Search results for: collaborative reading approach

Authors: Kashmir Johal

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Within a wider context of improving whole-life effectiveness of gas and oil fields, we have been researching how to generate power local to the wellhead. (Provision of external power to a subsea wellhead can be prohibitively expensive and results in uneconomic fields. This has been an oil/gas industry challenge for many years.) We have been developing a possible approach to “local” power generation and have been conducting technical, environmental, (and economic) research to develop a viable approach. We sought to create a workable design for a new type of power generation system that makes use of differential pressure that can exist between the sea surface and a gas (or oil reservoir). The challenge has not just been to design a system capable of generating power from potential energy but also to design it in such a way that it anticipates and deals with the wide range of technological, environmental, and chemical constraints faced in such environments. We believe this project shows the enormous opportunity in deriving clean, economic, and zero emissions renewable energy from offshore sources. Since this technology is not currently available, a patent has been filed to protect the advancement of this technology.

Keywords: renewable, energy, power, offshore

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Authors: Martha Karunakar

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There exists a wide divide between the urban and the rural students in a vast country like India. This dichotomy is seen in the resources available to them, like the learning facilities, the infra-structure, the learning ambience and meeting of their basic needs of food, clothing and shelter. This paper discusses the effect of English language teaching as a Bridge course on a select batch of rural students at an Engineering college in Chennai, one of the four Metros of India. The study aims to understand how the teacher input and the teacher- peer-student interaction facilitates the acquisition of the basic structures of the English language to a group that is minimally exposed to the language. The objective in conducting the Bridge Course is to integrate these rural students into the mainstream and empower them in terms of English speaking ability; to enable them to comprehend their respective engineering classes where the medium of instruction is English and also to be able to interact with their urban peers. This program is conducted prior to the start of a regular academic session to equip them face the rigors of engineering education. The study is placed within the framework of Interaction theory in second language acquisition. The study evaluates the impact of linking theory and practice by implementing meaningful interaction not only within classrooms but also in the common areas. By providing intensive comprehensible input, it is anticipated that participant’s level of English language improves. The teaching methods and classroom activities included individual and group participation, encompassing all the four skills of listening, speaking, reading and writing (LSRW). The diagnostic tests that were administered before the commencement of the course and the exit test after the completion were used to record the impact of the training.

Keywords: comprehensible input, interaction, rural students, teaching English

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Authors: Hanaa A. S. Oraby, Amal A. M. Hassan, Mahmoud M. Sakr, Atef A. A. Haiba

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Applying the profiling approach for the assessment of proteomic variations due to genetic modification of the Egyptian tomato cultivar "Edkawy", three complementary approaches were used. These methods are amino acids analysis, gel electrophoresis, and Gas chromatography coupled with mass spectrometry (GC/MS). The results of the present study Show evidence of proteomic variations between both modified tomato and its non-modified counterpart. Amino acids concentrations, and the protein patterns separation on the 1D SDS-PAGE were not similar in the case of transformed tomato compared to that of the non-transformed counterpart. These detected differences are most likely derived from the process of transformation. Results also revealed that the efficiency of GC/MS approach to identify a mixture of unknown proteins is limited. GC/MS analysis was only able to identify few number of protein molecules. Therefore, more advanced and specific technologies like MALDI-TOF-MS are recommended to be employed.

Keywords: GMOs, unintended effects, proteomic variations, 1D SDS-PAGE, GC/MS

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: TDNN, neural networks, noise, speech recognition

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Authors: Deon de Jager, Yahya Zweiri, Dimitrios Makris

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The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission.

Keywords: cognitive robotics, semantic memory, episodic memory, maximum entropy principle, particle swarm optimization

