Search results for: machine learning approach for neurological disorder assessment

Authors: Pornnapat Berndt

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The purpose of this research is to explore challenge and opportunities to establish cultural learning center of Wayang Artwork for creative tourism within the house of Mr. Sa-ngat Jaiprom. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, questionnaires (pretest and posttest), basic interviews, in-depth interviews and interviewed of key local informants. The study also uses both primary data and secondary data. From research result, it is revealed that the sample groups more realized valuable heritage value after learning about the history of wayang and the way to practices. The sample group indicated that it not too difficult for them to carving Wayang artwork as they have knowledge about Thai art before. However, in their opinion, they comment that it might difficult for others who have no basic knowledge to learn to carve wayang artwork.

Keywords: creative tourism, local community, cultural learning center, wayang artwork  

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Pooja Booluck

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A U-shaped learning curve entails a three-step process: a good performance followed by a bad performance followed by a good performance again. U-shaped curves have been observed not only in language acquisition but also in various fields such as temperature face recognition object permanence to name a few. Building on previous studies of the curve child language acquisition and Second Language Acquisition this empirical study seeks to investigate the relevance of the U-shaped learning model to the acquisition of the difference between cest and il est in the English Learners of French context. The present study was developed to assess whether older learners of French in the ELF context follow the same acquisition pattern. The empirical study was conducted on 15 English learners of French which lasted six weeks. Compositions and questionnaires were collected from each subject at three time intervals (after one week after three weeks after six weeks) after which students work were graded as being either correct or incorrect. The data indicates that there is evidence of a U-shaped learning curve in the acquisition of cest and il est and students did follow the same acquisition pattern as children in regards to rote-learned terms and subject clitics. This paper also discusses the need to introduce modules on U-shaped learning curve in teaching curriculum as many teachers are unaware of the trajectory learners undertake while acquiring core components in grammar. In addition this study also addresses the need to conduct more research on the acquisition of rote-learned terms and subject clitics in SLA.

Keywords: child language acquisition, rote-learning, subject clitics, u-shaped learning model

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Pamela Del Rosario, Elisabetta Palumbo, Marzia Traverso

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The building sector plays an important role in addressing pressing environmental issues such as climate change and resource scarcity. The energy performance of buildings is considerably affected by the external envelope. In fact, a considerable proportion of the building energy demand is due to energy losses through the windows. Nevertheless, according to literature, to pay attention only to the contribution of windows to the building energy performance, i.e., their influence on energy use during building operation, could result in a partial evaluation. Hence, it is important to consider not only the building energy performance but also the environmental performance of windows, and this not only during the operational stage but along its complete life cycle. Life Cycle Assessment (LCA) according to ISO 14040:2006 and ISO 14044:2006+A1:2018 is one of the most adopted and robust methods to evaluate the environmental performance of products throughout their complete life cycle. This life-cycle based approach avoids the shift of environmental impacts of a life cycle stage to another, allowing to allocate them to the stage in which they originated and to adopt measures that optimize the environmental performance of the product. Moreover, the LCA method is widely implemented in the construction sector to assess whole buildings as well as construction products and materials. LCA is regulated by the European Standards EN 15978:2011, at the building level, and EN 15804:2012+A2:2019, at the level of construction products and materials. In this work, the environmental performance of wooden windows was assessed by implementing the LCA method and adopting primary data. More specifically, the emphasis is given to embedded and operational impacts. Furthermore, correlations are made between these environmental impacts and aspects such as type of wood and window transmittance. In the particular case of the operational impacts, special attention is set on the definition of suitable maintenance scenarios that consider the potential climate influence on the environmental impacts. For this purpose, a literature review was conducted, and expert consultation was carried out. The study underlined the variability of the embedded environmental impacts of wooden windows by considering different wood types and transmittance values. The results also highlighted the need to define appropriate maintenance scenarios for precise assessment results. It was found that both the service life and the window maintenance requirements in terms of treatment and its frequency are highly dependent not only on the wood type and its treatment during the manufacturing process but also on the weather conditions of the place where the window is installed. In particular, it became evident that maintenance-related environmental impacts were the highest for climate regions with the lowest temperatures and the greatest amount of precipitation.

