Search results for: data comparison

Authors: Jamal A. Gledan, Othman A. Azzeidani

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During the last decade, Libya established a new Geodetic Datum called Libyan Geodetic Datum 2006 (LGD 2006) by using GPS, whereas the ground traversing method was used to establish the last Libyan datum which was called the Europe Libyan Datum 79 (ELD79). The current research paper introduces ELD79 to LGD2006 coordinate transformation technique, the accurate comparison of transformation between multiple regression equations and the three-parameters model (Bursa-Wolf). The results had been obtained show that the overall accuracy of stepwise multi regression equations is better than that can be determined by using Bursa-Wolf transformation model.

Keywords: geodetic datum, horizontal control points, traditional similarity transformation model, unconventional transformation techniques

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Mohammad Shabani

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The physical activities depending on the nature of the mechanical stresses they induce on bone sometimes have brought about different results. The purpose of this study was to compare bone mineral density (BMD) of the lumbar spine between the high-level cyclists and sedentary. Materials and Methods: In the present study, 73 cyclists senior (age: 25.81 ± 4.35 years; height: 179.66 ± 6.31 cm; weight: 71.55 ± 6.31 kg) and 32 sedentary subjects (age: 28.28 ± 4.52 years; height: 176.56 ± 6.2 cm; weight: 74.47 ± 8.35 kg) participated voluntarily. All cyclists belonged to the different teams from the International Cycling Union and they trained competitively for 10 years. BMD of the lumbar spine of the subjects was measured using DXA X-ray (Lunar). Descriptive statistics calculations were performed using computer software data processing (Statview 5, SAS Institute Inc. USA). The comparison of two independent distributions (BMD high level cyclists and sedentary) was made by the Student T Test standard. Probability 0.05 (p≤0 / 05) was adopted as significance. Results: The result of this study showed that the BMD values of the lumbar spine of sedentary subjects were significantly higher for all measured segments. Conclusion and Discussion: Cycling is firstly a common sport and on the other hand endurance sport. It is now accepted that weight bearing exercises have an osteogenic effect compared to non-weight bearing exercises. Thus, endurance sports such as cycling, compared to the activities imposing intense force in short time, seem not to really be osteogenic. Therefore, it can be concluded that cycling provides low stimulates osteogenic because of specific biomechanical forces of the sport and its lack of impact.

Keywords: BMD, lumbar spine, high level cyclist, cycling

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Authors: Volker Koch, Sigrid Swobodnik, Bernd M. Zunk

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The success of companies on globalized markets is significantly influenced by the performance of purchasing departments and, of course, the individuals employed as professional purchasers. Nonetheless, this is generally accepted in practice, in literature as well as in empirical research, only insufficient attention was given to the assessment of this relationship between the personality of professional purchasers and their individual performance. This paper aims to describe the relationship against the background of the 'Big Five Inventory'. Based on the five dimensions of a personality (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) a research model was designed. The research model divides the individual performance of professional purchasers into two major dimensions: operational and strategic. The operational dimension consists of the items 'cost', 'quality delivery' and 'flexibility'; the strategic dimension comprises the positions 'innovation', 'supplier satisfaction' as wells as 'purchasing and supply management integration in the organization'. To test the research model, a survey study was performed, and an online questionnaire was sent out to purchasing professionals in Austrian companies. The data collected from 78 responses was used to test the research model applying a group comparison. The comparison points out that there is (i) an influence of the purchasers’ personality on the individual performance of professional purchasers and (ii) a link between purchasers’ personality to a high or a low individual performance of professional purchasers. The findings of this study may help human resource managers during staff recruitment processes to identify the 'right performing personality' for an operational and/or a strategic position in purchasing departments.

