Search results for: electronic data interchange

Authors: Yogita Mishra, Arijit Roy, Dhruval Bhavsar

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The study estimates the nitrogen concentration in selected vegetation community’s i.e. chir pine (pinusroxburghii) by using hyperspectral satellite data and also identified the appropriate spectral bands and nitrogen indices. The Short Wave InfraRed reflectance spectrum at 1790 nm and 1680 nm shows the maximum possible absorption by nitrogen in selected species. Among the nitrogen indices, log normalized nitrogen index performed positively and negatively too. The strong positive correlation is taken out from 1510 nm and 760 nm for the pinusroxburghii for leaf nitrogen concentration and leaf nitrogen mass while using NDNI. The regression value of R² developed by using linear equation achieved maximum at 0.7525 for the analysis of satellite image data and R² is maximum at 0.547 for ground truth data for pinusroxburghii respectively.

Keywords: hyperspectral, NDNI, nitrogen concentration, regression value

Procedia PDF Downloads 295

Authors: Laurent Thiry, Michel Hassenforder

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This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries.

Keywords: data transformation, functional programming, information server, optimization

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Authors: Jianqin Qu

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This paper proposes a rigid point set matching algorithm in arbitrary dimensions based on the idea of symmetric covariant function. A group of functions of the points in the set are formulated using rigid invariants. Each of these functions computes a pair of correspondence from the given point set. Then the computed correspondences are used to recover the unknown rigid transform parameters. Each computed point can be geometrically interpreted as the weighted mean center of the point set. The algorithm is compact, fast, and dimension free without any optimization process. It either computes the desired transform for noiseless data in linear time, or fails quickly in exceptional cases. Experimental results for synthetic data and 2D/3D real data are provided, which demonstrate potential applications of the algorithm to a wide range of problems.

Keywords: covariant point, point matching, dimension free, rigid registration

Procedia PDF Downloads 168

Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

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Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Tabinda Sadiq, Tehmina Ashfaq Qazi, Hoor Shumail

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Recently, the advent of emerging technology has created an emerging generation of restaurant marketing. It explores the aspects that influence customers’ decision-making process in selecting a restaurant after reading food bloggers' reviews online. The motivation behind this research is to investigate the correlation between the credibility of the source and their attitude toward restaurant visits. The researcher collected the data by distributing a survey questionnaire through google forms by employing the Source credibility theory. Non- probability purposive sampling technique was used to collect data. The questionnaire used a predeveloped and validated scale by Ohanian to measure the relationship. Also, the researcher collected data from 250 respondents in order to investigate the influence of food bloggers on Gen Z's decision-making process. SPSS statistical version 26 was used for statistical testing and analyzing the data. The findings of the survey revealed that there is a moderate positive correlation between the variables. So, it can be analyzed that food bloggers do have an impact on Generation Z's decision making process.

Keywords: credibility, decision making, food bloggers, generation z, e-wom

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Authors: Pornpimol Chaiwuttisak

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The study aims to compare the performance of the logistics for Thailand’s wholesale and retail trade industries (except motor vehicles, motorcycle, and stalls) by using data (data envelopment analysis). Thailand Standard Industrial Classification in 2009 (TSIC - 2009) categories that industries into sub-group no. 45: wholesale and retail trade (except for the repair of motor vehicles and motorcycles), sub-group no. 46: wholesale trade (except motor vehicles and motorcycles), and sub-group no. 47: retail trade (except motor vehicles and motorcycles. Data used in the study is collected by the National Statistical Office, Thailand. The study consisted of four input factors include the number of companies, the number of personnel in logistics, the training cost in logistics, and outsourcing logistics management. Output factor includes the percentage of enterprises having inventory management. The results showed that the average relative efficiency of small-sized enterprises equals to 27.87 percent and 49.68 percent for the medium-sized enterprises.

