Search results for: Forensic anthropology population data

Authors: J. A. Batsis, G. G. Boateng, L. M. Seo, C. L. Petersen, K. L. Fortuna, E. V. Wechsler, R. J. Peterson, S. B. Cook, D. Pidgeon, R. S. Dokko, R. J. Halter, D. F. Kotz

Abstract:

Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting are fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time, and conducted three usability studies in two mixed-aged groups of participants (n=6 each) and a group of older adults with obesity participating in a weight-loss intervention (n=20). The system was favorable, acceptable and provided iterative information that could assist in future deployment on ubiquitous platforms. Our formative work provides the foundation to deliver home-based monitoring interventions in a high-risk, older adult population.

Keywords: Application, mHealth, older adult, resistance exercise band, sarcopenia.

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Authors: Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay KumarSingh, Chhaya Dule, Vivek Parganiha

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Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.

Keywords: Data Mining, Candidate Sets, Frequent Item set, Pruning.

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Authors: Anjum Iqbal, Mohd Aizaini Maarof

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Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.

Keywords: artificial immune system, danger theory, intelligent processing, system calls

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Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo

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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

Keywords: Neural networks, groundwater depth, forecast.

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Authors: B. Ferdousi, J. Bari

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With advancement of mobile computing, employees are increasingly doing their job-related works using personally owned mobile devices or organization owned devices. The Bring Your Own Device (BYOD) model allows employees to use their own mobile devices for job-related works, while Corporate Owned, Personally Enabled (COPE) model allows both organizations and employees to install applications onto organization-owned mobile devices used for job-related works. While there are many benefits of using mobile computing for job-related works, there are also serious concerns of different levels of threats to the organizational data security. Consequently, it is crucial to know the level of threat to the organizational data security in the BOYD and COPE models. It is also important to ensure that employees comply with the organizational data security policy. This paper discusses the organizational data security issues in perspective of ownership of mobile devices used by employees, especially in BYOD and COPE models. It appears that while the BYOD model has many benefits, there are relatively more data security risks in this model than in the COPE model. The findings also showed that in both BYOD and COPE environments, a more practical approach towards achieving secure mobile computing in organizational setting is through the development of comprehensive cybersecurity policies balancing employees’ need for convenience with organizational data security. The study helps to figure out the compliance and the risks of security breach in BYOD and COPE models.

Keywords: Data security, mobile computing, BYOD, COPE, cybersecurity policy, cybersecurity compliance.

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Authors: Haythem Yahyaoui, Samir Moalla, Mounir Bouden, Skander Ghorbel

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Nowadays, cloud environments are becoming a need for companies, this new technology gives the opportunities to access to the data anywhere and anytime. It also provides an optimized and secured access to the resources and gives more security for the data which is stored in the platform. However, some companies do not trust Cloud providers, they think that providers can access and modify some confidential data such as bank accounts. Many works have been done in this context, they conclude that encryption methods realized by providers ensure the confidentiality, but, they forgot that Cloud providers can decrypt the confidential resources. The best solution here is to apply some operations on the data before sending them to the provider Cloud in the objective to make them unreadable. The principal idea is to allow user how it can protect his data with his own methods. In this paper, we are going to demonstrate our approach and prove that is more efficient in term of execution time than some existing methods. This work aims at enhancing the quality of service of providers and ensuring the trust of the customers. 

Keywords: Confidentiality, cryptography, security issues, trust issues.

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Authors: Radek Malinský, Ivan Jelínek

Abstract:

Web 2.0 (social networking, blogging and online forums) can serve as a data source for social science research because it contains vast amount of information from many different users. The volume of that information has been growing at a very high rate and becoming a network of heterogeneous data; this makes things difficult to find and is therefore not almost useful. We have proposed a novel theoretical model for gathering and processing data from Web 2.0, which would reflect semantic content of web pages in better way. This article deals with the analysis part of the model and its usage for content analysis of blogs. The introductory part of the article describes methodology for the gathering and processing data from blogs. The next part of the article is focused on the evaluation and content analysis of blogs, which write about specific trend.

