Search results for: boosted

Authors: S. Abdulsalam, A. D. I. Suleiman, N. M. Musa, M. Yusuf

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Environment free of pollutants should be the concern of every individual but with industrialization and urbanization it is difficult to achieve. In view of achieving a pollution limited environment at low cost, a study was conducted on the use of bioremediation technology to remediate hydrocarbons and three heavy metals namely; copper (Cu), zinc (Zn) and iron (Fe) from a typical petroleum refinery wastewater in a closed system. Physicochemical and microbiological characteristics on the wastewater sample revealed that it was polluted with the aforementioned pollutants. Isolation and identification of microorganisms present in the wastewater sample revealed the presence of Bacillus subtilis, Micrococcus luteus, Staphylococcus aureus and Staphylococcus epidermidis. Bioremediation experiments carried out on five batch reactors with different compositions but at same environmental conditions revealed that treatment T5 (boosted with the association of Bacillus subtilis, Micrococcus luteus) gave the best result in terms of oil and grease content removal (i.e. 67% in 63 days). In addition, these microorganisms were able of reducing the concentrations of heavy metals in the sample. Treatments T5, T3 (boosted with Bacillus subtilis only) and T4 (boosted with Micrococcus luteus only) gave optimum percentage uptakes of 65, 75 and 25 for Cu, Zn and Fe respectively.

Keywords: boosted, bioremediation, closed system, aeration, uptake, wastewater

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Esraa Mohamed El-Fawal

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A facile solvothermal procedure was applied to fabricate zinc ferrite nanoparticles (ZnFe₂O₄ NPs). Activated carbon (AC) derived from peanut shells is synthesized using a microwave through the chemical activation method. The ZnFe₂O₄-AC composite is then mixed with a cerium-based metal-organic framework (CeMOF) by solid-state adding to formulate ZnFe₂O₄-AC/CeMOF composite. The synthesized photo materials were tested by scanning/transmission electron microscope (SEM/TEM), Photoluminescence (PL), (XRD) X-Ray diffraction, (FTIR) Fourier transform infrared, (UV-Vis/DRS) ultraviolet-visible/diffuse reflectance spectroscopy. The prepared ZnFe₂O₄-AC/CeMOFphotomaterial shows significantly boosted efficiency for photodegradation of methyl orange /methylene blue (MO/MB) compared with the pristine ZnFe₂O₄ and ZnFe₂O₄-AC composite under the irradiation of visible-light. The favorable ZnFe₂O₄-AC/CeMOFphotocatalyst displays the highest photocatalytic degradation efficiency of MB/MO (R: 91.5-88.6%, consecutively) compared with the other as-prepared materials after 30 min of visible-light irradiation. The apparent reaction rate K: 1.94-1.31 min-1 is also calculated. The boosted photocatalytic proficiency is ascribed to the heterojunction at the interface of prepared photo material that assists the separation of the charge carriers. To reach optimization, statistical analysis using response surface methodology was applied. The effect of independent parameters (such as A (pH), B (irradiation time), and (c) initial pollutants concentration on the response function (%)photodegradation of MB/MO dyes (as examples of azodyes) was investigated via using central composite design. At the optimum condition, the photodegradation efficiency (%) of the MB/MO is 99.8-97.8%, respectively. ZnFe2O₄-AC/CeMOF hybrid reveals good stability over four consecutive cycles.

