Search results for: predictive mining

Authors: Mohsen Rabbani, Trista McLaughlin, Ehsan Vahidi

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the remediation of polluted sites with heavy metals, such as rare earth elements (REEs), has been a primary concern of researchers to decontaminate the soil. Among all developed methods to address this concern, phytoremediation has been established as efficient, cost-effective, easy-to-use, and environmentally friendly way, providing a long-term solution for addressing this global concern. Furthermore, this technology has another great potential application in the metals production sector through returning metals buried in soil via metals cropping. Considering the significant metal concentration in hyper-accumulators, the utilization of bioaccumulated metals to extract metals from plant matter has been proposed as a sub-economic area called phytomining. As a recent, more advanced technology to eliminate such pollutants from the soil and produce critical metals, bioharvesting (phytomining/agromining) has been considered another compromising way to produce metals and meet the global demand for critical/target metals. The bio-ore obtained from phytomining can be safely disposed of or introduced to metal production pathways to obtain the most demanded metals, such as REEs. It is well-known that some hyperaccumulators, e.g., fern Dicranopteris linearis, can be used to absorb REE metals from the polluted soils and accumulate them in plant organs, such as leaves and stems. After soil remediation, the plant species can be harvested and introduced to the downstream steps, namely crushing/grinding, leaching, and purification processes, to extract REEs from plant matter. This novel interdisciplinary field can fill the gap between agriculture, mining, metallurgy, and the environment. Despite the advantages of agromining for the REEs production industry, key issues related to the environmental sustainability of the entire life cycle of this new concept have not been assessed yet. Hence, a comparative life cycle assessment (LCA) study was conducted to quantify the environmental footprints of REEs phytomining. The current LCA study aims to estimate and calculate environmental effects associated with phytomining by considering critical factors, such as climate change, land use, and ozone depletion. The results revealed that phytomining is an easy-to-use and environmentally sustainable approach to either eliminate REEs from polluted sites or produce REEs, offering a new source of such metals production. This LCA research provides guidelines for researchers active in developing a reliable relationship between agriculture, mining, metallurgy, and the environment to encounter soil pollution and keep the earth green and clean.

Keywords: phytoremediation, phytomining, life cycle assessment, environmental impacts, rare earth elements, hyperaccumulator

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Authors: Han Kuikui

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Adolescents, serving as the reserve force for technological innovation talents, possess STEM creativity that is not only pivotal to achieving STEM education goals but also provides a viable path for reforming science curricula in compulsory education and cultivating innovative talents in China. To investigate the relationship among adolescents' grit, creative self-efficacy, future time perspective, and STEM creativity, a survey was conducted in 2023 using stratified random sampling. A total of 1263 junior high school students from the main urban areas of Chongqing, from grade 7 to grade 9, were sampled. The results indicated that (1) Grit positively predicts adolescents' creative self-efficacy and STEM creativity significantly; (2) Creative self-efficacy mediates the positive relationship between grit and adolescents' STEM creativity; (3) The mediating role of creative self-efficacy is moderated by future time perspective, such that with a higher future time perspective, the positive predictive effect of grit on creative self-efficacy is more substantial, which in turn positively affects their STEM creativity.

Keywords: grit, stem creativity, creative self-efficacy, future time perspective

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Authors: Carolina A. Santos, Alexandra B. Ribeiro

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Climate change and environmental degradation are threats to humanity. Europe has been addressing these problems, namely through the Green Deal, with the use of batteries in mobility and energy fields. However, these require the use of critical raw materials, like lithium, which demand is estimated to grow 60 times in the next 30 years. Thus, it is fundamental to promote a circular economy with lithium recovery from secondary resources. These are nowadays key topics, which will be even more relevant in the future, so a new way to approach them is needed and must be encouraged. Therefore, one of our main goals is to analyse two methods of lithium retrieval from secondary sources, bioleaching, and electrodialysis, and assess them regarding their sustainability. The latest results show good efficiency of removal with both methods, even though there are some matrix interferences. Hence, further investment and research are needed in order to make this process sustainable and our society more circular.

