Search results for: topic clustering

Authors: Noawanit Songkram

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This paper is a report on the findings of a study conducted on e–learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education. The methodology of this study was R&D research. The subjects were 18 undergraduate students in Faculty of Education, Chulalongkorn University in the academic year of 2013. The research instruments were a problem solving ability assessment, a team learning evaluation form, and an attitude questionnaire. The data was statistically analyzed using mean, standard deviation, one way repeated measure ANOVA and t–test. The research findings discovered the e –learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education consisted of five components:(1) online collaborative tools, (2) active learning activities, (3) creative thinking, (4) knowledge sharing process, (5) evaluation and nine processes which were (1) preparing in group working, (2) identifying interested topic, (3) analysing interested topic, (4) collecting data, (5) concluding idea (6) proposing idea, (7) creating workings, (8) workings evaluation, (9) sharing knowledge from empirical experience.

Keywords: e-learning system, problem solving ability, team leaning, virtual learning environment

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Authors: Patricia Helena Dos Santos Martins, Mateus Atique, Lucas Oliveira Gomes Ferreira

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There is a growing body of literature that recognizes the importance of bibliometric analysis through the evolutionary nuances of a specific field while shedding light on the emerging areas in that field. Surprisingly, its application in the manufacturing research of vitiviniculture is relatively new and, in many instances, underdeveloped. The aim of this study is to present an overview of the bibliometric methodology, with a particular focus on the Meta-Analytical Approach Theory model – TEMAC, while offering step-by-step results on the available techniques and procedures for carrying out studies about the elements associated with vitiviniculture. Where TEMAC is a method that uses metadata to generate heat maps, graphs of keyword relationships and others, with the aim of revealing relationships between authors, articles and mainly to understand how the topic has evolved over the period study and thus reveal which subthemes were worked on, main techniques and applications, helping to understand that topic under study and guide researchers in generating new research. From the studies carried out using TEMAC, it is possible to raise which are the techniques within the statistical control of processes that are most used within the wine industry and thus assist professionals in the area in the application of the best techniques. It is expected that this paper will be a useful resource for gaining insights into the available techniques and procedures for carrying out studies about vitiviniculture, the cultivation of vineyards, the production of wine, and all the ethnography connected with it.

Keywords: TEMAC, vitiviniculture, statical control of process, quality

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Authors: Francisco Miroslav Ulloa-Stanojlovic, Gina Polo, Ricardo Augusto Dias

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In Brazil, there is a high incidence of rabies in herbivores of economic interest (HEI) transmitted by the common vampire bat Desmodus rotundus, the presence of human rabies cases and the huge economic losses in the world's largest cattle industry, it is important to assist the National Program for Control of Rabies in herbivores in Brazil, that aims to reduce the incidence of rabies in HEI populations, mainly through epidemiological surveillance, vaccination of herbivores and control of vampire-bat roosts. Material and Methods: A spatiotemporal retrospective Kulldorff's spatial scan statistic based on a Poisson model and Monte Carlo simulation and an Anselin's Local Moran's I statistic were used to uncover spatial clustering of HEI rabies from 2000 – 2014. Results: Were identify three important clusters with significant year-to-year variation (Figure 1). In 2000, was identified one area of clustering in the North region, specifically in the State of Tocantins. Between the year 2000 and 2004, a cluster centered in the Midwest and Southeast region including the States of Goiás, Minas Gerais, Rio de Janeiro, Espirito Santo and São Paulo was prominent. And finally between 2000 and 2005 was found an important cluster in the North, Midwest and South region. Conclusions: The HEI rabies is endemic in the country, in addition, appears to be significant differences among the States according to their surveillance services, that may be difficulting the control of the disease, also other factors could be influencing in the maintenance of this problem like the lack of information of vampire-bat roosts identification, and limited human resources for realization of field monitoring. A review of the program control by the authorities it’s necessary.

