Search results for: multidimensional process mining

Authors: Rim Bouhaouel, Naoufel Kraïem, Zuhoor Al Khanjari

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Unified Modeling Language (UML) is considered as one of the widespread modeling language standardized by the Object Management Group (OMG). Therefore, the model driving engineering (MDE) community attempts to provide reuse of UML diagrams, and do not construct it from scratch. The UML model appears according to a specific software development process. The existing method generation models focused on the different techniques of transformation without considering the development process. Our work aims to construct an UML component from fragments of UML diagram basing on an agile method. We define UML fragment as a portion of a UML diagram, which express a business target. To guide the generation of fragments of UML models using an agile process, we need a flexible approach, which adapts to the agile changes and covers all its activities. We use the software product line (SPL) to derive a fragment of process agile method. This paper explains our approach, named RECUP, to generate UML fragments following an agile process, and overviews the different aspects. In this paper, we present the approach and we define the different phases and artifacts.

Keywords: UML, component, fragment, agile, SPL

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Authors: Ahmad Kavousi, Ali Delaviz

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In this project, the landing process is simulated by using of multi-body dynamics commercial software. Various factors, including landing situations, aircraft structures and climate are used in this simulation. The purpose of this project is to determine the forces exerted on the aircraft landing gears in landing process in various landing conditions. For this purpose, the ADAMS multi-body dynamics software is used. Different scenarios based on FAR-25, including level landing, tail-down landing, crab landing are simulated. Results of dynamic simulation software with landing load factor obtained from the analytical solution are compared. The effect of fuselage elasticity on the landing load is studied. For this purpose, both of elastic and rigid body assumptions are used in the simulation process, and the results are compared and some conclusions are made.

Keywords: landing gear, landing process, aircraft, multi-body dynamics

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Authors: Huijing Wen, Daibao Guo

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Writing is thought to be the most complex facet of language arts. Assessing writing is difficult and subjective, and there are few scientifically validated assessments exist. Research has proposed evaluating writing using a multidimensional approach, including both qualitative and quantitative measures of handwriting, spelling and prose. Given that narrative writing has historically been a staple of literacy instruction in primary grades and is one of the three major genres Common Core State Standards required students to acquire starting in kindergarten, it is essential for teachers to understand how to measure beginning writers writing development and sources of writing difficulties through narrative writing. Guided by the theoretical models of early written expression and using empirical data, this study examines ways teachers can enact a comprehensive approach to understanding beginning writer’s narrative writing through three writing rubrics developed for a Curriculum-based Measurement (CBM). The goal is to help classroom teachers structure a framework for assessing early writing in primary classrooms. Participants in this study included 380 first-grade students from 50 classrooms in 13 schools in three school districts in a Mid-Atlantic state. Three writing tests were used to assess first graders’ writing skills in relation to both transcription (i.e., handwriting fluency and spelling tests) and translational skills (i.e., a narrative prompt). First graders were asked to respond to a narrative prompt in 20 minutes. Grounded in theoretical models of earlier expression and empirical evidence of key contributors to early writing, all written samples to the narrative prompt were coded three ways for different dimensions of writing: length, quality, and genre elements. To measure the quality of the narrative writing, a traditional holistic rating rubric was developed by the researchers based on the CCSS and the general traits of good writing. Students' genre knowledge was measured by using a separate analytic rubric for narrative writing. Findings showed that first-graders had emerging and limited transcriptional and translational skills with a nascent knowledge of genre conventions. The findings of the study provided support for the Not-So-Simple View of Writing in that fluent written expression, measured by length and other important linguistic resources measured by the overall quality and genre knowledge rubrics, are fundamental in early writing development. Our study echoed previous research findings on children's narrative development. The study has practical classroom application as it informs writing instruction and assessment. It offered practical guidelines for classroom instruction by providing teachers with a better understanding of first graders' narrative writing skills and knowledge of genre conventions. Understanding students’ narrative writing provides teachers with more insights into specific strategies students might use during writing and their understanding of good narrative writing. Additionally, it is important for teachers to differentiate writing instruction given the individual differences shown by our multiple writing measures. Overall, the study shed light on beginning writers’ narrative writing, indicating the complexity of early writing development.