Keywords: DEA, wholesales and retails, logistics, Thailand

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Authors: M. I. Benamrani, H. Benamrani

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In the research to replace silicon and other thin-film semiconductor technologies and to develop long-term technology that is environmentally friendly, low-cost, and abundant, there is growing interest today given to organic materials. Our objective is to prepare polymeric layers containing metal particles deposited on a surface of semiconductor material which can have better electrical properties and which could be applied in the fields of nanotechnology as an alternative to the existing processes involved in the design of electronic circuits. This work consists in the development of composite materials by complexation and electroreduction of copper in a film of poly (pyrrole benzoic acid). The deposition of the polymer film on a monocrystalline silicon substrate is made by electrochemical oxidation in an organic medium. The incorporation of copper particles into the polymer is achieved by dipping the electrode in a solution of copper sulphate to complex the cupric ions, followed by electroreduction in an aqueous solution to precipitate the copper. In order to prepare the monocrystalline silicon substrate as an electrode for electrodeposition, an in-depth study on its surface state was carried out using photoacoustic spectroscopy. An analysis of the optical properties using this technique on the effect of pickling using a chemical solution was carried out. Transmission-photoacoustic and impedance spectroscopic techniques give results in agreement with those of photoacoustic spectroscopy.

Keywords: photoacoustic, spectroscopy, copper sulphate, chemical solution

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Parvin Ghafarian, Mohammadreza Mohammadpur Panchah, Mehri Fallahi

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Synoptic patterns from surface up to tropopause are very important for forecasting the weather and atmospheric conditions. There are many tools to prepare and analyze these maps. Reanalysis data and the outputs of numerical weather prediction models, satellite images, meteorological radar, and weather station data are used in world forecasting centers to predict the weather. The forecasting extreme precipitating on the southern coast of the Caspian Sea (CS) is the main issue due to complex topography. Also, there are different types of climate in these areas. In this research, we used two reanalysis data such as ECMWF Reanalysis 5th Generation Description (ERA5) and National Centers for Environmental Prediction /National Center for Atmospheric Research (NCEP/NCAR) for verification of the numerical model. ERA5 is the latest version of ECMWF. The temporal resolution of ERA5 is hourly, and the NCEP/NCAR is every six hours. Some atmospheric parameters such as mean sea level pressure, geopotential height, relative humidity, wind speed and direction, sea surface temperature, etc. were selected and analyzed. Some different type of precipitation (rain and snow) was selected. The results showed that the NCEP/NCAR has more ability to demonstrate the intensity of the atmospheric system. The ERA5 is suitable for extract the value of parameters for specific point. Also, ERA5 is appropriate to analyze the snowfall events over CS (snow cover and snow depth). Sea surface temperature has the main role to generate instability over CS, especially when the cold air pass from the CS. Sea surface temperature of NCEP/NCAR product has low resolution near coast. However, both data were able to detect meteorological synoptic patterns that led to heavy rainfall over CS. However, due to the time lag, they are not suitable for forecast centers. The application of these two data is for research and verification of meteorological models. Finally, ERA5 has a better resolution, respect to NCEP/NCAR reanalysis data, but NCEP/NCAR data is available from 1948 and appropriate for long term research.

Keywords: synoptic patterns, heavy precipitation, reanalysis data, snow

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Authors: Hung Lin-Zin, Lai Mei-Yen

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Prostate cancer is the most commonly diagnosed male cancer in the U.S. The prevalence is around 1 in 8. The etiology of prostate cancer is still unknown, but some predisposing factors, such as age, black race, family history, and obesity, may increase the risk of the disease. In 2020, a total of 7,178 Taiwanese people were nearly diagnosed with prostate cancer, accounting for 5.88% of all cancer cases, and the incidence rate ranked fifth among men. In that year, the total number of deaths from prostate cancer was 1,730, accounting for 3.45% of all cancer deaths, and the death rate ranked 6th among men, accounting for 94.34% of the cases of male reproductive organs. Looking for domestic and foreign literature on the use of OMOP (Observational Medical Outcomes Partnership, hereinafter referred to as OMOP) database analysis, there are currently nearly a hundred literature published related to nursing-related health problems and nursing measures built in the OMOP general data model database of medical institutions are extremely rare. The OMOP common data model construction analysis platform is a system developed by the FDA in 2007, using a common data model (common data model, CDM) to analyze and monitor healthcare data. It is important to build up relevant nursing information from the OMOP- CDM database to assist our daily practice. Therefore, we choose prostate cancer patients who are our popular care objects and use the OMOP- CDM database to explore the common associated health problems. With the assistance of OMOP-CDM database analysis, we can expect early diagnosis and prevention of prostate cancer patients' comorbidities to improve patient care.