Keywords: Brazil, Desmodus rotundus, herbivores, rabies

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Authors: Aileen Editha

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The legal recognition of property rights over human genes is a divisive topic to which there is no resolution. As a frequently discussed topic, scholars and practitioners often highlight the inadequacies of a proprietary regime. However, little has been said in regard to the nature of human genetic materials (HGMs). This paper proposes approaching the issue of property over HGMs from an alternative perspective that looks at the personal and social value and valuation of HGMs. This paper will highlight how the unique and unresolved status of HGMs is incompatible with the main tenets of property and, consequently, contributes to legal ambiguity and uncertainty in the regulation of property rights over human genes. HGMs are perceived as part of nature and a free-for-all while also being within an individual’s private sphere. Additionally, it is also considered to occupy a unique “not-private-nor-public” status. This limbo-like position clashes with property’s fundamental characteristic that relies heavily on a clear public/private dichotomy. Moreover, as property is intrinsically linked to the legal recognition of one’s personhood, this irresolution benefits some while disadvantages others. In particular, it demands the publicization of once-private genes for the “common good” but subsequently encourages privatization (through labor) of these now-public genes. This results in the gain of some (already privileged) individuals while enabling the disenfranchisement of members of minority groups, such as Indigenous communities. This paper will discuss real and intellectual property rights over human genes, such as the right to income or patent rights, in Canada and the US. This paper advocates for a sui generis approach to governing rights and interests over human genes that would not rely on having a strict public/private dichotomy. Not only would this improve legal certainty and clarity, but it would also alleviate—or, at the very least, minimize—the role that the current law plays in further entrenching existing systemic inequalities. Despite the specificity of this topic, this paper argues that there are broader lessons to be learned. This issue is an insightful case study on the interconnection of various principles in law, society, and property, and what must be done when discordance between one or more of those principles has detrimental societal outcomes. Ultimately, it must be remembered that property is an adaptable and malleable instrument that can be developed to ensure it contributes to equity and flourishing.

Keywords: property rights, human genetic materials, critical legal scholarship, systemic inequalities

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Ana-Marija Stjepić, Dalia Suša Vugec

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Today, digital transformation is one of the leading topics that occupy the attention of scientific circles and business experts. Organizational success is most often reflected through the successful managing of business processes. Given the growing market for digital innovations and its ever-increasing impact on business, organizations need to be prepared for organizational changes that come with the digital era. In order to maintain their competitive advantage in the global market, organizations must adapt their processes to new digitalization conditions. The main goal of this study is to point out the link between the digital transformation and the business process management concept. Therefore, in order to contribute to the scientific field that explores the potential relation between business process management concept and digital transformation, a literature review has been conducted. Papers have been searched within the Business Process Management Journal by keywords related to the term digital transformation. Selected papers have been analyzed according to the topic, type of publication, year of publication, keywords, etc. The results reveal a growing number of papers published on the topic of digital transformation to the Business Process Management Journal, but the lack of case studies. This paper contributes to the extension of academic literature in this important, yet insufficiently researched, scientific field that creates the bond between two strong concepts of digital transformation and business process management.

Keywords: business process management, digital transformation, digitalization, process change

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Authors: Shivahari Revathi Venkateswaran

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Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.

Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering

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Authors: Marthese Azzopardi, Liberato Camilleri

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This research was performed to investigate the study habits and level of difficulty perceived by post-secondary students in Biology at Advanced-level topics after completing their first year of study. At the end of a two-year ‘sixth form’ course, Maltese students sit for the Matriculation and Secondary Education Certificate (MATSEC) Advanced-level biology exam as a requirement to pursue science-related studies at the University of Malta. The sample was composed of 23 students (16 taking Chemistry and seven taking some ‘Other’ subject at the Advanced Level). The cohort comprised seven males and 16 females. A questionnaire constructed by the authors, was answered anonymously during the last lecture at the end of the first year of study, in May 2016. The Chi square test revealed that gender plays no effect on the various study habits (c² (6) = 5.873, p = 0.438). ‘Reading both notes and textbooks’ was the most common method adopted by males (71.4%), whereas ‘Writing notes on each topic’ was that mostly used by females (81.3%). The Mann-Whitney U test showed no significant difference in the study habits of students and the mean assessment mark obtained at the end of the first year course (p = 0.231). Statistical difference was found with the One-ANOVA test when comparing the mean assessment mark obtained at the end of the first year course when students are clustered by their Secondary Education Certificate (SEC) grade (p < 0.001). Those obtaining a SEC grade of 2 and 3 got the highest mean assessment of 68.33% and 66.9%, respectively [SEC grading is 1-7, where 1 is the highest]. The Friedman test was used to compare the mean difficulty rating scores provided for the difficulty of each topic. The mean difficulty rating score ranges from 1 to 4, where the larger the mean rating score, the higher the difficulty. When considering the whole group of students, nine topics out of 21 were perceived as significantly more difficult than the other topics. Protein synthesis, DNA Replication and Biomolecules were the most difficult, in that order. The Mann-Whitney U test revealed that the perceived level of difficulty in comprehending Biomolecules is significantly lower for students taking Chemistry compared to those not choosing the subject (p = 0.018). Protein Synthesis was claimed as the most difficult by Chemistry students and Biomolecules by those not studying Chemistry. DNA Replication was the second most difficult topic perceived by both groups. The Mann-Whitney U test was used to examine the effect of gender on the perceived level of difficulty in comprehending various topics. It was found that females have significantly more difficulty in comprehending Biomolecules than males (p=0.039). Protein synthesis was perceived as the most difficult topic by males (mean difficulty rating score = 3.14), while Biomolecules, DNA Replication and Protein synthesis were of equal difficulty for females (mean difficulty rating score = 3.00). Males and females perceived DNA Replication as equally difficult (mean difficulty rating score = 3.00). Discovering the students’ study habits and perceived level of difficulty of specific topics is vital for the lecturer to offer guidance that leads to higher academic achievement.

