Search results for: English language learning experiences

Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Azzah Alghamdi

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The Interactive White Boards (IWBs) as one of the innovative educational technologies have been extensively investigated in advanced countries such as the UK, US, and Australia. However, there is a significant lack of research studies, which mainly examine the use of IWBs in Saudi Arabia. Therefore, this study aims to investigate the attitudes of primary teachers towards using IWBs in both the teaching and learning processes. Moreover, it aims to investigate if there is any significant difference between male teachers and females regarding their attitudes towards using this technology. This study concentrated on teachers in primary schools, which participated in Tatweer project in the city of Jeddah, in Saudi Arabia. Mixed methods approach was employed in this study using a designed questionnaire, classroom observations, and a semi-structured interview. 587 teachers (286 men and 301 women) from Tatweer primary schools were completed the questionnaire as well as twenty teachers were interviewed including seven female teachers were observed in their classrooms. The findings of this study indicated that approximately 11% of the teachers within the sample (n=587) had negative attitudes towards the use of IWBs in the teaching and learning processes. However, the majority of them nearly 89% agreed about the benefits of using IWBs in their classrooms. Additionally, all the twenty teachers who were interviewed (including the seven observed female teachers) had positive attitudes towards the use of these technologies. Moreover, 87% of male teachers and 91% of female teachers who completed the questionnaire accepted the usefulness of using IWBs in improving their teaching and students' learning. Thus, this indicates that there was no significant difference between male and female teachers in Tatweer primary schools in terms of their views about using these innovative technologies in their lessons. The findings of the current study will help the Ministry of Education to improve the policies of using IWBs in Saudi Arabia. Indeed, examining teachers’ attitudes towards IWBs is a very important issue because they are the main users in classrooms. Hence, their views should be considered to addressing the powers and boundaries of using IWBs. Moreover, students will feel comfortable to use IWBs if their teachers accept and use them well.

Keywords: IWBs, Saudi teachers’ views, Tatweer schools, teachers' gender

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Authors: Fernanda Mary R. G. Da Silva, Adriana C. F. Mozzambani, Marcelo F. Mello

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Introduction: Sexual assault against children and adolescents is a public health problem with serious consequences on their quality of life, especially for those who develop post-traumatic stress disorder (PTSD). The broad literature in this research area points to greater losses in verbal learning, explicit memory, speed of information processing, attention and executive functioning in PTSD. Objective: To compare the neuropsychological functions of adolescents from 14 to 17 years of age, victims of sexual violence with PTSD with those of healthy controls. Methodology: Application of a neuropsychological battery composed of the following subtests: WASI vocabulary and matrix reasoning; Digit subtests (WISC-IV); verbal auditory learning test RAVLT; Spatial Span subtest of the WMS - III scale; abbreviated version of the Wisconsin test; concentrated attention test - D2; prospective memory subtest of the NEUPSILIN scale; five-digit test - FDT and the Stroop test (Trenerry version) in adolescents with a history of sexual violence in the previous six months, referred to the Prove (Violence Care and Research Program of the Federal University of São Paulo), for further treatment. Results: The results showed a deficit in the word coding process in the RAVLT test, with impairment in A3 (p = 0.004) and A4 (p = 0.016) measures, which compromises the verbal learning process (p = 0.010) and the verbal recognition memory (p = 0.012), seeming to present a worse performance in the acquisition of verbal information that depends on the support of the attentional system. A worse performance was found in list B (p = 0.047), a lower priming effect p = 0.026, that is, lower evocation index of the initial words presented and less perseveration (p = 0.002), repeated words. Therefore, there seems to be a failure in the creation of strategies that help the mnemonic process of retention of the verbal information necessary for learning. Sustained attention was found to be impaired, with greater loss of setting in the Wisconsin test (p = 0.023), a lower rate of correct responses in stage C of the Stroop test (p = 0.023) and, consequently, a higher index of erroneous responses in C of the Stroop test (p = 0.023), besides more type II errors in the D2 test (p = 0.008). A higher incidence of total errors was observed in the reading stage of the FDT test p = 0.002, which suggests fatigue in the execution of the task. Performance is compromised in executive functions in the cognitive flexibility ability, suggesting a higher index of total errors in the alternating step of the FDT test (p = 0.009), as well as a greater number of persevering errors in the Wisconsin test (p = 0.004). Conclusion: The data from this study suggest that sexual violence and PTSD cause significant impairment in the neuropsychological functions of adolescents, evidencing risk to quality of life in stages that are fundamental for the development of learning and cognition.