Keywords: Blog, Sentiment Analysis, Web 2.0, Webometrics

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Authors: Orkide Donma, Mustafa M. Donma

Abstract:

Obesity, a disease associated with a low-grade inflammation, is a risk factor for the development of metabolic syndrome (MetS). So far, MetS risk factors such as parameters related to glucose and lipid metabolisms as well as blood pressure were considered for the evaluation of this disease. There are still some ambiguities related to the characteristic features of MetS observed particularly in pediatric population. Hormonal imbalance is also important, and quite a lot information exists about the behaviour of some hormones in adults. However, the hormonal profiles in pediatric metabolism have not been cleared yet. The aim of this study is to investigate the profiles of cortisol, insulin, and thyroid hormones in children with MetS. The study population was composed of morbid obese (MO) children without (Group 1) and with (Group 2) MetS components. WHO BMI-for age and sex percentiles were used for the classification of obesity. The values above 99 percentile were defined as morbid obesity. Components of MetS (central obesity, glucose intolerance, high blood pressure, high triacylglycerol levels, low levels of high density lipoprotein cholesterol) were determined. Anthropometric measurements were performed. Ratios as well as obesity indices were calculated. Insulin, cortisol, thyroid stimulating hormone (TSH), free T₃ and free T₄ analyses were performed by electrochemiluminescence immunoassay. Data were evaluated by statistical package for social sciences program. p<0.05 was accepted as the degree for statistical significance. The mean ages±SD values of Group 1 and Group 2 were 9.9±3.1 years and 10.8±3.2 years, respectively. Body mass index (BMI) values were calculated as 27.4±5.9 kg/m² and 30.6±8.1 kg/m², successively. There were no statistically significant differences between the ages and BMI values of the groups. Insulin levels were statistically significantly increased in MetS in comparison with the levels measured in MO children. There was not any difference between MO children and those with MetS in terms of cortisol, T₃, T₄ and TSH. However, T₄ levels were positively correlated with cortisol and negatively correlated with insulin. None of these correlations were observed in MO children. Cortisol levels in both MO as well as MetS group were significantly correlated. Cortisol, insulin, and thyroid hormones are essential for life. Cortisol, called the control system for hormones, orchestrates the performance of other key hormones. It seems to establish a connection between hormone imbalance and inflammation. During an inflammatory state, more cortisol is produced to fight inflammation. High cortisol levels prevent the conversion of the inactive form of the thyroid hormone T₄ into active form T₃. Insulin is reduced due to low thyroid hormone. T₃, which is essential for blood sugar control- requires cortisol levels within the normal range. Positive association of T₄ with cortisol and negative association of it with insulin are the indicators of such a delicate balance among these hormones also in children with MetS.

Keywords: Children, cortisol, insulin, metabolic syndrome, thyroid hormones.

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Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour

Abstract:

In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.

Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.

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Authors: Mohammad Ali Jabraeil Jamali, Ahmad Khademzadeh, Hasan Asil, Amir Asil

Abstract:

This method decrease usage power (expenditure) in networks on chips (NOC). This method data coding for data transferring in order to reduces expenditure. This method uses data compression reduces the size. Expenditure calculation in NOC occurs inside of NOC based on grown models and transitive activities in entry ports. The goal of simulating is to weigh expenditure for encoding, decoding and compressing in Baseline networks and reduction of switches in this type of networks. KeywordsNetworks on chip, Compression, Encoding, Baseline networks, Banyan networks.

Keywords: Networks on chip, Compression, Encoding, Baseline networks, Banyan networks

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Authors: Ejaz Ashraf, Raheel Babar, Muhammad Yaseen, Hafiz Khurram Shurjeel, Nosheen Fatima

Abstract:

Main purpose of this study was to assess adoption level of farmers for quinoa cultivation after they had been taught through training and visit extension approach. At this time of the 21^st century, population structure, climate change, food requirements and eating habits of people are changing rapidly. In this scenario, farmers must play their key role in sustainable crop development and production through adoption of new crops that may also be helpful to overcome the issue of food insecurity as well as reducing poverty in rural areas. Its cultivation in Pakistan is at the early stages and there is a need to raise awareness among farmers to grow quinoa crops. In the middle of the 2015, a training and visit extension approach was used to raise awareness and convince farmers to grow quinoa in the area. During training and visit extension program, 80 farmers were randomly selected for the training of quinoa cultivation. Later on, these farmers trained 60 more farmers living into their neighborhood. After six months, a survey was conducted with all 140 farmers to assess the impact of the training and visit program on adoption level of respondents for the quinoa crop. The survey instrument was developed with the help of literature review and other experts of the crop. Validity and reliability of the instrument were checked before complete data collection. The data were analyzed by using SPSS. Multiple regression analysis was used for interpretation of the results from the survey, which indicated that factors like information/ training, change in agronomic and plant protection practices play a key role in the adoption of quinoa cultivation by respondents. In addition, the model explains more than 50% of variation in the adoption level of respondents. It is concluded that farmers need timely information for improved knowledge of agronomic and plant protection practices to adopt cultivation of the quinoa crop in the area.