Keywords: biology, perceived difficulty, post-secondary, study habits

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Authors: Muhammad Saleem Ullah Khan Sumbal, Eric Tsui, Ricky Cheong, Eric See To

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Organizations are posed to a threat of valuable knowledge loss when employees leave either due to retirement, resignation, job change or because of disabilities e.g. death, etc. Due to changing economic conditions, globalization, and aging workforce, organizations are facing challenges regarding retention of valuable knowledge. On the one hand, large number of employees are going to retire in the organizations whereas on the other hand, younger generation does not want to work in a company for a long time and there is an increasing trend of frequent job change among the new generation. Because of these factors, organizations need to make sure that they capture the knowledge of employee before (s)he walks out of the door. The first step in this process is to know what type of knowledge employee possesses and whether this knowledge is important for the organization. Researchers reveal in the literature that despite the serious consequences of knowledge loss in terms of organizational productivity and competitive advantage, there has not been much work done in the area of knowledge loss assessment of departing employees. An important step in the knowledge retention process is to determine the critical ‘at risk’ knowledge. Thus, knowledge loss risk assessment is a process by which organizations can gauge the importance of knowledge of the departing employee. The purpose of this study is to explore this topic of knowledge loss risk assessment by conducting a qualitative study in oil and gas sector. By engaging in dialogues with managers and executives of the organizations through in-depth interviews and adopting a grounded methodology approach, the research will explore; i) Are there any measures adopted by organizations to assess the risk of knowledge loss from departing employees? ii) Which factors are crucial for knowledge loss assessment in the organizations? iii) How can we prioritize the employees for knowledge retention according to their criticality? Grounded theory approach is used when there is not much knowledge available in the area under research and thus new knowledge is generated about the topic through an in-depth exploration of the topic by using methods such as interviews and using a systematic approach to analyze the data. The outcome of the study will generate a model for the risk of knowledge loss through factors such as the likelihood of knowledge loss, the consequence/impact of knowledge loss and quality of the knowledge loss of departing employees. Initial results show that knowledge loss assessment is quite crucial for the organizations and it helps in determining what types of knowledge employees possess e.g. organizations knowledge, subject matter expertise or relationships knowledge. Based on that, it can be assessed which employee is more important for the organizations and how to prioritize the knowledge retention process for departing employees.

Keywords: knowledge loss, risk assessment, departing employees, Hong Kong organizations

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Authors: Laurence Doremus

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Freud as the builder of psychoanalysis as a discipline had created the science with Carl Gustav Jung (1875-1961), a psychiatry specialist from Zurich who was very important in the Freudian theory. The knowledge about the foundation of psychoanalysis is often focused on the influence of the works from Breuer or Charcot in the Freudian praxis, at least at the beginning of his career, and Jung's influence is often under-estimated. The paper focuses on the importance of the Jungian contributions in Freud's theories at the beginning of the creation of the discipline in the 1910s. We often meet Jungian schools on the first hand and Freudian schools, on the other hand in the academic field, but the Freudian field has to admit the importance of the Jungian theories in Freudian science. And also, the dialectical energy which appears in the letters exchanged between both of the fathers of psychoanalysis is important to understand the foundations of Freud Theory. That's why the paper will analyze in detail the correspondence between them in an epistemological and historical approach. Effectively the letters were translated and published (in French but also in English and other languages) lately in history and are still not well knew by the researchers in the psychoanalytical field. We well explain how Freud was helped by Jung despite his desire to build the theory. We analyze how the second topic named 'unconscious, preconscious, and conscious', is the result of the first topic that Jung built with Freud. The paper is a contribution to the knowledge we should have about the intense friendship between the two protagonists.