Keywords: embedded impacts, environmental performance, life cycle assessment, LCA, maintenance stage, operational impacts, wooden windows

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: G. Medkour Ishak-Boushaki, A. Taibi, M. Allab

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Quantitative environmental radioactivity measurements are needed to determine the level of exposure of a population to ionizing radiations and for the assessment of the associated risks. Gamma spectrometry remains a very powerful tool for the analysis of radionuclides present in an environmental sample but the basic problem in such measurements is the low rate of detected events. Using large environmental samples could help to get around this difficulty but, unfortunately, new issues are raised by gamma rays attenuation and self-absorption. Recently, a new method has been suggested, to detect and identify without quantification, in a short time, a gamma ray of a low count source. This method does not require, as usually adopted in gamma spectrometry measurements, a pulse height spectrum acquisition. It is based on a chronological record of each detected photon by simultaneous measurements of its energy ε and its arrival time τ on the detector, the pair parameters [ε,τ] defining an event mode sequence (EMS). The EMS serials are analyzed sequentially by a Bayesian approach to detect the presence of a given radioactive source. The main object of the present work is to test the applicability of this sequential approach in radioactive environmental materials detection. Moreover, for an appropriate health oversight of the public and of the concerned workers, the analysis has been extended to get a reliable quantification of the radionuclides present in environmental samples. For illustration, we consider as an example, the problem of detection and quantification of 238U. Monte Carlo simulated experience is carried out consisting in the detection, by a Ge(Hp) semiconductor junction, of gamma rays of 63 keV emitted by 234Th (progeny of 238U). The generated EMS serials are analyzed by a Bayesian inference. The application of the sequential Bayesian approach, in environmental radioactivity analysis, offers the possibility of reducing the measurements time without requiring large environmental samples and consequently avoids the attached inconvenient. The work is still in progress.

Keywords: Bayesian approach, event mode sequence, gamma spectrometry, Monte Carlo method

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Authors: Sadeq Al Yaari, Fayza Al Hammadi, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari, Salah Al Yami

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Background: When we further take Arabic grammatical issues into account in accordance with applied linguistic investigations on Arabic as Second Language (ASL) learners, a fundamental issue arises at this point as to the production of speech in Arabic: Oral grammatical errors committed by ASL learners. Aims: Using manual rating as well as computational analytic methodology to test a corpus of recorded speech by Second Language (ASL) learners of Arabic, this study aims to find the areas of difficulties in learning Arabic grammar. More specifically, it examines how and why ASL learners make grammatical errors in their oral speech. Methods: Tape recordings of four (4) Arabic as Second Language (ASL) learners who ranged in age from 23 to 30 were naturally collected. All participants have completed an intensive Arabic program (two years) and 20 minute-speech was recorded for each participant. Having the collected corpus, the next procedure was to rate them against Arabic standard grammar. The rating includes four processes: Description, analysis and assessment. Conclusions: Outcomes made from the issues addressed in this paper can be summarized in the fact that ASL learners face many grammatical difficulties when studying Arabic word order, tenses and aspects, function words, subject-verb agreement, verb form, active-passive voice, global and local errors, processes-based errors including addition, omission, substitution or a combination of any of them.

Keywords: grammar, error, oral, Arabic, second language, learner, applied linguistics.

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: Florin Leon

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This paper describes a simulator of traffic scenarios tailored to facilitate autonomous driving model training for urban environments. With the rising prominence of self-driving vehicles, the need for diverse datasets is very important. The proposed simulator provides a flexible framework that allows the generation of custom scenarios needed for the validation and enhancement of trajectory prediction algorithms. Its controlled yet dynamic environment addresses the challenges associated with real-world data acquisition and ensures adaptability to diverse driving scenarios. By providing an adaptable solution for scenario creation and algorithm testing, this tool proves to be a valuable resource for advancing autonomous driving technology that aims to ensure safe and efficient self-driving vehicles.