Keywords: Farmers, quinoa, adoption, contact, training and visit.

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Authors: H. Y. Jung, Ju H. Park, S. M. Lee

Abstract:

Sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.

Keywords: Sampled-data control, Sector bound, Solid oxide fuel cell, Time-delay.

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Authors: Yuki. Akiyama, Hiroaki. Sengoku, Ryosuke. Shibasaki

Abstract:

In this study, the locations and areas of commercial accumulations were detected by using digital yellow page data. An original buffering method that can accurately create polygons of commercial accumulations is proposed in this paper.; by using this method, distribution of commercial accumulations can be easily created and monitored over a wide area. The locations, areas, and time-series changes of commercial accumulations in the South Kanto region can be monitored by integrating polygons of commercial accumulations with the time-series data of digital yellow page data. The circumstances of commercial accumulations were shown to vary according to areas, that is, highly- urbanized regions such as the city center of Tokyo and prefectural capitals, suburban areas near large cities, and suburban and rural areas.

Keywords: Commercial accumulations, Spatio-temporal analysis, Urban monitoring, Yellow page data

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Authors: Maan M. Shaker

Abstract:

The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.

Keywords: Bioinformatics, DWT, EEG waves, FFT.

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

Abstract:

We propose obstacle classification method based on 2D LIDAR Database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width and intensity data; the first classification was processed by the width data; the second classification was processed by the intensity data; classification was processed by comparing to database; result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

Keywords: Obstacle, Classification, LIDAR, Segmentation, Width, Intensity, Database.

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Authors: Sajjad Farashi, Mohammadjavad Abolhassani, Mostafa Taghavi Kani

Abstract:

Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately.

Keywords: EMD, neural data processing, spike detection, wavelet decomposition.

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Authors: Mohammad Soliman, Mohamed A. Abou-Shouk

Abstract:

Knowledge management focuses on the development, storage, retrieval, and dissemination of information and expertise. It has become an important tool to improve performance in tourism enterprises. This includes improving decision-making, developing customer services, and increasing sales and profits. Knowledge management adoption depends on human, organizational and technological factors. This study aims to explore the concept of knowledge management in travel agents in Egypt. It explores the requirements of adoption and its impact on performance in these agencies. The study targets Category A travel agents in Egypt. The population of the study encompasses Category A travel agents having online presence. An online questionnaire is used to collect data from managers of travel agents. This study is useful for travel agents who are in urgent need to restructure their intermediary role and support their survival in the global travel market. The study sheds light on the requirements of adoption and the expected impact on performance. This could help travel agents identify their situation and the determine the extent to which they are ready to adopt knowledge management. This study is contributing to knowledge by providing insights from the tourism sector in a developing country where the concept of knowledge management is still in its infancy stages.

Keywords: Benefits, determinants, Egypt, knowledge management, travel agents.

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Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

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In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.

Keywords: Privacy enforcement, Platform-as-a-Service privacy awareness, cloud computing privacy.

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Authors: Thashmee Karunaratne, Henrik Böstrom

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Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

Keywords: Machine learning, Structure classification, Propositionalization.

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Authors: P. Jomsri

Abstract:

Currently, web usage make a huge data from a lot of user attention. In general, proxy server is a system to support web usage from user and can manage system by using hit rates. This research tries to improve hit rates in proxy system by applying data mining technique. The data set are collected from proxy servers in the university and are investigated relationship based on several features. The model is used to predict the future access websites. Association rule technique is applied to get the relation among Date, Time, Main Group web, Sub Group web, and Domain name for created model. The results showed that this technique can predict web content for the next day, moreover the future accesses of websites increased from 38.15% to 85.57 %. This model can predict web page access which tends to increase the efficient of proxy servers as a result. In additional, the performance of internet access will be improved and help to reduce traffic in networks.

Keywords: Association rule, proxy server, data mining.