Keywords: azo-dyes, photo-catalysis, zinc ferrite, response surface methodology

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Jill Steel

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Introduction: Animal-assisted learning (AAL) interventions are increasing exponentially, yet a paucity of quality research in the field exists. The aim of this study was to evaluate how the promotion of emotional wellbeing, through AAL, in this case, a dog, may support children’s engagement with reading in a Primary 1 classroom. Research indicates that dogs can provide emotional support to children; by forming a trusting attachment with a non-critical ‘friend’ who confers unconditional positive regard on the child, confidence may be boosted and anxiety reduced. By promoting emotional wellbeing through interactions with the dog, it is hoped that children begin to associate reading with feelings of wellbeing, which then results in increased engagement with reading. Methodology: A review of the literature was conducted. The relationship between emotional wellbeing and learning was explored, followed by an examination of the literature relating to Animal-Assisted Therapy and AAL. Scottish educational policy and legislation were analysed to establish the extent to which AAL might be suitable for the Scottish pedagogical context. An empirical study was conducted in a mainstream Primary 1 classroom over a four-week period. An inclusive approach was adopted whereby all children that wanted to interact with the dog were given the opportunity to do so, and all 25 children subsequently chose to participate. Children were not withdrawn from the classroom. Primary methods included interviews, observations, and questionnaires. Three focus children were selected for closer study. Main Results: Results were remarkably close to previous research and literature. Children’s emotional wellbeing was boosted, and engagement in reading improved. Principal Conclusions and Implications for Field: It was concluded that AAL could support emotional wellbeing and, in turn, promote children’s engagement with reading. The main limitation of the study was its short-term nature, and a longer randomised controlled trial with a larger sample, currently being undertaken by the author, would provide a fuller answer to the research question. Barriers to AAL include health and safety concerns and steps to ensure the welfare of the dog.

Keywords: animal-assisted learning, emotional wellbeing, reading, reading to dogs

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Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

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Authors: Rajat Kapoor

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India is a developing country and has experienced a rapid and tumultuous urban growth in the 20th century. The total urban population of the city increased ten-fold between 1901 and 2001. The share of urban population to the total population increased from less than 11 percent to over 28 percent in the same period. Except few examples, most of the Indian cities have grown in a haphazard manner; concentration of population followed by the planning exercises. In this era of global competitiveness and rapid urbanization there is no scope for malpractices in development strategies. It is expected that the Indian cities shall be planned comprehensively and holistically. The study reveals the land transformations the city of Faridabad is witnessing due to development which is largely boosted by the virtue of its location in the Delhi NCR.

Keywords: Delhi NCR, Faridabad, urban growth patterns, India

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Authors: Abigail Mosetsanagape Mooketsi, Itumeleng Pleasure Mongale, Joel Hinaunye Eita

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The aim of this study is to analyse the impact of transport infrastructure on economic growth in South Africa through Engle Granger two step approach using the data from 1970 to 2013. GDP is used as a proxy for economic growth whilst rail transport (rail lines, rail goods transported) and air transport(air passengers carried, air freight) are used as proxies for transport infrastructure. The results showed that there is a positive long-run relationship between transport infrastructure and economic growth. The results show that South Africa’s economic growth can be boosted by providing transport infrastructure. The estimated models were simulated and the results that the model is a good fit. The findings of this research will be beneficial to policy makers, academics and it will also enhance the ability of the investors to make informed decisions about investing in South Africa.

Keywords: transport, infrastructure, economic growth, South Africa

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Authors: Gauravi Agarkar, Mahendra Rai

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Fungi are closely associated with the plants in various types of interactions such as mycorrhizal, parasitic or endophytic. Some of these interactions are beneficial and a few are harmful to the host plants. It has been suggested that these plant-associated fungi are able to change their lifestyle abd this means endophyte may become parasite or vice versa. This phenomenon may have profound effect on plant-fungal interactions and various ecological niches. Therefore, it is necessary to identify the factors that trigger the change in fungal lifestyle and understand whether these different lifestyles are interconnected at some points either by physiological, biochemical or molecular routes. This review summarizes the factors affecting plant fungal interactions and discusses the possible mechanisms for lifestyles switching of fungi based on available experimental evidences. Research should be boosted in this direction to fetch more advantages in future and to avoid the severe consequences in agriculture and other related fields.

