Search results for: domain ontologies

Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: fake reviews, feature collection, opinion spam, spam detection

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Authors: Yi-De Liu

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Destination branding is a popular practice adopted by many cities in the context of intensified tourism competition. However, branding for major event is a relatively new domain in the studies of destination marketing. Based on a case study of Liverpool as the 2008 European Capital of Culture, the aim of this paper is to explore the effectiveness of the key branding campaign - the ‘Look of the City’ programme. This study looks at quantitative data collected from on-street face-to-face survey. 611 questionnaires were distributed to and collected from local residents, visitors from the immediate hinterland, domestic tourists and overseas visitors. The analysis is done, first by investigating respondents’ impression on the Liverpool 08 brand and the branding campaign, and then by exploring the effects of campaign. The positioning of Liverpool compared with other similar cities is addressed in the end. The final section extracts lessons from this empirical investigation.

Keywords: destination branding, major event, European capital of culture, Liverpool

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Authors: Omar Abdillah, Mirna Adriani

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In this work, we perform new approach for analyzing public sentiment towards the presidential candidate in the 2014 Indonesian election that expressed in Twitter. In this study we propose such procedure for analyzing sentiment over Indonesian political message by understanding the behavior of Indonesian society in sending message on Twitter. We took different approach from previous works by utilizing punctuation mark and Indonesian sentiment lexicon that completed with the new procedure in determining sentiment towards the candidates. Our experiment shows the performance that yields up to 83.31% of average precision. In brief, this work makes two contributions: first, this work is the preliminary study of sentiment analysis in the domain of political message that has not been addressed yet before. Second, we propose such method to conduct sentiment analysis by creating decision making procedure in which it is in line with the characteristic of Indonesian message on Twitter.

Keywords: unsupervised sentiment analysis, political message, lexicon based, user behavior understanding

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Authors: Liming Chen, Baoxin Xu, Zhaoyun Ding, Bin Liu, Xianqiang Zhu

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In the era of big data, public investors are faced with more complicated information related to investment decisions than ever before. To survive in the fierce competition, it has become increasingly urgent for investors to combine multi-source knowledge and evaluate the companies’ true value efficiently. For this, a rule-based ontology reasoning method is proposed to support steel companies’ value assessment. Considering the delay in financial disclosure and based on cost-benefit analysis, this paper introduces the supply chain enterprises financial analysis and constructs the ontology model used to value the value of steel company. In addition, domain knowledge is formally expressed with the help of Web Ontology Language (OWL) language and SWRL (Semantic Web Rule Language) rules. Finally, a case study on a steel company in China proved the effectiveness of the method we proposed.

Keywords: financial ontology, steel company, supply chain, ontology reasoning

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Authors: Yunzhe Tong, Jun Fan, Bin Wang

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The vibroacoustic behavior of an immersed infinite cylindrical shell with periodic lengthwise ribs has been studied in this paper. The motions of the shell are described by the Donnell equations. Each lengthwise rib is modeled as an elastic beam. The motions of the bulkheads are decomposed into the longitudinal motions and flexural motions. The analytical expressions of the shell motions can be obtained through circumferential mode expansion, Fourier Transform and periodic boundary condition in the circumferential direction. Furthermore, the far-field radiated pressure has been obtained using the stationary phase. The analysis of wavenumber domain shows that periodic lengthwise stiffeners in the circumferential direction can produce flexural Bloch waves. The dominant feature in far-field pressure amplitude is the resonance of the supersonic components of the flexural Bloch waves in the circumferential direction.

Keywords: flexural Bloch wave, stiffened shell, vibroacoustics, wavenumber analysis

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Authors: Amna Asif, Rabia Naseem

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In the domain of consumer-brand relationship, love for a fashion brand is a dominant idea. Brand executives incline to build more endearing brands, for example, Levi’s “Quality never goes out of style”. Though, the significance of this notion is not often debated in the literature of marketing. Moreover, the effect of brand image and personality on brand love has not been examined in any quantitative study in Pakistan. The current research aims to fill this study gap by evolving a causal framework integrating word-of-mouth, brand love, image, and personality to examine the relationships among them. Data was gathered through questionnaires survey, and it was filled by 409 university students. AMOS 20 was used to draw a path analysis and test the hypotheses. Results discovered that brand personality and brand image leads to brand love that ultimately impacts word-of-mouth. Results give thorough suggestions on which future research can be constructed.