Keywords: Carl Gustav Jung, correspondence, Freud's letters, psychoanalytic theory

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Authors: Samer Muthana Sarsam, Abdul Samad Shibghatullah, Chit Su Mon, Abd Aziz Alias, Hosam Al-Samarraie

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Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education. However, their specific role within the educational context and the level of legal support for their implementation remain unclear. Therefore, this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. To achieve this objective, English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the collected tweets. User sentiments, including disgust, surprise, sadness, anger, fear, joy, anticipation, and trust, as well as positive and negative sentiments, were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey.

Keywords: generative artificial intelligence chatbots, bard, higher education, topic modelling, sentiment analysis

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Authors: Sana Ilyas, Kishalaya Mukherjee, Suresh Shetty

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Lip Augmentation has become more common in recent years. Patients do not expect to experience facial palsy after having lip augmentation. This poster will present the findings of such a presentation and will discuss the possible pathophysiology and management. (This poster has been published as a paper in the dental update, June 2022) Aim: The aim of the study was to explore the link between facial nerve palsy and lip fillers, to explore the literature surrounding facial nerve palsy, and to discuss the case of a patient who presented with facial nerve palsy with seemingly unknown cause. Methodology: There was a thorough assessment of the current literature surrounding the topic. This included published papers in journals through PubMed database searches and printed books on the topic. A case presentation was discussed in detail of a patient presenting with peripheral facial nerve palsy and associating it with lip augmentation that she had a day prior. Results and Conclusion: Even though the pathophysiology may not be clear for this presentation, it is important to highlight uncommon presentations or complications that may occur after treatment. This can help with understanding and managing similar cases, should they arise.It is also important to differentiate cause and association in order to make an accurate diagnosis. This may be difficult if there is little scientific literature. Therefore, further research can help to improve the understanding of the pathophysiology of similar presentations. This poster has been published as a paper in dental update, June 2022, and therefore shares a similar conclusiom.

Keywords: facial palsy, lip augmentation, causation and correlation, dental cosmetics

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: Mahmoud Meckawey

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This research is dealing with one of the most important aspects of maintenance fields, that is Maintenance Strategy. It's the branch which concerns the concepts and the schematic thoughts in how to manage maintenance and how to deal with the defects in the engineering products (buildings, machines, etc.) in general. Through the papers we will act with the followings: i) The Engineering Product & the Technical Systems: When we act with the maintenance process, in a strategic view, we act with an (engineering product) which consists of multi integrated systems. In fact, there is no engineering product with only one system. We will discuss and explain this topic, through which we will derivate a developed definition for the maintenance process. ii) The factors or basis of the functionality efficiency: That is the main factors affect the functional efficiency of the systems and the engineering products, then by this way we can give a technical definition of defects and how they occur. iii) The legality of occurrence of defects (Legal defects and Illegal defects): with which we assume that all the factors of the functionality efficiency been applied, and then we will discuss the results. iv) The Guarantee, the Functional Span Age and the Technical surplus concepts: In the complementation with the above topic, and associated with the Reliability theorems, where we act with the Probability of Failure state, with which we almost interest with the design stages, that is to check and adapt the design of the elements. But in Maintainability we act in a different way as we act with the actual state of the systems. So, we act with the rest of the story that means we have to act with the complementary part of the probability of failure term which refers to the actual surplus of the functionality for the systems.

Keywords: engineering product and technical systems, functional span age, legal and illegal defects, technical and functional surplus

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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Authors: Megi Surmanidze

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The relations between tourists and the population is essentially important to develop tourism. This global branch is an incoming stream and plays the huge economy role in the country is development. This branch is important for adjarian region as well as for Batumi as a touristic city. When lots of tourists visit our city, the relationship and attitudes between tourists and the local population is too important and play the determinant role whether they will visit the country again or not. Receptivness is on actual accompanying process typical for tourism as a growing incoming business direction, and its necessary to be studied the problems and touristic relationships. The aim of the article is to show the importance of receptibility in the business touristic industry also, the topic was to show the dimension scale of receptibility revearing in Batumi population. The topic itself is of hight importance as the touristic business is growing day by day, though the helpful and disturbing social couses were left out of focus were dencely connected to the relations between tourists and the local population. The value is real and directly proportional to the relationship between tourists and the population. The qualitative social research was fulfield. The method to get information was deepened interview. the method gave us the opportunity to get acquented to the actors points of view, also it was suitable points of view, also it was suitable to create favourable conditions to respodents be sincere during interviev and don’t hide their real emotions and the opinions.

