Search results for: supervised learning algorithm

Authors: Aliya Moreira, Blake Duffy, Sam Kosinski, Kate Heckman, Erika Steensma

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Introduction: The One Health initiative promotes recognition of the interrelatedness between people, animals, plants, and their shared environment. The field of comparative medicine studies the similarities and differences between humans and animals for the purpose of advancing medical sciences. Currently, medical school education is narrowly focused on human anatomy and physiology, but as the COVID-19 pandemic has demonstrated, a holistic understanding of health requires comprehension of the interconnection between health and the lived environment. To prepare future physicians for unique challenges from emerging zoonoses to climate change, medical students can benefit from exposure to and experience with One Health and Comparative Medicine content. Methods: In January 2020, an elective course for medical students on One Health and Comparative Medicine was created to provide medical students with the background knowledge necessary to understand the applicability of animal and environmental health in medical research and practice. The 2-week course was continued in January 2021, with didactic and experiential activities taking place virtually due to the COVID-19 pandemic. In response to student feedback, lectures were added to expand instructional content on zoonotic and wildlife diseases for the second iteration of the course. Other didactic sessions included interprofessional lectures from 20 physicians, veterinarians, public health professionals, and basic science researchers. The first two cohorts of students were surveyed regarding One Health and Comparative Medicine concepts at the beginning and conclusion of the course. Results: 16 medical students have completed the comparative medicine course thus far, with 87.5% (n=14) completing pre-and post-course evaluations. 100% of student respondents indicated little to no exposure to comparative medicine or One Health concepts during medical school. Following the course, 100% of students felt familiar or very familiar with comparative medicine and One Health concepts. To assess course efficacy, questions were evaluated on a five-point Likert scale. 100% agreed or strongly agreed that learning Comparative Medicine and One Health topics augmented their medical education. 100% agreed or strongly agreed that a course covering this content should be regularly offered to medical students. Conclusions: Data from the student evaluation surveys demonstrate that the Comparative Medicine course was successful in increasing medical student knowledge of Comparative Medicine and One Health. Results also suggest that interprofessional training in One Health and Comparative Medicine is applicable and useful for medical trainees. Future iterations of this course could capitalize on the inherently interdisciplinary nature of these topics by enrolling students from veterinary and public health schools into a longitudinal course. Such recruitment may increase the course’s value by offering multidisciplinary student teams the opportunity to conduct research projects, thereby strengthening both the individual learning experience as well as sparking future interprofessional research ventures. Overall, these efforts to educate medical students in One Health topics should be reproducible at other institutions, preparing more future physicians for the diverse challenges they will encounter in practice.

Keywords: medical education, interprofessional instruction, one health, comparative medicine

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Authors: Reza Alikhani

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This research focuses on the establishment of horizontal cooperation among companies to enhance their operational efficiency and competitiveness. The study proposes an approach to horizontal collaboration, called coalition configuration, which involves partnering companies sharing distribution centers in a network design problem. The paper investigates which coalition should be formed in each distribution center to minimize the total cost of the network. Moreover, potential uncertainties, such as operational and disruption risks, are considered during the collaborative design phase. To address this problem, a two-stage robust optimization model for collaborative distribution network design under surging demand and facility disruptions is presented, along with a column-and-constraint generation algorithm to obtain exact solutions tailored to the proposed formulation. Extensive numerical experiments are conducted to analyze solutions obtained by the model in various scenarios, including decisions ranging from fully centralized to fully decentralized settings, collaborative versus non-collaborative approaches, and different amounts of uncertainty budgets. The results show that the coalition formation mechanism proposes some solutions that are competitive with the savings of the grand coalition. The research also highlights that collaboration increases network flexibility and resilience while reducing costs associated with demand and capacity uncertainties.