Keywords: brand love, brand personality, brand image, fashion brands, word-of-mouth

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Authors: Yingying Ding

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This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: contrastive studies, specialised translation, corpus-based approach, teaching

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Authors: Elke Mackensen

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Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: K. N. Umesh, K. S. Srinivasan

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The influence of rotor rub was studied with respect to light rub and heavy rub conditions. The investigations were carried out for both below and above critical speeds. The time domain waveform has revealed truncation of the waveform during rubbing conditions. The quantum of rubbing has been indicated by the quantum of truncation. The orbits for light rub have indicated a single loop whereas for heavy rub multi looped orbits have been observed. In the heavy rub condition above critical speed both sub harmonics and super harmonics are exhibited. The orbit precess in a direction opposite to the direction of the rotation of the rotor. When the rubbing was created above the critical speed the orbit shape was of '8' shape indicating the rotor instability. Super-harmonics and sub-harmonics of vibration signals have been observed for light rub and heavy rub conditions and for speeds above critical.

Keywords: rotor rub, orbital analysis, frequency analysis, vibration signatures

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, a causal model, modelica, horizontal and vertical sorting

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Authors: Ti-An Tsai, Chun-Chih Wang, Hung-Wen Wang, I-Ling Chang, Lien-Wen Chen

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In this study, we demonstrate a high-resolution refractive index sensor based on a magnetic photonic crystal (MPC) composed of a triangular lattice array of air holes embedded in Si matrix. A microcavity is created by changing the radius of an air hole in the middle of the photonic crystal. The cavity filled with gyrotropic materials can serve as a refractive index sensor. The shift of the resonant frequency of the sensor is obtained numerically using finite difference time domain method under different ambient conditions having refractive index from n = 1.0 to n = 1.1. The numerical results show that a tiny change in refractive index of Δn = 0.0001 is distinguishable. In addition, the spectral response of the MPC sensor is studied while an external magnetic field is present. The results show that the MPC sensor exhibits a dramatic improvement in resolution.

Keywords: magnetic photonic crystal, refractive index sensor, sensitivity, high-resolution

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Authors: Prashant Shrivastava

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The purpose of this study is to explore re3dat.org registry to identify research data repositories registration workflow process. Further objective is to depict a graph for present development of research data repositories in India. Preliminarily with an approach to understand re3data.org registry framework and schema design then further proceed to explore the status of research data repositories of India in re3data.org registry. Research data repositories are getting wider relevance due to e-research concepts. Now available registry re3data.org is a good tool for users and researchers to identify appropriate research data repositories as per their research requirements. In Indian environment, a compatible National Research Data Policy is the need of the time to boost the management of research data. Registry for Research Data Repositories is a crucial tool to discover specific information in specific domain. Also, Research Data Repositories in India have not been studied. Re3data.org registry and status of Indian research data repositories both discussed in this study.

Keywords: research data, research data repositories, research data registry, re3data.org

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Authors: A. Errasti, F. Doffagne, O. Foucrier, S. Kao, A. Meigne, H. Pellae, T. Rouland

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Renewable energy recovery is an important domain of research in past few years in view of protection of our ecosystem. Several industrial companies are setting up widespread recovery systems to exploit wave energy. Most of them have a large size, are implanted near the shores and exploit current flows. However, as oceans represent 70% of Earth surface, a huge space is still unexploited to produce energy. Present analysis focuses on surface small scale wave energy recovery. The principle is exactly the opposite of wheel damper for a car on a road. Instead of maintaining the car body as non-oscillatory as possible by adapted control, a system is designed so that its oscillation amplitude under wave action will be maximized with respect to a boat carrying it in view of differential potential energy recuperation. From parametric analysis of system equations, interesting domains have been selected and expected energy output has been evaluated.

Keywords: small scale wave, potential energy, optimized energy recovery, auto-adaptive system

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Soulef Amamria, Mohamed Sadok Bensalem, Mohamed Ghanmi

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The structural and sedimentological study of fault-related- folds in the Southern Tunisian Atlas is distinguished by a special geometry of the gravitational structures. This distinct geometry is observable in the example of a flap structure in Jebel Ben Zannouch with the formation of a stuck syncline. This geometry can be explained by the mechanism of major thrusting in Orbata anticline in the occidental extremity of Gafsa chains, with asymmetrical flank dips and hinge migration kinematics. These kinematics was originally controlled by the Breakthrough structure; the study of this special geometry of gravity flap structure depends on the sedimentation domain, shortening ratios, and erosion speed. This study constitutes one of the complete examples of kinematic model validation on a field scale.