Keywords: endophytic, lifestyle switching, mycorrhizal, parasitic, plant-fungal interactions

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Authors: Egboja Simon Oga

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One of the most important keys to the success of a nation is to ensure steady development and national economic self-sufficiency and independence. It is therefore in this regard that this paper is designed to identify food security to be crucial to all nations’ effort toward sustainable national development. Nigeria as a case study employed various effort by the successive government towards food security. Emphasis were placed on the extent to which government has boosted food security situation on the basis of the identified limitations, conclusion was drawn, recommendation/suggestions proffered, that subsidization of the process of farm inputs like fertilizer, improved seeds and agrochemical, education of farmers on modern methods of farming through extension services, improvisation of village-based food storage mechanism and provision of infrastructural facilities in rural areas to facilitate the preservation and easy evacuation of farm produces are necessary.

Keywords: food, governance, development, security

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Authors: Alpana Agarwal, Akhil Sharma

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This work presents a fully differential CMOS amplifier consisting of two self-biased gain boosted inverter stages, that provides an alternative to the power hungry operational amplifier. The self-biasing avoids the use of external biasing circuitry, thus reduces the die area, design efforts, and power consumption. In the present work, regulated cascode technique has been employed for gain boosting. The Miller compensation is also applied to enhance the phase margin. The circuit has been designed and simulated in 1.8 V 0.18 µm CMOS technology. The simulation results show a high DC gain of 100.7 dB, Unity-Gain Bandwidth of 107.8 MHz, and Phase Margin of 66.7^o with a power dissipation of 286 μW and makes it suitable candidate for the high resolution pipelined ADCs.

Keywords: CMOS amplifier, gain boosting, inverter-based amplifier, self-biased inverter

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

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Authors: Priyadarshini Alok

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The present situation in India envisages that because of the surge in population and the economy, cities are expected to spill over to hinterland areas. The consumption-led factors such as land, labor, etc. will be boosted. Hence, the need for regional connectivity becomes obligatory. But, there is enormous pressure upon the land; proving itself through rising traffic congestion, roads, and railway accidents. Air transport is practical, but due to decreasing availability of land, this will not be a wise solution. What with the introduction of seaplanes in the country which was once the vital asset in the world prior to Second World War. Maldives has proved it. Seaplanes offer natural landing site and are time and cost-efficient. Seaplanes in accordance with UDAN can prove to be the solution in linking various regions with other states. This research paper aims to offer the feasibility analysis along with site justification of the potential areas in the state of Andhra Pradesh, India; for the operation of seaplanes. The standards are taken from the US Department of Transportation, Federal Aviation Administration for the analysis. The conflation of Seaplanes with UDAN will offer an alternate mode of air connectivity, strengthen the transport network by simulation of connectivity to unserved and under-served areas and boost the nation's economy.

Keywords: connectivity, seaplanes, transport, UDAN

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Authors: Erfan Ghasemi, Mohammadreza Khani, Hamidreza Mahmoudi, Mohammad Ali Nilforoushzadeh, Babak Shokri, Pouria Akbartehrani

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Given the global significance of treating infectious wounds, the goal of this study is to use spark plasma as a new treatment for infectious wounds. To generate spark plasma, a high-voltage (7 kV) and high-frequency (75 kHz) source was used. Infectious wounds in the peritoneum of mice were divided into control and plasma-treated groups at random. The plasma-treated animals received plasma radiation every 4 days for 12 days, for 60 seconds each time. On the 15th day after the first session, the wound in the plasma-treated group had completely healed. The spectra of spark plasma emission and tissue properties were studied. The mechanical resistance of the wound healed in the plasma treatment group was considerably higher than in the control group (p<0.05), according to the findings. Furthermore, histological evidence suggests that wound re-epithelialization is faster in comparison to controls. Angiogenesis and fibrosis (collagen production) were also dramatically boosted in the plasma-treated group, whereas the stage of wound healing inflammation was significantly reduced. Plasma therapy accelerated wound healing by causing considerable wound constriction. The results of this investigation show that spark plasma has an influence on the treatment of infectious wounds.