Keywords: tourism industry, receptiveness, cultural identity, xenophobia

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Authors: Anderson S. Fonseca, Italo F. S. Da Silva, Robert D. A. Santos, Mayara G. Da Silva, Pedro H. C. Vieira, Antonio M. S. Sobrinho, Victor H. B. Lemos, Petterson S. Diniz, Anselmo C. Paiva, Eliana M. G. Monteiro

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In Brazil, the National Electric Energy Agency (ANEEL) establishes that electrical energy companies are responsible for measuring and billing their customers. Among these regulations, it’s defined that a company must bill your customers within 27-33 days. If a relocation or a change of period is required, the consumer must be notified in writing, in advance of a billing period. To make it easier to organize a workday’s measurements, these companies create a reading plan. These plans consist of grouping customers into reading groups, which are visited by an employee responsible for measuring consumption and billing. The creation process of a plan efficiently and optimally is a capacitated clustering problem with constraints related to homogeneity and compactness, that is, the employee’s working load and the geographical position of the consuming unit. This process is a work done manually by several experts who have experience in the geographic formation of the region, which takes a large number of days to complete the final planning, and because it’s human activity, there is no guarantee of finding the best optimization for planning. In this paper, the GBKMeans method presents a technique based on K-Means and genetic algorithms for creating a capacitated cluster that respects the constraints established in an efficient and balanced manner, that minimizes the cost of relocating consumer units and the time required for final planning creation. The results obtained by the presented method are compared with the current planning of a real city, showing an improvement of 54.71% in the standard deviation of working load and 11.97% in the compactness of the groups.

Keywords: capacitated clustering, k-means, genetic algorithm, districting problems

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Authors: Kayode O. Afolabi, Benson C. Iweriebor, Anthony I. Okoh, Larry C. Obi

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Porcine circovirus type 2 (PCV2) is the major etiological viral agent of porcine multisystemic wasting syndrome (PWMS) and other porcine circovirus-associated diseases (PCVAD) of great economic importance in pig industry globally. In an effort to determine the status of swine herds in the Province as regarding the ‘small but powerful’ viral pathogen; a total of 375 blood, faecal and nasal swab samples were obtained from seven pig farms (commercial and communal) in Amathole, O.R. Tambo and Chris-Hani District Municipalities of Eastern Cape Province between the year 2015 and 2016. Three hundred and thirty nine (339) samples out of the total sample were subjected to molecular screening using PCV2 specific primers by conventional polymerase chain reaction (PCR). Selected sequences were further analyzed and confirmed through genome sequencing and phylogenetic analyses. The data obtained revealed that 15.93% of the screened samples (54/339) from the swine herds of the studied areas were positive for PCV2; while the severity of occurrence of the viral pathogen as observed at farm level ranges from approximately 5.6% to 60% in the studied farms. The Majority, precisely 15 out of 17 (88%) analyzed sequences were found clustering with other PCV2b reference strains in the phylogenetic analysis. More interestingly, two other sequences obtained were also found clustering within PCV2d genogroup, which is presently another fast-spreading genotype with observable higher virulence in global swine herds. This finding confirmed the presence of this all-important viral pathogen in pigs of the region; which could result in a serious outbreak of PCVAD and huge economic loss at the instances of triggering factors if no appropriate measures are taken to curb its spread effectively.