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Authors: Yah-Ling Hung

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eHealth is undoubtedly emerging as a promising vehicle to provide information for individual self-care management. However, the accessing ability, reading strategies and navigating behavior between higher literacy users and lower literacy users are significantly different. Yet, ways to tailor audiences’ health literacy and develop appropriate eHealth to feed their need become a big challenge. The purpose of this study is to compare the educational effectiveness of eHealth to deliver health knowledge between higher literacy users and lower literacy users, thus establishing useful design strategies of eHealth for users with different level of health literacy. The study was implemented in four stages, the first of which developed a website as the testing media to introduce health care knowledge relating to children’s allergy. Secondly, a reliability and validity test was conducted to make sure that all of the questions in the questionnaire were good indicators. Thirdly, a pre-post knowledge test was conducted with 66 participants, 33 users with higher literacy and 33 users with lower literacy respectively. Finally, a usability evaluation survey was undertaken to explore the criteria used by users with different levels of health literacy to evaluate eHealth. The results demonstrated that the eHealth Intervention in both groups had a positive outcome. There was no significant difference between the effectiveness of eHealth intervention between users with higher literacy and users with lower literacy. However, the average mean of lower literacy group was marginally higher than the average mean of higher literacy group. The findings also showed that the criteria used to evaluate eHealth could be analyzed in terms of the quality of information, appearance, appeal and interaction, but the users with lower literacy have different evaluation criteria from those with higher literacy. This is an interdisciplinary research which proposes the sequential key steps that incorporate the planning, developing and accessing issues that need to be considered when designing eHealth for patients with varying degrees of health literacy.

Keywords: eHealth, health intervention, health literacy, usability evaluation

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Aline F. Marcon, Eduardo F. da Silva, Marina Bouzon

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The impact of lean management on environmental sustainability is the research line that receives the most attention from academicians. Therefore, the social dimension of sustainable development has so far received less attention. This paper aims to evaluate the impact of intra-plant lean manufacturing practices on social sustainability indicators extracted from the Global Reporting Initiative (GRI) parameters. The method is two-phased, including MCDM approach to uncover the most relevant practices regarding social performance and Interpretive Structural Modeling (ISM) method to reveal the structural relationship among lean practices. Professionals from the academic and industrial fields answered the questionnaires. From the results of this paper, it is possible to verify that practices such as “Safety Improvement Programs”, “Total Quality Management” and “Cross-functional Workforce” are the ones which have the most positive influence on the set of GRI social indicators.

Keywords: indicators, ISM, lean, social, sustainability

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Authors: Peter Burggräf, Moritz Krunke, Hanno Voet

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Due to shortening product and technology lifecycles, many companies use standardization approaches in product development and factory planning to reduce costs and time to market. Unlike large companies, where modular systems are already widely used, small and medium-sized companies often show a much lower degree of standardization due to lower scale effects and missing capacities for the development of these standards. To overcome these challenges, the development of industry sector specific standards in cooperations or by third parties is an interesting approach. This paper analyzes which branches that are mainly dominated by small or medium-sized companies might be especially interesting for the development of factory standards using the example of the German industry. For this, a key performance indicator based approach was developed that will be presented in detail with its specific results for the German industry structure.

Keywords: factory planning, factory standards, industry sector specific standardization, production planning

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Authors: Eric Mxolisi Mkhondo

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Geometrical deviations and interaction of mechanical parts influences the performance of miniature systems.These deviations tend to cause costly problems during assembly due to imperfections of components, which are invisible to a naked eye.They also tend to cause unsatisfactory performance during operation due to deformation cause by environmental conditions.One of the effective tools to manage the deviations and interaction of parts in the system is tolerance analysis.This is a quantitative tool for predicting the tolerance variations which are defined during the design process.Traditional tolerance analysis assumes that the assembly is static and the deviations come from the manufacturing discrepancies, overlooking the functionality of the whole system and deformation of parts due to effect of environmental conditions. This paper presents an integrated tolerance analysis approach for miniature system in operation.In this approach, a computer-aided design (CAD) model is developed from system’s specification.The CAD model is then used to specify the geometrical and dimensional tolerance limits (upper and lower limits) that vary component’s geometries and sizes while conforming to functional requirements.Worst-case tolerances are analyzed to determine the influenced of dimensional changes due to effects of operating temperatures.The method is used to evaluate the nominal conditions, and worse case conditions in maximum and minimum dimensions of assembled components.These three conditions will be evaluated under specific operating temperatures (-40°C,-18°C, 4°C, 26°C, 48°C, and 70°C). A case study on the mechanism of a zoom lens system is used to illustrate the effectiveness of the methodology.

Keywords: geometric dimensioning, tolerance analysis, worst-case analysis, zoom lens mechanism

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Authors: Azna Zuberi

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Biofilm is a sessile bacterial accretion in which bacteria adapts different physiological and morphological behavior from planktonic form. It is the root cause of about 80% microbial infections in human. Among them, E. coli biofilms are most prevalent in medical devices associated nosocomial infections. The objective of this study was to inhibit biofilm formation by targeting LuxS gene, involved in quorum sensing using CRISPRi. luxS is a synthase, involved in the synthesis of Autoinducer-2(AI-2), which in turn guides the initial stage of biofilm formation. To implement CRISPRi system, we have synthesized complementary sgRNA to target gene sequence and co-expressed with dCas9. Suppression of luxS was confirmed through qRT-PCR. The effect of luxS gene on biofilm inhibition was studied through crystal violet assay, XTT reduction assay and scanning electron microscopy. We conclude that CRISPRi system could be a potential strategy to inhibit bacterial biofilm through mechanism base approach.