Keywords: adolescents, neuropsychological functions, PTSD, sexual violence

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Authors: L. R. Valencia Pérez, J. M. Peña Aguilar, A. Lamadrid Álvarez, A. Pastrana Palma, H. F. Valencia Pérez, M. Vivanco Vargas

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This work proposes a Cooperation-Competitive (Coopetitive) approach that allows coordinated work among the Secretary of Public Education (SEP), the Autonomous University of Querétaro (UAQ) and government funds from National Council for Science and Technology (CONACYT) or some other international organizations. To work on an overall knowledge transfer strategy with e-learning over the Cloud, where experts in junior high and high school education, working in multidisciplinary teams, perform analysis, evaluation, design, production, validation and knowledge transfer at large scale using a Cloud Computing platform. Allowing teachers and students to have all the information required to ensure a homologated nationally knowledge of topics such as mathematics, statistics, chemistry, history, ethics, civism, etc. This work will start with a pilot test in Spanish and initially in two regional dialects Otomí and Náhuatl. Otomí has more than 285,000 speaking indigenes in Queretaro and Mexico´s central region. Náhuatl is number one indigenous dialect spoken in Mexico with more than 1,550,000 indigenes. The phase one of the project takes into account negotiations with indigenous tribes from different regions, and the Information and Communication technologies to deliver the knowledge to the indigenous schools in their native dialect. The methodology includes the following main milestones: Identification of the indigenous areas where Otomí and Náhuatl are the spoken dialects, research with the SEP the location of actual indigenous schools, analysis and inventory or current schools conditions, negotiation with tribe chiefs, analysis of the technological communication requirements to reach the indigenous communities, identification and inventory of local teachers technology knowledge, selection of a pilot topic, analysis of actual student competence with traditional education system, identification of local translators, design of the e-learning platform, design of the multimedia resources and storage strategy for “Cloud Computing”, translation of the topic to both dialects, Indigenous teachers training, pilot test, course release, project follow up, analysis of student requirements for the new technological platform, definition of a new and improved proposal with greater reach in topics and regions. Importance of phase one of the project is multiple, it includes the proposal of a working technological scheme, focusing in the cultural impact in Mexico so that indigenous tribes can improve their knowledge about new forms of crop improvement, home storage technologies, proven home remedies for common diseases, ways of preparing foods containing major nutrients, disclose strengths and weaknesses of each region, communicating through cloud computing platforms offering regional products and opening communication spaces for inter-indigenous cultural exchange.

Keywords: Mexicans indigenous tribes, education, knowledge transfer, cloud computing, otomi, Náhuatl, language

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Sumalee Tientongdee

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In this study, it was aimed to investigate Physics achievement of the pre-service science teacher studying in general science program at Suan Sunandha Rajabhat University, Bangkok, Thailand. The program has provided the new curriculum that focuses on 21st-century skills development. Active learning approaches are used to teach in all subjects. One of the active learning approaches Peer Instruction, or PI was used in this study to teach physics of waves as a compulsory course. It was conducted in the second semester from January to May of 2017. The concept test was given to evaluate pre-service science teachers’ understanding in concept of waves. Problem-solving assessment form was used to evaluate their problem-solving skill. The results indicated that after they had learned through Peer Instruction in physics of waves course, their concepts in physics of waves was significantly higher at 0.05 confident levels. Their problem-solving skill from the whole class was at the highest level. Based on the group interview on the opinions of using Peer Instruction in Physics class, they mostly felt that it was very useful and helping them understand more about physics, especially for female students.

Keywords: peer instruction, physics of waves, pre-service science teacher, Suan Sunandha Rajabhat university