Keywords: big five inventory, individual performance, personality, purchasing professionals

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Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

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Authors: Ghoul Rachid Brahim

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Introduction: Back pain is a real public health problem with a significant socio-economic impact. It is the consequence of a degeneration of the lumbar intervertebral disc (IVD). This often asymptomatic pathology is compatible with an active life. As soon as it becomes symptomatic, conservative treatment is recommended in the majority of cases. The physical or functional disability is resistant to well-monitored conservative treatment, which justifies a surgical alternative which imposes a well-studied reflection on the objectives to be achieved. Objective: Evaluate the indication and short and medium term contribution of surgery in the management of painful degenerative lumbar disc disease. To prove the effectiveness of surgical treatment in the management of painful lumbar degenerative disc disease. Materials and methods: This is a prospective descriptive mono-centric study without comparison group, comprising a series of 104 patients suffering from lumbar painful degenerative disc disease treated surgically. Retrospective analysis of data collected prospectively. Comparison between pre and postoperative clinical status, by pain self-assessment scores and on the impact on pre and postoperative quality of life (3, 6 to 12 months). Results: This study showed that patients who received surgical treatment had great improvements in symptoms, function and several health-related quality of life in the first year after surgery. Conclusions: The surgery had a significantly positive impact on patients' pain, disability and quality of life. Overall, 97% of the patients were satisfied.

Keywords: degenerative disc disease, intervertebral disc, several health-related quality, lumbar painful

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Nidhi S. Chandra, Veena Agrawal, Debprasad Chattopadhyay

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Background: Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in developing countries. It spreads feco orally mainly due to contamination of drinking water by sewage. There is limited data on the genotypic comparison of HEV isolates from sewage water and humans. The aim of this study was to identify genotype and conduct phylogenetic analysis of HEV isolates from sewage water and humans. Materials and Methods: 14 sewage water and 60 serum samples from acute sporadic hepatitis E cases (negative for hepatitis A, B, C) were tested for HEV-RNA by nested polymerase chain reaction (RTnPCR) using primers designed with in RdRp (RNA dependent RNA polymerase) region of open reading frame-1 (ORF-1). Sequencing was done by ABI prism 310. The sequences (343 nucleotides) were compared with each other and were aligned with previously reported HEV sequences obtained from GeneBank, using Clustal W software. A Phylogenetic tree was constructed by using PHYLIP version 3.67 software. Results: HEV-RNA was detected in 49/ 60 (81.67%) serum and 5/14 (35.71%) sewage samples. The sequences obtained from 17 serums and 2 sewage specimens belonged to genotype I with 85% similarity and clustering with previously reported human HEV sequences from India. HEV isolates from human and sewage in North West India are genetically closely related to each other. Conclusion: These finding suggest that sewage acts as reservoir of HEV. Therefore it is important that measures are taken for proper waste disposal and treatment of drinking water to prevent outbreaks and epidemics due to HEV.

Keywords: hepatitis E virus, nested polymerase chain reaction, open reading frame-1, nucleotidies

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Authors: Sidi M. A. Ghaly, Mohammad O. Khan, Mohammed Shalaby, Khaled A. Al-Snaie

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Electrical capacitance tomography (ECT) is a valuable, non-invasive technique used to monitor multiphase flow processes, especially within industrial pipelines. This study focuses on the design, testing, and performance comparison of ECT sensors configured with 8, 12, and 16 electrodes, aiming to evaluate their effectiveness in imaging accuracy, resolution, and sensitivity. Each sensor configuration was designed to capture the spatial permittivity distribution within a pipeline cross-section, enabling visualization of phase distribution and flow characteristics such as oil and water interactions. The sensor designs were implemented and tested in closed pipes to assess their response to varying flow regimes. Capacitance data collected from each electrode configuration were reconstructed into cross-sectional images, enabling a comparison of image resolution, noise levels, and computational demands. Results indicate that the 16-electrode configuration yields higher image resolution and sensitivity to phase boundaries compared to the 8- and 12-electrode setups, making it more suitable for complex flow visualization. However, the 8 and 12-electrode sensors demonstrated advantages in processing speed and lower computational requirements. This comparative analysis provides critical insights into optimizing ECT sensor design based on specific industrial requirements, from high-resolution imaging to real-time monitoring needs.