Keywords: writing assessment, early writing, beginning writers, transcriptional skills, translational skills, primary grades, simple view of writing, writing rubrics, curriculum-based measurement

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Authors: Muhammad Naeem, Abdulrahman A. Al-Rabiah, Wasif Mughees

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Technical mixture of C4 containing 1-butene and n-butane are very close to each other with regard to their boiling points i.e. -6.3°C for 1-butene and -1°C for n-butane. Extractive distillation process is used for the separation of 1-butene from the existing mixture of C4. The solvent is the essential of extractive distillation, and an appropriate solvent plays an important role in the process economy of extractive distillation. Aspen Plus has been applied for the separation of these hydrocarbons as a simulator. Moreover, NRTL activity coefficient model was used in the simulation. This model indicated that the material balances in this separation process were accurate for several solvent flow rates. Mixture of acetonitrile and water used as a solvent and 99% pure 1-butene was separated. This simulation proposed the ratio of the feed to solvent as 1: 7.9 and 15 plates for the solvent recovery column. Previously feed to solvent ratio was more than this and the number of proposed plates were 30, which shows that the separation process can be economized.

Keywords: extractive distillation, 1-butene, aspen plus, ACN solvent

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Authors: Jaime A. Palma-Mendoza

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A supply chain consists of different processes and when conducting supply chain re-design is necessary to identify the relevant processes and select a target for re-design. A solution was developed which consists to identify first the relevant processes using the Supply Chain Operations Reference (SCOR) model, then to use Analytical Hierarchy Process (AHP) for target process selection. An application was conducted in an Airline MRO supply chain re-design project which shows this combination can clearly aid the identification of relevant supply chain processes and the selection of a target process for re-design.

Keywords: decision support systems, multiple criteria analysis, supply chain management

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Authors: S.V. Kleyn, S.Yu. Zagorodnov, А.А. Kokoulina

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Technogenic emissions of the mining and processing complex are characterized by a high content of chemical components and solid dust particles. However, each industrial enterprise and the surrounding area have features that require refinement and parameterization. Numerous studies have shown the negative impact of fine dust PM10 and PM2.5 on the health, as well as the possibility of toxic components absorption, including heavy metals by dust particles. The target of the study was the quantitative assessment of the fractional and particle size composition of ambient air dust in the area of impact by primary magnesium production complex. Also, we tried to describe the morphology features of dust particles. Study methods. To identify the dust emission sources, the analysis of the production process has been carried out. The particulate composition of the emissions was measured using laser particle analyzer Microtrac S3500 (covered range of particle size is 20 nm to 2000 km). Particle morphology and the component composition were established by electron microscopy by scanning microscope of high resolution (magnification rate - 5 to 300 000 times) with X-ray fluorescence device S3400N ‘HITACHI’. The chemical composition was identified by X-ray analysis of the samples using an X-ray diffractometer XRD-700 ‘Shimadzu’. Determination of the dust pollution level was carried out using model calculations of emissions in the atmosphere dispersion. The calculations were verified by instrumental studies. Results of the study. The results demonstrated that the dust emissions of different technical processes are heterogeneous and fractional structure is complicated. The percentage of particle sizes up to 2.5 micrometres inclusive was ranged from 0.00 to 56.70%; particle sizes less than 10 microns inclusive – 0.00 - 85.60%; particle sizes greater than 10 microns - 14.40% -100.00%. During microscopy, the presence of nanoscale size particles has been detected. Studied dust particles are round, irregular, cubic and integral shapes. The composition of the dust includes magnesium, sodium, potassium, calcium, iron, chlorine. On the base of obtained results, it was performed the model calculations of dust emissions dispersion and establishment of the areas of fine dust РМ 10 and РМ 2.5 distribution. It was found that the dust emissions of fine powder fractions PM10 and PM2.5 are dispersed over large distances and beyond the border of the industrial site of the enterprise. The population living near the enterprise is exposed to the risk of diseases associated with dust exposure. Data are transferred to the economic entity to make decisions on the measures to minimize the risks. Exposure and risks indicators on the health are used to provide named patient health and preventive care to the citizens living in the area of negative impact of the facility.