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Jaya Bishnu Pradhan

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Weaving mat is one of the common activities performed in different community generally in the rural part of Nepal. Mat weavers’ practice mathematical ideas and concepts implicitly in order to perform their job. This study is intended to uncover the mathematical ideas embedded in mat weaving that can help teachers and students for the teaching and learning of school geometry. The ethnographic methodology was used to uncover and describe the beliefs, values, understanding, perceptions, and attitudes of the mat weavers towards mathematical ideas and concepts in the process of mat weaving. A total of 4 mat weavers, two mathematics teachers and 12 students from grade level 6-8, who are used to participate in weaving, were selected for the study. The whole process of the mat weaving was observed in a natural setting. The classroom observation and in-depth interview were taken with the participants with the help of interview guidelines and observation checklist. The data obtained from the field were categorized according to the themes regarding mathematical ideas embedded in the weaving activities, and its possibilities in teaching learning of school geometry. In this study, the mathematical activities in different sectors of their lives, their ways of understanding the natural phenomena, and their ethnomathematical knowledge were analyzed with the notions of pluralism. From the field data, it was found that the mat weaver exhibited sophisticated geometrical ideas in the process of construction of frame of mat. They used x-test method for confirming if the mat is rectangular. Mat also provides a good opportunity to understand the space geometry. A rectangular form of mat may be rolled up when it is not in use and can be converted to a cylindrical form, which usually can be used as larder so as to reserve food grains. From the observation of the situations, this cultural experience enables students to calculate volume, curved surface area and total surface area of the cylinder. The possibilities of incorporation of these cultural activities and its pedagogical use were observed in mathematics classroom. It is argued that it is possible to use mat weaving activities in the teaching and learning of school geometry.

Keywords: ethnography, ethnomathematics, geometry, mat weaving, school pedagogy

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

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Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

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Authors: Preeti Yadav, Dandeswar Bisoyi, Debkumar Chakrabati

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For many years organization have been exercising a creative technique of applying brand mascots, which results in making a visual ‘ambassador’ of a brand. The goal of mascot’s is just not confined to strengthening the brand identity, improving customer perception, but also acting as a vehicle of anthropomorphic translation towards the consumer. Such that it helps in embracing the power of recognition and processing the experiences happening in our daily lives. The study examines the relationship between the specific mascot features and brand attitude. It eliminates that mascot trust is an important mediator of the mascot features on brand attitude. Anthropomorphic characters turn out to be the key players despite the application of brand mascots in today’s marketing.

Keywords: advertising, mascot, branding, recall

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Authors: Sarah Maiter

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Heightened globalization, migration, displacement of citizens, and refugee needs is putting increasing demand for approaches to social services for diverse populations that responds to families to ensure the safety and protection of vulnerable members while providing supports and services. Along with this social works re-focus on socially just approaches to practice increasingly asks social workers to consider the challenging circumstances of families when providing services rather than a focus on individual shortcomings alone. Child protection workers then struggle to ensure safety of children while assessing the needs of families. This assessment can prove to be difficult when providing services to immigrant, refugee, and racially diverse families as understanding of and familiarity with these families is often limited. Furthermore, child protection intervention in western countries is state mandated having legal authority when intervening in the lives of families where child protection concerns have been identified. Within this context, racialized immigrant and refugee families are at risk of misunderstandings that can result in interventions that are overly intrusive, unhelpful, and harsh. Research shows disproportionality and overrepresentation of racial and ethnic minorities, and immigrant families in the child protection system. Reasons noted include: a) possibilities of racial bias in reporting and substantiating abuse, b) struggles on the part of workers when working with families from diverse ethno-racial backgrounds and who are immigrants and may have limited proficiency in the national language of the country, c) interventions during crisis and differential ongoing services for these families, d) diverse contexts of these families that poses additional challenges for families and children, and e) possible differential definitions of child maltreatment. While cultural and ethnic diversity in child rearing approaches have been cited as contributors to child protection concerns, this approach should be viewed cautiously as it can result in stereotyping and generalizing that then results in inappropriate assessment and intervention. However, poverty and the lack of social supports, both well-known contributors to child protection concerns, also impact these families disproportionately. Child protection systems, therefore, need to continue to examine policy and practice approaches with these families that ensures safety of children while balancing the needs of families. This presentation provides data from several research studies that examined definitions of child maltreatment among a sample of racialized immigrant families, experiences of a sample of immigrant families with the child protection system, concerns of a sample of child protection workers in the provision of services to these families, and struggles of families in the transitions to their new country. These studies, along with others provide insights into areas of consideration for practice that can contribute to safety for children while ensuring just and equitable responses that have greater potential for keeping families together rather than premature apprehension and removal of children to state care.

Keywords: child protection, child welfare services, immigrant families, racial and ethnic diversity

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Shougi Suliman Abosuliman, Arun Kumar, Firoz Alam

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Disaster preparedness is a key success factor for any effective disaster management practices. This paper evaluates the disaster preparedness and management in Saudi Arabia using an empirical investigation approach. It presents the results of the survey conducted by interviewing representatives of the Saudi decision-makers and administrators responsible for disaster control in Jeddah before, during and after flooding in 2009 and 2010. First, demographics of the respondents are presented, followed by quantitative analysis of their views and experiences regarding the Kingdom’s readiness before and after each flood. This is shown as a series of dependent and independent variables. Following this is a list of respondents’ priorities for disaster preparation in the Kingdom.