Keywords: biofilm, CRISPRi, luxS, microbial

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Authors: Chandralal Shrestha

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In the developing countries like Nepal, the community based forest management approach has often been glorified as one of the best forest management alternatives to maximize the forest benefits. Though the approach has succeeded to construct a local level institution and conserve the forest biodiversity, how the local communities perceived about the forest benefits, the question always remains silent among the researchers and policy makers. The paper aims to explore the understanding of forest benefits from the perspective of local communities who used the forests in terms of institutional stability, equity and livelihood opportunity, and ecological stability. The paper revealed that the local communities have mixed understanding over the forest benefits. The institutional and ecological activities carried out by the local communities indicated that they have better understanding over the forest benefits. However, inequality while sharing the forest benefits, low pricing strategy and its negative consequences in valuation of forest products and limited livelihood opportunities indicated the poor understanding.

Keywords: community based forest management, forest benefits, lowland, Nepal

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Authors: Takahiro Hino, Michiharu Maeda

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Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms.

Keywords: combinatorial optimization problems, particle swarm optimization, set-based particle swarm optimization, traveling salesman problem

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Raquel Nieto, Marta Vázquez, Anita Drumond, Luis Gimeno

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One of the hot spots of climate change is the increment of ocean evaporation. The best estimation of evaporation, OAFlux data, shows strong increasing trends in evaporation from the oceans since 1978, with peaks during the hemispheric winter and strongest along the paths of the global western boundary currents and any inner Seas. The transport of moisture from oceanic sources to the continents is the connection between evaporation from the ocean and precipitation over the continents. A key question is to try to relate evaporative source regions over the oceans where trends have occurred in the last decades with their sinks over the continents to check if there have been also any trends in the precipitation amount or its characteristics. A Lagrangian approach based on FLEXPART and ERA-interim data is used to establish this connection. The analyzed period was 1980 to 2012. Results show that there is not a general pattern, but a significant agreement was found in important areas of climate interest.

Keywords: ocean evaporation, Lagrangian approaches, contiental precipitation, Europe

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Authors: Radim Sip, Denisa Denglerova

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It’s impossible to capture complex phenomena, such as inclusion, with reductionism. The most common form of reductionism is the objectivist approach, where processes and relationships are reduced to entities and clearly outlined phases, with a consequent search for relationships between them. Constructivism as a paradigm and situational analysis as a methodological research portfolio represent a way to avoid the dominant objectivist approach. They work with a situation, i.e. with the essential blending of actors and their environment. Primary transactions are taking place between actors and their surroundings. Researchers create constructs based on their need to solve a problem. Concepts therefore do not describe reality, but rather a complex of real needs in relation to the available options how such needs can be met. For examination of a complex problem, corresponding methodological tools and overall design of the research are necessary. Using an original research on inclusion in the Czech Republic as an example, this contribution demonstrates that inclusion is not a substance easily described, but rather a relationship field changing its forms in response to its actors’ behaviour and current circumstances. Inclusion consists of dynamic relationship between an ideal, real circumstances and ways to achieve such ideal under the given circumstances. Such achievement has many shapes and thus cannot be captured by description of objects. It can be expressed in relationships in the situation defined by time and space. Situational analysis offers tools to examine such phenomena. It understands a situation as a complex of dynamically changing aspects and prefers relationships and positions in the given situation over a clear and final definition of actors, entities, etc. Situational analysis assumes creation of constructs as a tool for solving a problem at hand. It emphasizes the meanings that arise in the process of coordinating human actions, and the discourses through which these meanings are negotiated. Finally, it offers “cartographic tools” (situational maps, socials worlds / arenas maps, positional maps) that are able to capture the complexity in other than linear-analytical ways. This approach allows for inclusion to be described as a complex of phenomena taking place with a certain historical preference, a complex that can be overlooked if analyzed with a more traditional approach.