Keywords: infectious wounds, mice, spark plasma, treatment

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Authors: Weili Shao, Qian Wang, Jianxin He

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Recently, the applications of graphene oxide in fabricating scaffolds for bone tissue engineering have been received extensive concern. In this work, poly l-lactic/graphene oxide composite nanofibers were successfully fabricated by electrospinning. The morphology structure, porosity and mechanical properties of the composite nanofibers were characterized using different techniques. And mouse mesenchymal stem cells were cultured on the composite nanofiber scaffolds to assess their suitability for bone tissue engineering. The results indicated that the composite nanofiber scaffolds had finer fiber diameter and higher porosity as compared with pure poly l-lactic nanofibers. Furthermore, incorporation of graphene oxide into the poly l-lactic nanofibers increased protein adsorptivity, boosted the Young’s modulus and tensile strength by nearly 4.2-fold and 3.5-fold, respectively, and significantly enhanced adhesion, proliferation, and osteogenesis in mouse mesenchymal stem cells. The results indicate that composite nanofibers could be excellent and versatile scaffolds for bone tissue engineering.

Keywords: poly l-lactic, graphene oxide, osteogenesis, bone tissue engineering

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Authors: Diogo de Carvalho Cabral

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This article offers a non-declensionist account of tropical deforestation, arguing that, rather than social stamp upon the environment or ecological endgame, deforestation is part of social site-making and remaking, the process through which humans produce sociality by carrying out nature-mediated – and therefore nature-transforming – practices that inevitably reset the very conditions of those practices. Human landscape-shaping inadvertently alters other species’ habitats –most often decimating them, but sometimes improving them–, the outcomes of which always resonate back upon human inhabitation and land use. Despite the overall tendency of biotic homogenization resulting from modern deforestation processes, there are always winners, i.e., species that gain competitive advantages enabling them to thrive in the novel ecosystems. Here it is examined one such case of deforestation-boosted species, namely leafcutter ants, which wrought havoc in the rural landscapes of nineteenth-century Brazil by defoliating a wide range of crops. By combining Historical GIS analysis and qualitative interpretation, it is shown how agricultural deforestation might have changed the ant species' biogeographies, and how in turn these changes – construed as 'infestation' – stimulated social innovations and rearrangements such as technical ingenuity, legal-administrative practices, and even local electoral arenas.

Keywords: deforestation, leafcutter ants, nineteenth-century Brazil, socio-ecological change

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Authors: Festus M. Epetimehin

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This study was conducted to identify the effect of National Directorate of Employment (NDE) training on the profit of Agricultural-Small and Medium Enterprises (SMEs) and to evaluate the factors that influenced farmers' participation in NDE training, as well as the type and frequency of training farmers and other agro-allied entrepreneurs in Nigeria. Using a multi-stage sampling procedure, a total of 384 respondents were sampled, including 192 beneficiaries and 192 non-beneficiaries in Oyo and Lagos States, respectively. Data were analysed using Binary Logit regression and Propensity Score Matching techniques. According to the binary logit analysis, respondents’ gender, availability to extension services, and the location of respondent’s operation were determinant factors influencing NDE training enrolment. All identified factors are related to the probability of respondents’ involvement in a positive way. Propensity score matching revealed that Agricultural-SMEs who participated in the NDE program boosted their profit by N341,072.18. The positive outcome of the effect implies that NDE training enhances Agri-SME performance in Nigeria. The study concluded that greater funding should be provided for the NDE for performance-enhancing training of the Agri-SMEs.