Keywords: fault-related-folds, southern Tunisian Atlas, flap structure, breakthrough

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Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: Carine Enow-Ayor Tarkang, Victorine Neh Akenji, Dmitri Rouwet, Jodephine Njdma, Andrew Ako Ako, Franco Tassi, Jules Remy Ngoupayou Ndam

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Groundwater is the major water resource in the whole of Bafia used for drinking, domestic, poultry and agricultural purposes, and being an area of intense agriculture, there is a great necessity to do a quality assessment. Bafia is one of the main food suppliers in the Centre region of Cameroon, and so to meet their demands, the farmers make use of fertilizers and other agrochemicals to increase their yield. Less than 20% of the population in Bafia has access to piped-borne water due to the national shortage, according to the authors best knowledge very limited studies have been carried out in the area to increase awareness of the groundwater resources. The aim of this study was to assess heavy metal contamination levels in ground and surface waters and to evaluate the effects of agricultural inputs on water quality in the Bafia area. 57 water samples (including 31 wells, 20 boreholes, 4 rivers and 2 springs) were analyzed for their physicochemical parameters, while collected samples were filtered, acidified with HNO3 and analyzed by ICP-MS for their heavy metal content (Fe, Ti, Sr, Al, Mn). Results showed that most of the water samples are acidic to slightly neutral and moderately mineralized. Ti concentration was significantly high in the area (mean value 130µg/L), suggesting another Ti source besides the natural input from Titanium oxides. The high amounts of Mn and Al in some cases also pointed to additional input, probably from fertilizers that are used in the farmlands. Most of the water samples were found to be significantly contaminated with heavy metals exceeding the WHO allowable limits (Ti-94.7%, Al-19.3%, Mn-14%, Fe-5.2% and Sr-3.5% above limits), especially around farmlands and topographic low areas. The heavy metal concentration was evaluated using the heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and degree of contamination (Cd), while the Ficklin diagram was used for the water based on changes in metal content and pH. The high mean values of HPI and Cd (741 and 5, respectively), which exceeded the critical limit, indicate that the water samples are highly contaminated, with intense pollution from Ti, Al and Mn. Based on the HPI and Cd, 93% and 35% of the samples, respectively, are unacceptable for drinking purposes. The lowest HPI value point also had the lowest EC (50 µS/cm), indicating lower mineralization and less anthropogenic influence. According to the Ficklin diagram, 89% of the samples fell within the near-neutral low-metal domain, while 9% fell in the near-neutral extreme-metal domain. Two significant factors were extracted from the PCA, explaining 70.6% of the total variance. The first factor revealed intense anthropogenic activity (especially from fertilizers), while the second factor revealed water-rock interactions. Agricultural activities thus have an impact on the heavy metal content of groundwater in the area; hence, much attention should be given to the affected areas in order to protect human health/life and thus sustainably manage this precious resource.

Keywords: Bafia, contamination, degree of contamination, groundwater, heavy metal pollution index

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Khald Anisse, Amina Radgui, Mohammed Rziza

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Tracking objects on video sequences is a very challenging task in many works in computer vision applications. However, there is no article that treats this topic in catadioptric vision. This paper is an attempt that tries to describe a new approach of omnidirectional images processing based on inverse stereographic projection in the half-sphere. We used the spherical model proposed by Gayer and al. For object tracking, our work is based on snake method, with optimization using the Greedy algorithm, by adapting its different operators. The algorithm will respect the deformed geometries of omnidirectional images such as spherical neighborhood, spherical gradient and reformulation of optimization algorithm on the spherical domain. This tracking method that we call "spherical snake" permitted to know the change of the shape and the size of object in different replacements in the spherical image.

Keywords: computer vision, spherical snake, omnidirectional image, object tracking, inverse stereographic projection

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Authors: Mehmet Kargaci

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Transformation of technology and strategy has been the main factor for the evolution of war. In addition to land, maritime, air and space domains, cyberspace has become the fifth domain with emerge of internet. The current security environment has become more complex and uncertain than ever before. Moreover, warfare has evaluated from conventional to irregular, asymmetric and hybrid war. Weak actors such as terrorist organizations and non-state actors has increasingly conducted cyber-attacks against strong adversaries. Besides, states has developed cyber capabilities in order to defense critical infrastructure regarding the cyber threats. Cyber warfare will be key in future security environment. Although what to do has been placed in operational plans, how to do has lacked and ignored as to cyber defense and attack. The purpose of the article is to put forward a model for how to conduct cyber capabilities in a conventional war. First, cyber operations capabilities will be discussed. Second put forward the necessities of cyberspace environment and develop a model for how to plan an operation using cyber operation capabilities, finally the assessment of the applicability of cyber operation capabilities and offers will be presented.

Keywords: cyber war, cyber threats, cyber operation capabilities, operation planning

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Authors: Soumaya Sallem, Marc Olivas

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This contribution presents a method for detecting, locating, and characterizing soft faults in a complex wired network. The proposed method is based on multi-carrier reflectometry MCTDR (Multi-Carrier Time Domain Reflectometry) combined with a multi-objective genetic algorithm. In order to ensure complete network coverage and eliminate diagnosis ambiguities, the MCTDR test signal is injected at several points on the network, and the data is merged between different reflectometers (sensors) distributed on the network. An adapted multi-objective genetic algorithm is used to merge data in order to obtain more accurate faults location and characterization. The proposed method performances are evaluated from numerical and experimental results.