Keywords: constructivism, situational analysis, objective realism, reductionism, inclusion

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Authors: Tingting Huang, Sha Li, Geoffrey Griffiths, Martin Lukac, Jianning Zhu

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Territorial landscapes have experienced a gradual loss of their typical characteristics during long-term human activities. In order to protect the integrity of regional landscapes, it is necessary to characterize, evaluate and protect them in a graded manner. The study takes Fujian, China, as an example and classifies the landscape characters of the site at the regional scale, middle scale, and detailed scale. A multi-scale approach combining parametric and holistic approaches is used to classify and partition the landscape character types (LCTs) and landscape character areas (LCAs) at different scales, and a multi-element landscape assessment approach is adopted to explore the conservation strategies of the landscape character. Firstly, multiple fields and multiple elements of geography, nature and humanities were selected as the basis of assessment according to the scales. Secondly, the study takes a parametric approach to the classification and partitioning of landscape character, Principal Component Analysis, and two-stage cluster analysis (K-means and GMM) in MATLAB software to obtain LCTs, combines with Canny Operator Edge Detection Algorithm to obtain landscape character contours and corrects LCTs and LCAs by field survey and manual identification methods. Finally, the study adopts the Landscape Sensitivity Assessment method to perform landscape character conservation analysis and formulates five strategies for different LCAs: conservation, enhancement, restoration, creation, and combination. This multi-scale identification approach can efficiently integrate multiple types of landscape character elements, reduce the difficulty of broad-scale operations in the process of landscape character conservation, and provide a basis for landscape character conservation strategies. Based on the natural background and the restoration of regional characteristics, the results of landscape character assessment are scientific and objective and can provide a strong reference in regional and national scale territorial spatial planning.

Keywords: parameterization, multi-scale, landscape character identify, landscape character assessment

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Authors: Wei Gao

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The current study investigates the change of listener responses in social conversations of the second language (L2) speakers who are staying abroad with Chinese L1 speakers in Australian universities and how their long-term stay abroad impacted their design for L2 recipient actions. There is a limited amount of empirical work on L2 English listener response acquisition, particularly regarding the influence of long-term stay abroad in English-speaking countries. Little is known whether the development of L2 listener responses and the improvement of interactional competence is affected by the prolonged residency in the target L2 country. Forty-eight participants were recruited, and they participated in the designed speaking task through Computer-Mediated Communication. Results showed that long-term stay abroad Chinese L1 speakers demonstrated an English-like pattern of listener responses in communication. Long-term stay abroad experience had a significant impact on L2 English listener responses production and organization in social conversation. Long-term stay abroad L1 Chinese speakers had an active and productive response in listenership than their non-stay abroad counterparts in terms of frequency and placement in producing listener responses. However, the L2 English listener response production only occurred to be partial in response tokens, such as backchannels and reactive expressions, also in resumptive openers' employment. This study shows that L2 English listener responses could be acquired during a long-term stay abroad in English-speaking countries but showed partial acquisition in collaborative finishes production. In addition, the most prominent finding was that Chinese L1 speakers changed their overall listener responses pattern from L1 Chinese to L2 English. The study reveals specific interactional changes in English L2 listener responses acquisition. It generates pedagogical implications for cross-cultural communication and L2 pragmatics acquisition during a long-term stay abroad.

Keywords: listener responses, stay abroad, interactional competence, L2 pragmatics acquisition

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Authors: Maria Valeria De Bonis, Gianpaolo Ruocco

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Nowadays, computational modeling is paving new design and verification ways in a number of industrial sectors. The technology is ripe to challenge some case in the Bioengineering and Medicine frameworks: for example, looking at the strategical and ethical importance of oncology research, efforts should be made to yield new and powerful resources to tumor knowledge and understanding. With these driving motivations, we approach this gigantic problem by using some standard engineering tools such as the mathematics behind the biomass transfer. We present here some bacterial colonization studies in complex structures. As strong analogies hold with some tumor proliferation, we extend our study to a benchmark case of solid tumor. By means of a commercial software, we model biomass and energy evolution in arbitrary media. The approach will be useful to cast virtualization cases of cancer growth in human organs, while augmented reality tools will be used to yield for a realistic aid to informed decision in treatment and surgery.

