categories, i.e., satisfactory, may be satisfactory, and unsatisfactory.

Keywords: cast-in-situ piles, characteristic reflectograms, pile integrity test, sonic integrity testing program

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Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

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Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: indoor navigation, low light, RGB-D camera, vision based

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

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Authors: Aroona Hashmi

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An economic recession can lead people to feel more insecure about their financial situation. The series of events leading into a recession can be especially distressing for Educated Youth. One of the most salient factors linking economic recession to psychological distress is unemployment. It is proved that a large number of educated young people are facing higher unemployment rate in Pakistan. Young people are likely to get frustrated at the lack of opportunities made available to them. If the young population increases more rapidly than job opportunities, then number of unemployment is likely to increase. The aim of present study was to investigate the relationship between economic instability, growing rate of aggression and frustration among educated youth. The study aimed to find out the impact of increased economic instability on the learning abilities of the students. Data was gathered from six university students of Punjab, Pakistan. The sample of the study consisted of three hundred male and female university students. The data was analyzed by applying Chi -square test. The results of the research indicate that there is a significant relationship between low household income and growing rate of aggression among educated youth. The increasing trend of economic instability significantly influences the learning abilities of the students. The study concludes that feeling of deprivation produce frustration and could be expressed through aggression. Therefore, if factors that are responsible for youth unemployment in Pakistan are addressed, psychological effects will be reduced. The right way of tackling the youth bulge is to turn the youth into a productive workforce. There is a dire need to transform the education system to societal needs. At the same time creating demand for the young workforce is achieved through dynamic changes in the economic structure.

Keywords: psychological effects, economic recession, educated youth, environmental factors

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Authors: Woo Young Jung, Sung Min Park, Ho Young Son, Viriyavudh Sim

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This study presents a way to reduce earthquake damage and emergency rehabilitation of critical structures such as schools, high-tech factories, and hospitals due to strong ground motions associated with climate changes. Regarding recent trend, a strong earthquake causes serious damage to critical structures and then the critical structure might be influenced by sequence aftershocks (or tsunami) due to fault plane adjustments. Therefore, in order to improve seismic performance of critical structures, retrofitted or strengthening study of the structures under aftershocks sequence after emergency rehabilitation of the structures subjected to strong earthquakes is widely carried out. Consequently, this study used composite material for emergency rehabilitation of the structure rather than concrete and steel materials because of high strength and stiffness, lightweight, rapid manufacturing, and dynamic performance. Also, this study was to develop or improve the seismic performance or seismic retrofit of critical structures subjected to strong ground motions and earthquake aftershocks, by utilizing GFRP-Corrugated Infill Panels (GCIP).

Keywords: aftershock, composite material, GFRP, infill panel

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Authors: Man Singh Meena

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Structural engineers are facing different kind of challenges due to innovative & bold ideas of architects who are trying to design every structure with uniqueness. In RCC frame structures different geometry of columns can be used in design and rectangular columns can be placed with different type orientation. The analysis is design of structures can also be carried out by different type of software available i.e., STAAD Pro, ETABS and TEKLA. In recent times high-rise building modeling & analysis is done by ETABS due to its certain features which are superior to other software. The case study in this paper mainly emphasizes on structural behavior of high rise building for different column shape configurations like Circular, Square, Rectangular and Rectangular with 90-degree Rotation and rectangular shape plan. In all these column shapes the areas of columns are kept same to study the effect on design of concrete area is same. Modelling of 20-storeys R.C.C. framed building is done on the ETABS software for analysis. Post analysis of the structure, maximum bending moments, shear forces and maximum longitudinal reinforcement are computed and compared for three different story structures to identify the effectiveness of geometry of column.