Keywords: logistics, warehouse sharing, robust facility location, collaboration for resilience

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: Panupong Makvichian

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Global Navigation Satellite System (GNSS) is nowadays a common technology that improves navigation functions in our life. Additionally, GNSS is also being employed on behalf of an accurate atmospheric sensor these times. Meteorology is a practical application of GNSS, which is unnoticeable in the background of people’s life. GNSS Precise Point Positioning (PPP) is a positioning method that requires data from a single dual-frequency receiver and precise information about satellite positions and satellite clocks. In addition, careful attention to mitigate various error sources is required. All the above data are combined in a sophisticated mathematical algorithm. At this point, the research is going to demonstrate how GNSS and PPP method is capable to provide high-precision estimates, such as 3D positions or Zenith tropospheric delays (ZTDs). ZTDs combined with pressure and temperature information allows us to estimate the water vapor in the atmosphere as precipitable water vapor (PWV). If the process is replicated for a network of GNSS sensors, we can create thematic maps that allow extract water content information in any location within the network area. All of the above are possible thanks to the advances in GNSS data processing. Therefore, we are able to use GNSS data for climatic trend analysis and acquisition of the further knowledge about the atmospheric water content.

Keywords: GNSS, precise point positioning, Zenith tropospheric delays, precipitable water vapor

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Authors: Juhee Lee, Yongjun Lee

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In designing a low-energy-consuming buildings, the heat transfer through a large glass or wall becomes critical. Multiple layers of the window glasses and walls are employed for the high insulation. The gravity driven air flow between window glasses or wall layers is a natural heat convection phenomenon being a key of the heat transfer. For the first step of the natural heat transfer analysis, in this study the development and application of a finite volume method for the numerical computation of viscous incompressible flows is presented. It will become a part of the natural convection analysis with high-order scheme, multi-grid method, and dual-time step in the future. A finite volume method based on a fully-implicit second-order is used to discretize and solve the fluid flow on unstructured grids composed of arbitrary-shaped cells. The integrations of the governing equation are discretised in the finite volume manner using a collocated arrangement of variables. The convergence of the SIMPLE segregated algorithm for the solution of the coupled nonlinear algebraic equations is accelerated by using a sparse matrix solver such as BiCGSTAB. The method used in the present study is verified by applying it to some flows for which either the numerical solution is known or the solution can be obtained using another numerical technique available in the other researches. The accuracy of the method is assessed through the grid refinement.

Keywords: finite volume method, fluid flow, laminar flow, unstructured grid

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Authors: Gulshad Azatova, Aleksandr Kapitonov, Natig Aminov

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Multiagent systems have gained significant attention in various fields, such as robotics, autonomous vehicles, and distributed computing, where multiple agents cooperate and communicate to achieve complex tasks. Efficient communication among agents is a crucial aspect of these systems, as it directly impacts their overall performance and scalability. This scholarly work provides an exploration of essential communication elements and conducts a comparative assessment of diverse protocols utilized in multiagent systems. The emphasis lies in scrutinizing the strengths, weaknesses, and applicability of these protocols across various scenarios. The research also sheds light on emerging trends within communication protocols for multiagent systems, including the incorporation of machine learning methods and the adoption of blockchain-based solutions to ensure secure communication. These trends provide valuable insights into the evolving landscape of multiagent systems and their communication protocols.

Keywords: communication, multi-agent systems, protocols, consensus

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Authors: Hao Wang, Shengchun Wang, Weidong Wang

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On the influence of train vibration and environmental noise on the measurement of track wear, we proposed a method for automatic extraction of circular arc on the inner or outer side of the rail waist and achieved the high-precision registration of rail profile. Firstly, a polynomial fitting method based on truncated residual histogram was proposed to find the optimal fitting curve of the profile and reduce the influence of noise on profile curve fitting. Then, based on the curvature distribution characteristics of the fitting curve, the interval search algorithm based on dynamic window’s maximum curvature entropy was proposed to realize the automatic segmentation of small circular arc. At last, we fit two circle centers as matching reference points based on small circular arcs on both sides and realized the alignment from the measured profile to the standard designed profile. The static experimental results show that the mean and standard deviation of the method are controlled within 0.01mm with small measurement errors and high repeatability. The dynamic test also verified the repeatability of the method in the train-running environment, and the dynamic measurement deviation of rail wear is within 0.2mm with high repeatability.