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Authors: Lucia Hrebeňárová, Jarmila Žolnová, Veronika Palková

Abstract:

Inclusive education of students with special educational needs has been on the increase in the Slovak Republic, facing many challenges. Preparedness of teachers for inclusive education is one of the most frequent issues; teachers lack skills when it comes to the use of effective instruction depending on the individual needs of students, improvement of classroom management and social skills, and support of inclusion within the classroom. Social support is crucial for the school success of students within inclusive settings. The aim of the paper is to analyse perception of the frequency and importance of peer social support by students with special educational needs in inclusive education. The data collection tool used was the Child and Adolescent Social Support Scale (CASSS). The research sample consisted of 953 fourth grade students – 141 students with special educational needs educated in an inclusive setting and 812 students of the standard population. No significant differences were found between the students with special educational needs and the students without special educational needs in an inclusive setting when it comes to the perception of frequency and importance of social support of schoolmates and friends. However, the perception of frequency and importance of a friend’s social support was higher than the perception of frequency and importance of a classmate’s social support in both groups of students.

Keywords: Inclusive education, peer social support, peer, student with special educational needs.

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Authors: Kwangho Cha, Hyeyoung Cho, Sungho Kim

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As many scientific applications require large data processing, the importance of parallel I/O has been increasingly recognized. Collective I/O is one of the considerable features of parallel I/O and enables application programmers to easily handle their large data volume. In this paper we measured and analyzed the performance of original collective I/O and the subgroup method, the way of using collective I/O of MPI effectively. From the experimental results, we found that the subgroup method showed good performance with small data size.

Keywords: Collective I/O, MPI, parallel file system.

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Authors: Y. N. Phang, E. F. Loh

Abstract:

Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling overdispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling overdispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling overdispered medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling overdispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling overdispersed medical count data when ZIP and ZINB are inadequate.

Keywords: Zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit.

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Authors: Yi Yang, Jiasong Sun

Abstract:

Almost every aspect of our daily lives is now intertwined with some degree of Human-Computer Interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology and more. The HCI courses in the Department of Electronics at Tsinghua University, known as the Media and Cognition course, is constantly updated to reflect the most advanced technological advances, such as virtual reality, augmented reality and artificial intelligence-based interaction. For more than a decade, this course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which has gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. The latest version of the HCI course practices a semantic communication platform based on AI-generated techniques. We explored a student-centered model and proposed an interest-based teaching method. Students are no longer just recipients of knowledge, but become active participants in the learning process driven by personal interests, thereby encouraging students to take responsibility for their own education. One of the latest results of this teaching approach in the course "Media and Cognition" is a student proposal to develop a semantic communication platform rooted in artificial intelligence generative technologies. The platform solves a key challenge in communications technology: the ability to preserve visual signals. The interest-based approach emphasizes personal curiosity and active participation, and the proposal of an artificial intelligence-generated semantic communication platform is an example and successful result of how students can exert greater creativity when they have the power to control their own learning.

Keywords: Human-computer interaction, media and cognition course, semantic communication, retain ability, prompts.

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Authors: Rosie Borup, Hanifa Shah

Abstract:

This paper aims to describe how student satisfaction is measured for work-based learners as these are non-traditional learners, conducting academic learning in the workplace, typically their curricula have a high degree of negotiation, and whose motivations are directly related to their employers- needs, as well as their own career ambitions. We argue that while increasing WBL participation, and use of SSD are both accepted as being of strategic importance to the HE agenda, the use of WBL SSD is rarely examined, and lessons can be learned from the comparison of SSD from a range of WBL programmes, and increased visibility of this type of data will provide insight into ways to improve and develop this type of delivery. The key themes that emerged from the analysis of the interview data were: learners profiles and needs, employers drivers, academic staff drivers, organizational approach, tools for collecting data and visibility of findings. The paper concludes with observations on best practice in the collection, analysis and use of WBL SSD, thus offering recommendations for both academic managers and practitioners.

Keywords: Student satisfaction data, work based learning, employer engagement, NSS.

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Authors: Ghalem Belalem, Yahya Slimani

Abstract:

Large scale systems such as computational Grid is a distributed computing infrastructure that can provide globally available network resources. The evolution of information processing systems in Data Grid is characterized by a strong decentralization of data in several fields whose objective is to ensure the availability and the reliability of the data in the reason to provide a fault tolerance and scalability, which cannot be possible only with the use of the techniques of replication. Unfortunately the use of these techniques has a height cost, because it is necessary to maintain consistency between the distributed data. Nevertheless, to agree to live with certain imperfections can improve the performance of the system by improving competition. In this paper, we propose a multi-layer protocol combining the pessimistic and optimistic approaches conceived for the data consistency maintenance in large scale systems. Our approach is based on a hierarchical representation model with tree layers, whose objective is with double vocation, because it initially makes it possible to reduce response times compared to completely pessimistic approach and it the second time to improve the quality of service compared to an optimistic approach.

