Search results for: WEKA data mining tool

Authors: Ming Lun Wang, Yan Wang, Ning Ruo Sun

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With the development of cloud computing, more and more users start to utilize the cloud storage service. However, there exist some issues: 1) cloud server steals the shared data, 2) sharers collude with the cloud server to steal the shared data, 3) cloud server tampers the shared data, 4) sharers and key generation center (KGC) conspire to steal the shared data. In this paper, we use advanced encryption standard (AES), hash algorithms, and accountable key-policy attribute-based encryption without key escrow (WOKE-AKP-ABE) to build a security cloud storage scheme. Moreover, the data are encrypted to protect the privacy. We use hash algorithms to prevent the cloud server from tampering the data uploaded to the cloud. Analysis results show that this scheme can resist conspired attacks.

Keywords: cloud storage security, sharing storage, attributes, Hash algorithm

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Authors: Chen Xiong, Tong Xin, Li Hao, Xu Jin-Sheng

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Experiment and simulation researches on the structural integrity of propellant grain in solid rocket motor (SRM) with high volumetric fraction were conducted. First, by using SRM parametric modeling functions with secondary development tool Python of ABAQUS, the three dimensional parameterized modeling programs of star shaped grain, wheel shaped grain and wing cylindrical grain were accomplished. Then, the mechanical properties under different loads for star shaped grain were obtained with the application of automatically established finite element model in ABAQUS. Next, several optimization algorithms are introduced to optimize the star shaped grain, wheel shaped grain and wing cylindrical grain. After meeting the demands of burning surface changes and volumetric fraction, the optimum three dimensional shapes of grain were obtained. Finally, by means of parametric modeling functions, pressure data of SRM’s cold pressurization test was directly applied to simulation of grain in terms of mechanical performance. The results verify the reliability and practical of parameterized modeling program of SRM.

Keywords: cold pressurization test, ğarametric modeling, structural integrity, propellant grain, SRM

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Authors: Binjuan Xu, Qian Zhao, Ping Jiang, Bo Guo, Zhijun Cheng, Xiaoyue Wu

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Astronautical valves are key units in engine systems of astronautical products; their reliability will influence results of rocket or missile launching, even lead to damage to staff and devices on the ground. Besides failure in engine system may influence the hitting accuracy and flight shot of missiles. Therefore high reliability is quite essential to astronautical products. There are quite a few literature doing research based on few failure test data to estimate valves’ reliability, thus this paper proposed a new method to estimate valves’ reliability, according to the corresponding tests of different failure modes, this paper takes advantage of tests data which acquired from temperature, vibration, and action tests to estimate reliability in every failure modes, then this paper has regarded these three kinds of tests as three stages in products’ process to integrate these results to acquire valves’ reliability. Through the comparison of results achieving from tests data and simulated data, the results have illustrated how to obtain valves’ reliability based on the few failure data with failure modes and prove that the results are effective and rational.

Keywords: censored data, temperature tests, valves, vibration tests

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Authors: Jutharat Nawarungreung

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As an international language, English is used as a medium in formal and informal settings including all kinds of entertainment. As it were, the use of English in such an arena is of no less importance and interest, and indeed it becomes a valuable tool for EFL learners to learn and improve their language. In addition, it is a social perspective in the way that English is incorporated in other nationalities’ music, as well as the attitudes of listeners toward it. This research principally aimed to find out the level of comprehensibility of English inserted in Thai pop music. There were three groups of participants, namely Thais, non-native speakers who are non-Thai and native speakers, 35 each group. The research tools comprised song lyrics, interviews, questionnaires, and video recorder. The participants listened to Thai songs and wrote down the English words and their meanings they heard. They were video-recorded when listening to the songs, and then asked on particular actions and facial expressions. Afterwards, they were interviewed to account for their attitudes toward the incorporation of English into Thai songs. Finally, the participants completed a questionnaire. Data was analysed by the way of comparison of all the participants’ pronunciation. In doing so, the number of correct and incorrect answers was revealed. The study has shown that those who attained the highest level of understanding the English words in Thai music were Thais, native speakers, and non-native speakers who are non-Thai respectively.

