Search results for: multimedia retrieval

Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: Charles Masulani Mwale

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Background: It is argued that 85% of children with the disability live in resource-poor countries where there are few available disability services. A majority of these children, including their parents, suffer a lot as a result of the disability and its associated stigmatization, leading to a marginalized life. These parents also experience more stress and mental health problems such as depression, compared with families of normal developing children. There is little research from Africa addressing these issues especially among parents of intellectually disabled children. WHO encourages research on the impact that child with a disability have on their family and appropriate training and support to the families so that they can promote the child’s development and well-being. This study investigated the parenting experiences, mechanisms of coping with these challenges and psychosocial needs while caring for children with intellectual disabilities in both rural and urban settings of Lilongwe and Mzuzu. Methods: This is part of a larger Mixed-methods study aimed at developing a contextualized psychosocial intervention for parents of intellectually disabled children. 16 focus group discussions and four in-depth interviews were conducted with parents in catchments areas for St John of God and Children of Blessings in Mzuzu and Lilongwe cities respectively. Ethical clearance was obtained from COMREC. Data were stored in NVivo software for easy retrieval and management. All interviews were tape-recorded, transcribed and translated into English. Note-taking was performed during all the observations. Data triangulation from the interviews, note taking and the observations were done for validation and reliability. Results: Caring for intellectually disabled children comes with a number of challenges. Parents experience stigma and discrimination; fear for the child’s future; have self-blame and guilt; get coerced by neighbors to kill the disabled child; and fear violence by and to the child. Their needs include respite relief, improved access to disability services, education on disability management and financial support. For their emotional stability, parents cope by sharing with others and turning to God while other use poor coping mechanisms like alcohol use. Discussion and Recommendation: Apart from neighbors’ coercion to eliminate the child life, the findings of this study are similar to those done in other countries like Kenya and Pakistan. It is recommended that parents get educated on disability, its causes, and management to array fears of unknown. Community education is also crucial to promote community inclusiveness and correct prevailing myths associated with disability. Disability institutions ought to intensify individual as well as group counseling services to these parents. Further studies need to be done to design culturally appropriate and specific psychosocial interventions for the parents to promote their psychological resilience.

Keywords: psychological distress, intellectual disability, psychosocial interventions, mental health, psychological resilience, children

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Authors: Bipasa Dey, Varsha Panwar, Tanmay Dutta

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Laccase, a multicopper oxidase (EC 1.10.3.2) have been at the forefront as a superior industrial biocatalyst. They are versatile in terms of bestowing sustainable and ecological catalytic reactions such as polymerisation, xenobiotic degradation and bioremediation of phenolic and non-phenolic compounds. Regardless of the wide biotechnological applications, the critical limiting factors viz. reusability, retrieval, and storage stability still prevail. This can cause an impediment in their applicability. Crosslinked enzyme aggregates (CLEAs) have emerged as a promising technique that rehabilitates these essential facets, albeit at the expense of their enzymatic activity. The carrier free crosslinking method prevails over the carrier-bound immobilisation in conferring high productivity, low production cost owing to the absence of additional carrier and circumvent any non-catalytic ballast which could dilute the volumetric activity. To the best of our knowledge, the ε-amino group of lysyl residue is speculated as the best choice for forming Schiff’s base with glutaraldehyde. Despite being most preferrable, excess glutaraldehyde can bring about disproportionate and undesirable crosslinking within the catalytic site and hence could deliver undesirable catalytic losses. Moreover, the surface distribution of lysine residues in Trametes versicolor laccase is significantly less. Thus, to mitigate the adverse effect of glutaraldehyde in conjunction with scaling down the degradation or catalytic loss of the enzyme, crosslinking with inert substances like gelatine, collagen, Bovine serum albumin (BSA) or excess lysine is practiced. Analogous to these molecules, sugars have been well known as a protein stabiliser. It helps to retain the structural integrity, specifically secondary structure of the protein during aggregation by changing the solvent properties. They are comprehended to avert protein denaturation or enzyme deactivation during precipitation. We prepared crosslinked enzyme aggregates (CLEAs) of laccase from T. versicolor with the aid of sugars. The sugar CLEAs were compared with the classic BSA and glutaraldehyde laccase CLEAs concerning physico-chemical properties. The activity recovery for the fructose CLEAs were found to be ~20% higher than the non-sugar CLEA. Moreover, the 𝐾𝑐𝑎𝑡𝐾𝑚⁄ values of the CLEAs were two and three-fold higher than BSA-CLEA and GACLEA, respectively. The half-life (t1/2) deciphered by sugar-CLEA was higher than the t1/2 of GA-CLEAs and free enzyme, portraying more thermal stability. Besides, it demonstrated extraordinarily high pH stability, which was analogous to BSA-CLEA. The promising attributes of increased storage stability and recyclability (>80%) gives more edge to the sugar-CLEAs over conventional CLEAs of their corresponding free enzyme. Thus, sugar-CLEA prevails in furnishing the rudimentary properties required for a biocatalyst and holds many prospects.