Keywords: Data Grid, replication, consistency, optimistic approach, pessimistic approach.

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Authors: Shigeaki Sakurai, Ken Ueno, Ryohei Orihara

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This paper proposes a method that discovers time series event patterns from textual data with time information. The patterns are composed of sequences of events and each event is extracted from the textual data, where an event is characteristic content included in the textual data such as a company name, an action, and an impression of a customer. The method introduces 7 types of time constraints based on the analysis of the textual data. The method also evaluates these constraints when the frequency of a time series event pattern is calculated. We can flexibly define the time constraints for interesting combinations of events and can discover valid time series event patterns which satisfy these conditions. The paper applies the method to daily business reports collected by a sales force automation system and verifies its effectiveness through numerical experiments.

Keywords: Text mining, sequential mining, time constraints, daily business reports.

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Authors: M. M. Shahidul Hassan, Omiya Hassan

Abstract:

Bangladesh has set its goal to reach upper middle-income country status by 2024. To attain this status, the country must satisfy the World Bank requirement of achieving minimum Gross National Income (GNI). Number of youth job seekers in the country is increasing. University graduates are looking for decent jobs. So, the vital issue of this country is to understand how the GNI and jobs can be increased. The objective of this paper is to address these issues and find ways to create more job opportunities for youths at home and abroad which will increase the country’s GNI. The paper studies proportion of different goods Bangladesh exported, and also the percentage of employment in different sectors. The data used here for the purpose of analysis have been collected from the available literature. These data are then plotted and analyzed. Through these studies, it is concluded that growth in sectors like agricultural, ready-made garments (RMG), jute industries and fisheries are declining and the business community is not interested in setting up capital-intensive industries. Under this situation, the country needs to explore other business opportunities for a higher economic growth rate. Knowledge can substitute the physical resource. Since the country consists of the large youth population, higher education will play a key role in economic development. It now needs graduates with higher-order skills with innovative quality. Such dispositions demand changes in a university’s curriculum, teaching and assessment method which will function young generations as active learners and creators. By bringing these changes in higher education, a knowledge-based society can be created. The application of such knowledge and creativity will then become the commodity of Bangladesh which will help to reach its goal as an upper middle-income country.

Keywords: Bangladesh, economic sectors, economic growth, higher education, knowledge-based economy, massifcation of higher education, teaching and learning, universities’ role in society.

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Authors: C. R. Danielle, S. Erik, T. Patrick, M. Hugh

Abstract:

In the last decade there has been an increase in demand for fine grinding due to the depletion of coarse-grained orebodies and an increase of processing fine disseminated minerals and complex orebodies. These ores have provided new challenges in concentrator design because fine and ultra-fine grinding is required to achieve acceptable recovery rates. Therefore, the correct design of a grinding circuit is important for minimizing unit costs and increasing product quality. The use of ball mills for grinding in fine size ranges is inefficient and, therefore, vertical stirred grinding mills are becoming increasingly popular in the mineral processing industry due to its already known high energy efficiency. This work presents a hypothesis of a methodology to predict the product size distribution of a vertical stirred mill using a Bond ball mill. The Population Balance Model (PBM) was used to empirically analyze the performance of a vertical mill and a Bond ball mill. The breakage parameters obtained for both grinding mills are compared to determine the possibility of predicting the product size distribution of a vertical mill based on the results obtained from the Bond ball mill. The biggest advantage of this methodology is that most of the minerals processing laboratories already have a Bond ball mill to perform the tests suggested in this study. Preliminary results show the possibility of predicting the performance of a laboratory vertical stirred mill using a Bond ball mill.

Keywords: Bond ball mill, population balance model, product size distribution, vertical stirred mill.

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Authors: Il-Do Jeong, Hang-Geun Jeong

Abstract:

This paper describes the design and fabrication of a clock and data recovery circuit (CDR). We propose a new clock and data recovery which is based on a 1/4-rate frequency detector (QRFD). The proposed frequency detector helps reduce the VCO frequency and is thus advantageous for high speed application. The proposed frequency detector can achieve low jitter operation and extend the pull-in range without using the reference clock. The proposed CDR was implemented using a 1/4-rate bang-bang type phase detector (PD) and a ring voltage controlled oscillator (VCO). The CDR circuit has been fabricated in a standard 0.18 CMOS technology. It occupies an active area of 1 x 1 and consumes 90 mW from a single 1.8V supply.

Keywords: Clock and data recovery, 1/4-rate frequency detector, 1/4-rate phase detector.

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