Keywords: capacitance tomography, modeling, simulation, electrode, permittivity, fluid dynamics, imaging sensitivity measurement

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Authors: Arun Goel

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Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper, attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly and Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly and Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicates the improvement in the performance of SVM (Poly and Rbf) in comparison to dimensional form of scour.

Keywords: modeling, pier scour, regression, prediction, SVM (Poly and Rbf kernels)

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Ivana Cestar, Ivica Stančerić, Saša Ahac, Vesna Dragčević, Tamara Džambas

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On special paved areas, such as road intersections, vehicles are usually moving through horizontal curves with smaller radii and occupy considerably greater area compared to open road segments. Planning procedure of these areas is mainly an iterative process that consists of designing project elements, assembling those elements to a design project, and analyzing swept paths for the design vehicle. If applied elements do not fulfill the swept path requirements for the design vehicle, the process must be carried out again. Application of specialized computer software for swept path analysis significantly facilitates planning procedure of special paved areas. There are various software of this kind available on the global market, and each of them has different specifications. In this paper, comparison of two software commonly used in Croatia (Auto TURN and Vehicle Tracking) is presented, their advantages and disadvantages are described, and their applicability on a particular paved area is discussed. In order to reveal which one of the analyszed software is more favorable in terms of swept paths widths, which one includes input parameters that are more relevant for this kind of analysis, and which one is more suitable for the application on a certain special paved area, the analysis shown in this paper was conducted on a number of different intersection types.

Keywords: software comparison, special paved areas, swept path analysis, swept path input parameters

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: Stephen Vera, Tabitha Black, Charalambos Cleanthous, Ryan Sain

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A gender comparison of USA commercial pilots’ demographics and views of CRM, social desirability and locus of control were surveyed. The Aviation safety locus of control (ASLOC) was used to measure external (ASLOC-E) or internal (ASLOC-I) aviation safety locus of control. The gender differences were explored using the ASLOC scores as a categorical variable. A differential comparison of crew resource management (CRM), based on the Federal Aviation Administration’s (FAA) guidelines was conducted. The results indicated that the proportion of female to male respondents matches the current ratio of USA commercial pilots. Moreover, there were no significant differences regarding overall education and the total number of communication classes one took. Regarding CRM issues, there were no significant differences on their views regarding the roles of the PIC, stress, time management, and managing a flight team. The females scored significantly lower on aeronautical decision making (ADM) and communications. There were no significant differences on either the Balanced Inventory of Desirable Responding (BIDR) impression management (IM) or self-deceptive enhancement (SDE). Although there were no overall significant differences on the ASLOC, the females did score higher on the internal subscale than did the males. An additional comparison of socially desirable responding indicates that all scores may be invalid, especially from the female respondents.

Keywords: social desirability, safety locus of control, crew resource management, commercial pilots

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Authors: Carla Palencia-Aguilar