Keywords: English throughout the world, varieties of English, English in Thai songs, intelligibility, attitudes

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Authors: Valery Yakubovich, Shuping wu

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Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

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Authors: C. Xavier Mendieta, J. J McArthur

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Governments are playing an increasingly active role in reducing carbon emissions, and a key strategy has been the introduction of mandatory energy disclosure policies. These policies have resulted in a significant amount of publicly available data, providing researchers with a unique opportunity to develop location-specific energy and carbon emission benchmarks from this data set, which can then be used to develop building archetypes and used to inform urban energy models. This study presents the development of such a benchmark using the public reporting data. The data from Ontario’s Ministry of Energy for Post-Secondary Educational Institutions are being used to develop a series of building archetype dynamic building loads and energy benchmarks to fill a gap in the currently available building database. This paper presents the development of a benchmark for college and university residences within ASHRAE climate zone 6 areas in Ontario using the mandatory disclosure energy and greenhouse gas emissions data. The methodology presented includes data cleaning, statistical analysis, and benchmark development, and lessons learned from this investigation are presented and discussed to inform the development of future energy benchmarks from this larger data set. The key findings from this initial benchmarking study are: (1) the importance of careful data screening and outlier identification to develop a valid dataset; (2) the key features used to develop a model of the data are building age, size, and occupancy schedules and these can be used to estimate energy consumption; and (3) policy changes affecting the primary energy generation significantly affected greenhouse gas emissions, and consideration of these factors was critical to evaluate the validity of the reported data.

Keywords: building archetypes, data analysis, energy benchmarks, GHG emissions

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

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Authors: Grecco M. Ante, Princess Rochelle O. Gan

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Plants that can absorb greater than 10,000 µg Ni/g dry mass in their stems and leaves are termed as ‘hypernickelophores’. Chelators are chemicals that make the metals in the soil more soluble, making them a potential enhancer for phytoextraction. This study aims to observe the effect of different concentrations of the chelating agent ethylene diamine tetraacetate (EDTA) on the metal uptake (or rate of phytoextraction) of Nickel by Phyllanthus sp. nov. The plant is found to be a hyperickelophore in normal conditions. The addition of EDTA increased the metal uptake of the plant. The increasing amount of the chelating agent causes a decrease in the phytoextraction of the plant but moves the onset of its peak of maximum nickel content in its tissue to an earlier time. The chelator-assisted phytoextraction of nickel by Phyllanthus sp. nov. is proven to be an efficient auxiliary mining operation for nickel laterite mines.

Keywords: phytomining, Phyllanthus sp. nov., EDTA, nickel, laterite

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Authors: Louella Nicole Gamao

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Writing in English is accounted as a complex skill and process for foreign language learners who commit errors in writing are found as an inevitable part of language learners' writing. This study aims to explore and analyze the learners of English-as-a foreign Language (EFL) freewriting in a University in Taiwan by identifying the category of mistakes that often appear in their freewriting activity and analyzing the learners' awareness of each error. Hopefully, this present study will be able to gain further information about students' errors in their English writing that may contribute to further understanding of the benefits of freewriting activity that can be used for future purposes as a powerful tool in English writing courses for EFL classes. The present study adopted the framework of error analysis proposed by Dulay, Burt, and Krashen (1982), which consisted of a compilation of data, identification of errors, classification of error types, calculation of frequency of each error, and error interpretation. Survey questionnaires regarding students' awareness of errors were also analyzed and discussed. Using quantitative and qualitative approaches, this study provides a detailed description of the errors found in the students'freewriting output, explores the similarities and differences of the students' errors in both academic writing and freewriting, and lastly, analyzes the students' perception of their errors.

Keywords: error, EFL, freewriting, taiwan, english

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Authors: Kylie Mueller, Nijole Bernaitis, Shailendra Anoopkumar-Dukie