Keywords: curvature entropy, profile registration, rail wear, structured light, train-running

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Authors: Arlene Villarama, Micol Grace Guanzon, Zenaida Ramos

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This paper provides insights into teaching English as a Foreign Language in the Philippines. The authors reviewed relevant theories and literature, and provide an analysis of the issues in teaching English in the Philippine setting in the light of these theories. The authors made an investigation in Bagong Barrio National High School (BBNHS) - a public school in Caloocan City. The institution has a population of nearly 3,000 students. The performances of randomly chosen 365 respondents were scrutinised. The study regarding the success of teaching English as a foreign language to Filipino children were highlighted. This includes the respondents’ family background, surroundings, way of living, and their behavior and understanding regarding education. The results show that there is a significant relationship between demonstrative, communal, and logical areas that touch the efficacy of introducing English as a foreign Dialectal. Filipino children, by nature, are adventurous and naturally joyful even for little things. They are born with natural skills and capabilities to discover new things. They highly consider activities and work that ignite their curiosity. They love to be recognised and are inspired the most when given the assurance of acceptance and belongingness. Fun is the appealing influence to ignite and motivate learning. The magic word is excitement. The study reveals the many facets of the accumulation and transmission of erudition, in introduction and administration of English as a foreign phonological; it runs and passes through different channels of diffusion. Along the way, there are particles that act as obstructions in protocols where knowledge are to be gathered. Data gained from the respondents conceals a reality that is beyond one’s imagination. One significant factor that touches the inefficacy of understanding and using English as a foreign language is an erroneous outset gained from an old belief handed down from generation to generation. This accepted perception about the power and influence of the use of language, gives the novices either a negative or a positive notion. The investigation shows that a higher number of dislikes in the use of English can be tracked down from the belief of the story on how the English language came into existence. The belief that only the great and the influential have the right to use English as a means of communication kills the joy of acceptance. A significant notation has to be examined so as to provide a solution or if not eradicate the misconceptions that lie behind the substance of the matter. The result of the authors’ research depicts a substantial correlation between the emotional (demonstrative), social (communal), and intellectual (logical). The focus of this paper is to bring out the right notation and disclose the misconceptions with regards to teaching English as a foreign language. This will concentrate on the emotional, social, and intellectual areas of the Filipino learners and how these areas affect the transmittance and accumulation of learning. The authors’ aim is to formulate logical ways and techniques that would open up new beginnings in understanding and acceptance of the subject matter.

Keywords: accumulation, behaviour, facets, misconceptions, transmittance

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Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

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Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Shao-Hsun Keng, Hwang-Ruey Song

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This paper examines the causal effect of the tenure track system on instructors’ grading practices and teaching effectiveness by taking advantage of a natural experiment in Taiwan. The results show that assistant professors subject to the tenure track policy are more likely to grade leniently and fail fewer students. The course grade is 5% higher in classes taught by assistant professors subject to the tenure system. However, the tendency to grade leniently is reversed after assistant professors subject to the tenure system are promoted to a higher rank. Our findings are consistent with the exchange theory. We also show that teaching and student efforts are adversely affected by the tenure policy, which could reduce student learning and the quality of the workforce in the long run.

Keywords: tenure track system, grading leniency, study time, grade inflation

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: Firdaous Zair, Zoubir Elfelsoufi, Mohammed Fourka