Keywords: OMOP, nursing diagnosis, health problem, prostate cancer

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Authors: Yannick Verbelen, Sam De Winne, Niek Blondeel, Ann Peeters, An Braeken, Abdellah Touhafi

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The growing popularity of solid state thermoelectric devices in cooling applications has sparked an increasing diversity of thermoelectric coolers (TECs) on the market, commonly known as “Peltier modules”. They can also be used as generators, converting a temperature difference into electric power, and opportunities are plentiful to make use of these devices as thermoelectric generators (TEGs) to supply energy to low power, autonomous embedded electronic applications. Their adoption as energy harvesters in this new domain of usage is obstructed by the complex thermoelectric models commonly associated with TEGs. Low cost TECs for the consumer market lack the required parameters to use the models because they are not intended for this mode of operation, thereby urging an alternative method to obtain electric power estimations in specific operating conditions. The design of the test setup implemented in this paper is specifically targeted at benchmarking commercial, off-the-shelf TECs for use as energy harvesters in domestic environments: applications with limited temperature differences and space available. The usefulness is demonstrated by testing and comparing single and multi stage TECs with different sizes. The effect of a boost converter stage on the thermoelectric end-to-end efficiency is also discussed.

Keywords: thermoelectric cooler, TEC, complementary balanced energy harvesting, step-up converter, DC/DC converter, energy harvesting, thermal harvesting

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Authors: Argaw T. Gurmu, Muhammad N. Mahmood

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The objective of this research is to identify the architecture and construction management students’ learning challenges of the building measurement. This research used the survey data obtained collected from the students who completed the building measurement unit. NVivo qualitative data analysis software was used to identify relevant themes. The analysis of the qualitative data revealed the major learning difficulties such as inadequacy of practice questions for the examination, inability to work as a team, lack of detailed understanding of the prerequisite units, insufficiency of the time allocated for tutorials and incompatibility of lecture and tutorial schedules. The output of this research can be used as a basis for improving the teaching and learning activities in construction measurement units.

Keywords: building measurement, construction management, learning challenges, evaluate survey

Procedia PDF Downloads 138

Authors: Ing-Jen Hung, Tzu-Chien Wang

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Nowadays, customers can interact with firms through miscellaneous online ads on different channels easily. In other words, customer now has innumerable options and limitless time to accomplish their commercial activities with firms, individualizing their own online customer journey. This kind of convenience emphasizes the importance of online advertisement allocation on different channels. Therefore, profound understanding of customer behavior can make considerable benefit from optimizing fund allocation on diverse ad channels. To achieve this objective, multiple firms utilize numerical methodology to create data-driven advertisement policy. In our research, we aim to exploit online customer click data to discover the correlations between each channel and their sequential relations. We use LSTM to deal with sequential property of our data and compare its accuracy with other non-sequential methods, such as CART decision tree, logistic regression, etc. Besides, we also classify our customers into several groups by their behavioral characteristics to perceive the differences between all groups as customer portrait. As a result, we discover distinct customer journey under each customer portrait. Our article provides some insights into marketing research and can help firm to formulate online advertising criteria.

Keywords: LSTM, customer journey, marketing, channel ads

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Authors: W. H. Zheng, J. Li, F. J. Hong

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Thin liquid film evaporation in ultrathin nanoporous membranes, which reduce the viscous resistance while still maintaining high capillary pressure and efficient liquid delivery, is a promising thermal management approach for high-power electronic devices cooling. Given the challenges and technical limitations of experimental studies for accurate interface temperature sensing, complex manufacturing process, and short duration of membranes, a dimensionless lattice Boltzmann method capable of restoring thermophysical properties of working fluid is particularly derived. The evaporation of R134a to its pure vapour ambient in nanoporous membranes with the pore diameter of 80nm, thickness of 472nm, and three porosities of 0.25, 0.33 and 0.5 are numerically simulated. The numerical results indicate that the highest heat transfer coefficient is about 1740kW/m²·K; the highest heat flux is about 1.49kW/cm² with only about the wall superheat of 8.59K in the case of porosity equals to 0.5. The dissipated heat flux scaled with porosity because of the increasing effective evaporative area. Additionally, the self-regulation of the shape and curvature of the meniscus under different operating conditions is also observed. This work shows a promising approach to forecast the membrane performance for different geometry and working fluids.