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Background: Vitamin K antagonists particularly warfarin is the most frequently used oral medication for deep vein thrombosis (DVT) treatment and prophylaxis. Time in therapeutic range (TITR) of the international normalised ratio (INR) is widely accepted as a measure to assess the quality of warfarin therapy. Multiple factors can affect warfarin control and the subsequent adverse outcomes including thromboembolic and bleeding events. Predictor models have been developed to assess potential contributing factors and measure the individual risk of these adverse events. These predictive models have been validated in atrial fibrillation (AF) patients, however, there is a lack of literature on whether these can be successfully applied to other warfarin users including DVT patients. Therefore, the aim of the study was to assess the ability of these risk models (HAS BLED and CHADS2) to predict haemorrhagic and ischaemic incidences in DVT patients treated with warfarin. Methods: A retrospective analysis of DVT patients receiving warfarin management by a private pathology clinic was conducted. Data was collected from November 2007 to September 2014 and included demographics, medical and drug history, INR targets and test results. Patients receiving continuous warfarin therapy with an INR reference range between 2.0 and 3.0 were included in the study with mean TITR calculated using the Rosendaal method. Bleeding and thromboembolic events were recorded and reported as incidences per patient. The haemorrhagic risk model HAS BLED and ischaemic risk model CHADS2 were applied to the data. Patients were then stratified into either the low, moderate, or high-risk categories. The analysis was conducted to determine if a correlation existed between risk assessment tool and patient outcomes. Data was analysed using GraphPad Instat Version 3 with a p value of <0.05 considered to be statistically significant. Patient characteristics were reported as mean and standard deviation for continuous data and categorical data reported as number and percentage. Results: Of the 533 patients included in the study, there were 268 (50.2%) female and 265 (49.8%) male patients with a mean age of 62.5 years (±16.4). The overall mean TITR was 78.3% (±12.7) with an overall haemorrhagic incidence of 0.41 events per patient. For the HAS BLED model, there was a haemorrhagic incidence of 0.08, 0.53, and 0.54 per patient in the low, moderate and high-risk categories respectively showing a statistically significant increase in incidence with increasing risk category. The CHADS2 model showed an increase in ischaemic events according to risk category with no ischaemic events in the low category, and an ischaemic incidence of 0.03 in the moderate category and 0.47 high-risk categories. Conclusion: An increasing haemorrhagic incidence correlated to an increase in the HAS BLED risk score in DVT patients treated with warfarin. Furthermore, a greater incidence of ischaemic events occurred in patients with an increase in CHADS2 category. In an Australian population of DVT patients, the HAS BLED and CHADS2 accurately predicts incidences of haemorrhage and ischaemic events respectively.

Keywords: anticoagulant agent, deep vein thrombosis, risk assessment, warfarin

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: Narges Sadat Myrmousavy, Maryam Eslampanah

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The aim of this study was to investigate the relationship between social capital and knowledge sharing is the Ministry of Culture and Islamic Guidance. They are descriptive correlation study. The study sample consisted of all the experts in the Ministry of Culture and Islamic Guidance helping professionals headquarters in Tehran in the summer period is 2012, the number is 650. Random sampling is targeted. The sample size is 400. The data collection tool was a questionnaire that was used for the preparation of a standard questionnaire. They also examine the assumptions of the regression coefficient for the relationship between variables in order to investigate the main hypothesis test is used. The findings suggest that the structural and knowledge-sharing between components, there is a direct relationship. The components of the relationship between Impression management and knowledge sharing, there is a direct relationship. There was no significant relationship between Individual pro-social motives and knowledge sharing. Both components of the cognitive aspects of open mindedness and competence are directly related with knowledge sharing. Finally, the comparison between the different dimensions of social capital, the largest of its structure, and its relationship with knowledge sharing is the least relation.

Keywords: social capital, knowledge sharing, ministry of culture and Islamic guidance (Iran), open mindedness, pro-social motives

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Authors: Hiesik Kim, Yong-Beom Kim, Jaheon Gu

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Internet of Things (IoT) applications are widely serviced and spread worldwide. Local wireless data transmission technique must be developed to rate up with some technique. Bluetooth wireless data communication is wireless technique is technique made by Special Inter Group (SIG) using the frequency range 2.4 GHz, and it is exploiting Frequency Hopping to avoid collision with a different device. To implement experiment, equipment for experiment transmitting measured data is made by using Arduino as open source hardware, gas sensor, and Bluetooth module and algorithm controlling transmission rate is demonstrated. Experiment controlling transmission rate also is progressed by developing Android application receiving measured data, and controlling this rate is available at the experiment result. It is important that in the future, improvement for communication algorithm be needed because a few error occurs when data is transferred or received.