Keywords: wired network, reflectometry, network distributed diagnosis, multi-objective genetic algorithm

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Pardeep Bishnoi, Mayank Srivastava, Mrityunjay Kumar Sinha

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This article represents the numerical investigation of the pressure and velocity field variation of the dynamics of pendant drop formation through a capillary tube. Numerical simulations are executed using volume of fluid (VOF) method in the computational fluid dynamics (CFD). In this problem, Non Newtonian fluid is considered as dispersed fluid whereas air is considered as a continuous fluid. Pressure contours at various time steps expose that pressure varies nearly hydrostatically at each step of the dynamics of drop formation. A result also shows the pressure variation of the liquid droplet during free fall in the computational domain. The evacuation of the fluid from the necking region is also shown by the contour of the velocity field. The role of surface tension in the Pressure contour of the dynamics of drop formation is also studied.

Keywords: pressure contour, surface tension, volume of fluid, velocity field

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Authors: Dung Nguyen, Yu Wei, Eric Henderson

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Analytical tools have been used for decades to assist in the development of forest plans. In 2016, a new decision support system, PRISM, was jointly developed by United States Forest Service (USFS) Northern Region and Colorado State University to support the forest planning process. Prism has a friendly user interface with functionality for database management, model development, data visualization, and sensitivity analysis. The software is tailored for USFS planning, but it is flexible enough to support planning efforts by other forestland owners and managers. Here, the core capability of PRISM and its applications in developing plans for several United States national forests are presented. The strengths of PRISM are also discussed to show its potential of being a preferable tool for managers and experts in the domain of forest management and planning.

Keywords: decision support, forest management, forest plan, graphical user interface, software

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Nouby M. Ghazaly, Ahmed O. Moaaz, Mostafa Makrahy

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The main purpose of a car suspension system is to reduce the vibrations resulting from road roughness. The main objective of this research paper is to decrease vibration and improve passenger comfort through controlling car suspension system using sliding mode control techniques. The mathematical model for passive and active suspensions systems for quarter car model which subject to excitation from different road profiles is obtained. The active suspension system is synthesized based on sliding mode control for a quarter car model. The performance of the sliding mode control is determined through computer simulations using MATLAB and SIMULINK toolbox. The simulated results plotted in time domain, and root mean square values. It is found that active suspension system using sliding mode control improves the ride comfort and decrease vibration.

Keywords: quarter car model, active suspension system, sliding mode control, road profile

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

Abstract:

Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

Procedia PDF Downloads 301

Authors: R. K. Apalowo, D. Chronopoulos

Abstract:

A Finite Element (FE) based scheme is presented for quantifying guided wave interaction with Localised Nonlinear Structural Damage (LNSD) within structures of arbitrary layering and geometric complexity. The through-thickness mode-shape of the structure is obtained through a wave and finite element method. This is applied in a time domain FE simulation in order to generate time harmonic excitation for a specific wave mode. Interaction of the wave with LNSD within the system is computed through an element activation and deactivation iteration. The scheme is validated against experimental measurements and a WFE-FE methodology for calculating wave interaction with damage. Case studies for guided wave interaction with crack and delamination are presented to verify the robustness of the proposed method in classifying and identifying damage.

Keywords: layered structures, nonlinear ultrasound, wave interaction with nonlinear damage, wave finite element, finite element

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Authors: Nigus Maregu Demewoz, Shu-Kai Yeh

Abstract:

Low-density nanocellular foam is a fascinating new-generation advanced material due to its mechanical strength and thermal insulation properties. In nanocellular foam, reducing the density increases the insulation ability. However, producing a nanocellular foam of densities less than 0.3 with a cell size of less than 100 nm is very challenging. In this study, poly (methyl methacrylate) (PMMA) was blended with Polyether block amide (PEBAX) to study the effects of PEBAX on the nanocellular foam structure of the PMMA matrix. We added 2 wt% of PEBAX in the PMMA matrix, and the PEBAX nanostructured domain size of 45 nm was well dispersed in the PMMA matrix. The foaming result produced a new generation special bouquet-like nanocellular foam of cell size less than 50 nm with a relative density of 0.24. Also, we were able to produce a nanocellular foam of a relative density of about 0.17. In addition to thermal insulation applications, bouquet-like nanocellular foam may be expected for filtration applications.

Keywords: nanocellular foam, low-density, cell size, relative density, PMMA/PEBAX

Procedia PDF Downloads 78