Keywords: high heat flux, ultrathin nanoporous membrane, thin film evaporation, lattice Boltzmann method

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Authors: Made Ayu Aristyana Dewi

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Due to the importance of knowledge in today’s competitive world, an understanding of how to enhance employee knowledge sharing has become critical. This study discerning employees’ knowledge sharing intentions according to the type of knowledge to be shared, whether tacit or explicit. This study provides a critical and systematic review of the current literature on knowledge sharing, with a particular focus on the most critical factors influencing employees’ tacit and explicit knowledge sharing intentions. The extant literature was identified through four electronic databases, from 2006 to 2016. The findings of this review reveal that most of the previous studies only focus on individual and social factors as the antecedents of knowledge sharing intention. Therefore, those previous studies did not consider some other potential factors, like organizational and technological factors that may hinder the progress of knowledge sharing processes. Based on the findings of the critical review, a conceptual framework is proposed, which presents the antecedents of employees’ tacit and explicit knowledge sharing intentions and its impact on innovation and sustainable competitive advantage.

Keywords: antecedents, explicit knowledge, individual factors, innovation, intentions, knowledge sharing, organizational factors, social factors, sustainable competitive advantage, tacit knowledge, technological factors

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Authors: Lucas Viani, Benedetta Mennucci, Soo Young Park, Johannes Gierschner

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Organic semiconductors have attracted the attention of the scientific community in the past decades due to their unique physicochemical properties, allowing new designs and alternative device fabrication methods. Until today, organic electronic devices are largely based on conjugated polymers mainly due to their easy processability. In the recent years, due to moderate ET and CT efficiencies and the ill-defined nature of polymeric systems the focus has been shifting to small conjugated molecules with well-defined chemical structure, easier control of intermolecular packing, and enhanced CT and ET properties. It has led to the synthesis of new small molecules, followed by the growth of their crystalline structure and ultimately by the device preparation. This workflow is commonly followed without a clear knowledge of the ET and CT properties related mainly to the macroscopic systems, which may lead to financial and time losses, since not all materials will deliver the properties and efficiencies demanded by the current standards. In this work, we present a theoretical workflow designed to predict the key properties of ET of these new materials prior synthesis, thus speeding up the discovery of new promising materials. It is based on quantum mechanical, hybrid, and classical methodologies, starting from a single molecule structure, finishing with the prediction of its packing structure, and prediction of properties of interest such as static and averaged excitonic couplings, and exciton diffusion length.

Keywords: organic semiconductor, organic crystals, energy transport, excitonic couplings

Procedia PDF Downloads 253

Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

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Due to the rapid growth in modern communication systems, fault tolerance and data security are two important issues in a secure transaction. During the transmission of data between the sender and receiver, errors may occur frequently. Therefore, the sender must re-transmit the data to the receiver in order to correct these errors, which makes the system very feeble. To improve the scalability of the scheme, we present a secure proxy signature scheme with fault tolerance over an efficient and secure authenticated key agreement protocol based on RSA system. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties.

Keywords: proxy signature, fault tolerance, rsa, key agreement protocol

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Authors: Zahzouh Zoubir, Bouzaouit Azzeddine, Gahgah Mounir

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The use of more and more electronic power switches with a nonlinear behavior generates non-sinusoidal currents in distribution networks, which causes damage to domestic and industrial equipment. The multi-level shunt power active filter is subsequently shown to be an adequate solution to the problem raised. Nevertheless, the difficulty of adjusting the active filter DC supply voltage requires another technology to ensure it. In this article, a photovoltaic generator is associated with the DC bus power terminals of the active filter. The proposed system consists of a field of solar panels, three multi-level voltage inverters connected to the power grid and a non-linear load consisting of a six-diode rectifier bridge supplying a resistive-inductive load. Current control techniques of active and reactive power are used to compensate for both harmonic currents and reactive power as well as to inject active solar power into the distribution network. An algorithm of the search method of the maximum power point of type Perturb and observe is applied. Simulation results of the system proposed under the MATLAB/Simulink environment shows that the performance of control commands that reassure the solar power injection in the network, harmonic current compensation and power factor correction.