Keywords: disaster response policy, crisis management, effective service delivery, Jeddah

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Arash Khosravi, Mohamad Nazir Ahmad

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Today’s economy is a knowledge-based economy in which knowledge is a crucial facilitator to individuals, as well as being an instigator of success. Due to the impact of globalization, universities face new challenges and opportunities. Accordingly, they ought to be more innovative and have their own competitive advantages. One of the most important goals of universities is the promotion of students as professional knowledge workers. Therefore, knowledge sharing and transferring at tertiary level between students and supervisors is vital in universities, as it decreases the budget and provides an affordable way of doing research. Knowledge-sharing impact factors can be categorized into three groups, namely: organizational, individual and technical factors. There are some individual barriers to knowledge sharing, namely: lack of time and trust, lack of communication skills and social networks. IT systems such as e-learning, blogs and portals can increase knowledge sharing capability. However, it must be stated that IT systems are only tools and not solutions. Individuals are still responsible for sharing information and knowledge. This paper proposes new research model to examine the effect of individual factors and organisational factors, namely: learning strategy, trust culture, supervisory support, as well as technological factor on knowledge sharing in a research supervision process at the University of Technology Malaysia.

Keywords: knowledge management, knowledge sharing, research supervision, knowledge transferring

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Authors: Andrea Knezevic, Padmini Pai, Julaine Allan, Katarzyna Olcoń, Louisa Smith

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Healthcare staff are on the frontline during times of disaster and are required to support the health and wellbeing of communities despite any personal adversity and trauma they are experiencing as a result of the disaster. This study explored the experiences of healthcare staff trained as ‘Wellness Warriors’ following the 2019-2020 Australian bushfires. The findings indicated that healthcare staff developed interpersonal skills around deep listening and connecting with others which allowed them to feel differently about work and restored their faith in healthcare leadership.

Keywords: Australian bushfires, burnout, health care providers, mental health, occupational trauma, post-disaster, wellbeing, workplace wellness

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Authors: Afshan Liaquat

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Pakistan is a multicultural country, and people are divided across political and religious values. The major objective of this study is to investigate the pedagogical relevance of the poetry of Rumi and Blake for culturally diverse undergraduate classes in Pakistani universities in Lahore. The study was based on a survey research design. A closed-ended questionnaire was developed for data collection from 100 students purposively selected from two universities in Lahore. The findings of the study indicate that intercultural poetry with the theme of Love, written by poets like Rumi and Blake, needs to be taught at the undergraduate level. The study has implications for students, teachers, and genre-based syllabus designers associated with teaching English Literature in Pakistani universities.

Keywords: intercultural literature, globalization, spiritual love, teaching of cross-cultural literature

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Authors: Paul Flynn, Luke O’Donnell

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There is a pressing societal need for educators in formal and non-formal settings to develop pedagogical frameworks, programmes, and interventions that support the development of transversal skills for life beyond the classroom. These skills include communication, collaboration, interpersonal relationship building, problem-solving, and planning, and organizational skills; or lifeworld literacies encountered first hand. This is particularly true for young people aged between 15-18. This demographic represents both the future of society and those best positioned to take advantage of well-designed, structured educational supports within and across formal and non-formal settings. Secondary school history has been identified as an appropriate area of study which deftly develops many of those transversal skills so crucial to positive societal engagement. However, in the formal context, students often challenge history’s relevance to their own lived experience and dismiss it as a study option. In response to such challenges, teachers will often design stimulating lessons which are often well-received. That said, some students continue to question modern-day connections, presenting a persistent and pervasive classroom distraction. The continuing decline in numbers opting to study second-level history indicates an erosion of what should be a critical opportunity to develop all-important lifeworld literacies within formal education. In contrast, students readily acknowledge relevance in non-formal settings where many participants meaningfully engage with history by way of student-focused activities. Furthermore, many do so without predesigned pedagogical aids which support transversal skills development as embodied learning outcomes. As this paper will present, there is a dearth of work pertaining to the circular subject of history and its embodied learning outcomes, including lifeworld literacies, in formal and non-formal settings. While frequently challenging to reconcile formal (often defined by strict curricula and examination processes), and non-formal engagement with history, opportunities do exist. In the Irish context, this is exemplified by a popular university outreach programme: breaking the SEAL. This programme supports second-level history students as they fulfill curriculum requirements in completing a research study report. This report is a student-led research project pulling on communication skills, collaboration with peers and teachers, interpersonal relationships, problem-solving, and planning and organizational skills. Completion of this process has been widely recognized as excellent preparation not only for higher education (third level) but work-life demands as well. Within a formal education setting, the RSR harnesses non-formal learning virtues and exposes students to limited aspects of independent learning that relate to a professional work setting –a lifeworld literacy. Breaking the SEAL provides opportunities for students to enhance their lifeworld literacy by engaging in an independent research and learning process within the protective security of the classroom and its teacher. This paper will highlight the critical role this programme plays in preparing participating students (n=315) for life after compulsory education and presents examples of how lifeworld literacies may be developed through a scaffolded process of historical research and reporting anchored in non-formal contexts.