Keywords: autonomous driving, car simulator, machine learning, model training, urban simulation environment

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Authors: Ofelia A. Damag

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The study was focused on the development and assessment of the illustrated language activities of the 1996 Edition of the Portage Guide to Early Education. It determined the extent of appropriateness, applicability, time efficiency and aesthetics of the illustrated language activities to be used as instructional material not only by teachers, but parents and caregivers as well. The eclectic research design was applied in this study using qualitative and quantitative methods. To determine the applicability and time efficiency of the study, a try out was done. Since the eclectic research design was used, it made use of a researcher-made survey questionnaire and focus group discussion. Analysis of the data was done through weighted mean and ANOVA. The respondents of the study were representatives of Special Education (SPED) teachers, caregivers and parents of a special-needs child, particularly with difficulties in learning basic language skills. The results of the study show that a large number of respondents are SPED teachers and caregivers and are mostly college graduates. Many of them have earned units towards Master’s studies. Moreover, a majority of the respondents have not attended seminars or in-service training in early intervention for them to be more competent in the area of specialization. It is concluded that the illustrated language activities under review in this study are appropriate, applicable, time efficient and aesthetic for use as a tool in teaching. The recommendations are focused on the advocacy for SPED teachers, caregivers and parents of special-needs children to be more consistent in the implementation of the new instructional materials as an aid in an intervention program.

Keywords: illustrated language activities, inclusion, portage guide to early education, special educational needs

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Authors: Alexander A. Tokmakov

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Cell-free protein synthesis is widely used to synthesize recombinant proteins. It allows genome-scale expression of various polypeptides under strictly controlled uniform conditions. However, only a minor fraction of all proteins can be successfully expressed in the systems of protein synthesis that are currently used. The factors determining expression success are poorly understood. At present, the vast volume of data is accumulated in cell-free expression databases. It makes possible comprehensive bioinformatics analysis and identification of multiple features associated with successful cell-free expression. Here, we describe an approach aimed at identification of multiple physicochemical and structural properties of amino acid sequences associated with protein solubility and aggregation and highlight major correlations obtained using this approach. The developed method includes: categorical assessment of the protein expression data, calculation and prediction of multiple properties of expressed amino acid sequences, correlation of the individual properties with the expression scores, and evaluation of statistical significance of the observed correlations. Using this approach, we revealed a number of statistically significant correlations between calculated and predicted features of protein sequences and their amenability to cell-free expression. It was found that some of the features, such as protein pI, hydrophobicity, presence of signal sequences, etc., are mostly related to protein solubility, whereas the others, such as protein length, number of disulfide bonds, content of secondary structure, etc., affect mainly the expression propensity. We also demonstrated that amenability of polypeptide sequences to cell-free expression correlates with the presence of multiple sites of post-translational modifications. The correlations revealed in this study provide a plethora of important insights into protein folding and rationalization of protein production. The developed bioinformatics approach can be of practical use for predicting expression success and optimizing cell-free protein synthesis.

Keywords: bioinformatics analysis, cell-free protein synthesis, expression success, optimization, recombinant proteins

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Authors: Ann Dashwood

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Using Standard Australian English with confidence is a cultural expectation of parents of primary school aged children who want to engage effectively with their children’s teachers and school administration. That confidence in support of their children’s learning at school is seldom experienced by parents whose first language is not English. Sharing language with competence in an intercultural environment is the common denominator for meaningful communication and engagement to occur in a school community. Experience in relevant, interactive sessions is known to enhance engagement and participation. The purpose of this paper is to identify a pedagogy for parents otherwise isolated from daily use of functional Australian cultural language learned to engage effectively in their children’s learning at school. The outcomes measure parents’ intercultural engagement with classroom teachers and attention to the school’s administrative procedures using quantitative and qualitative methods. A principled communicative task-based language learning approach, combined with intercultural communication strategies provide the theoretical base for intensive English inquiry-based learning and engagement. The quantitative analysis examines data samples collected by classroom teachers and administrators and parents’ writing samples. Interviews and observations qualitatively inform the study. Currently, significant numbers of projects are active in community centers and schools to enhance English language knowledge of parents from Language Backgrounds Other Than English (LBOTE). The study is significant to explore the effects of an intensive English pedagogy with parents of varied English language backgrounds, by targeting inquiry-based language use for social interactions in the school and wider community, specific engagement and cultural interaction with teachers and school activities and procedures.