Keywords: lithium, sustainable mining, social license to operate, bioleaching, electrodialysis

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Authors: Alireza Jalilifar, Nadia Mayahi

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The dissertation defense as a complicated conflict-prone context entails the adoption of elegant interactional strategies, one of which is self-denigration. This study aimed to develop and validate a self-denigration model that fits the context of doctoral defense sessions in applied linguistics. Two focus group discussions provided the basis for developing this conceptual model, which assumed 10 functions for self-denigration, namely good manners, modesty, affability, altruism, assertiveness, diffidence, coercive self-deprecation, evasion, diplomacy, and flamboyance. These functions were used to design a 40-item questionnaire on the attitudes of applied linguists concerning self-denigration in defense sessions. The confirmatory factor analysis of the questionnaire indicated the predictive ability of the measurement model. The findings of this study suggest that self-denigration in doctoral defense sessions is the social representation of the participants’ values, ideas and practices adopted as a negotiation strategy and a conflict management policy for the purpose of establishing harmony and maintaining resilience. This study has implications for doctoral students and academics and illuminates further research on self-denigration in other contexts.

Keywords: academic discourse, politeness, self-denigration, grounded theory, dissertation defense

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Fangtian Fan, Zhaoguo Liu, Yin Lu

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Early recurrence (ER) (< 1 year) after liver resection is one of the most important factors that impacts the prognosis of patients with hepatocellular carcinoma (HCC). However, the molecular mechanisms and predictive indexes of ER after curative resection remain largely unknown. The present study aimed to exploit the role of EGFR signaling in EMT and early recurrence of HCC after curative resection and elucidate the molecular mechanisms. Our results showed that nuclear beta-catenin / PKM2 was a independent predictor of early recurrence after curative resection in EGFR-overexpressed HCC. Mechanistic investigation indicated that nuclear accumulation of beta-catenin and PKM2 induced by EGFR signal promoted HCC cell invasion and proliferation, which were required for early recurrence of HCC. These effects were mediated by PI3K/AKT and ERK pathways rather than the canonical Wnt signaling. In conclusions, EGFR signal induced-nuclear translocation of beta-catenin and PKM2 promotes HCC malignancy and indicates early recurrence after curative resection.

Keywords: beta-catenin, early recurrence, hepatocellular carcinoma, malignancy, PKM2

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Authors: Ayoub El Bourtali, Abdessamed Najine, Amrou Moussa Benmoussa

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It is becoming more and more necessary to manage flood risk, and it must include all stakeholders and all possible means available. The goal of this work is to map the vulnerability of the Oued Derna-region Tagzirt flood zone in the semi-arid region. This is about implementing predictive models and flood control. This allows for the development of flood risk prevention plans. In this study, A resistivity survey was conducted over the area to locate and evaluate soil characteristics in order to calculate discharges and prevent flooding for the study area. The development of a one-dimensional (1D) hydrodynamic model of the Derna River was carried out in HEC-RAS 5.0.4 using a combination of survey data and spatially extracted cross-sections and recorded river flows. The study area was hit by several extreme floods, causing a lot of property loss and loss of life. This research focuses on the most recent flood events, based on the collected data, the water level, river flow and river cross-section were analyzed. A set of flood levels were obtained as the outputs of the hydraulic model and the accuracy of the simulated flood levels and velocity.

Keywords: derna river, 1D hydrodynamic model, flood modelling, HEC-RAS 5.0.4

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Authors: Nelson Barrientos, Matthew J. Davies, Marco C. Marques, Darren N. Nesbeth, Gary J. Lye, Nicolas Szita

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Microbioreactor technology is making important advances towards its application in cell culture and bioprocess development. In particular, the technology promises flexible and controllable devices capable to perform parallelised experimentation at low cost. Currently, state of the art methods (e.g. optical sensors) allow the accurate monitoring of the microbioreactor operation. In addition, the laminar flow regime encountered in these devices allows more predictive fluid dynamics modelling, improving the control over the soluble, physical and mechanical environment of the cells. This work describes the development and characterisation of a novel microbioreactor cassette system (microbioreactor volume is 150 μL. The volumetric oxygen transfer coefficient (KLa) and mixing time have been characterised to be between 25 to 113 h-1 and 0.5 and 0.1 s, respectively. In addition, the Residence time distribution (RTD) analysis confirms that the reactor operates at well mixed conditions. Finally, Staphylococcus carnosus TM300 growth is demonstrated via batch culture experiments. Future work consists in expanding the optics of the microbioreactor design to include the monitoring of variables such as fluorescent protein expression, among others.