Keywords: high-rise building, column geometry, building modelling, ETABS analysis, building design, structural analysis, structural optimization

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Authors: M.Ayazi, N. Golshan Ebrahimi

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The development of antibacterial surface has devoted extensive researches and important field due to the growing antimicrobial resistance strains. The superhydrophobic surface has raised attention because of reducing bacteria adhesion in the absence of antibiotic agents. Evaluating the current development antibacterial surface has to be investigating to consider the potential of applying superhydrophobic surface to reduce bacterial adhesion or role of patterned surfaces on it. In this study, we present different samples with bio-inspired hierarchical and microstructures to consider their ability in reducing bacterial adhesion. The structures have inspired from rice-like pattern and lotus-leaf that developed on the polydimethylsiloxane (PDMS) and polypropylene (PP). The results of the attachment behaviors have considered on two bacteria strains of gram-negative Escherichia coli (E. coli) bacteria and gram-positive Staphylococcus aureus (S. aureus). The reduction of bacteria adhesion on these roughness surfaces demonstrated the effectiveness of rinsing ability on removing bacterial cells on structured plastic surfaces. Results have also offered the important role of bacterial species, material chemistry and hierarchical structure to prevent bacterial adhesion.

Keywords: hierarchical structure, self-cleaning, lotus-effect, bactericidal

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Authors: Joanna Skopinska-Wisniewska, Anna Bajek, Marta Ziegler-Borowska, Alina Sionkowska

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Hydrogels are a class of materials widely used in medicine for many years. Proteins, such as collagen, due to the presence of a large number of functional groups are easily wettable by polar solvents and can create hydrogels. The supramolecular network capable to swelling is created by cross-linking of the biopolymers using various reagents. Many cross-linking agents has been tested for last years, however, researchers still are looking for a new, more secure reactants. Squaric acid, 3,4-dihydroxy 3-cyclobutene 1,2- dione, is a very strong acid, which possess flat and rigid structure. Due to the presence of two carboxyl groups the squaric acid willingly reacts with amino groups of collagen. The main purpose of this study was to investigate the influence of addition of squaric acid on the chemical, physical and biological properties of collagen materials. The collagen type I was extracted from rat tail tendons and 1% solution in 0.1M acetic acid was prepared. The samples were cross-linked by the addition of 5%, 10% and 20% of squaric acid. The mixtures of all reagents were incubated 30 min on magnetic stirrer and then dialyzed against deionized water. The FTIR spectra show that the collagen structure is not changed by cross-linking by squaric acid. Although the mechanical properties of the collagen material deteriorate, the temperature of thermal denaturation of collagen increases after cross-linking, what indicates that the protein network was created. The lyophilized collagen gels exhibit porous structure and the pore size decreases with the higher addition of squaric acid. Also the swelling ability is lower after the cross-linking. The in vitro study demonstrates that the materials are attractive for 3T3 cells. The addition of squaric acid causes formation of cross-ling bonds in the collagen materials and the transparent, stiff hydrogels are obtained. The changes of physicochemical properties of the material are typical for cross-linking process, except mechanical properties – it requires further experiments. However, the results let us to conclude that squaric acid is a suitable cross-linker for protein materials for medicine and tissue engineering.

Keywords: collagen, squaric acid, cross-linking, hydrogel

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: N. F. F. Ebecken, G. C. Pereira

Abstract:

Although networks provide a powerful approach to the study of a wide variety of ecological systems, their formulation usually does not include various types of interactions, interactions that vary in space and time, and interconnected systems such as networks. The emerging field of 'multilayer networks' provides a natural framework for extending ecological systems analysis to include these multiple layers of complexity as it specifically allows for differentiation and modeling of intralayer and interlayer connectivity. The structure provides a set of concepts and tools that can be adapted and applied to the ecology, facilitating research in high dimensionality, heterogeneous systems in nature. Here, ecological multilayer networks are formally defined based on a review of prior and related approaches, illustrates their application and potential with existing data analyzes, and discusses limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers a largely untapped potential to further address ecological complexity, to finally provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.