Keywords: engagement, intercultural communication, language teaching pedagogy, LBOTE, school community

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Authors: Franco Primo Soffietti, Diego Fernando Turello, Federico Pinto

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Shear waves that propagate through the ground impose deformations that must be taken into account in the design and assessment of buried longitudinal structures such as tunnels, pipelines, and piles. Conventional engineering approaches for seismic evaluation often rely on a Euler-Bernoulli beam models supported by a Winkler foundation. This approach, however, falls short in capturing the distortions induced when the structure is subjected to shear waves. To overcome these limitations, in the present work an analytical solution is proposed considering a Timoshenko beam and including transverse and rotational springs. The present research proposes ground springs derived as closed-form analytical solutions of the equations of elasticity including the seismic wavelength. These proposed springs extend the applicability of previous plane-strain models. By considering variations in displacements along the longitudinal direction, the presented approach ensures the springs do not approach zero at low frequencies. This characteristic makes them suitable for assessing pseudo-static cases, which typically govern structural forces in kinematic interaction analyses. The results obtained, validated against existing literature and a 3D Finite Element model, reveal several key insights: i) the cutoff frequency significantly influences transverse and rotational springs; ii) neglecting displacement variations along the structure axis (i.e., assuming plane-strain deformation) results in unrealistically low transverse springs, particularly for wavelengths shorter than the structure length; iii) disregarding lateral displacement components in rotational springs and neglecting variations along the structure axis leads to inaccurately low spring values, misrepresenting interaction phenomena; iv) transverse springs exhibit a notable drop in resonance frequency, followed by increasing damping as frequency rises; v) rotational springs show minor frequency-dependent variations, with radiation damping occurring beyond resonance frequencies, starting from negative values. This comprehensive analysis sheds light on the complex behavior of embedded longitudinal structures when subjected to shear waves and provides valuable insights for the seismic assessment.

Keywords: shear waves, Timoshenko beams, Winkler springs, sol-structure interaction

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: Jiongbin Liu, William Yeoh, Shang Gao, Xiaoliang Meng, Yuhan Zhu

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The metaverse concept has sparked significant interest in both academic and industrial spheres. As educational institutions increasingly adopt this technology, understanding its implementation becomes crucial. In response, we conducted a comprehensive case study at a large university, systematically analyzing the nine stages of metaverse development initiatives. Our study unveiled critical insights into the planning, assessment, and execution processes, offering invaluable guidance for stakeholders. The findings highlight both the opportunities for enhanced learning experiences and the challenges related to technological integration and social interaction in higher education.

Keywords: metaverse, metaverse development framework, higher education, case study

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Authors: Khodi Morgan, Kasey Crowe, Amanda Morgan

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Canadian High School Students' Attitudes & Objective: To survey Canadian high school students regarding their attitudes and perspectives towards people with disabilities and explore how age, gender, and personal experience with a disability may impact these views. Methods: A survey was developed using the standardized Attitude Toward Persons With Disability Scale as its base, with the addition of questions specifically about Autism and Attention Deficit Hyperactivity Disorder (ADHD). The survey also gathered information about the participant’s age and gender and whether or not they, or a close family member, had any disabilities. Participants were recruited at a public Canadian high school by fellow student researchers. Results: A total of 219 (N=219) students ranging from 13 - 19 years old participated in the study (m= 15.9 years of age). Gender was equally split, with 44% male, 42% female and 14% undeclared. Experience with a disability was common amongst participants, with 25% self-identifying as having a personal disability and 48% claiming to have a close family member with a disability. Exploratory trends indicated that females, people with self-identified disabilities, and people with close family members with disabilities trended towards having more positive attitudes toward persons with disabilities.

Keywords: disability, autism, ADHD, high school, adolescence, community research, acceptance

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Authors: Mathias Linden, André Schneider, Harald F. O. von Korflesch

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The risk of train accidents is an ongoing concern for railway organizations, governments, insurance companies and other depended sectors. Safety technologies are installed to reduce and to prevent potential damages of train accidents. Since the budgetary for the safety of railway organizations is limited, it is necessary not only to achieve a high availability and high safety standard but also to be cost effective. Therefore, an economic assessment of safety technologies is fundamental to create an accurate risk analysis. In order to conduct an economical assessment of a railway safety technology and a quantification of the costs of the accident causes, the Global Railway Accident Database & Evaluation (GRADE) has been developed. The aim of this paper is to describe the structure of this accident database and to show how it can be used for risk analyses. A number of risk analysis methods, such as the probabilistic safety assessment method (PSA), was used to demonstrate this accident database’s different possibilities of risk analysis. In conclusion, it can be noted that these analyses would not be as accurate without GRADE. The information gathered in the accident database was not available in this way before. Our findings are relevant for railway operators, safety technology suppliers, assurances, governments and other concerned railway organizations.