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Calculations of Net Carbon Balance can be obtained by means of Net Biome Productivity (NBP), Net Ecosystem Productivity (NEP), and Net Primary Production (NPP). The latter is an important component of the biosphere carbon cycle and is easily obtained data from MODIS MOD17A3HGF; however, the results are only available yearly. To overcome data availability, bands 33 to 36 from MODIS MYD021KM (obtained on a daily basis) were analyzed and compared with NPP data from the years 2000 to 2021 in 7 sites where surface mining takes place in the Colombian territory. Coal, Gold, Iron, and Limestone were the minerals of interest. Scales and Units as well as thermal anomalies, were considered for net carbon balance per location. The NPP time series from the satellite images were filtered by using two Matlab filters: First order and Discrete Transfer. After filtering the NPP time series, comparing the graph results from the satellite’s image value, and running a linear regression, the results showed R2 from 0,72 to 0,85. To establish comparable units among NPP and bands 33 to 36, the Greenhouse Gas Equivalencies Calculator by EPA was used. The comparison was established in two ways: one by the sum of all the data per point per year and the other by the average of 46 weeks and finding the percentage that the value represented with respect to NPP. The former underestimated the total CO2 emissions. The results also showed that coal and gold mining in the last 22 years had less CO2 emissions than limestone, with an average per year of 143 kton CO2 eq for gold, 152 kton CO2 eq for coal, and 287 kton CO2 eq for iron. Limestone emissions varied from 206 to 441 kton CO2 eq. The maximum emission values from unfiltered data correspond to 165 kton CO2 eq. for gold, 188 kton CO2 eq. for coal, and 310 kton CO2 eq. for iron and limestone, varying from 231 to 490 kton CO2 eq. If the most pollutant limestone site improves its production technology, limestone could count with a maximum of 318 kton CO2 eq emissions per year, a value very similar respect to iron. The importance of gathering data is to establish benchmarks in order to attain 2050’s zero emissions goal.

Keywords: carbon dioxide, NPP, MODIS, MINING

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Jagroop Singh Sidhu, Buta Singh

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A low computational deblocking filter including three frequency related modes (smooth mode, intermediate mode, and non-smooth mode for low-frequency, mid-frequency, and high frequency regions, respectively) is proposed. The suggested approach requires zero additions, zero subtractions, zero multiplications (for intermediate region), no divisions (for non-smooth region) and no comparison. The suggested method thus keeps the computation lower and thus suitable for image coding systems based on blocks. Comparison of average number of operations for smooth, non-smooth, intermediate (per pixel vector for each block) using filter suggested by Chen and the proposed method filter suggests that the proposed filter keeps the computation lower and is thus suitable for fast processing algorithms.

Keywords: blocking artifacts, computational complexity, non-smooth, intermediate, smooth

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Antoaneta Stefanova, Rositsa Gencheva, Pavlin Groudev

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This paper presents an application of the ASTEC V2r3p3 computer code for simulation of QUENCH-12 experiment. The test has been performed to investigate the behavior of VVER type of fuel assemblies during severe accident conditions. In the performed analyses it has been assessed the mass of generated hydrogen during the experiment flooding of overheated core. The comparison of ASTECv2r3p3 calculated results with measured test data shows good agreement.

Keywords: hydrogen production, VVER, QUENCH facility, severe accident, reactor core

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Authors: Amirhossein Safi

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Internet of things (IOT) is a kind of advanced information technology which has drawn societies’ attention. Sensors and stimulators are usually recognized as smart devices of our environment. Simultaneously, IOT security brings up new issues. Internet connection and possibility of interaction with smart devices cause those devices to involve more in human life. Therefore, safety is a fundamental requirement in designing IOT. IOT has three remarkable features: overall perception, reliable transmission, and intelligent processing. Because of IOT span, security of conveying data is an essential factor for system security. Hybrid encryption technique is a new model that can be used in IOT. This type of encryption generates strong security and low computation. In this paper, we have proposed a hybrid encryption algorithm which has been conducted in order to reduce safety risks and enhancing encryption's speed and less computational complexity. The purpose of this hybrid algorithm is information integrity, confidentiality, non-repudiation in data exchange for IOT. Eventually, the suggested encryption algorithm has been simulated by MATLAB software, and its speed and safety efficiency were evaluated in comparison with conventional encryption algorithm.