Keywords: ecological networks, multilayered networks, sea ecology, Brazilian Coastal Area

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Authors: Huai-Cong Liu，Tae Chul Jeong，Ju Lee

Abstract:

This paper describes characteristic analysis of a synchronous reluctance motor (SynRM)’s rotor with the Multi-segment and Multi-layer structure. The magnetic-saturation phenomenon in SynRM is often appeared. Therefore, when modeling analysis of SynRM the calculation of nonlinear magnetic field needs to be considered. An important influence factor on the convergence process is how to determine the relative permeability. An improved method, which ensures the calculation, is convergence by linear iterative method for saturated magnetic field. If there are inflection points on the magnetic curve,an optimum convergence method of solution for nonlinear magnetic field was provided. Then the equivalent magnetic circuit is calculated, and d,q-axis inductance can be got. At last, this process is applied to design a 7.5Kw SynRM and its validity is verified by comparing with the result of finite element method (FEM) and experimental test data.

Keywords: SynRM, magnetic-saturation, magnetic circuit, analytical modeling

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Authors: Ángel de J. López-Pérez, Sonia E. Ruiz, Vanessa A. Segovia

Abstract:

Buildings vulnerability due to seismic activity has been highly studied since the middle of last century. As a solution to the structural and non-structural damage caused by intense ground motions, several seismic energy dissipating devices, such as buckling-restrained braces (BRB), have been proposed. BRB have shown to be effective in concentrating a large portion of the energy transmitted to the structure by the seismic ground motion. A design approach for buildings with BRB elements, which is based on a seismic Displacement-Based formulation, has recently been proposed by the coauthors in this paper. It is a practical and easy design method which simplifies the work of structural engineers. The method is used here for the design of the structure-BRB damper system. The objective of the present study is to extend and apply a methodology to find the best combination of design parameters on multiple-degree-of-freedom (MDOF) structural frame – BRB systems, taking into account simultaneously: 1) initial costs and 2) an adequate engineering demand parameter. The design parameters considered here are: the stiffness ratio (α = K_frame/K_total), and the strength ratio (γ = V_damper/V_total); where K represents structural stiffness and V structural strength; and the subscripts "frame", "damper" and "total" represent: the structure without dampers, the BRB dampers and the total frame-damper system, respectively. The selection of the best combination of design parameters α and γ is based on an initial costs analysis and on the structural dynamic response of the structural frame-damper system. The methodology is applied to a 12-story 5-bay steel building with BRB, which is located on the intermediate soil of Mexico City. It is found the best combination of design parameters α and γ for the building with BRB under study.

Keywords: best combination of design parameters, BRB, buildings with energy dissipating devices, buckling-restrained braces, initial costs

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: P. M. Fonseca Alfonso, E. A. Murillo Ruiz, M. Diaz Lagos

Abstract:

Understanding the physicochemical properties and mineralogy of fly ash from a particular source is essential for to protect the environment and considering its possible applications, specifically, in the production of geopolymeric materials that solidify/stabilize heavy metals ions. The results of the characterization of three fly ash samples are shown in this paper. The samples were produced in the TERMOPAIPA IV thermal power plant in the State of Boyaca, Colombia. The particle size distribution, chemical composition, mineralogy, and molecular structure of three samples were analyzed using laser diffraction, X-ray fluorescence, inductively coupled plasma mass spectrometry, X-ray diffraction, and infrared spectroscopy respectively. The particle size distribution of the three samples probably ranges from 0.128 to 211 μm. Approximately 59 elements have been identified in the three samples. It is noticeable that the ashes are made up of aluminum and silicon compounds. Besides, the iron phase in low content was also found. According to the results found in this study, the fly ash samples type F has a great potential to be used as raw material for the manufacture of geopolymers with potential use in the stabilization/solidification of heavy metals; mainly due to the presence of amorphous aluminosilicates typical of this type of ash, which react effectively with alkali-activator.

Keywords: fly ash, geopolymers, molecular structure, physicochemical properties.

Procedia PDF Downloads 123