Keywords: pigs, polymerase chain reaction, porcine circovirus type 2, South Africa

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Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

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Authors: Kyung Bae Park, Sung Ho Ha

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Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: latent dirichlet allocation, R program, text mining, topic model, user generated contents, visualization

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Authors: Renata Martins Pacheco, João Claro

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Wildfires are becoming an increasing concern worldwide, causing substantial social, economic, and environmental disruptions. This situation is especially relevant in Mediterranean-climate regions, present in all the five continents of the world, in which fire is not only a natural component of the environment but also perhaps one of the most important evolutionary forces. The rise in wildfire occurrences and their associated impacts suggests the need for identifying knowledge gaps and enhancing the basis of scientific evidence on how managers and policymakers may act effectively to address them. Considering that the main goal of a systematic map is to collate and catalog a body of evidence to describe the state of knowledge for a specific topic, it is a suitable approach to be used for this purpose. In this context, the aim of this study is to systematically map the research trends in wildfire management practices in Mediterranean-climate regions. A total of 201 wildfire management studies were analyzed and systematically mapped in terms of their: Year of publication; Place of study; Scientific outlet; Research area (Web of Science) or Research field (Scopus); Wildfire phase; Central research topic; Main objective of the study; Research methods; and Main conclusions or contributions. The results indicate that there is an increasing number of studies being developed on the topic (most from the last 10 years), but more than half of them are conducted in few Mediterranean countries (60% of the analyzed studies were conducted in Spain, Portugal, Greece, Italy or France), and more than 50% are focused on pre-fire issues, such as prevention and fuel management. In contrast, only 12% of the studies focused on “Economic modeling” or “Human factors and issues,” which suggests that the triple bottom line of the sustainability argument (social, environmental, and economic) is not being fully addressed by fire management research. More than one-fourth of the studies had their objective related to testing new approaches in fire or forest management, suggesting that new knowledge is being produced on the field. Nevertheless, the results indicate that most studies (about 84%) employed quantitative research methods, and only 3% of the studies used research methods that tackled social issues or addressed expert and practitioner’s knowledge. Perhaps this lack of multidisciplinary studies is one of the factors hindering more progress from being made in terms of reducing wildfire occurrences and their impacts.

Keywords: wildfire, Mediterranean-climate regions, management, policy

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: Dimitris Varsamis, Christos Talagkozis, Alkiviadis Tsimpiris, Paris Mastorocostas

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The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated.

Keywords: K-means algorithm, clustering, parallel computations, Matlab

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Authors: Sora Suh, Catherine Michener

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This paper examines the collaborative forms of advocacy that a group of four public school teachers took for their emergent bilingual students in one public school district. While teacher advocacy takes many forms in and out of the classroom, much advocacy work is done by individuals and less by collective action. As a result, individual teachers risk isolation or marginalization in their school contexts when they advocate for immigrant youth. This paper is intended to contribute to the documentation and understanding of teachers’ advocacy work as a collaborative act in teacher education research. The increase of ELs in US classrooms and a corresponding lack of teacher preparation to meet the needs of ELs has motivated the training of educators in linguistically responsive education (e.g., ESL, sheltered English instruction [SEI], bilingual education). Drawing from educational theories of linguistically responsive teaching for preparing educators, we trace the linguistically responsive advocacy work of the teachers. The paper is a multiple case study that tracks how teachers’ discussions on advocacy during a teacher preparation program leading to collaborative actions in their daily teaching lives in and out of school. Data collected includes online discussion forums on the topic of advocacy, course assignments on the topic of advocacy, video-audio recordings of classroom teaching observations, and video-audio recordings of individual and focus group interviews. The findings demonstrate that the teachers’ understanding of advocacy developed through collaborative partnerships formed in the teacher preparation program and grew into active forms of collaborative advocacy in their teaching practice in and out of school. The teachers formed multi-level and collaborative partnerships with teachers, families, community members, policymakers from the local government, and educational researchers to advocate for their emergent bilingual students by planning advocacy events such as new family orientations for emergent bilinguals, professional development for general education teachers on the topic of linguistically responsive instruction, and family nights hosted by the district. The paper’s findings present types of advocacy work in which teachers engage (pedagogical, curricular, out-of-school work) and provide evidence of collaborative advocacy work by a group of engaged educators. The paper highlights the increased agency and effective advocacy of teachers through teacher education and collaborative partnerships and suggests a need for more research on collaborative forms of teacher advocacy for emergent bilinguals.