Keywords: bacteria, simulation, tumor, precision medicine

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Qi Wu, Dian-Yong Chen, Feng-Kun Guo, Gang Li

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In the past decade, a growing number of new hadron states have been observed, which are dubbed as XYZ states in the heavy quarkonium mass regions. In this work, we present our study on the production of some new hadron states. In particular, we investigate the processes Υ(5S,6S)→ Zb (10610)/Zb (10650)π, Bc→ Zc (3900)/Zc (4020)π and Λb→ Pc (4312)/Pc (4440)/Pc (4457)K. (1) For the production of Zb (10610)/Zb (10650) from Υ(5S,6S) decay, two types of bottom-meson loops were discussed within a nonrelativistic effective field theory. We found that the loop contributions with all intermediate states being the S-wave ground state bottom mesons are negligible, while the loops with one bottom meson being the broad B₀* or B₁' resonance could provide the dominant contributions to the Υ(5S)→ Zb⁽'⁾ π. (2) For the production of Zc (3900)/Zc (4020) from Bc decay, the branching ratios of Bc⁺→ Z (3900)⁺ π⁰ and Bc⁺→ Zc (4020)⁺ π⁰ are estimated to be of order of 10⁽⁻⁴⁾ and 10⁽⁻⁷⁾ in an effective Lagrangian approach. The large production rate of Zc (3900) could provide an important source of the production of Zc (3900) from the semi-exclusive decay of b-flavored hadrons reported by D0 Collaboration, which can be tested by the exclusive measurements in LHCb. (3) For the production of Pc (4312), Pc (4440) and Pc (4457) from Λb decay, the ratio of the branching fraction of Λb→ Pc K was predicted in a molecular scenario by using an effective Lagrangian approach, which is weakly dependent on our model parameter. We also find the ratios of the productions of the branching fractions of Λb→ Pc K and Pc→ J/ψ p can be well interpreted in the molecular scenario. Moreover, the estimated branching fractions of Λb→ Pc K are of order 10⁽⁻⁶⁾, which could be tested by further measurements in LHCb Collaboration.

Keywords: effective Lagrangian approach, hadron loops, molecular states, new hadron states

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Authors: Tony Mann

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This paper sets out the case for involving and engaging employees/workers/stakeholders/staff in any significant change that is being considered by the senior executives of the organization. It establishes the rationale, the approach, the methodology of engagement and the benefits of a participative approach. It challenges the new norm of imposing change for fear of resistance and instead suggests that involving people has better outcomes and a longer-lasting impact. Various strategic models are introduced and illustrated to explain how the process can be most effective. The paper highlights one model in particular (the Process Iceberg® Organizational Change model) that has proven to be instrumental in developing effective change. Its use is demonstrated in its various forms and explains why so much change fails to address the key elements and how we can be more productive in managing change. ‘Participation’ in change is too often seen as negative, expensive and unwieldy. The paper aims to show that another model: UIA=O+E, can offset the difficulties and, in fact, produce much more positive and effective change.

Keywords: facilitation, stakeholders, buy-in, digital workshops

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Authors: Lukholo Raxangana

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Fourth Industrial Revolution (4IR)-era teaching offers in-service mathematics teachers opportunities to use blended approaches to engage learners while teaching mathematics. This study explores in-service high school teachers' experiences with a blended teaching approach to mathematics. This qualitative case study involved eight pre-service teachers from four selected schools in the Sedibeng West District of the Gauteng Province. The study used the community of inquiry model as its analytical framework for data analysis. Data collection was through semi-structured interviews and focus-group discussions to explore in-service teachers' experiences with the influence of blended teaching (BT) on learning mathematics. The study results are the impact of load-shedding, benefits of BT, and perceptions of in-service and hindrances of BT. Based on these findings, the study recommends that further research should focus on developing data-free BT tools to assist during load-shedding, regardless of location.

Keywords: bended teaching, teachers, in-service, and mathematics

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Authors: Reiko Yamamoto

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This paper reports on how synchrony occurs between children and their teacher, and what prevents or facilitates synchrony. The aim of the experiment conducted in this study was to precisely analyze their movements and synchrony and reveal the process of synchrony in a real-world classroom. Specifically, the experiment was conducted for around 20 minutes during an English as a foreign language (EFL) lesson. The participants were 11 fourth-grade school children and their classroom teacher in a public elementary school in Japan. Previous researchers assert that synchrony causes the state of flow in a class. For checking the level of flow, Short Flow State Scale (SFSS) was adopted. The experimental procedure had four steps: 1) The teacher read aloud the first half of an English storybook to the children. Both the teacher and the children were at their own desks. 2) The children were subjected to an SFSS check. 3) The teacher read aloud the remaining half of the storybook to the children. She made the children remove their desks before reading. 4) The children were again subjected to an SFSS check. The movements of all participants were recorded with a video camera. From the movement analysis, it was found that the children synchronized better with the teacher in Step 3 than in Step 1, and that the teacher’s movement became free and outstanding without a desk. This implies that the desk acted as a barrier between the children and the teacher. Removal of this barrier resulted in the children’s reactions becoming synchronized with those of the teacher. The SFSS results proved that the children experienced more flow without a barrier than with a barrier. Apparently, synchrony is what caused flow or social emotions in the classroom. The main conclusion is that synchrony leads to cognitive outcomes such as children’s academic performance in EFL learning.