Keywords: Arduino, Bluetooth, gas sensor, IoT, transmission

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Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

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Authors: Adewole A. Ayoade, Marshall R. Sweatt, John P. H. Steele, Qi Han, Khaled Al-Wahedi, Hamad Karki, William A. Yearsley

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Understanding the behavioral characteristics of sensors is a crucial step in fusing data from several sensors of different types. This paper introduces a practical, real-time approach to integrate heterogeneous sensor data to achieve higher accuracy than would be possible from any one individual sensor in localizing a mobile robot. We use this approach in both indoor and outdoor environments and it is especially appropriate for those environments like oil and gas refineries due to their sparse and featureless nature. We have studied the individual contribution of each sensor data to the overall combined accuracy achieved from the fusion process. A Sequential Update Extended Kalman Filter(EKF) using validation gates was used to integrate GPS data, Compass data, WiFi data, Inertial Measurement Unit(IMU) data, Vehicle Velocity, and pose estimates from Fiducial marker system. Results show that the approach can enable a mobile robot to navigate autonomously in any environment using a priori information.

Keywords: inspection mobile robot, navigation, sensor fusion, sequential update extended Kalman filter

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Authors: Juliet Ramohai

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This paper explores how women departmental heads in higher education experience leadership in a time of the COVID-19 crises. The focus is mostly on their care and coping as they work in virtual spaces. Most scholars have looked at the effects and challenges that different employees face while working from home during a lockdown. However, very few take a dedicated focus on women in leadership and the coping mechanisms and resources that they use for effective leadership during this difficult time. The paper draws on two aspects of Sen’s Capabilities approach, functionings, and agency, to cast a closer understanding of the institutional and individual coping mechanisms that might be at these women's disposal. The qualitative approach used for this paper and a feminist lens provides a critical and in-depth understanding of the real-life stories of the women and how they make sense of their virtual leadership. Data for this paper was collected through semi-structured interviews with 10 women in the positions of head of departments and analysed thematically using capabilities approach concepts as an analytical tool. The findings in this paper indicate that functionings and freedoms are tightly linked to institutional ethnographies. These ethnographies might support or hamper coping for women leaders, especially during times of crisis.

Keywords: capability approach, women leaders, higher education, COVID-19

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Authors: Muhammed Jamiu Mustapha, Jamiu Folarin, Stephen Obiri Agyei, Rasheed Ademola Adebiyi, Mutiu Iyanda Lasisi

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The role of information in societal development and growth cannot be over-emphasized. It has remained an age-long strategy to adopt the information flow to make an egalitarian society. The same has become a tool for throwing society into chaos and anarchy. It has been adopted as a weapon of war and a veritable instrument of psychological warfare with a variety of uses. That is why some scholars posit that information could be deployed as a weapon to wreak “Mass Destruction" or promote “Mass Development". When used as a tool for destruction, the effect on society is like an atomic bomb which when it is released, pollutes the air and suffocates the people. Technological advancement has further exposed the latent power of information and many societies seem to be overwhelmed by its negative effect. While information remains one of the bedrock of democracy, the information ecosystem across the world is currently facing a more difficult battle than ever before due to information pluralism and technological advancement. The more the agents involved try to combat its menace, the difficult and complex it is proving to be curbed. In a region like Africa with dangling democracy enfolds with complexities of multi-religion, multi-cultures, inter-tribes, ongoing issues that are yet to be resolved, it is important to pay critical attention to the case of information disorder and find appropriate ways to curb or mitigate its effects. The media, being the middleman in the distribution of information, needs to build capacities and capabilities to separate the whiff of misinformation and disinformation from the grains of truthful data. From quasi-statistical senses, it has been observed that the efforts aimed at fighting information pollution have not considered the built resilience of media organisations against this disorder. Apparently, the efforts, resources and technologies adopted for the conception, production and spread of information pollution are much more sophisticated than approaches to suppress and even reduce its effects on society. Thus, this study seeks to interrogate the phenomenon of information pollution and the capabilities of select media organisations in Sub-Saharan Africa. In doing this, the following questions are probed; what are the media actions to curb the menace of information pollution? Which of these actions are working and how effective are they? And which of the actions are not working and why they are not working? Adopting quantitative and qualitative approaches and anchored on the Dynamic Capability Theory, the study aims at digging up insights to further understand the complexities of information pollution, media capabilities and strategic resources for managing misinformation and disinformation in the region. The quantitative approach involves surveys and the use of questionnaires to get data from journalists on their understanding of misinformation/disinformation and their capabilities to gate-keep. Case Analysis of select media and content analysis of their strategic resources to manage misinformation and disinformation is adopted in the study while the qualitative approach will involve an In-depth Interview to have a more robust analysis is also considered. The study is critical in the fight against information pollution for a number of reasons. One, it is a novel attempt to document the level of media capabilities to fight the phenomenon of information disorder. Two, the study will enable the region to have a clear understanding of the capabilities of existing media organizations to combat misinformation and disinformation in the countries that make up the region. Recommendations emanating from the study could be used to initiate, intensify or review existing approaches to combat the menace of information pollution in the region.