Keywords: dust emissions, еxposure assessment, PM 10, PM 2.5

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Authors: V. Makis, L. Jafari

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In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart.

Keywords: Bayesian u-chart, economic design, optimal stopping, semi-Markov decision process, statistical process control

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

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This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

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Authors: Md Habibur Rahman, Jaeho Kim

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Efficient process scheduling is a crucial factor in ensuring optimal system performance and resource utilization in computer systems. While various algorithms have been proposed over the years, there are still limitations to their effectiveness. This paper introduces a new Wait-Optimized Scheduler (WOS) algorithm that aims to minimize process waiting time by dividing them into two layers and considering both process time and waiting time. The WOS algorithm is non-preemptive and prioritizes processes with the shortest WOS. In the first layer, each process runs for a predetermined duration, and any unfinished process is subsequently moved to the second layer, resulting in a decrease in response time. Whenever the first layer is free or the number of processes in the second layer is twice that of the first layer, the algorithm sorts all the processes in the second layer based on their remaining time minus waiting time and sends one process to the first layer to run. This ensures that all processes eventually run, optimizing waiting time. To evaluate the performance of the WOS algorithm, we conducted experiments comparing its performance with traditional scheduling algorithms such as First-Come-First-Serve (FCFS) and Shortest-Job-First (SJF). The results showed that the WOS algorithm outperformed the traditional algorithms in reducing the waiting time of processes, particularly in scenarios with a large number of short tasks with long wait times. Our study highlights the effectiveness of the WOS algorithm in improving process scheduling efficiency in computer systems. By reducing process waiting time, the WOS algorithm can improve system performance and resource utilization. The findings of this study provide valuable insights for researchers and practitioners in developing and implementing efficient process scheduling algorithms.

Keywords: process scheduling, wait-optimized scheduler, response time, non-preemptive, waiting time, traditional scheduling algorithms, first-come-first-serve, shortest-job-first, system performance, resource utilization

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Authors: Nesrine Bougarech, Habib Affes

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This research lays the emphasis on the business process reengineering (BPR) which consists in radically altering the organizational processes through the optimal use of information technology (IT) to attain major enhancements in terms of quality, performance and productivity. A survey of the business process reengineering (BPR) was carried out in three French groups and their subsidiaries in Tunisia. The data collected were qualitatively analyzed in an attempt to test the main indicators of the success of a business process reengineering project (BPR) and to compare the importance of these indicators in the context of France versus Tunisia. The study corroborates that the respect of the inherent principles of the business process reengineering (BPR) and the diversity of the human resources involved in the project can lead to better productivity, higher quality of the goods or services and lower cost. Additionally, our results mirror the extent to which the respect of the principles and the diversity of resources are more important in the French companies than in their Tunisian subsidiaries.

Keywords: business process reengineering (BPR), human resources management (HRM), information technology (IT), management

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Authors: Tulasi Rayasa, Phani Kumar Pullela

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A systematic review is a research method that involves collecting and evaluating the information on a specific topic in order to provide a comprehensive and unbiased review. This type of review aims to improve the software development process by ensuring that the research is thorough and accurate. To ensure objectivity, it is important to follow systematic guidelines and consider multiple sources, such as literature reviews, interviews, and surveys. The evaluation process should also be streamlined by incorporating research from journals and other sources, such as grey literature. The main goal of a systematic review is to identify the consistency of current models in the field of computer application and software engineering.