Keywords: history, education, literacy, transversal skills

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Hussah Alotaishan

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In today’s globally-driven modern cities diverse groups of new visitors face various challenges when attempting to find their desired location if culture and language are barriers. The most common way-showing tools such as directional and identificational signs are the most problematic and their usefulness can be limited or even non-existent. It is argued new methods should be implemented that could support or replace such conventional literacy and language dependent way-finding aids. It has been concluded in recent research studies that local urban features in complex pedestrian spaces are worthy of further study in order to reveal if they do function as way-showing clues. Some researchers propose a more comprehensive approach to the complex perception of buildings, façade design and surface patterns, while some have been questioning whether we necessarily need directional signs or can other methods deliver the same message but in a clearer manner for a wider range of users. This study aimed to test to what extent do existent environmental and urban features through the city center area of Swansea in the UK facilitate the way-finding process of a first time visitor. The three-hour experiment was set to attempt to find 11 visitor attractions ranging from recreational, historical, educational and religious locations. The challenge was attempting to find as many as possible when no prior geographical knowledge of their whereabouts was established. The only clues were 11 pictures representing each of the locations that had been acquired from the city of Swansea official website. An iPhone and a heart-rate tracker wristwatch were used to record the route was taken and stress levels, and take record photographs of destinations or decision-making points throughout the journey. This paper addresses: current limitations in understanding the ways that the physical environment can be intentionally deployed to facilitate pedestrians while finding their way around, without or with a reduction in language dependent signage; investigates visitor perceptions of their surroundings by indicating what urban elements manifested an impact on the way-finding process. The initial findings support the view that building facades and street features, such as width, could facilitate the decision-making process if strategically employed. However, more importantly, the anticipated features of a specific place construed from a promotional picture can also be misleading and create confusion that may lead to getting lost.

Keywords: pedestrian way-finding, environmental features, urban way-showing, environmental affordance

Procedia PDF Downloads 155

Authors: Sunita Boro, Saurabh Kumar Dixit

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Terroir, an intricate fusion of geography, climate, soil, and human expertise, has long been acknowledged as a defining factor in the character of wines and foods. This research embarks on an exploration of terroir's profound influence on gastronomic tourism, shedding light on the intricate interplay between the physical environment and culinary artistry. Delving into the intricate science of terroir, we scrutinize its role in shaping the sensory profiles of wines and foods, emphasizing the profound impact of specific regions on flavor, aroma, and texture. We deploy a multifaceted methodology, amalgamating sensory analysis, chemical profiling, geographical information systems, and qualitative interviews to unearth the nuanced nuances of terroir expression. Through an exhaustive review of the literature, we elucidate the historical roots of terroir, unveil the intricate cultural dimensions shaping it, and provide a comprehensive examination of prior studies in the field. Our findings underscore the pivotal role of terroir in promoting regional identities, enhancing the economic viability of locales, and attracting gastronomic tourists. The paper also dissects the marketing strategies employed to promote terroir-driven food and wine experiences. We elucidate the utilization of storytelling, branding, and collaborative endeavors in fostering a robust terroir-based tourism industry. This elucidates both the potential for innovation and the challenges posed by oversimplification or misrepresentation of terroir. Our research spotlights the intersection of terroir and sustainability, emphasizing the significance of environmentally conscious practices in terroir-driven productions. We discern the harmonious relationship between sustainable agriculture, terroir preservation, and responsible tourism, encapsulating the essence of ecological integrity in gastronomic tourism. Incorporating compelling case studies of regions and businesses excelling in the terroir-based tourism realm, we offer in-depth insights into successful models and strategies, with an emphasis on their replicability and adaptability to various contexts. Ultimately, this paper not only contributes to the scholarly understanding of terroir's role in the world of food and wine tourism but also provides actionable recommendations for stakeholders to leverage terroir's allure, preserve its authenticity, and foster sustainable and enriching culinary tourism experiences.