Keywords: disinformation, information pollution, misinformation, media capabilities, sub-Saharan Africa

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Authors: Nicodemus Tiendem, Arrey Mbayong Napoleon

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The heightening of competition and the quest for market share has left business persons and research communities re-examining and reinventing their competitive practices. A case in point is Porter’s generic strategy which has received a lot of criticism lately regarding its inability to maintain a company’s competitive power. This is because it focuses more on the organisation and ignores her external partners, who have a strong bearing on the company’s performance. This paper, therefore, sought to examine Porter’s generic strategies alongside supply chain quality management practices in terms of their effectiveness in building the competitive power of manufacturing companies in Cameroon. This was done with the use of primary data captured from a survey study across the supply chains of 20 manufacturing companies in Cameroon using a five-point Likert scale questionnaire. For each company, four 1st tier suppliers and four 1st tier distributors were carefully chosen to participate in the study alongside the companies themselves. In each case, attention was directed to persons involved in the supply chains of the companies. This gave a total of 180 entities comprising the supply chains of the 20 manufacturing companies involved in the study, making a total of 900 participants. The data was analysed using three multiple regression models to assess the effect of Porter’s generic strategy and supply chain quality management on the marketing performance of the companies. The findings proved that in such a competitive atmosphere, supply chain quality management is a better tool for marketing performance over Porter’s generic strategies and hence building the competitive power of the companies at all levels of the study. Although the study made use of convenience sampling, where sample selectivity biases the results, the findings aligned with many other recent developments in line with building the competitive power of manufacturing companies and thereby made the findings suitable for generalisation.

Keywords: supply chain quality management, Porter’s generic strategies, competitive power, marketing performance, manufacturing companies, Cameroon

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Authors: Abrar Hussain Qureshi

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Vocabulary has been highlighted as a key indicator in any foreign language learning program, especially English as a foreign language (EFL). It is often considered a potential tool in foreign language curriculum, and its deficiency impedes successful communication in the target language. The knowledge of the lexicon is very significant in getting communicative competence and performance. Nouns constitute a considerable bulk of English vocabulary. Rather, they are the bones of the English language and are the main semantic carrier in spoken and written discourse. As nouns dominate the bulk of the English lexicon, their role becomes all the more potential. The undertaken research is a systematic effort in this regard to work out a list of highly frequent list of Pakistani English nouns for the EFL learners at the secondary level. It will encourage autonomy for the EFL learners as well as will save their time. The corpus used for the research has been developed locally from leading English newspapers of Pakistan. Wordsmith Tools has been used to process the research data and to retrieve word list of frequent Pakistani English nouns. The retrieved list of core Pakistani English nouns is supposed to be useful for English language learners at the secondary level as it covers a wide range of speech events.

Keywords: corpus, EFL, frequency list, nouns

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Authors: Q. Giraud, J. Gonçalvès, B. Paris

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Remediation of dense non-aqueous phase liquids (DNAPLs) represents a challenging issue because of their persistent behaviour in the environment. This pilot-scale study investigates, by means of in situ experiments and numerical modelling, the feasibility of the pulsed pumping process of a large amount of a DNAPL in an alluvial aquifer. The main compound of the DNAPL is hexachlorobutadiene, an emerging organic pollutant. A low-permeability keyed enclosure was built at the location of the DNAPL source zone in order to isolate a finite undisturbed volume of soil, and a 3-month pulsed pumping process was applied inside the enclosure to exclusively extract the DNAPL. The water/DNAPL interface elevation at both the pumping and observation wells and the cumulated pumped volume of DNAPL were also recorded. A total volume of about 20m³ of purely DNAPL was recovered since no water was extracted during the process. The three-dimensional and multiphase flow simulator TMVOC was used, and a conceptual model was elaborated and generated with the pre/post-processing tool mView. Numerical model consisted of 10 layers of variable thickness and 5060 grid cells. Numerical simulations reproduce the pulsed pumping process and show an excellent match between simulated, and field data of DNAPL cumulated pumped volume and a reasonable agreement between modelled and observed data for the evolution of the water/DNAPL interface elevations at the two wells. This study offers a new perspective in remediation since DNAPL pumping system optimisation may be performed where a large amount of DNAPL is encountered.