Keywords: engagement in a class, English as a foreign language (EFL) learning, interactional synchrony, social emotions

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Authors: Sahbi Farhani

Abstract:

This study investigates the relationship between external debt and military spending in case of India over the period of 1970–2012. In doing so, we have applied the structural break unit root tests to examine stationarity properties of the variables. The Auto-Regressive Distributed Lag (ARDL) bounds testing approach is used to test whether cointegration exists in presence of structural breaks stemming in the series. Our results indicate the cointegration among external debt, military spending, debt servicing, and economic growth. Moreover, military spending and debt servicing add in external debt. Economic growth helps in lowering external debt. The Vector Error Correction Model (VECM) analysis and Granger causality test reveal that military spending and economic growth cause external debt. The feedback effect also exists between external debt and debt servicing in case of India.

Keywords: external debt, military spending, ARDL approach, India

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Authors: Ibrahim A. Alhammad, Suleiman A. Al-Otaibi, Khalid S. Al-Gahtani, Naïf Al-Otaibi, Abdulaziz A. Bubshait

Abstract:

In the last decade, the use of project delivery system of design build engineering contracts is increasing in North America due to the reasons of reducing the project duration and minimizing costs. The shift from traditional approach of Design-Bid-Build to Design-Build contracts have been attributed to many factors such as evolution of the regulatory and legal frameworks governing the engineering contracts and improvement in integrating design and construction. The aforementioned practice of contracting is more appropriate in North America; yet, it may not be the case in Saudi Arabia where the traditional approach of construction contracting remains dominant. The authors believe there are number of factors related to the gaps in the level of sophistication of the engineering and management of the construction projects in both countries. A step towards improving the Saudi construction practice by adopting the new trend of construction contracting, this paper identifies the reasons why Design/Build form of contracting are not frequently utilized. A field survey, which includes the questionnaire addressing the research problem, is distributed to three main parties of the construction contracts: clients, consultants, and contractors. The analyzed collected data were statistically sufficient to finding the reasons of not adopting the new trend of good practice of deign build approach in Saudi Arabia. In addition, the reasons are: (1) lack of regulation and legal framework; (2) absence of clear criteria of the owner for the trade-off between competing contractors, (3) and lack of experience, knowledge and skill.

Keywords: design built projects, Saudi Arabia, GCC, mega projects

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Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

Abstract:

Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Jean Berger, Nassirou Lo, Martin Noel

Abstract:

Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

Procedia PDF Downloads 376

Authors: Stephanie Ho

Abstract:

This project utilizes principles derived from the Surrealist movement to prioritize creative and critical thinking in secondary English Language Arts (ELA). The implementation of Surrealist-style pedagogies within an ELA classroom will be rooted in critical, radical pedagogy, which addresses the injustices caused by economic-oriented educational systems. The use of critical pedagogy will enable the subversive artistic and political aims of Surrealism to be transmitted to a classroom context. Through aesthetic reading strategies, appreciative questioning and dialogue, students will actively critique the power dynamics which structure (and often restrict) their lives. Within the ELA domain, cost-effective approaches often replace the actual “arts” of ELA. This research will therefore explore how Surrealist-oriented pedagogies could restore imaginative freedom and deconstruct conceptual barriers (normative standards, curricular constraints, and status quo power relations) in secondary ELA. This research will also examine how Surrealism can be used as a political and pedagogical model to treat societal problems mirrored in ELA classrooms. The stakeholders are teachers, as they experience constant pressure within their practices. Similarly, students encounter rigorous, results-based pressures. These dynamics contribute to feelings of powerlessness, thus reinforcing a formulaic model of ELA. The ELA curriculum has potential to create laboratories for critical discussion and active movement towards social change. This proposed research strategy of Surrealist-oriented pedagogies could enable students to experiment with social issues and develop senses of agency and voice that reflect awareness of contemporary society while simultaneously building their ELA skills.

Keywords: arts-informed pedagogies, language arts, literature, surrealism

Procedia PDF Downloads 137