Keywords: terroir, food tourism, wine tourism, sustainability

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Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

Procedia PDF Downloads 221

Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

Procedia PDF Downloads 74

Authors: Kais A. Kadhim

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This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches.

Keywords: modals, meaning, persuasion, speech

Procedia PDF Downloads 385

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Valery Yakubovich, Shuping wu

Abstract:

Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

Procedia PDF Downloads 106

Authors: Jan Schmitt, Sophie Fischer

Abstract:

After the Corona pandemic, climate change is a further, long-lasting challenge the society must deal with. An ongoing climate change need to be prevented. Nevertheless, the adoption tothe already changed climate conditionshas to be focused in many sectors. Recently, the decisive role of the economic sector with high value added can be seen in the Corona crisis. Hence, manufacturing industry as such a sector, needs to be prepared for climate change and adaption. Several examples from the manufacturing industry show the importance of a strategic effort in this field: The outsourcing of a major parts of the value chain to suppliers in other countries and optimizing procurement logistics in a time-, storage- and cost-efficient manner within a network of global value creation, can lead vulnerable impacts due to climate-related disruptions. E.g. the total damage costs after the 2011 flood disaster in Thailand, including costs for delivery failures, were estimated at 45 billion US dollars worldwide. German car manufacturers were also affected by supply bottlenecks andhave close its plant in Thailand for a short time. Another OEM must reduce the production output. In this contribution, a game-based learning approach is presented, which should enable manufacturing companies to derive their own strategies for climate adaption out of a mix of different actions. Based on data from a regional study of small, medium and large manufacturing companies in Mainfranken, a strongly industrialized region of northern Bavaria (Germany) the game-based learning approach is designed. Out of this, the actual state of efforts due to climate adaption is evaluated. First, the results are used to collect single actions for manufacturing companies and second, further actions can be identified. Then, a variety of climate adaption activities can be clustered according to the scope of activity of the company. The combination of different actions e.g. the renewal of the building envelope with regard to thermal insulation, its benefits and drawbacks leads to a specific strategy for climate adaption for each company. Within the game-based approach, the players take on different roles in a fictionalcompany and discuss the order and the characteristics of each action taken into their climate adaption strategy. Different indicators such as economic, ecologic and stakeholder satisfaction compare the success of the respective measures in a competitive format with other virtual companies deriving their own strategy. A "play through" climate change scenarios with targeted adaptation actions illustrate the impact of different actions and their combination onthefictional company.

Keywords: business strategy, climate change, climate adaption, game-based learning

Procedia PDF Downloads 191

Authors: Asmaa Fathy Mohamed Desoky

Abstract:

The subject of this research is the inference of intentionality and context from the verses of the Meccan surahs, which include the paradox of reward and punishment, applied to the duality of disbelief and faith; The Holy Quran is the most important sacred linguistic reference in the Arabic language because it is rich in all the rules of the language in addition to the linguistic miracle. the Quranic text is a first-class intentional text, sent down to convey something to the recipient (Muhammad first and then communicates it to Muslims) and influence and convince him, which opens the door to many Ijtihad; a desire to reach the will of Allah and his intention from his words Almighty. Intentionality as a term is one of the most important deliberative terms, but it will be modified to suit the Quranic discourse, especially since intentionality is related to intention-as it turned out earlier - that is, it turns the reader or recipient into a predictor of the unseen, and this does not correspond to the Quranic discourse. Hence, in this research, a set of dualities will be identified that will be studied in order to clarify the meaning of them according to the opinions of previous interpreters in accordance with the sanctity of the Quranic discourse, which is intentionally related to the dualities of reward and punishment, such as: the duality of disbelief and faith, noting that it is a duality that combines opposites and Paradox on one level, because it may be an external paradox between action and reaction, and may be an internal paradox in matters related to faith, and may be a situational paradox in a specific event or a certain fact. It should be noted that the intention of the Qur'anic text is fully realized in form and content, in whole and in part, and this research includes a presentation of some applied models of the issues of intention and context that appear in the verses of the paradox of reward and punishment in the Meccan surahs in Quraan.

Keywords: intentionality, context, the paradox, reward, punishment, Meccan surahs

Procedia PDF Downloads 57