Keywords: Actif power filter, MPPT, pertub&observe algorithm, PV array, PWM-control

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Authors: Yalda Zarnegarnia, Shari Messinger

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Receiver operating characteristic (ROC) curves have been widely used in medical research to illustrate the performance of the biomarker in correctly distinguishing the diseased and non-diseased groups. Correlated biomarker data arises in study designs that include subjects that contain same genetic or environmental factors. The information about correlation might help to identify family members at increased risk of disease development, and may lead to initiating treatment to slow or stop the progression to disease. Approaches appropriate to a case-control design matched by family identification, must be able to accommodate both the correlation inherent in the design in correctly estimating the biomarker’s ability to differentiate between cases and controls, as well as to handle estimation from a matched case control design. This talk will review some developed methods for ROC curve estimation in settings with correlated data from case control design and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using Conditional ROC curves will be demonstrated, to provide appropriate ROC curves for correlated paired data. The proposed approach will use the information about the correlation among biomarker values, producing conditional ROC curves that evaluate the ability of a biomarker to discriminate between diseased and non-diseased subjects in a familial paired design.

Keywords: biomarker, correlation, familial paired design, ROC curve

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Authors: Adeela Abu Bakar, Minder Kaur, Parthaman Singh

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In the Malaysian education system, a formal setting of English language learning takes place in a content-based classroom (CBC). Until recently, there is less study in Malaysia, which researched the effects of code-switching (CS) behaviour towards the students’ knowledge sharing (KS) with their peers. The aim of this study is to investigate the frequency, reasons, and effect that CS, from the English language to Bahasa Melayu, has among local STEM and N-STEM undergraduates towards KS in a content-based classroom. The study implies a mixed-method research design with questionnaire and interviews as the instruments. The data is collected through distribution of questionnaires and interviews with the undergraduates. The quantitative data is analysed using SPSS in simple frequencies and percentages, whereas qualitative data involves organizing the data into themes, followed by analysis. Findings found that N-STEM undergraduates code-switch more as compared to STEM undergraduates. In addition to that, both the STEM and N-STEM undergraduates agree that CS acts as a catalyst towards KS in a content-based classroom. However, they also acknowledge that excess use of CS can be a hindrance towards KS. The findings of the study can benefit STEM and N-STEM undergraduates, education policymakers, language teachers, university educators, and students with significant insights into the role of CS towards KS in a content-based classroom. Some of the recommendations that can be applied for future studies are that the number of participants can be increased, an observation to be included for the data collection.

Keywords: switching, content-based classroom, content and language integrated learning, knowledge sharing, STEM and N-STEM undergraduates

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Authors: Jolly Puri, Shiv Prasad Yadav

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Multi-component data envelopment analysis (MC-DEA) is a popular technique for measuring aggregate performance of the decision making units (DMUs) along with their components. However, the conventional MC-DEA is limited to crisp input and output data which may not always be available in exact form. In real life problems, data may be imprecise or fuzzy. Therefore, in this paper, we propose (i) a fuzzy MC-DEA (FMC-DEA) model in which shared and undesirable fuzzy resources are incorporated, (ii) the proposed FMC-DEA model is transformed into a pair of crisp models using cut approach, (iii) fuzzy aggregate performance of a DMU and fuzzy efficiencies of components are defined to be fuzzy numbers, and (iv) a numerical example is illustrated to validate the proposed approach.

Keywords: multi-component DEA, fuzzy multi-component DEA, fuzzy resources, decision making units (DMUs)

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

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Authors: Suelane Garcia Fontes, Silvio Luiz Stanzani, Pedro L. Pizzigatti Corrła Ronaldo G. Morato

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Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation.