Keywords: language education, teacher advocacy, language instruction, teacher education

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

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We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

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Authors: Alireza Behfar, Mohammad Mahdavi

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Nowadays, in the light of progress in the world of science, technology and communications, mastery of learning international languages is a sure and needful matter. In learning any language as a second language, progress and achieving a desirable level in speaking is indeed important for approximately all learners. In this research, we find out how preparation can influence L2 learners' oral fluency with respect to individual differences in working memory capacity. The participants consisted of sixty-one advanced L2 learners including MA students of TEFL at Isfahan University as well as instructors teaching English at Sadr Institute in Isfahan. The data collection consisted of two phases: A working memory test (reading span test) and a picture description task, with a one-month interval between the two tasks. Speaking was elicited through speech generation task in which the individuals were asked to discuss four topics emerging in two pairs. The two pairs included one simple and one complex topic and was accompanied by planning time and without any planning time respectively. Each topic was accompanied by several relevant pictures. L2 fluency was assessed based on preparation. The data were then analyzed in terms of the number of syllables, the number of silent pauses, and the mean length of pauses produced per minute. The study offers implications for strategies to improve learners’ both fluency and working memory.

Keywords: two factors, fluency, working memory capacity, preparation, L2 speech production reading span test picture description

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Authors: Saimir Heta, Kers Kapaj, Rialda Xhizdari, Ilma Robo

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Dealing with emerging cases of dental malpractice with justifications that stem from the clear rules of dental ethics is a phenomenon with an increasing trend in today's dental practice. Dentists should clearly understand how far the limit of malpractice goes, with or without minimal or major consequences, for the affected patient, which can be justified as a complication of dental treatment, in support of the rules of dental ethics in the dental office. Indeed, malpractice can occur in cases of lack of professionalism, but it can also come as a consequence of anatomical and physiological limitations in the implementation of the dental protocols, predetermined and indicated by the patient in the paragraph of the treatment plan in his personal card. This study is of the review type with the aim of the latest findings published in the literature about the problem of dealing with these phenomena. The combination of keywords is done in such a way with the aim to give the necessary space for collecting the right information in the networks of publications about this field, always first from the point of view of the dentist and not from that of the lawyer or jurist. From the findings included in this article, it was noticed the diversity of approaches towards the phenomenon depends on the different countries based on the legal basis that these countries have. There is a lack of or a small number of articles that touch on this topic, and these articles are presented with a limited number of data on the same topic. Conclusions: Dental malpractice should not be hidden under the guise of various dental complications that we justify with the strict rules of ethics for patients treated in the dental chair. The individual experience of dental malpractice must be published with the aim of serving as a source of experience for future generations of dentists.

Keywords: dental ethics, malpractice, professional protocol, random deviation

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

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Authors: Julius M. Ndambuki, Gislar E. Kifanyi, Samuel N. Odai, Charles Gyamfi

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Conjunctive management of surface and groundwater resources is a challenging task due to the spatial and temporal variability nature of hydrology as well as hydrogeology of the water storage systems. Surface water-groundwater hydrogeology is highly uncertain; thus it is imperative that this uncertainty is explicitly accounted for, when managing water resources. Various methodologies have been developed and applied by researchers in an attempt to account for the uncertainty. For example, simulation-optimization models are often used for conjunctive water resources management. However, direct application of such an approach in which all realizations are considered at each iteration of the optimization process leads to a very expensive optimization in terms of computational time, particularly when the number of realizations is large. The aim of this paper, therefore, is to introduce and apply an efficient approach referred to as Retrospective Optimization Approximation (ROA) that can be used for optimizing conjunctive use of surface water and groundwater over a multiple hydrogeological model simulations. This work is based on stochastic simulation-optimization framework using a recently emerged technique of sample average approximation (SAA) which is a sampling based method implemented within the Retrospective Optimization Approximation (ROA) approach. The ROA approach solves and evaluates a sequence of generated optimization sub-problems in an increasing number of realizations (sample size). Response matrix technique was used for linking simulation model with optimization procedure. The k-means clustering sampling technique was used to map the realizations. The methodology is demonstrated through the application to a hypothetical example. In the example, the optimization sub-problems generated were solved and analysed using “Active-Set” core optimizer implemented under MATLAB 2014a environment. Through k-means clustering sampling technique, the ROA – Active Set procedure was able to arrive at a (nearly) converged maximum expected total optimal conjunctive water use withdrawal rate within a relatively few number of iterations (6 to 7 iterations). Results indicate that the ROA approach is a promising technique for optimizing conjunctive water use of surface water and groundwater withdrawal rates under hydrogeological uncertainty.

Keywords: conjunctive water management, retrospective optimization approximation approach, sample average approximation, uncertainty

Procedia PDF Downloads 209