Keywords: accident causes, accident costs, accident database, global railway accident database & evaluation, GRADE, probabilistic safety assessment, PSA, railway accidents, risk analysis

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Authors: Maide Yesilyurt, Saide Faydalı

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Patients experience pain at different intensities in postoperative. The diagnosis of the pain, the assessment and the success of the treatment and care make the measurement of this finding compulsory. The aim of the study is to determine the evaluation differences numeric pain scales. The descriptive study was conducted with 360 patients with in postoperative. The data were obtained from questionnaires related to six numeric pain scales most preferred in clinical use, and a face-to-face interview technique was used by the researcher. Regarding to numeric pain scale, questions include forth positive and one negative statement. In evaluating the data; chi-square and Pearson correlation tests were used. For the study, the patients’ informed consents, the institution and the ethics committee received permission. In this study, patients' ages are between 18-80, 95.8% of the patients were not informed about pain assessment. Patients evaluated the 5-item numeric scale as the easy, can be answered quickly, accurate, and appropriate for clinical use and the 101 items numeric scale as complex than other scales. Regarding to numeric pain scales with positive statements between age, marital status, educational status, previous surgery, having chronic disease and getting information about pain assessment significant difference has been detected. All numeric pain scales are correlated to each other. As a result, it was determined that as the items in the numerical scales decreased, the patients were able to perceive the scales better, and the items in the scales increased, the patients were in trouble to understand.

Keywords: numeric pain scales, nurse, pain assessment, patient

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Authors: Bekir O. Bartin, Kaan Ozbay, Sandeep Mudigonda, Hong Yang

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The use of microscopic traffic simulation in evaluating the operational and safety conditions at toll plazas is demonstrated. Two toll plazas in New Jersey are selected as case studies and were developed and validated in Paramics traffic simulation software. In order to simulate drivers’ lane selection behavior in Paramics, a utility-based lane selection approach is implemented in Paramics Application Programming Interface (API). For each vehicle approaching the toll plaza, a utility value is assigned to each toll lane by taking into account the factors that are likely to impact drivers’ lane selection behavior, such as approach lane, exit lane and queue lengths. The results demonstrate that similar operational conditions, such as lane-by-lane toll plaza traffic volume can be attained using this approach. In addition, assessment of safety at toll plazas is conducted via a surrogate safety measure. In particular, the crash index (CI), an improved surrogate measure of time-to-collision (TTC), which reflects the severity of a crash is used in the simulation analyses. The results indicate that the spatial and temporal frequency of observed crashes can be simulated using the proposed methodology. Further analyses can be conducted to evaluate and compare various different operational decisions and safety measures using microscopic simulation models.

Keywords: microscopic simulation, toll plaza, surrogate safety, application programming interface

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Authors: Antti Nurminen, Avleen Malhi

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Significant progress in intelligent systems in combination with exceedingly wide application domains having machine learning as the core technology are usually opaque, non-intuitive, and commonly complex for human users. We use innovative IoT technology which monitors and analyzes moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants. For plant health monitoring, we construct a system yielding the Normalized Difference Vegetation Index (NDVI), supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions, and bootstrap our AI with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.

Keywords: explainable artificial intelligence, intelligent agent, IoT, NDVI

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Authors: Ruilin Zhu, Katarzyna Olcoń, Rose M. Pulliam, Dorie J. Gilbert

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Cultural humility is increasingly important in social work literature, given its emphasis on mitigating power imbalances in helping relationships, particularly across cultural differences. Consequently, there is a need to understand whether and how cultural humility can be taught in social work education. Relying on ethnographic observations and reflective journals from a cultural immersion program, this study identified the learning process required to develop cultural humility: confusion and discomfort, re-moulding, and humility in action.

Keywords: social work education, cultural humility, transformative learning theory, study abroad, ethnographic observations

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Authors: Narendra Babu Bommenahalli Veerabhadrappa

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Teaching sustainability concepts along with profit maximizing philosophy of business in management education is a challenge. This paper explores and evaluates various learning models to inculcate sustainability concepts in management education. The paper explains about a new pedagogy that was tested in a business management school (Indus Business Academy, Bangalore, India) to teach sustainability. The pedagogy was designed by intertwining concepts related to sustainability with digital marketing concepts. As part of this experimental method, students (in groups) were assigned with various topics of sustainability and were asked to work with concepts of digital marketing and thus market the concepts of sustainability. The paper explains as a case study as to how sustainability was integrated with digital marketing tools and how learning towards sustainability was facilitated. It also explains the outcomes of this pedagogical method, in terms of inculcating sustainability concepts amongst management students as well as marketing and proliferation of sustainability concepts to bring about the behavioral changes amongst target audience towards sustainability.