Keywords: internet of things, security, hybrid algorithm, privacy

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Authors: Ahmed Elaksher

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Over the last few years, significant progresses have been made and new approaches have been proposed for efficient collection of 3D spatial data from Unmanned aerial vehicles (UAVs) with reduced costs compared to imagery from satellite or manned aircraft. In these systems, a low-cost GPS unit provides the position, velocity of the vehicle, a low-quality inertial measurement unit (IMU) determines its orientation, and off-the-shelf cameras capture the images. Structure from Motion (SfM) and photogrammetry are the main tools for 3D surface reconstruction from images collected by these systems. Unlike traditional techniques, SfM allows the computation of calibration parameters using point correspondences across images without performing a rigorous laboratory or field calibration process and it is more flexible in that it does not require consistent image overlap or same rotation angles between successive photos. These benefits make SfM ideal for UAVs aerial mapping. In this paper, a direct comparison between SfM Digital Elevation Models (DEM) and those generated through traditional photogrammetric techniques was performed. Data was collected by a 3DR IRIS+ Quadcopter with a Canon PowerShot S100 digital camera. Twenty ground control points were randomly distributed on the ground and surveyed with a total station in a local coordinate system. Images were collected from an altitude of 30 meters with a ground resolution of nine mm/pixel. Data was processed with PhotoScan, VisualSFM, Imagine Photogrammetry, and a photogrammetric algorithm developed by the author. The algorithm starts with performing a laboratory camera calibration then the acquired imagery undergoes an orientation procedure to determine the cameras’ positions and orientations. After the orientation is attained, correlation based image matching is conducted to automatically generate three-dimensional surface models followed by a refining step using sub-pixel image information for high matching accuracy. Tests with different number and configurations of the control points were conducted. Camera calibration parameters estimated from commercial software and those obtained with laboratory procedures were comparable. Exposure station positions were within less than few centimeters and insignificant differences, within less than three seconds, among orientation angles were found. DEM differencing was performed between generated DEMs and few centimeters vertical shifts were found.

Keywords: UAV, photogrammetry, SfM, DEM

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Authors: Khin Fai Chen, Jee-Hou Ho, Eng Hwa Yap

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An acoustic micro-energy harvester (AMEH) is developed to convert wasted acoustical energy into useful electrical energy. AMEH is mathematically modeled using lumped element modelling (LEM) and Euler-Bernoulli beam (EBB) modelling. An experiment is designed to validate the mathematical model and assess the feasibility of AMEH. Comparison of theoretical and experimental data on critical parameter value such as Mm, Cms, dm and Ceb showed the variances are within 1% to 6%, which is reasonably acceptable. Hence, AMEH mathematical model is validated. Then, AMEH undergoes bandwidth tuning for performance optimization for further experimental work. The AMEH successfully produces 0.9 V⁄(m⁄s^2) and 1.79 μW⁄(m^2⁄s^4) at 60Hz and 400kΩ resistive load which only show variances about 7% compared to theoretical data. By integrating a capacitive load of 200µF, the discharge cycle time of AMEH is 1.8s and the usable energy bandwidth is available as low as 0.25g. At 1g and 60Hz resonance frequency, the averaged power output is about 2.2mW which fulfilled a range of wireless sensors and communication peripherals power requirements. Finally, the design for AMEH is assessed, validated and deemed as a feasible design.

Keywords: piezoelectric, acoustic, energy harvester

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Authors: Collins Ekpiwre Augustine

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Taking into account the impact of teacher training on primary and secondary teachers’ classroom competencies and practices, as revealed by many empirical studies, this study investigated the impact of teacher qualifications on the pedagogical competencies of university teachers in Northwest Nigeria.Four research questions were answered while four hypotheses were tested. Both descriptive statistic of frequencies/arithmetic mean and inferential statistic oft-test were used to analyze the data collected. In order to provide a focus to the study,an observational rating scale titled “University Teachers’ Pedagogical Competency Observation Rating Scale” (UTPCORS) was used to collect data for the study. The population for the study comprised all the university teachers in the three Federal Universities in Northwest Nigeria totaling about 3,401. However, this pilot study was administered on 8 teachers - with 4 participants in each comparison group in Bayero University, Kano.The findings of the study revealed that there was no significant difference in the four hypotheses postulated for the study.