Keywords: dense non-aqueous phase liquid (DNAPL), hexachlorobutadiene, in situ pulsed pumping, multiphase flow, numerical modelling, porous media

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Authors: Salman Naseer

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One of the main challenges of operating a smart city (SC) is collecting the massive data generated from multiple data sources (DS) and to transmit them to the control units (CU) for further data processing and analysis. These ever-increasing data demands require not only more and more capacity of the transmission channels but also results in resource over-provision to meet the resilience requirements, thus the unavoidable waste because of the data fluctuations throughout the day. In addition, the high energy consumption (EC) and carbon discharges from these data transmissions posing serious issues to the environment we live in. Therefore, to overcome the issues of intensive EC and carbon emissions (CE) of massive data dissemination in Smart Cities, we propose an energy efficient and carbon reduction approach by utilizing the daily mobility of the existing vehicles as an alternative communications channel to accommodate the data dissemination in smart cities. To illustrate the effectiveness and efficiency of our approach, we take the Auckland City in New Zealand as an example, assuming massive data generated by various sources geographically scattered throughout the Auckland region to the control centres located in city centre. The numerical results show that our proposed approach can provide up to 5 times lower delay as transferring the large volume of data by utilizing the existing daily vehicles’ mobility than the conventional transmission network. Moreover, our proposed approach offers about 30% less EC and CE than that of conventional network transmission approach.

Keywords: smart city, delay tolerant network, infrastructure offloading, opportunistic network, vehicular mobility, energy consumption, carbon emission

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Authors: Tonga Agadi Danladi, Ajao Wasiu Bamidele, Terdue Dyeko

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Recent advancement in microwave communications technologies especially in telecommunications and broadcasting have resulted in congestion on the frequencies below 10GHz. This has forced microwave designers to look for high frequencies. Unfortunately for frequencies greater than 10GHz rain becomes one of the main factors of attenuation in signal strength. At frequencies from 10GHz upwards, rain drop sizes leads to outages that compromises the availability and quality of service this making it a critical factor in satellite link budget design. Rain rate and rain attenuation predictions are vital steps to be considered when designing microwave satellite communication link operating at Ku-band frequencies (112-18GHz). Unreliable rain rates data in the tropical regions of the world like Nigeria from radio communication group of the international Telecommunication Union (ITU-R) makes it difficult for microwave engineers to determine a realistic rain margin that needs to be accommodated in satellite link budget design in such region. This work presents an empirical tool for predicting the amount of signal due to rain on DBS signal operating at the Ku-band.

Keywords: attenuation, Ku-Band, microwave communication, rain rates

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Authors: Truong H. Le, Ngoc M. To, Quang N. Tran, Luu T. Cao, Chi V. Le