Keywords: PSM, binary logit model, Agri-SME

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Authors: Ngozi N. Sydney-Agbor, Chioma N. Ihegboro

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Stress and personality as factors influencing nurses’ aggressive behaviour were investigated. The participants comprised of one hundred and fifty nurses selected through convenience sampling technique from four (4) private hospitals in Imo State, Nigeria; namely: Eastern Summit Specialist Clinics and Maternity, St. David Hospital, New Cross Hospital, and Christian Teaching Hospital. The nurses were all females with ages between 20–35 and a mean age of 25.10 years and a standard deviation of 4.15. The participants were administered with Job Related Tension Scale, Type A Behaviour Scale and Buss- Perry Aggressive Behaviour Scale. Two hypotheses were postulated and tested. Cross- sectional survey and Regression Analysis were adopted as design and statistics respectively. Results showed that as stress increased, nurses aggression also increased. Personality also predicted nurses aggressive behaviour with Type As’ exhibiting higher aggression than Type Bs’.The study recommended that hospital management board should improve the welfare of the nurses and their morale should be boosted by involving them in policy-making concerning their welfare and care of their patients, this will help minimise situations capable of increasing aggressive behaviour. There should also be sensitization on the negative impact of aggressive behaviour to patients especially amongst the personality Type A’s who are more susceptible to aggression.

Keywords: aggressive behaviour, nurses, personality, stress

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Authors: Arwa Abdelhay, Inshad Jum’h, Abeer Albsoul, Khalideh Alrawashdeh, Dina Al Tarazi

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Treated wastewater reuse has been considered recently as one of the successful management strategies to overcome water shortage in countries suffering from water scarcity. The non-readily biodegradable and recalcitrant pollutants in wastewater cannot be destructed by conventional treatment methods. This paper deals with the electrochemical treatment of dairy wastewater using a promising non-conventional Boron-Doped Diamond (BDD) anode. During the electrochemical process, different operating parameters were investigated, such as electrolysis time, current density, supporting electrolyte, chemical oxygen demand (COD), turbidity as well as absorbance/color. The experimental work revealed that electrochemical oxidation carried out with no added electrolyte has significantly reduced the COD, turbidity, and color (absorbance) by 72%, 76%, and 78% respectively. Results also showed that raising the current density from 5.1 mA/cm² to 7.7 mA/cm² has boosted COD, and color removal to 82.5%, and 83% respectively. However, the current density did not show any significant effect on the turbidity. Interestingly, it was observed that adding Na₂SO₄ and FeCl₃ as supporting electrolytes brought the COD removal to 91% and 97% respectively. Likewise, turbidity and color removal has been enhanced by the addition of the same supporting electrolytes.

Keywords: boron doped-diamond anode, dairy wastewater, electrochemical oxidation, supporting electrolytes

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Miju Choi, Ava Seo

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Japan has been positioned as a major inbound market to Korea, accounting for about 31% of total inbound visitors until 2012. The percentage has sharply dropped each year since and remained in second place, reaching 13.33% in 2016. Meanwhile, China has been boosted as a major inbound market, reaching 46.79% in 2016. Chinese tourists mainly visit Korea with the major purpose of shopping. They consume Korean cosmetic/beauty products and clothes while Japanese tourists prefer to purchase healthy food such as ginseng and seaweed. This study aims to investigate and compare tourist shopping patterns across two major inbound markets, China and Japan. A quantitative approach using survey was applied from 2009 to 2016. Findings suggest Chinese visit Korea due to quality of product, value for money, and accessibility, and trust. Meanwhile, Japanese choose Korea as a shopping destination mainly due to convenience, affordability, and tourist attractions. Also, there were significant differences in shopping venues. For example, Japanese tourists prefer shopping at department stores while Chinese tourists prefer retail outlets and local markets. This study contributes to deeper understanding on two major inbound markets to Korea and suggests future marketing strategies.