Keywords: management-education, pedagogy, sustainability, behavior

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Authors: Haoyu Zhao, Qianshu Ma, Min Xie, Yunqi Huang, Yunjia Liu, Huan Song, Hongsheng Gui, Mingli Li, Qiang Wang

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Rationale: Bipolar disorder (BD) and major depressive disorder (MDD), as severe chronic illnesses that restrict patients’ psychosocial functioning and reduce their quality of life, are both categorized into mood disorders. Emerging evidence has suggested that the reliability of self-rated health (SRH) was wellvalidated and that the risk of various health outcomes, including mortality and health care costs, could be predicted by SRH. Compared with other lengthy multi-item patient-reported outcomes (PRO) measures, SRH was proven to have a comparable predictive ability to predict mortality and healthcare utilization. However, to our knowledge, no study has been conducted to assess the association between SRH and hospitalization among people with mental disorders. Therefore, our study aims to determine the association between SRH and subsequent all-cause hospitalizations in patients with BD and MDD. Methods: We conducted a prospective cohort study on people with BD or MDD in the UK from 2006 to 2010 using UK Biobank touchscreen questionnaire data and linked administrative health databases. The association between SRH and 2-year all-cause hospitalizations was assessed using proportional hazard regression after adjustment for sociodemographics, lifestyle behaviors, previous hospitalization use, the Elixhauser comorbidity index, and environmental factors. Results: A total of 29,966 participants were identified, experiencing 10,279 hospitalization events. Among the cohort, the average age was 55.88 (SD 8.01) years, 64.02% were female, and 3,029 (10.11%), 15,972 (53.30%), 8,313 (27.74%), and 2,652 (8.85%) reported excellent, good, fair, and poor SRH, respectively. Among patients reporting poor SRH, 54.19% had a hospitalization event within 2 years compared with 22.65% for those having excellent SRH. In the adjusted analysis, patients with good, fair, and poor SRH had 1.31 (95% CI 1.21-1.42), 1.82 (95% CI 1.68-1.98), and 2.45 (95% CI 2.22, 2.70) higher hazards of hospitalization, respectively, than those with excellent SRH. Conclusion: SRH was independently associated with subsequent all-cause hospitalizations in patients with BD or MDD. This large study facilitates rapid interpretation of SRH values and underscores the need for proactive SRH screening in this population, which might inform resource allocation and enhance high-risk population detection.

Keywords: severe mental illnesses, hospitalization, risk prediction, patient-reported outcomes

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Authors: Nikhil Byanna, Abdolghani Ebrahimi, Diego Klabjan

Abstract:

How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman’s performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.

Keywords: offensive lineman, player performance, NFL, machine learning

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Authors: J. Tamosaitiene

Abstract:

The article focuses on the identification and classification of key risk management criteria that represent the most important sustainability aspects of the construction industry. The construction sector is one of the most important sectors in Lithuania. Nowadays, the assessment of the risk level of a construction project is especially important for the quality of construction projects, the growth of enterprises and the sector. To establish the most important criteria for successful growth of the sector, a questionnaire for experts was developed. The analytic hierarchy process (AHP), the expert judgement method and other multicriteria decision making (MCDM) methods were used to develop the hybrid model. The results were used to develop an integrated knowledge system for the measurement of a risk level particular to construction projects. The article presents a practical case that details the developed system, sustainable aspects, and risk assessment.