Keywords: computer application, software engineering, process research, data science

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Authors: Byungmin Cho, Dongchul Lee, Taejin Kim, Minhee Lee

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The procedure for the seismic retrofit of existing buildings includes the seismic evaluation. In the evaluation step, it is assessed whether the buildings have satisfactory performance against seismic load. Based on the results of that, the buildings are upgraded. To evaluate seismic performance of the buildings, it usually goes through the model transformation from elastic analysis to inelastic analysis. However, when the data is not delivered through the interwork, engineers should manually input the data. In this process, since it leads to inaccuracy and loss of information, the results of the analysis become less accurate. Therefore, in this study, the process for the seismic evaluation of existing buildings using structural information modeling is suggested. This structural information modeling makes the work economic and accurate. To this end, it is determined which part of the process could be computerized through the investigation of the process for the seismic evaluation based on ASCE 41. The structural information modeling process is developed to apply to the seismic evaluation using Perform 3D program usually used for the nonlinear response history analysis. To validate this process, the seismic performance of an existing building is investigated.

Keywords: existing building, nonlinear analysis, seismic performance, structural information modeling

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Authors: Bahadir Aydin

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Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

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Authors: M. T. Alodat

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In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model.

Keywords: extended skew normal distribution, Gaussian process for regression, predictive distribution, ESGPr model

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Authors: A. Hamidi, S. Khelladi, L. Illoul, A. Tcharkhtchi

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Reactive rotational molding (RRM) is a process to manufacture hollow plastic parts with reactive material has several advantages compared to conventional roto molding of thermoplastic powders: process cycle time is shorter; raw material is less expensive because polymerization occurs during processing and high-performance polymers may be used such as thermosets, thermoplastics or blends. However, several phenomena occur during this process which makes the optimization of the process quite complex. In this study, we have used a mixture of isocyanate and polyol as a reactive system. The chemical transformation of this system to polyurethane has been studied by thermal analysis and rheology tests. Thanks to these results of the curing process and rheological measurements, the kinetic and rheokinetik of polyurethane was identified. Smoothed Particle Hydrodynamics, a Lagrangian meshless method, was chosen to simulate reactive fluid flow in 2 and 3D configurations of the polyurethane during the process taking into account the chemical, and chemiorehological results obtained experimentally in this study.

Keywords: reactive rotational molding, simulation, smoothed particle hydrodynamics, surface tension, rheology, free surface flows, viscoelastic, interpolation

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Authors: Sanaa Chafik, Imane Daoudi, Mounim A. El Yacoubi, Hamid El Ouardi

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Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH.

Keywords: approximate nearest neighbor search, content based image retrieval (CBIR), curse of dimensionality, locality sensitive hashing, multidimensional indexing, scalability

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Authors: Ivonne Tshuma, Ralf Cord-Ruwisch, Wendell Ela

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Water crisis is a world-wide problem because of population growth and climate change. Hence, desalination is a solution to water scarcity, which threatens the world. Reverse osmosis (RO) is the most used technique for desalination; unfortunately, this process, usually requires high-pressure requirement hence requires a lot of energy about 3 – 5.5 KWhr/m³ of electrical energy. The pressure requirements of RO can be alleviated by the use of PRO (pressure retarded osmosis) to drive the RO process. This paper proposes a process of utilizing the energy directly from PRO to drive an RO process. The paper mostly analyses the PRO process parameters such as cross-flow velocity, density, and buoyancy and how these have an effect on PRO hence ultimately the RO process. The experimental study of the PRO with various feed solution concentrations and cross-flow velocities at fixed applied pressure with different orientations of the PRO cell was performed. The study revealed that without cross-flow velocity, buoyancy effects were observed but not with cross-flow velocity.