Keywords: microbioreactor, cell-culture, fermentation, microfluidics

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Authors: Lagnajeet Mohanty, Mohnish Mishra, Pratham Tapdiya, Himanshu Sekhar Nayak, Swetapadma Singh

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In the era of global interconnectedness, effective risk assessment and management are critical for organizational resilience. This review explores the integration of machine learning (ML) into risk processes, examining its transformative potential and the challenges it presents. The literature reveals ML's success in sectors like consumer credit, demonstrating enhanced predictive accuracy, adaptability, and potential cost savings. However, ethical considerations, interpretability issues, and the demand for skilled practitioners pose limitations. Looking forward, the study identifies future research scopes, including refining ethical frameworks, advancing interpretability techniques, and fostering interdisciplinary collaborations. The synthesis of limitations and future directions highlights the dynamic landscape of ML in risk management, urging stakeholders to navigate challenges innovatively. This abstract encapsulates the evolving discourse on ML's role in shaping proactive and effective risk management strategies in our interconnected and unpredictable global landscape.

Keywords: machine learning, risk assessment, ethical considerations, financial inclusion

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.Methods: A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. Results: The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. Conclusion: This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: physical activity, motivation, level of physical activity, type of physical activity

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Authors: Mikhail Sandzhiev

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The monitoring of metal pollution in environmental waters is crucial for the protection of ecosystems, human health and agricultural activities. Traditional laboratory-based metal analysis methods are time-consuming and expensive, which often leads to delays in the availability of information. This study presents an approach to real-time water quality monitoring using portable X-ray fluorescence (p-XRF) technology coupled with geographic information systems (GIS). Using a custom Python script, p-XRF data is processed and formatted into a GIS-compatible format, facilitating spatial visualization of metal concentrations in ǪGIS. Field-usable filters, especially bisphosphonate-functionalized thermally carbonized porous silicon (BP-TCPSi), preformed metals such as Mn, Ni, Cu, Zn, and Pb allow direct detection in the field by using p-XRF. Key objectives include robust data collection, spatial visualization and validation processes to ensure accuracy and efficiency. This provides quick and efficient insights into metal contamination trends and allows proactive decision-making.

Keywords: metal concentrations, predictive mapping, environmental monitoring, environmental waters

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Authors: Siriporn Poolsuwan

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This research aims to explore the information needs, information usages, and problems of information usage of the older people club’s members in Dusit District, Bangkok. There are 12 clubs and 746 club’s members in this district. The research results use for older person service in this district. Data is gathered from 252 club’s members by using questionnaires. The quantitative approach uses in research by percentage, means and standard deviation. The results are as follows (1) The older people need Information for entertainment, occupation and academic in the field of short story, computer work, and religion and morality. (2) The participants use Information from various sources. (3) The Problem of information usage is their language skills because of the older people’s literacy problem.

Keywords: information behavior, older person, information seeking, knowledge discovery and data mining

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Authors: Le Chen

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This paper presents an efficient tool platform scheme to ensure quality protection throughout the software development process. The main principle is to manage the information of requirements, design, development, testing, operation and maintenance process with proper tools, and to set up the quality standards of each process. Through the tools’ display and summary of quality standards, the quality standards can be visualizad and ready for policy decision, which is called Quality Gate in this paper. In addition, the tools are also integrated to achieve the exchange and relation of information which highly improving operational efficiency. In this paper, the feasibility of the scheme is verified by practical application of development projects, and the overall information display and data mining are proposed to be further improved.

Keywords: efficiency, quality gate, software process, tools

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Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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This research addresses the need for a comprehensive framework that can guide the design and assessment of multi-level public spaces and public realms and their impact on the built environment. The study aims to understand and measure the neural mechanisms involved in this process. By doing so, it can lay the foundation for vertical and volumetric urbanism and ensure consistency and excellence in the field while also supporting scientific research methods for urban design with cognitive neuroscientists. To investigate these aspects, the paper focuses on the neighborhood scale in Hong Kong, specifically examining multi-level public spaces and quasi-public spaces within both commercial and residential complexes. The researchers use predictive Artificial Intelligence (AI) as a methodology to assess and comprehend the applicability of the urban design framework for vertical and volumetric urbanism. The findings aim to identify the factors that contribute to successful public spaces within a vertical living environment, thus introducing a new typology of public spaces.

Keywords: vertical urbanism, scientific research methods, spatial cognition, urban design guideline

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Jean Marie Tshimula, Atsushi Togashi

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We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

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Authors: Karim Kootahi, Seyed Abolhasan Naeini

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The soil-water characteristic curve (SWCC), which represents the relationship between suction and water content (or degree of saturation), is an important property of unsaturated soils. The conventional method for determining SWCC is through specialized testing procedures. Since these procedures require specialized unsaturated soil testing apparatus and lengthy testing programs, several index properties-based correlations have been developed for estimating the SWCC of fine-grained soils. There are, however, considerable inconsistencies among the published correlations and there is no validation study on the predictive ability of existing correlations. In the present study, all existing index properties-based correlations are evaluated using a high quality worldwide database. The performances of existing correlations are assessed both graphically and quantitatively using statistical measures. The results of the validation indicate that most of the existing correlations provide unacceptable estimates of degree of saturation but the most recent model appears to be promising.