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The allocation of resources is an issue that is needed on the tactical and operational strategic plan. This work considers the allocation of resources in the case of pure players, manufacturers and Click & Mortars that have launched online sales. The aim is to improve the level of customer satisfaction and maintaining the benefits of e-retailer and of its cooperators and reducing costs and risks. Our contribution is a decision support system and tool for improving the allocation of resources in logistics chains e-commerce B2C context. We first modeled the B2C chain with all operations that integrates and possible scenarios since online retailers offer a wide selection of personalized service. The personalized services that online shopping companies offer to the clients can be embodied in many aspects, such as the customizations of payment, the distribution methods, and after-sales service choices. In addition, every aspect of customized service has several modes. At that time, we analyzed the optimization problems of supply chain resource allocation in customized online shopping service mode, which is different from the supply chain resource allocation under traditional manufacturing or service circumstances. Then we realized an optimization model and algorithm for the development based on the analysis of the allocation of the B2C supply chain resources. It is a multi-objective optimization that considers the collaboration of resources in operations, time and costs but also the risks and the quality of services as well as dynamic and uncertain characters related to the request.

Keywords: e-commerce, supply chain, B2C, optimisation, resource allocation

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Authors: Houxiang Zhu, Chun Liang

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The CRISPR-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different gRNA sequences. The published CRISPR-Cpf1 gRNA data was reanalyzed. Many sequences and structural features of gRNAs (e.g., the position-specific nucleotide composition, position-nonspecific nucleotide composition, GC content, minimum free energy, and melting temperature) correlated with target efficiency were found. Using machine learning technology, a support vector machine (SVM) model was created to predict target efficiency for any given gRNAs. The first web service application, CRISPR-DT (CRISPR DNA Targeting), has been developed to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing.

Keywords: CRISPR-Cpf1, genome editing, target efficiency, target specificity

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Nidhi Kaushal, Sanjit Mishra

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Narrative writings in literature are ample source of knowledge and easily understandable. In every old tradition, we found that people learn ethics from oral tales. They had their leaders and lessons of leadership in their stories. In India, we have sufficient amount of stories of leaders. Whether the story is of an ordinary person or a corporate leader of large firm, it always has a unique message of motivation. The objective of this paper is to elaborate the story lines in literature and get the leadership lessons from them, so that we can set up a new concept of leadership based on scholarship of literature. This is our hypothesis that leadership lessons can be learned from the study of literary writings and it can also act an innovative way of learning the management skills through literature. The role of the leader can be familiarly communicated in the form of the tales. Describing a positive psychological narrative from the text is the best way to manifesting an idea into the minds of people. We accomplished this paper that leadership as an attribute can be learned from the folk psychological literary writings.

Keywords: leadership, literature, management, psychology

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Authors: Chunfang Zhou

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The growing issues of sustainability have increased the discussions on how to foster “green engineers” from diverse perspectives in both contexts of education and organizations. As creativity has been considered as the first stage of innovation process that can also be regarded as a path to sustainability, this paper will particularly propose creativity as a scientific literacy meaning a collection of awareness, ability, and skills about sustainability. From this sense, creativity should be an element in IT engineering education and organizational learning programmes, since IT engineers are one group of key actors in designing, researching and developing social media products that are most important channels of improving public awareness of sustainability. This further leads this paper to discuss by which pedagogical strategies and by which training methods in organizations, creativity and sustainability can be integrated into IT engineering education and IT enterprise innovation process in order to meeting the needs of ‘creative engineers’ in the society changes towards sustainability. Accordingly, this paper contributes to future work on the links between creativity, innovation, sustainability, and IT engineering development both theoretically and practically.

Keywords: creativity, innovation, IT engineers, sustainability

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Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: Mikko Salminen, Simo Järvelä, Niklas Ravaja