Keywords: cross-flow velocity, pressure retarded osmosis, density, buoyancy

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Authors: Nam-Uk Kim, Sung-Hwan Kim, Tai-Myoung Chung

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Typical security management systems are not suitable for detecting APT attack, because they cannot draw the big picture from trivial events of security solutions. Although SIEM solutions have security analysis engine for that, their security analysis mechanisms need to be verified in academic field. Although this paper proposes merely an architectural model for APT detection, we will keep studying on correlation analysis mechanism in the future.

Keywords: advanced persistent threat, anomaly detection, data mining

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Authors: Rizwan Rizwan

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This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

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Authors: Hyun-Koo Kim, Yong-Hun Kim, Soo-Young Suk, Ju H. Park, Ho-Youl Jung

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This paper presents an effective speed hump detection process at the day-time. we focus only on round types of speed humps in the day-time dynamic road environment. The proposed speed hump detection scheme consists mainly of two process as stereo matching and speed hump detection process. Our proposed process focuses to speed hump detection process. Speed hump detection process consist of noise reduction step, data fusion step, and speed hemp detection step. The proposed system is tested on Intel Core CPU with 2.80 GHz and 4 GB RAM tested in the urban road environments. The frame rate of test videos is 30 frames per second and the size of each frame of grabbed image sequences is 1280 pixels by 670 pixels. Using object-marked sequences acquired with an on-vehicle camera, we recorded speed humps and non-speed humps samples. Result of the tests, our proposed method can be applied in real-time systems by computation time is 13 ms. For instance; our proposed method reaches 96.1 %.

Keywords: data fusion, round types speed hump, speed hump detection, surface filter

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Authors: Kun Li, Yuzheng Liu, Xiuqi Fan

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High-security standard and high efficiency of screening seem to be contradictory to each other in the airport security check process. Improving the efficiency as far as possible while maintaining the same security standard is significantly meaningful. This paper utilizes the knowledge of Operation Research and Stochastic Process to establish mathematical models to explore this problem. We analyze the current process of airport security check and use the M/G/1 and M/G/k models in queuing theory to describe the process. Then we find the least efficient part is the pre-check lane, the bottleneck of the queuing system. To improve passenger throughput and reduce the variance of passengers’ waiting time, we adjust our models and use Monte Carlo method, then put forward three modifications: adjust the ratio of Pre-Check lane to regular lane flexibly, determine the optimal number of security check screening lines based on cost analysis and adjust the distribution of arrival and service time based on Monte Carlo simulation results. We also analyze the impact of cultural differences as the sensitivity analysis. Finally, we give the recommendations for the current process of airport security check process.

Keywords: queue theory, security check, stochatic process, Monte Carlo simulation

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Authors: S. J. Taylor, S. Chowdhury, T. A. Pychyl

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Attention Deficit Hyperactivity Disorder (ADHD) and procrastination are often discussed in relation to problems with self-regulation and executive functioning (EF). The small body of extant research that has explored the relations between these variables has many limitations particularly in terms of the samples used and the measurement of procrastination. In this study, we recruited a sample of undergraduate students with a confirmed clinical diagnosis of ADHD (n = 48, 66.7% females) as well as a sample of student volunteers without ADHD (n = 68, 75.8% females) to investigate the relations between ADHD subtypes, EF, procrastination and other forms of delay. We used the newly developed Multidimensional Measure of Academic Procrastination and Delay Questionnaire. As hypothesized, the results revealed that individuals with ADHD displayed significantly more irrational delay, general procrastination and academic procrastination compared to individuals without ADHD. This study contributed to the research literature indicating that individuals with ADHD struggle with procrastination as a result of symptoms of ADHD and EF deficits. Theses results provide support for adopting a new language when describing procrastination problems among individuals with ADHD, and they have implications for the nature of academic accommodations and interventions for individuals with ADHD.