Keywords: SWCC, correlations, index properties, validation

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Authors: William Wong Xiu Shun, Yoonjin Hyun, Mingyu Kim, Seongi Choi, Namgyu Kim

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Recently, the demand of extracting the R&D keywords from the issues and using them in retrieving R&D information is increasing rapidly. But it is hard to identify the related issues or to distinguish them. Although the similarity between the issues cannot be identified, but with the R&D lexicon, the issues that always shared the same R&D keywords can be determined. In details, the R&D keywords that associated with particular issue is implied the key technology elements that needed to solve the problem of the particular issue. Furthermore, the related issues that sharing the same R&D keywords can be showed in a more systematic way through the issue clustering constructed from the perspective of R&D. Thus, sharing of the R&D result and reusable of the R&D technology can be facilitated. Indirectly, the redundancy of investment on the same R&D can be reduce as the R&D information can be shared between those corresponding issues and reusability of the related R&D can be improved. Therefore, a methodology of constructing an issue clustering from the perspective of common R&D keywords is proposed to satisfy the demands mentioned.

Keywords: clustering, social network analysis, text mining, topic analysis

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Authors: Mesfin Angaw Tesfay

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Uridine-5’-diphosphate (UDP)-glucose is one of the most versatile building blocks within the metabolism of prokaryotes and eukaryotes, serving as an activated sugar donor during the glycosylation of natural products. It is formed by the enzyme UDP-glucose pyrophosphorylase (UGPase) using uridine-5′-triphosphate (UTP) and α-d-glucose 1-phosphate as a substrate. Herein, two UGPase genes from Lactobacillus kefiranofaciens ZW3 (LkUGPase) and Rhodococcus wratislaviensis IFP 2016 (RwUGPase) were identified through genome mining approaches. The LkUGPase and RwUGPase have 299 and 306 amino acids, respectively. Both UGPase has the conserved UTP binding site (G-X-G-T-R-X-L-P) and the glucose -1-phosphate binding site (V-E-K-P). The LkUGPase and RwUGPase were cloned in E. coli, and SDS-PAGE analysis showed the expression of both enzymes forming about 36 KDa of protein band after induction. LkUGPase and RwUGPase have an activity of 1549.95 and 671.53 U/mg, respectively. Currently, their kinetic properties are under investigation.

Keywords: UGPase, LkUGPase, RwUGPase, UDP-glucose, glycosylation

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Authors: K.Narasimhulu, Y. Pydi Setty

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The objective of this present study is the optimization of process parameters in biosorption of Ni(II) and Cd(II) ions by Pseudomonas putida using Response Surface Methodology in a Packed bed bioreactor. The experimental data were also tested with theoretical models to find the best fit model. The present paper elucidates RSM as an efficient approach for predictive model building and optimization of Ni(II) and Cd(II) ions using Pseudomonas putida. In packed bed biosorption studies, comparison of the breakthrough curves of Ni(II) and Cd(II) for Agar immobilized and PAA immobilized Pseudomonas putida at optimum conditions of flow rate of 300 mL/h, initial metal ion concentration of 100 mg/L and bed height of 20 cm with weight of biosorbent of 12 g, it was found that the Agar immobilized Pseudomonas putida showed maximum percent biosorption and bed saturation occurred at 20 minutes. Optimization results of Ni(II) and Cd(II) by Pseudomonas putida from the Design Expert software were obtained as bed height of 19.93 cm, initial metal ion concentration of 103.85 mg/L, and flow rate of 310.57 mL/h. The percent biosorption of Ni(II) and Cd(II) is 87.2% and 88.2% respectively. The predicted optimized parameters are in agreement with the experimental results.