Keywords: impact, university teachers, teachers' qualifications, competencies

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Authors: Junaid Mahmood Alam, Howarh Humaira Ali, Ishrat Sultana

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Long term irregularity in the glycemic state, especially in Type 2 diabetes mellitus (T2DM) patients, depicted by higher levels of HbA1c, is noted to be correlated with the development of microalbuminuria. The aim of the current study is to investigate the association of urinary microalbumin with HbA1c and with duration of diabetes mellitus in selected male and female T2DM patients. This cross-sectional study was carried out in a total of 70 patients, thirty-five each male and females with diagnosed T2DM, within the age group of 35-60 years. Biochemical parameters of urea, creatinine, urinary microalbumin, HbA1c, fasting blood glucose and post- parendial blood glucose were determined by standard methods. Data was statistically examined by student’s t-test and Pearson’s correlation. Results showed that comparison of healthy control subjects with both male and female T2DM patients depicted significantly elevated levels of all parameters in (P < 0.05 to P < 0.001). Comparison of duration of T2DM with the existence of urinary microalbumin was moderately significant (P < 0.05) when duration was less than 4 years, significant (P < 0.01) with duration of 4-6 years and markedly significant (P < 0.001) with duration of more than 6 years. It is concluded that in male and female T2DM patients, duration of DM as well as poor glycemic control, depicted by higher levels of HbA1c is significantly correlated with elevated levels of urinary microalbumin.

Keywords: type 2 diabetes mellitus, glycosylated hemoglobin, urinary microalbumin, T2DM, HbA1c

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Authors: A. Er-Raki, D. Hartmann, J. P. Belaud, S. Negny

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This paper has a double purpose: firstly, a literature review of the life cycle analysis (LCA) and secondly a comparison between conventional (static) LCA and multi-level dynamic LCA on the following items: (i) inventories evolution with time (ii) temporal evolution of the databases. The first part of the paper summarizes the state of the art of the static LCA approach. The different static LCA limits have been identified and especially the non-consideration of the spatial and temporal evolution in the inventory, for the characterization factors (FCs) and into the databases. Then a description of the different levels of integration of the notion of temporality in life cycle analysis studies was made. In the second part, the dynamic inventory has been evaluated firstly for a single nuclear plant and secondly for the entire French nuclear power fleet by taking into account the construction durations of all the plants. In addition, the databases have been adapted by integrating the temporal variability of the French energy mix. Several iterations were used to converge towards the real environmental impact of the energy mix. Another adaptation of the databases to take into account the temporal evolution of the market data of the raw material was made. An identification of the energy mix of the time studied was based on an extrapolation of the production reference values of each means of production. An application to the construction of the French nuclear power plants from 1971 to 2000 has been performed, in which a dynamic inventory of raw material has been evaluated. Then the impacts were characterized by the ILCD 2011 characterization method. In order to compare with a purely static approach, a static impact assessment was made with the V 3.4 Ecoinvent data sheets without adaptation and a static inventory considering that all the power stations would have been built at the same time. Finally, a comparison between static and dynamic LCA approaches was set up to determine the gap between them for each of the two levels of integration. The results were analyzed to identify the contribution of the evolving nuclear power fleet construction to the total environmental impacts of the French energy mix during the same period. An equivalent strategy using a dynamic approach will further be applied to identify the environmental impacts that different scenarios of the energy transition could bring, allowing to choose the best energy mix from an environmental viewpoint.

Keywords: LCA, static, dynamic, inventory, construction, nuclear energy, energy mix, energy transition

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Afshin Karimzadeh Fard

Abstract:

Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process.

Keywords: artificial neural networks, milling process, rotational speed, temperature

Procedia PDF Downloads 405

Authors: M. Naidoo

Abstract:

Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

Procedia PDF Downloads 106

Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

Abstract:

In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

Procedia PDF Downloads 176