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Introduction: In Vietnam, the current monitoring and treatment for ordinary diabetic patient mostly based on glucose monitoring with HbA1c test for every three months (recommended goal is HbA1c < 6.5%~7%). For diabetes in pregnant women or Gestational diabetes mellitus (GDM), glycemic control until the time of delivery is extremly important because it could reduce significantly medical implications for both the mother and the child. Besides, GDM requires continuos glucose monitoring at least every two weeks and therefore an alternative marker of glycemia for short-term control is considering a potential tool for the healthcare providers. There are published studies have indicated that the glycosylated serum protein is a better indicator than glycosylated hemoglobin in GDM monitoring. Based on the actual practice in Vietnam, this study was designed to evaluate the role of serum fructosamine as a monitoring tool in GDM treament and its correlations with fasting blood glucose (G0), 2-hour postprandial glucose (G2) and glycosylated hemoglobin (HbA1c). Methods: A cohort study on pregnant women diagnosed with GDM by the 75-gram oralglucose tolerance test was conducted at Endocrinology Department, Cho Ray hospital, Vietnam from June 2014 to March 2015. Cho Ray hospital is the final destination for GDM patient in the southern of Vietnam, the study population has many sources from other pronvinces and therefore researchers belive that this demographic characteristic can help to provide the study result as a reflection for the whole area. In this study, diabetic patients received a continuos glucose monitoring method which consists of bi-weekly on-site visit every 2 weeks with glycosylated serum protein test, fasting blood glucose test and 2-hour postprandial glucose test; HbA1c test for every 3 months; and nutritious consultance for daily diet program. The subjects still received routine treatment at the hospital, with tight follow-up from their healthcare providers. Researchers recorded bi-weekly health conditions, serum fructosamine level and delivery outcome from the pregnant women, using Stata 13 programme for the analysis. Results: A total of 500 pregnant women was enrolled and follow-up in this study. Serum fructosamine level was found to have a light correlation with G0 ( r=0.3458, p < 0.001) and HbA1c ( r=0.3544, p < 0.001), and moderately correlated with G2 ( r=0.4379, p < 0.001). During study timeline, the delivery outcome of 287 women were recorded with the average age of 38.5 ± 1.5 weeks, 9% of them have macrosomia, 2.8% have premature birth before week 35th and 9.8% have premature birth before week 37th; 64.8% of cesarean section and none of them have perinatal or neonatal mortality. The study provides a reference interval of serum fructosamine for GDM patient was 112.9 ± 20.7 μmol/dL. Conclusion: The present results suggests that serum fructosamine is as effective as HbA1c as a reflection of blood glucose control in GDM patient, with a positive result in delivery outcome (0% perinatal or neonatal mortality). The reference value of serum fructosamine measurement provided a potential monitoring utility in GDM treatment for hospitals in Vietnam. Healthcare providers in Cho Ray hospital is considering to conduct more studies to test this reference as a target value in their GDM treatment and monitoring.

Keywords: gestational diabetes mellitus, monitoring tool, serum fructosamine, Vietnam

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: Alexis D'Agostino, Celeste Sununtunasuk, Jack Fiedler

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Background: Iron-folic acid (IFA) supplementation during antenatal care (ANC) has existed in many countries for decades. Despite this, low national coverage persists and women do not often consume appropriate amounts during pregnancy. USAID’s SPRING Project investigated pregnant women’s access to, and consumption of, IFA tablets through ANC. Cross-country analysis provided a global picture of the state of IFA-supplementation, while country-specific results noted key contextual issues, including geography, wealth, and ANC attendance. The analysis can help countries prioritize strategies for systematic performance improvements within one of the most common micronutrient supplementation programs aimed at reducing maternal anemia. Methodology: Using falter point analysis on Demographic and Health Survey (DHS) data collected from 162,958 women across 22 countries, SPRING identified four sequential falter points (ANC attendance, IFA receipt or purchase, IFA consumption, and number of tablets taken) where pregnant women fell out of the IFA distribution structure. SPRING analyzed data on IFA intake from DHS surveys with women of reproductive age. SPRING disaggregated these data by ANC participation during the most recent pregnancy, residency, and women’s socio-economic status. Results: Average sufficient IFA tablet use across all countries was only eight percent. Even in the best performing countries, only about one-third of pregnant women consumed 180 or more IFA tablets during their most recent pregnancy. ANC attendance was an important falter point for a quarter of women across all countries (with highest falter rates in Democratic Republic of the Congo, Nigeria, and Niger). Further analysis reveals patterns, with some countries having high ANC coverage but low IFA provision during ANC (DRC and Haiti), others having high ANC coverage and IFA provision but few women taking any tablets (Nigeria and Liberia), and countries that perform well in ANC, supplies, and initial consumption but where very few women consume the recommended 180 tablets (Malawi and Cambodia). Country-level analysis identifies further patterns of supplementation. In Indonesia, for example, only 62% of women in the poorest quintile took even one IFA tablet, while 86% of the wealthiest women did. This association between socioeconomic status and IFA intake held across nearly all countries where these data are available and was also visible in rural/urban comparisons. Analysis of ANC attendance data also suggests that higher numbers of ANC visits are associated with higher tablet intake. Conclusions: While it is difficult to disentangle which specific aspects of supply or demand cause the low rates of consumption, this tool allows policy-makers to identify major bottlenecks to scaling-up IFA supplementation during ANC. In turn, each falter point provides possible explanations of program performance and helps strategically identify areas for improved IFA supplementation. For example, improving the delivery of IFA supplementation in Ethiopia relies on increasing access to ANC, but also on identifying and addressing program gaps in IFA supply management and health workers’ practices in order to provide quality ANC services. While every country requires a customized approach to improving IFA supplementation, the multi-country analysis conducted by SPRING is a helpful first step in identifying country bottlenecks and prioritizing interventions.