Keywords: packed bed bioreactor, response surface mthodology, pseudomonas putida, biosorption, waste water

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Authors: Zakari Mustapha, Clinto Aigbavboa, Wellinton Didi Thwala

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Occupational Health and Safety (OHS) issues has been a major challenge to the Ghanaian government. The purpose of the study was to examine OHS practices in Ghana. The study looked at various views from different scholars about OHS practices in order to achieve the objective of the study. Literature review was conducted on OHS in Ghana. Findings from the study shows Ministry of Roads and Transport (MRT) and Ministry of Water Resources, Works and Housing (MWRWH) are two government ministries in charge of construction and implementation of the construction sector policy. The Factories, Offices and Shops Act 1970, Act 328 and the Mining Regulations 1970 LI 665 are the two major edicts. The study presents a strong background on OHS practices in Ghana and contribute to the body of knowledge on the solution to the current trends and challenges of OHS in the construction sector.

Keywords: ILO convention, OHS challenges, OHS practices, OHS improvement

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Authors: Nouini Adrâa

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Background: The management of dizziness and vertigo can be challenging in the emergency department (ED). It is important to rapidly diagnose vertebrobasilar stroke (VBS), as therapeutic options such as thrombolysis and anticoagulation require prompt decisions. Objective: This study aims to assess the rate of misdiagnosis in patients with dizziness caused by VBS in the ED. Methods and Results: The cohort was comprised of 82 patients with a mean age of 55 years; 51% were women and 49% were men. Among dizzy patients, 15% had VBS. We used Cohen’s kappa test to quantify the agreement between two raters – namely, emergency physicians and neurologists – regarding the causes of dizziness in the ED. The agreement between emergency physicians and neurologists is low for the final diagnosis of central vertigo disorders and moderate for the final diagnosis of VBS. The sensitivity of ED clinal examination for benign conditions such as BPPV was low at 56%. The positive predictive value of the ED clinical examination for VBS was also low at 50%. Conclusion: There is a substantial rate of misdiagnosis in patients with dizziness caused by VBS in the ED. To reduce the number of missing diagnoses of VBS in the future, there is a need to train emergency physicians in neuro vestibular examinations, including the HINTS examination for acute vestibular syndrome (AVS) and the Dix-Hallpike (DH) maneuver for episodic vestibular syndrome. Using video head impulse tests could help reduce the rate of misdiagnosis of VBS in the ED.

Keywords: dizziness, vertigo, vestibular disease, emergency

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Authors: Saif Al Ghafri, Thomas Hughes, Armand Karimi, Kumarini Seneviratne, Jordan Oakley, Michael Johns, Eric F. May

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Knowledge of the thermophysical properties of complex mixtures at extreme conditions of pressure and temperature have always been essential to the Liquefied Natural Gas (LNG) industry’s evolution because of the tremendous technical challenges present at all stages in the supply chain from production to liquefaction to transport. Each stage is designed using predictions of the mixture’s properties, such as density, viscosity, surface tension, heat capacity and phase behaviour as a function of temperature, pressure, and composition. Unfortunately, currently available models lead to equipment over-designs of 15% or more. To achieve better designs that work more effectively and/or over a wider range of conditions, new fundamental property data are essential, both to resolve discrepancies in our current predictive capabilities and to extend them to the higher-pressure conditions characteristic of many new gas fields. Furthermore, innovative experimental techniques are required to measure different thermophysical properties at high pressures and over a wide range of temperatures, including near the mixture’s critical points where gas and liquid become indistinguishable and most existing predictive fluid property models used breakdown. In this work, we present a wide range of experimental measurements made for different binary and ternary mixtures relevant to LNG processing, with a particular focus on viscosity, surface tension, heat capacity, bubble-points and density. For this purpose, customized and specialized apparatus were designed and validated over the temperature range (200 to 423) K at pressures to 35 MPa. The mixtures studied were (CH4 + C3H8), (CH4 + C3H8 + CO2) and (CH4 + C3H8 + C7H16); in the last of these the heptane contents was up to 10 mol %. Viscosity was measured using a vibrating wire apparatus, while mixture densities were obtained by means of a high-pressure magnetic-suspension densimeter and an isochoric cell apparatus; the latter was also used to determine bubble-points. Surface tensions were measured using the capillary rise method in a visual cell, which also enabled the location of the mixture critical point to be determined from observations of critical opalescence. Mixture heat capacities were measured using a customised high-pressure differential scanning calorimeter (DSC). The combined standard relative uncertainties were less than 0.3% for density, 2% for viscosity, 3% for heat capacity and 3 % for surface tension. The extensive experimental data gathered in this work were compared with a variety of different advanced engineering models frequently used for predicting thermophysical properties of mixtures relevant to LNG processing. In many cases the discrepancies between the predictions of different engineering models for these mixtures was large, and the high quality data allowed erroneous but often widely-used models to be identified. The data enable the development of new or improved models, to be implemented in process simulation software, so that the fluid properties needed for equipment and process design can be predicted reliably. This in turn will enable reduced capital and operational expenditure by the LNG industry. The current work also aided the community of scientists working to advance theoretical descriptions of fluid properties by allowing to identify deficiencies in theoretical descriptions and calculations.