Keywords: ADHD, delay, executive functioning, procrastination, self-regulation

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Authors: Ismene Ithai Bras-Ruiz

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Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: A. T. Muruviwa

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Empirical studies of the current CSR agenda have been dominated by literature from the North at the expense of the nations from the South where most TNCs are located. Therefore, owing to the limitations of the current discourse that is dominated by Western ideas such as voluntarism, philanthropy, business case and economic gains, scholars have been calling for a new CSR agenda that is South-centred and addresses the needs of developing nations. The development theme has dominated in the recent literature as scholars concerned with the relationship between business and society have tried to understand its relationship with CSR. Despite a plethora of literature on the roles of corporations in local communities and the impact of CSR initiatives, there is lack of adequate empirical evidence to help us understand the nexus between CSR and development. For all the claims made about the positive and negative consequences of CSR, there is surprisingly little information about the outcomes it delivers. This study is a response to these claims made about the developmental aspect of CSR in developing countries. It offers some empirical bases for assessing the major CSR projects that have been fulfilled by a major mining company, Zimplats in Mhondoro-Ngezi Zimbabwe. The neo-liberal idea of capitalism and market dominations has empowered TNCs to stamp their authority in the developing countries. TNCs have made their mark in developing nations as they stamp their global private authority, rivalling or implicitly challenging the state in many functions. This dominance of corporate power raises great concerns over their tendencies of abuses in terms of environmental, social and human rights concerns as well as how to make them increasingly accountable. The hegemonic power of TNCs in the developing countries has had a tremendous impact on the overall CSR practices. While TNCs are key drivers of globalization they may be acting responsibly in their Global Northern home countries where there is a combination of legal mechanisms and the fear of civil society activism associated with corporate scandals. Using a triangulated approach in which both qualitative and quantitative methods were used the study found out that most CSR projects in Zimbabwe are dominated and directed by Zimplats because of the power it possesses. Most of the major CSR projects are beneficial to the mining company as they serve the business plans of the mining company. What was deduced from the study is that the infrastructural development initiatives by Zimplats confirm that CSR is a tool to advance business obligations. This shows that although proponents of CSR might claim that business has a mandate for social obligations to society, we need not to forget the dominant idea that the primary function of CSR is to enhance the firm’s profitability.

Keywords: hegemonic power, projects, reciprocity, stakeholders

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Authors: Radhey Sham Rajoria

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Pressure to increase the competitiveness in the manufacturing sector and for the survival in the market has led to the development of machining centres, which enhance productivity, improve quality, shorten the lead time, and reduce the manufacturing cost. With the innovation of machining centres in the manufacturing sector the production lines have been replaced by these machining centers, having the ability to machine various processes and multiple tooling with automatic tool changer (ATC) for the same part. Also the process plans can be easily generated for complex components. Some means are required to utilize the machining center at its best. The present work is concentrated on the automated part program generation, and in turn automated process plan generation for the turned components on Denford “MIRAC” 8 stations ATC lathe machining centre. A package in C++ on DOS platform is developed which generates the complete CNC part program, process plan and process sequence for the turned components. The input to this system is in the form of a blueprint in graphical format with machining parameters and variables, and the output is the CNC part program which is stored in a .mir file, ready for execution on the machining centre.

Keywords: CNC, MIRAC, ATC, process planning

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Authors: Suleiman Obeidat, Nabeel Mandahawi

Abstract:

In this work, the packaging process for a move is improved. The customers of this move need their household stuff to be moved from their current house to the new one with minimum damage, in an organized manner, on time and with the minimum cost. Our goal was to improve the process between 10% and 20% time efficiency, 90% reduction in damaged parts and an acceptable improvement in the cost of the total move process. The expected ROI was 833%. Many improvement techniques have been used in terms of the way the boxes are prepared, their preparation cost, packing the goods, labeling them and moving them to a place for moving out. DMAIC technique is used in this work: SIPOC diagram, value stream map of “As Is” process, Root Cause Analysis, Maps of “Future State” and “Ideal State” and an Improvement Plan. A value of ROI=624% is obtained which is lower than the expected value of 833%. The work explains the techniques of improvement and the deficiencies in the old process.