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One of the central models of emotional intelligence (EI) is that of Mayer and Salovey’s, which includes ability to monitor own feelings and emotions and those of others, ability to discriminate different emotions, and to use this information to guide thinking and actions. There is vast amount of previous research where positive links between EI and, for example, leadership successfulness, work outcomes, work wellbeing and organizational climate have been reported. EI has also a role in the effectiveness of work teams, and the effects of EI are especially prominent in jobs requiring emotional labor. Thus, also the organizational context must be taken into account when considering the effects of EI on work outcomes. Based on previous research, it is suggested that EI can also protect managers from the negative consequences of stress. Stress may have many detrimental effects on the manager’s performance in essential work tasks. Previous studies have highlighted the effects of stress on, not only health, but also, for example, on cognitive tasks such as decision-making, which is important in managerial work. The motivation for the current study came from the notion that, unfortunately, many stressed individuals may not be aware of the circumstance; periods of stress-induced physiological arousal may be prolonged if there is not enough time for recovery. To tackle this problem, physiological stress levels of managers were collected using recording of heart rate variability (HRV). The goal was to use this data to provide the managers with feedback on their stress levels. The managers could access this feedback using a www-based learning environment. In the learning environment, in addition to the feedback on stress level and other collected data, also developmental tasks were provided. For example, those with high stress levels were sent instructions for mindfulness exercises. The current study focuses on the relation between the measured physiological stress levels and EI of the managers. In a pilot study, 33 managers from various fields wore the Firstbeat Bodyguard HRV measurement devices for three consecutive days and nights. From the collected HRV data periods (minutes) of stress and recovery were detected using dedicated software. The effects of EI on HRV-calculated stress indexes were studied using Linear Mixed Models procedure in SPSS. There was a statistically significant effect of total EI, defined as an average score of Schutte’s emotional intelligence test, on the percentage of stress minutes during the whole measurement period (p=.025). More stress minutes were detected on those managers who had lower emotional intelligence. It is suggested, that high EI provided managers with better tools to cope with stress. Managing of own emotions helps the manager in controlling possible negative emotions evoked by, e.g., critical feedback or increasing workload. High EI managers may also be more competent in detecting emotions of others, which would lead to smoother interactions and less conflicts. Given the recent trend to different quantified-self applications, it is suggested that monitoring of bio-signals would prove to be a fruitful direction to further develop new tools for managerial and leadership coaching.

Keywords: emotional intelligence, leadership, heart rate variability, personality, stress

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Authors: Ma'amun Muhammed

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The ability of smartphones in enhancing communication, providing access to business and serving as a pool for information retrieval has a far reaching and potentially beneficial impacts on enhancing teaching in higher institutions in the developing countries like Nigeria. Nigeria as one of the fast growing economies in Africa, whose citizens patronize smartphones can utilize this opportunity by inculcating the culture of using smartphones not only for communication, business transaction, banking etc. but also for enhancing teaching in the higher institutions. Smartphones have become part and parcel of our lives, particularly among young people. The primary objective of this paper is to ascertain the use of smartphones in enhancing teaching in Nigeria’s higher institutions, to achieve this, content analysis was used thoroughly. This paper examines the opportunities offered by smartphones to the students of higher institutions of learning, the challenges being faced by lecturers of these institutions in classrooms. Lastly, it offers solution on how some of these critical challenges will be overcame, so as to utilize the technology of these devices.

Keywords: communication, information retrieval, mobile phone, smartphones teaching

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Authors: S. S. Kenny Lee, C. C. Kong, S. K. Ting

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Teaching engineering lab provides opportunity for students to practice theories learned through physical experiment in the laboratory. However, building laboratories to accommodate increased number of students are expensive, making it impossible for an educational institution to afford the high expenses. In this paper, we develop a low cost and remote platform to aid teaching undergraduate students. The platform is constructed where the real experiment setting up in laboratory can be reconfigure and accessed remotely, the aim is to increase student’s desire to learn at which they can interact with the physical experiment using network enabled devices at anywhere in the campus. The platform is constructed with Raspberry Pi as a main control board that provides communication between computer interfaces to the actual experiment preset in the laboratory. The interface allows real-time remote viewing and triggering the physical experiment in the laboratory and also provides instructions and learning guide about the experimental.