Keywords: data mining, data science, trajectory, animal behavior

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Authors: Ewa Rudnik

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Poster shows a comparison of hydrometallurgical routes of copper recovery from low-grade e-waste. Electronic scrap was smelted to produce Cu–Zn–Ag alloy. The alloy was then treated in the following ways: (a) anodic dissolution with simultaneous metal electrodeposition using ammoniacal and sulfuric acid solutions. This resulted in the separation of metals, where lead, silver and tin accumulated mainly in the slimes, while copper was transferred to the electrolyte and then recovered on the cathode. The best conditions of the alloy treatment were obtained in the sulfuric acid, where the final product was metal of high purity (99% Cu) at the current efficiency of 90%. (b) leaching in ammoniacal solutions of various compositions and then copper electrowinning. Alloy was leached in chloride, carbonate, sulfate and thiosulfate baths. This resulted in the separation of the metals, wherein copper and zinc were transferred to the electrolyte, while metallic tin and silver as well as lead salts remained in the slimes. Copper was selectively recovered from the ammoniacal solutions by the electrolysis, leaving zinc ions in the electrolyte. The best conditions of the alloy treatment were obtained in the ammonia-carbonate system, where the final product was copper of high purity (99.9%) at the current efficiency of 60%. Thiosulfate solution was not applicable for the leaching of the copper alloy due to secondary reactions of the formation of copper (I) thiosulfate complexes and precipitation of copper (I) sulfide.

Keywords: alloy, electrolysis, e-waste, leaching

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Authors: Rei-Heng Cheng, Wen-Pinn Fang

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A wireless sensor network (WSN) is composed by a large number of sensors and one or a few base stations, where the sensor is responsible for detecting specific event information, which is sent back to the base station(s). However, how to save electricity consumption to extend the network lifetime is a problem that cannot be ignored in the wireless sensor networks. Since the sensor network is used to monitor a region or specific events, how the information can be reliably sent back to the base station is surly important. Network coding technique is often used to enhance the reliability of the network transmission. When a node needs to send out M data packets, it encodes these data with redundant data and sends out totally M + R packets. If the receiver can get any M packets out from these M + R packets, it can decode and get the original M data packets. To transmit redundant packets will certainly result in the excess energy consumption. This paper will explore relationship between the quality of wireless transmission and the number of redundant packets. Hopefully, each sensor can overhear the nearby transmissions, learn the wireless transmission quality around it, and dynamically determine the number of redundant packets used in network coding.

Keywords: energy consumption, network coding, transmission reliability, wireless sensor networks

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Authors: Chun-Yuan Chen, Ming-Chang Li, Xiu-Hui Wen, Yi-Ching Tu

Abstract:

The objective of this study explores GPS (Global Positioning System) applied to trace construction vehicles such as trucks or cranes, help to pattern the earth-dumping stations of traffic construction in Taiwan. Traffic construction in this research is defined as the engineering of high-speed railways, expressways, and which that distance more than kilometers. Audit the location and check the compliance with regulations of earth-dumping stations is one of important tasks in Taiwan EPA. Basically, the earth-dumping station was known as one source of particulate matter from air pollution during construction process. Due to GPS data can be analyzed quickly and be used conveniently, this study tried to find out dumping stations by modeling vehicles tracks from GPS data during work cycle of construction. The GPS data updated from 13 vehicles related to an expressway construction in central Taiwan. The GPS footprints were retrieved to Keyhole Markup Language (KML) files so that can pattern the tracks of trucks by computer applications, the data was collected about eight months- from Feb. to Oct. in 2017. The results of GPS footprints identified dumping station and outlined the areas of earthwork had been passed to the Taiwan EPA for on-site inspection. Taiwan EPA had issued advice comments to the agency which was in charge of the construction to prevent the air pollution. According to the result of this study compared to the commonly methods in inspecting environment by manual collection, the GPS with KML patterning and modeling method can consumes less time. On the other hand, through monitoring the GPS data from construction vehicles could be useful for administration to development and implementation of strategies in environmental management.