Keywords: packaging, lean tools, six sigma, DMAIC methodology, SIPOC

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Authors: Emanuel Koseos

Abstract:

Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems.

Keywords: competency-based learning, data-driven decision making, machine learning, secondary schools

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Authors: F. Moghtaderi, M. Burke, J. Troelsen

Abstract:

There has been a significant decline in active travel as well as the massive increase use of car-dependent travel mode in many countries during past two decades. Evidential risks for people’s physical and mental health problems are followed by this increased use of motorized travel mode. These problems range from overweight and obesity to increasing air pollution. In response to these rising concerns, local councils and other interested organizations around the world have introduced a variety of initiatives regarding reduce the dominance of cars for the daily journeys. However, the nature of these kinds of interventions, which related to the human behavior, make lots of complexities. People’s travel behavior and changing this behavior, has two different aspects. People’s attitudes and perceptions toward the sustainable and healthy modes of travel, and motorized travel modes (especially private car use) is one these two aspects. The other one related to people’s behavior change processes. There are no comprehensive model in order to guide policy interventions to increase the level of succeed of such interventions. A comprehensive theoretical framework is required in accordance to facilitate and guide the processes of data collection and analysis to achieve the best possible guidelines for policy makers. Regarding this gaps in the travel behavior change research, this paper attempted to identify and suggest a multidimensional framework in order to facilitate planning interventions. A structured mixed-method is suggested regarding the expand the scope and improve the analytic power of the result according to the complexity of human behavior. In order to recognize people’s attitudes, a theory with the focus on people’s attitudes towards a particular travel behavior was needed. The literature around the theory of planned behavior (TPB) was the most useful, and had been proven to be a good predictor of behavior change. Another aspect of the research, related to the people’s decision-making process regarding explore guidelines for the further interventions. Therefore, a theory was needed to facilitate and direct the interventions’ design. The concept of the transtheoretical model of behavior change (TTM) was used regarding reach a set of useful guidelines for the further interventions with the aim to increase active travel and sustainable modes of travel. Consequently, a combination of these two theories (TTM and TPB) had presented as an appropriate concept to identify and design implemented travel behavior change interventions.

Keywords: behavior change theories, theoretical framework, travel behavior change interventions, urban research

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Authors: Naoki Yamamoto, Hidenobu Ozaki, Taiichiro Ookawa, Youming Liu, Kazunori Okada, Aiping Zheng

Abstract:

Rice sheath blight is one of the destructive fungal diseases in rice. We have thought that rice sheath blight resistance is a polygenic trait. Host-pathogen interactions and secondary metabolites such as lignin and phytoalexins are likely to be involved in defense against R. solani. However, to our knowledge, it is still unknown how sheath blight resistance can be enhanced in rice breeding. To seek for an alternative genetic factor that contribute to sheath blight resistance, we mined relevant allelic variations from rice core collections created in Japan. Based on disease lesion length on detached leaf sheath, we selected 30 varieties of the top tail-end and the bottom tail-end, respectively, from the core collections to perform genome-wide association mapping. Re-sequencing reads for these varieties were used for calling single nucleotide polymorphisms among the 60 varieties to create a SNP panel, which contained 1,137,131 homozygous variant sites after filitering. Association mapping highlighted a locus on the long arm of chromosome 11, which is co-localized with three sheath blight QTLs, qShB11-2-TX, qShB11, and qSBR-11-2. Based on the localization of the trait-associated alleles, we identified an ankyryn repeat-containing protein gene (ANK-M) as an uncharacterized candidate factor for rice sheath blight resistance. Allelic distributions for ANK-M in the whole rice population supported the reliability of trait-allele associations. Gene expression characteristics were checked to evaluiate the functionality of ANK-M. Since an ANK-M homolog (OsPIANK1) in rice seems a basal defense regulator against rice blast and bacterial leaf blight, ANK-M may also play a role in the rice immune system.

Keywords: allele mining, GWAS, QTL, rice sheath blight

Procedia PDF Downloads 79