Keywords: tourist shopping patterns, Korea, China, Japan, historical data

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Authors: Boyu Li

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Mixed matrix membranes (MMMs), as a separation technology, can improve CO₂ recycling efficiency and reduce the environmental impacts associated with huge emissions. Nevertheless, many challenges must be overcome to design excellent selectivity and permeability performance MMMs. Herein, this work demonstrates the design of nano-scale GNPs (Cu-BDC@PEG) with strong compatibility and high free friction volume (FFV) is an effective way to construct non-interfacial voids MMMs with a desirable combination of selectivity and permeability. Notably, the FFV boosted thanks to the chain length and shape of the GNPs. With this, the permeability and selectivity of Cu-BDC@PEG/PVDF MMMs had also been significantly improved. As such, compatible Cu-BDC@PEG proves very efficient for resolving challenges of MMMs with poor compatibility on the basis of the interfacial defect. Poly (Ethylene Glycol) (PEG) with oxygen groups can be finely coordinated with Cu-MOFs to disperse Cu-BDC@PEG homogenously and form hydrogen bonds with matrix to achieve continuous phase. The resultant MMMs exhibited a simultaneous enhancement of gas permeability (853.1 Barrer) and ideal CO₂/N selectivity (41.7), which has surpassed Robenson's upper bound. Moreover, Cu-BDC@PEG/PVDF has a high-temperature resistance and a long time sustainably. This attractive separation performance of Cu-BDC@PEG/PVDF offered an exciting platform for the development of composite membranes for sustainable CO₂ separations.

Keywords: metal organic framework, CO₂ separation, mixed matrix membrane, polymer

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Authors: Fatima Zohra Rahou, A. Guen Bouazza, B. Bouazza

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Multi-gate SOI MOSFETs has shown better results in subthreshold performances. The replacement of SiO2 by high-k dielectric can fulfill the requirements of Multi-gate MOSFETS with a scaling trend in device dimensions. The advancement in fabrication technology has also boosted the use of different high -k dielectric materials as oxide layer at different places in MOSFET structures. One of the most important multi-gate structures is square GAA SOI MOSFET that is a strong candidate for the next generation nanoscale devices; show an even stronger control of short channel effects. In this paper, GAA SOI MOSFET structure with using high -k dielectrics materials Al2O3 (k~9), HfO2 (k~20), La2O3 (k~30) and metal gate TiN are simulated by using 3-D device simulator DevEdit and Atlas of SILVACO TCAD tools. Square GAA SOI MOSFET transistor with High-k HfO2 gate dielectrics and TiN metal gate exhibits significant improvements performances compared to Al2O3 and La2O3 dielectrics for the same structure. Simulation results of GAA SOI MOSFET transistor with HfO2 dielectric show the increase in saturation current and Ion/Ioff ratio while leakage current, subthreshold slope and DIBL effect are decreased.

Keywords: technology SOI, short-channel effects (SCEs), multi-gate SOI MOSFET, square GAA SOI MOSFET, high-k dielectric, Silvaco software

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Authors: Ai Chin Wong, Wan Ahmad Jaafar Wan Yahaya, Balakrishnan Muniandy

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This study aims to explore the impact of Virtual Reality (VR) using visual signaling principles in learning about the science laboratory safety guide; this study involves students with different spatial ability. There are two types of science laboratory safety lessons, which are Virtual Reality with Signaling (VRS) and Virtual Reality Non Signaling (VRNS). This research has adopted a 2 x 2 quasi-experimental factorial design. There are two types of variables involved in this research. The two modes of courseware form the independent variables with the spatial ability as the moderator variable. The dependent variable is the students’ performance. This study sample consisted of 141 students. Descriptive and inferential statistics were conducted to analyze the collected data. The major effects and the interaction effects of the independent variables on the independent variable were explored using the Analyses of Covariance (ANCOVA). Based on the findings of this research, the results exhibited low spatial ability students in VRS outperformed their counterparts in VRNS. However, there was no significant difference in students with high spatial ability using VRS and VRNS. Effective learning in students with different spatial ability can be boosted by implementing the Virtual Reality with Signaling (VRS) in the design as well as the development of Virtual Science Laboratory (ViSLab).