Keywords: iron and folic acid, supplementation, antenatal care, micronutrient

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Authors: Hyun-Woo Cho

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Automatic process monitoring schemes are designed to give early warnings for unusual process events or abnormalities as soon as possible. For this end, various techniques have been developed and utilized in various industrial processes. It includes multivariate statistical methods, representation skills in reduced spaces, kernel-based nonlinear techniques, etc. This work presents a nonlinear empirical monitoring scheme for batch type production processes with incomplete process measurement data. While normal operation data are easy to get, unusual fault data occurs infrequently and thus are difficult to collect. In this work, noise filtering steps are added in order to enhance monitoring performance by eliminating irrelevant information of the data. The performance of the monitoring scheme was demonstrated using batch process data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: batch process, monitoring, measurement, kernel method

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Authors: Mohammed M. Algaradah

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A chelating mercapto- nanoscavenger has been developed exploiting the high surface area of monodisperse nano-sized mesoporous silica. The nanoscavenger acts as a solid phase trace metal extractant whilst suspended as a quasi-stable sol in aqueous samples. This mode of extraction requires no external agitation as the particles move naturally through the sample by Brownian motion, convection and slow sedimentation. Careful size selection enables the nanoscavenger to be easily recovered together with the extracted analyte by conventional filtration or centrifugation. The research describes the successful attachment of chelator mercapto to ca. 136 ± 15 nm high surface area (BET surface area = 1006 m2 g-1) mesoporous silica particles. The resulting material had a copper capacity of ca. 1.34 ± 0.10 mmol g-1 and was successfully applied to the collection of a trace element from water. Essentially complete recovery of Cu (II) has been achieved from freshwater samples giving typical preconcentration factors of 100 from 50 µg/l samples. Data obtained from a nanoscavenger-based extraction of copper from samples were not significantly different from those obtained by using a conventional colorimetric procedure employing complexation/solvent extraction.

Keywords: nano scavenger, mesoporous silica, trace metal, preconcentration

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Authors: S. Murr, S. Phillips

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One of the biggest challenges entering a market with a carsharing or any other shared mobility (SM) service is sound investment decision-making. To support this process, the authors think that a city index evaluating different criteria is necessary. The goal of such an index is to benchmark cities along a set of external measures to answer the main two challenges: financially viability and the understanding of its specific requirements. The authors have consulted several shared mobility projects and industry experts to create such a Shared Mobility City Index (SMCI). The current proposal of the SMCI consists of 11 individual index measures: general data (demographics, geography, climate and city culture), shared mobility landscape (current SM providers, public transit options, commuting patterns and driving culture) and political vision and goals (vision of the Mayor, sustainability plan, bylaws/tenders supporting SM). To evaluate the suitability of the index, 16 cities on the East Coast of North America were selected and secondary research was conducted. The main sources of this study were census data, organisational records, independent press releases and informational websites. Only non-academic sources where used because the relevant data for the chosen cities is not published in academia. Applying the index measures to the selected cities resulted in three major findings. Firstly, density (city area divided by number of inhabitants) is not an indicator for the number of SM services offered: the city with the lowest density has five bike and carsharing options. Secondly, there is a direct correlation between commuting patterns and how many shared mobility services are offered. New York, Toronto and Washington DC have the highest public transit ridership and the most shared mobility providers. Lastly, except one, all surveyed cities support shared mobility with their sustainability plan. The current version of the shared mobility index is proving a practical tool to evaluate cities, and to understand functional, political, social and environmental considerations. More cities will have to be evaluated to refine the criteria further. However, the current version of the index can be used to assess cities on their suitability for shared mobility services and will assist investors deciding which city is a financially viable market.

Keywords: carsharing, transportation, urban planning, shared mobility city index

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