Keywords: engineering lab, low cost, network, remote platform, reconfigure, real-time

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Authors: Qing Zhang, Jiachen Nie, Yujia Wen, Guanyuan Kou, Peng Yu, Kun Xia, Qin Yang, Li Ding

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BACKGROUND: A facility layout problem (FLP) is an NP-complete (non-deterministic polynomial) problem, which is hard to obtain an exact optimal solution. FLP has been widely studied in various limited spaces and workflows. For example, cafeterias with many types of equipment for troops cause chaotic processes when dining. OBJECTIVE: This article tried to optimize the layout of troops’ cafeteria and to improve the overall efficiency of the dining process. METHODS: First, the original cafeteria layout design scheme was analyzed from an ergonomic perspective and two new design schemes were generated. Next, three facility layout models were designed, and further simulation was applied to compare the total time and density of troops between each scheme. Last, an experiment of the dining process with video observation and analysis verified the simulation results. RESULTS: In a simulation, the dining time under the second new layout is shortened by 2.25% and 1.89% (p<0.0001, p=0.0001) compared with the other two layouts, while troops-flow density and interference both greatly reduced in the two new layouts. In the experiment, process completing time and the number of interference reduced as well, which verified corresponding simulation results. CONCLUSIONS: Our two new layout schemes are tested to be optimal by a series of simulation and space experiments. In future research, similar approaches could be applied when taking layout-design algorithm calculation into consideration.

Keywords: layout optimization, dining efficiency, troops’ cafeteria, anylogic simulation, field experiment

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Authors: Mohamed Nabhan

Abstract:

The research aims to build a measure of sensory preferences for (wrestling and boxing) players. The researchers used the descriptive approach and a sample of (8) consisting of (40) wrestling players, (40) boxing players with different scales, and they were chosen in a deliberate random way, and the most important results were that there were statistically significant differences between wrestlers and boxers in the sensory preferences of their senses. There is no indication in the sensory preferences for the senses of “sight and hearing” and that the significance is in favor of the wrestlers in the senses of “sight and touch,” and there is a convergence in the sense of hearing. Through the value of the averagesAfter collecting the data and statistical treatments and the results reached by the researcher, it was possible to reach: The following conclusions and recommendations: There are differences between wrestling and boxing players in their sensory preferences, the senses used in learning, due to several reasons, the most important of which may be as follows:- Scales for the player and for each sport separately. The nature of the game, the performance of skills, and dealing with the opponent or competitor.Tools used in performance and training.

Keywords: sensory preferences, sensory scale, wrestling players, boxing players

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Authors: Ya-Hui Lee, Ching-Yi Lu, Hui-Chuan Wei

Abstract:

The purpose of this research was to explore the relation between proactive coping and well-being of middle-aged adults. We conducted survey research that with t-test, one way ANOVA, Pearson correlation and stepwise multiple regression to analyze. This research drew on a sample of 395 participants from the senior learning centers of Taiwan. The results provided the following findings: 1.The participants from different residence areas associated significant difference with proactive coping, but not with well-being. 2. The participants’ perceived of financial level associated significant difference with both proactive coping and well-being. 3. There was significant difference between participants’ income and well-being. 4. The proactive coping was positively correlated with well-being. 5. From stepwise multiple regression analysis showed that two dimensions of proactive coping had positive predictability. Finally, these results of this study can be provided as references for designing older adult educational programs in Taiwan.

Keywords: middle-age and older adults, learners, proactive coping, well-being

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Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde

Abstract:

In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affects the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.

Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance

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Authors: Rahime Filiz Kiremit

Abstract:

This study identifies six different articles to explain which strategies are most effective for kindergarten English Language Learners. The literature review project has information about six different research articles, purpose of the studies, and results of the studies. There are several strategies can be used for ELL students to help them to develop their English language skills. Some articles mention technology as a multimedia integrated into the curriculum, some of them mention writing as a method of learning English as a second language. However, they all have a common strategy that is shared reading. According to these six articles, shared reading has a big role of ELL students’ language developmental process. All in all, read-aloud with multimedia enhancement strategy is the best strategy to use in the classroom, because this strategy is based on shared reading and also integrated with technology.

Keywords: bilingual education, effective strategies, english language learners, kindergarten

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

Abstract:

In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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Authors: Ajai Singh

Abstract:

Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

Procedia PDF Downloads 346