Keywords: LNG, thermophysical, viscosity, density, surface tension, heat capacity, bubble points, models

Procedia PDF Downloads 274

Authors: Zachary Blanks, Solomon Sonya

Abstract:

Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

Procedia PDF Downloads 294

Authors: Marc Romero-Estonllo, Judith Ramos-Castro, Yaiza San Miguel, Beatriz Rodríguez-Garrido, Carmela Monterroso

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Mining activity is among the main sources of trace and heavy metal(loid) pollution worldwide, which is a hazard to human and environmental health. That is why several projects have been emerging for the remediation of such polluted places. Phytomanagement strategies draw good performances besides big side benefits. In this work, a glasshouse assay with trace element polluted soils from an old Cu mine ore (NW of Spain) which forms part of the PhytoSUDOE network of phytomanaged contaminated field sites (PhytoSUDOE Project (SOE1/P5/E0189)) was set. The objective was to evaluate improvements induced by the following phytoremediation-related treatments. Three increasingly complex amendments alone or together with plant growth (Populus nigra L. alone and together with Tripholium repens L.) were tested. And three different rhizosphere bioinocula were applied (Plant Growth Promoting Bacteria (PGP), mycorrhiza (MYC), or mixed (PGP+MYC)). After 110 days of growth, plants were collected, biomass was weighed, and tree length was measured. Physical-chemical analyses were carried out to determine pH, effective Cation Exchange Capacity, carbon and nitrogen contents, bioavailable phosphorous (Olsen bicarbonate method), pseudo total element content (microwave acid digested fraction), EDTA extractable metals (complexed fraction), and NH4NO3 extractable metals (easily bioavailable fraction). On plant material, nitrogen content and acid digestion elements were determined. Amendment usage, plant growth, and bioinoculation were demonstrated to improve soil fertility and/or plant health within the time span of this study. Particularly, pH levels increased from 3 (highly acidic) to 5 (acidic) in the worst-case scenario, even reaching 7 (neutrality) in the best plots. Organic matter and pH increments were related to polluting metals’ bioavailability decrements. Plants grew better both with the most complex amendment and the middle one, with few differences due to bioinoculation. Using the less complex amendment (just compost) beneficial effects of bioinoculants were more observable, although plants didn’t thrive very well. On unamended soils, plants neither sprouted nor bloomed. The scheme assayed in this study is suitable for phytomanagement of these kinds of soils affected by mining activity. These findings should be tested now on a larger scale.

Keywords: aided phytoremediation, mine pollution, phytostabilization, soil pollution, trace elements

Procedia PDF Downloads 67

Authors: E. Benga, T. Tengen, A. Alugongo

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Periodic and continuous inventory models are the two classical management tools used to handle inventories. These models have advantages and disadvantages. The implementation of both continuous (r,Q) inventory and periodic (R, S) inventory models in most manufacturing plants comes with higher cost. Such high inventory costs are due to the fact that most manufacturing plants are not flexible enough. Since demand and lead-time are two important variables of every inventory models, their effect on the flexibility of the manufacturing plant matter most. Unfortunately, these effects are not clearly understood by managers. The reason is that the decision parameters of the continuous (r, Q) inventory and periodic (R, S) inventory models are not designed to effectively deal with the issues of uncertainties such as poor manufacturing performances, delivery performance supplies performances. There is, therefore, a need to come up with a predictive and hybrid inventory model that can combine in some sense the feature of the aforementioned inventory models. A linear combination technique is used to hybridize both continuous (r, Q) inventory and periodic (R, S) inventory models. The behavior of such hybrid inventory model is described by a differential equation and then optimized. From the results obtained after simulation, the continuous (r, Q) inventory model is more effective than the periodic (R, S) inventory models in the short run, but this difference changes as time goes by. Because the hybrid inventory model is more cost effective than the continuous (r,Q) inventory and periodic (R, S) inventory models in long run, it should be implemented for strategic decisions.

Keywords: periodic inventory, continuous inventory, hybrid inventory, optimization, manufacturing plant

Procedia PDF Downloads 382

Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

Abstract:

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

Procedia PDF Downloads 235