Keywords: risk, system, model, construction

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Authors: Heeyeul Kwon, Yongtae Park

Abstract:

Despite the highly touted benefits, emerging technologies have unleashed pervasive concerns regarding unintended and unforeseen social impacts. Thus, those wishing to create safe and socially acceptable products need to identify such side effects and mitigate them prior to the market proliferation. Various methodologies in the field of technology assessment (TA), namely Delphi, impact assessment, and scenario planning, have been widely incorporated in such a circumstance. However, literatures face a major limitation in terms of sole reliance on participatory workshop activities. They unfortunately missed out the availability of a massive untapped data source of futuristic information flooding through the Internet. This research thus seeks to gain insights into utilization of futuristic data, future-oriented documents from the Internet, as a supplementary method to generate social impact scenarios whilst capturing perspectives of experts from a wide variety of disciplines. To this end, network analysis is conducted based on the social keywords extracted from the futuristic documents by text mining, which is then used as a guide to produce a comprehensive set of detailed scenarios. Our proposed approach facilitates harmonized depictions of possible hazardous consequences of emerging technologies and thereby makes decision makers more aware of, and responsive to, broad qualitative uncertainties.

Keywords: emerging technologies, futuristic data, scenario, text mining

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Authors: Priti Saha, Biswajit Paul

Abstract:

Rapid urbanization and industrialization have increased the demand of groundwater. However, the present era has evident an enormous level of groundwater pollution. Therefore, water quality assessment is paramount importance to evaluate its suitability for drinking, irrigation and industrial use. This study focus to evaluate the groundwater quality of an industrial city in eastern India through interdisciplinary techniques. The multi-purpose Water Quality Index (WQI) assess the suitability for drinking as well as irrigation of forty sampling locations, where 2.5% and 15% of sampling locations have excellent water quality (WQI:0-25) as well as 15% and 40% have good quality (WQI:25-50), which represents its suitability for drinking and irrigation respectively. However, the industrial water quality was assessed through Ryznar Stability Index (LSI), which affirmed that only 2.5% of sampling locations have neither corrosive nor scale forming properties (RSI: 6.2-6.8). These techniques with the integration of geographical information system (GIS) for spatial assessment indorsed its effectiveness to identify the regions where the water bodies are suitable to use for drinking, irrigation as well as industrial activities. Further, the sources of these contaminants were identified through factor analysis (FA), which revealed that both the geogenic as well as anthropogenic sources were responsible for groundwater pollution. This research demonstrates the effectiveness of statistical and GIS techniques for the analysis of environmental contaminants.

Keywords: groundwater, water quality analysis, water quality index, WQI, factor analysis, FA, spatial assessment

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Authors: Brooke Sweeney, David White, Felix Amparano, Nick A. Clark, Amy R. Beck, Mathew Lindquist, Lora Edwards, Julie Vandal, Jennifer Lisondra, Katie Cox, Renee Arensberg, Allen Cummins, Jazmine Cedeno, Jason D. Fraser, Kelsey Dean, Helena H. Laroche, Cristina Fernandez

Abstract:

Background: National organizations call for standardized pre-surgical requirements and education to optimize postoperative outcomes. Since 2017 our surgery program has used defined protocols and educational curricula pre- and post-surgery. In response to patient outcomes, our educational content was refined to include quizzes to assess patient knowledge and surgical preparedness. We aim to optimize adolescent pre-bariatric surgery preparedness by improving overall aggregate pre-surgical assessment performance from 68% to 80% within 12 months. Methods: A multidisciplinary improvement team was developed within the weight management clinic (WMC) of our tertiary care, free-standing children’s hospital. A manual has been utilized since 2017, with limitations in consistent delivery and patient uptake of information. The curriculum has been improved to include quizzes administered during WMC visits prior to bariatric surgery. The initial outcome measure is the pre-surgical quiz score of adolescents preparing for bariatric surgery. Process measure was the number of questions answered correctly to test the questions. Baseline performance was determined by a patient assessment survey of pre-surgical preparedness at patient visits. Plan-Do-Study-Act cycles (PDSA) included: 1) creation and implementation of a refined curriculum, 2) development of 5 new quizzes based upon learning objectives, and 3) improving provider-lead teaching and quiz administration within clinic workflow. Run charts assessed impact over time. Results: A total of 346 quiz questions were administered to 34 adolescents. The outcome measure improved from a baseline mean of 68% to 86% following PDSA 2 cycles, and it was sustained. Conclusion/Implication: Patient/family comprehension of surgical preparedness improved with standardized education via team member-led teaching and assessment using quizzes during pre-surgical clinic visits. The next steps include launching redesigned teaching materials with modules correlated to quizzes and assessment of comprehension and outcomes post-surgically.

Keywords: bariatric surgery, adolescent, clinic, pre-bariatric training

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