Keywords: automatic management, earth-dumping station, environmental management, Global Positioning System (GPS), particulate matter, traffic construction

Procedia PDF Downloads 164

Authors: Javeria Ahmad, Ayesha Maryam, Zahid Rizwan, Nadeem Nasir, Yasir Nawab, Hafiz Shehbaz Ahmad

Abstract:

Mitigation of electromagnetic interference (EMI) through thin, lightweight, and cost-effective materials is critical for electronic appliances as well as human health. The present research work discusses the design of composites that are suitable to minimize EMI through various stacking sequences. The carbon fibers reinforced composite structures impregnated with dielectric (MWCNTs) and magnetic nanofillers (Fe₂O₃) were developed to investigate their microwave absorption properties. The composite structure comprising a single type of nanofillers, each of MWCNTs & Fe₂O₃, was developed, and then their layers were stacked over each other with various stacking sequences to investigate the best stacking sequence, which presents good microwave absorption characteristics. A vector network analyzer (VNA) was used to analyze the microwave absorption properties of these developed composite structures. The composite structures impregnated with the layers of a dielectric nanofiller and sandwiched between the layers of a magnetic nanofiller show the highest EMI shielding value of 59 dB and a dielectric conductivity of 35 S/cm in the frequency range of 0.1 to 13.6 GHz. The results also demonstrate that the microwave absorption properties of the developed composite structures were dominant over reflection properties. The absence of an external peak in X-ray diffraction (XRD), marked the purity of the added nanofillers.

Keywords: nanocomposites, microwave absorption, EMI shielding, skin depth, reflection loss

Procedia PDF Downloads 52

Authors: Javad Sadidi, Ehsan Babaei, Hani Rezayan

Abstract:

The advent of web 2.0 gives a possibility to scale down the costs of data collection and mapping, specifically if the process is done by volunteers. Every volunteer can be thought of as a free and ubiquitous sensor to collect spatial, descriptive as well as multimedia data for tourist services. The lack of large-scale information, such as real-time climate and weather conditions, population density, and other related data, can be considered one of the important challenges in developing countries for tourists to make the best decision in terms of time and place of travel. The current research aims to design and implement a spatiotemporal web map service using volunteer-submitted data. The service acts as a tourist-guide service in which tourists can search interested places based on their requested time for travel. To design the service, three tiers of architecture, including data, logical processing, and presentation tiers, have been utilized. For implementing the service, open-source software programs, client and server-side programming languages (such as OpenLayers2, AJAX, and PHP), Geoserver as a map server, and Web Feature Service (WFS) standards have been used. The result is two distinct browser-based services, one for sending spatial, descriptive, and multimedia volunteer data and another one for tourists and local officials. Local official confirms the veracity of the volunteer-submitted information. In the tourist interface, a spatiotemporal search engine has been designed to enable tourists to find a tourist place based on province, city, and location at a specific time of interest. Implementing the tourist-guide service by this methodology causes the following: the current tourists participate in a free data collection and sharing process for future tourists, a real-time data sharing and accessing for all, avoiding a blind selection of travel destination and significantly, decreases the cost of providing such services.

Keywords: VGI, tourism, spatiotemporal, browser-based, web mapping

Procedia PDF Downloads 98

Authors: Mati Ullah, Shams Ur Rahman

Abstract:

The purpose of this research study is to investigate the effect of macroeconomic and bank-specific factors on credit risk in Islamic banking in Pakistan. The future of financial institutions largely depends on how well they manage risks. Credit risk is an important type of risk affecting the banking sector. The current study has taken quarterly data for the period of 6 years, from 1st July 2014 to 30 Jun 2020. The data set consisted of secondary data. Data was extracted from the websites of the State Bank and World Bank and from the financial statements of the concerned banks. In this study, the Ordinary least square model was used for the analysis of the data. The results supported the hypothesis that macroeconomic factors and bank-specific factors have a significant effect on credit risk. Macroeconomic variables, Inflation and exchange rates have positive significant effects on credit risk. However, gross domestic product has a negative significant relationship with credit risk. Moreover, the corporate rate has no significant relation with credit risk. Internal variables, size, management efficiency, net profit share income and capital adequacy have been proven to influence positively and significantly the credit risk. However, loan to deposit-has a negative insignificance relationship with credit risk. The contribution of this article is that similar conclusions have been made regarding the influence of banking factors on credit risk.

Keywords: credit risk, Islamic banks, macroeconomic variables, banks specific variable

Procedia PDF Downloads 19