Keywords: spatial ability, science laboratory safety, visual signaling principles, virtual reality

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Authors: Zhen Li

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In contemporary China, the rise of consumer culture and feminism has facilitated the ‘She-Economy’, where Chinese women’s consumption power has been boosted along with the thriving Chinese economy. Domestic reality TV shows such as Sisters Who Make Waves (hereafter SWMW) target female audiences by bringing women's issues such as age, appearance, and balance between family and career to the discussion. Against this backdrop, this study adopted multimodal content analysis to investigate how SWMW failed to live up to the feminist goals the show had claimed and how serious women’s issues were consumed and capitalized by the consumer media culture from consumer culture and feminist perspectives. The findings reveal that while the female-themed work claims to uncover the charm that age brings to women over their thirties, it merely mentions female anxiety and uses feminism in disguise to achieve commercial success without in-depth thinking and discussion of what real-life issues women in China are tackling. They further show that the mass media-promoted modern femininity combined with consumerism deepens anxiety over aging among female audiences. The study sheds light on understanding the new development of Chinese femininity and the impact of consumer culture on feminist consciousness in contemporary China.

Keywords: consumer culture, feminism, multimodal content analysis, she-economy

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Authors: Elena Tkachenko, Vladimir Kokh, Alina Osipenko, Vladislav Surkov

Abstract:

The modern period of development of Russian economy is fraught with a number of problems related to limitations in the use of traditional planning and financial management tools. Restrictions in the use of foreign software when performing an order of the Russian Government, on the one hand, and sanctions limiting the support of the major ERP and MRP II systems in the Russian Federation, on the other hand, entail the necessity to appeal to the basics of developing budgeting and analysis systems for industrial enterprises. Thus, cost calculation theory becomes the theoretical foundation for the development of industrial cost management systems. Based on the foregoing, it would be fair to make an assumption that the development of a working managerial accounting model on an industrial enterprise using an automated enterprise resource management system should rest upon the concept of the inevitability of alterations of business processes. On the other hand, optimized business processes make the architecture of financial analytics more transparent and permit the use of all the benefits of data cubes. The metrics and indicator slices provide online assessment of the state of key business processes at a given moment of time, which improves the quality of managerial decisions considerably. Therefore, the bilateral sanctions situation boosted the development of corporate business analytics and took industrial companies to the next level of understanding of business processes.

Keywords: cost culculation, ERP, OLAP, modern Russian economy

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Authors: Bárbara Magalhães Bravo, Cláudia Figueiras

Abstract:

The economical globalization through the liberalization of the markets and capitals boosted the economical development of the nations and the needs for sorting out the disputes arising from the foreign investment. The arbitration, for all the inherent advantages, such as swiftness, arbitrators’ specialise skills and impartiality sets a pacifier tool for the interest in account. Safeguarded the public interest, we face the problem of the confidentiality in the arbitration. The urgent development of impelling mechanisms concerning transparency, guaranty and protection of the interest in account, reveals itself urgent. Through a bibliography review, we will dense the state of art, by going through the several solutions concerning, and pointing out the most suitable. Through the jurisprudential analysis we will point out the solution for the conflict confidentiality/public interest. The transparency, inextricable from the public interest, imposes the arbitration process can be open to all citizens. Transparency rules have been considered at the UNCITRAL in attempting to conciliate the necessity of publicity and the public interest, however still insufficient. The arbitration of foreign investment carries consequences to the citizens of the State. Articulating mechanisms between the arbitral procedures secrecy and the public interest should be adopted. The arbitration of foreign investment, being a tertius genius between the international arbitration and the administrative arbitration would claim its own regulation in each and every States where the confidentiality rules and its exceptions could be identified. One should enquiry where the limit of the citizens’ individual rights protection and the public interest should give way to the principle of transparency

Keywords: arbitration, foreign investment, transparency, confidenciality, International Centre for Settlement of Investment Disputes UNCITRAL

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