Keywords: cross-linked enzyme aggregates, laccase immobilization, enzyme reusability, enzyme stability

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Authors: Megan Glyde, Louise Dye, David Keane, Ed Sutherland

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Background: Typically patient education is provided either verbally, in the form of written material, or with a multimedia-based tool such as videos, CD-ROMs, DVDs, or via the internet. By providing patients with effective educational tools, this can help to meet their information needs and subsequently empower these patients and allow them to participate within medical-decision making. Video education may have some distinct advantages compared to other modalities. For instance, whilst eHealth is emerging as a promising modality of patient education, an individual’s ability to access, read, and navigate through websites or online modules varies dramatically in relation to health literacy levels. Literacy levels may also limit patients’ ability to understand written education, whereas video education can be watched passively by patients and does not require high literacy skills. Other benefits of video education include that the same information is provided consistently to each patient, it can be a cost-effective method after the initial cost of producing the video, patients can choose to watch the videos by themselves or in the presence of others, and they can pause and re-watch videos to suit their needs. Health information videos are not only viewed by patients in formal educational sessions, but are increasingly being viewed on websites such as YouTube. Whilst there is a lot of anecdotal and sometimes misleading information on YouTube, videos from government organisations and professional associations contain trustworthy and high-quality information and could enable YouTube to become a powerful information dissemination platform for patients and carers. This systematic review will examine the efficacy of video education to improve treatment or illness-related knowledge in patients with various long-term conditions, in comparison to other modalities of education. Methods: Only studies which match the following criteria will be included: participants will have a long-term physical health condition, video education will aim to improve treatment or illness related knowledge and will be tested in isolation, and the study must be a randomised controlled trial. Knowledge will be the primary outcome measure, with modality preference, anxiety, and behaviour change as secondary measures. The searches have been conducted in the following databases: OVID Medline, OVID PsycInfo, OVID Embase, CENTRAL and ProQuest, and hand searching for relevant published and unpublished studies has also been carried out. Screening and data extraction will be conducted independently by 2 researchers. Included studies will be assessed for their risk of bias in accordance with Cochrane guidelines, and heterogeneity will also be assessed before deciding whether a meta-analysis is appropriate or not. Results and Conclusions: Appropriate synthesis of the studies in relation to each outcome measure will be reported, along with the conclusions and implications.

Keywords: long-term condition, patient education, systematic review, video

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Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

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Authors: Robert L. Fine, Zhiyong Yang, Christy Spivey, Bonnie Boardman, Maureen Courtney

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Importance: Barriers to traditional advance care planning (ACP) and advance directive (AD) creation have limited the promise of ACP/AD for individuals and families, the healthcare team, and society. Reengineering ACP by using a web-based, consumer-driven process has recently been suggested. We report early experience with such a process. Objective: Begin to analyze the potential of the creation and use of ACP/ADs as generated by a consumer-friendly, digital process by 1) assessing the likelihood that consumers would create ACP/ADs without structured intervention by medical or legal professionals, and 2) analyzing the responses to determine if the plans can help doctors better understand a person’s goals, preferences, and priorities for their medical treatments and the naming of healthcare agents. Design: The authors chose 900 users of MyDirectives.com, a digital ACP/AD tool, solely based on their state of residence in order to achieve proportional representation of all 50 states by population size and then reviewed their responses, summarizing these through descriptive statistics including treatment preferences, demographics, and revision of preferences. Setting: General United States population. Participants: The 900 participants had an average age of 50.8 years (SD = 16.6); 84.3% of the men and 91% of the women were in self-reported good health when signing their ADs. Main measures: Preferences regarding the use of life-sustaining treatments, where to spend final days, consulting a supportive and palliative care team, attempted cardiopulmonary resuscitation (CPR), autopsy, and organ and tissue donation. Results: Nearly 85% of respondents prefer cessation of life-sustaining treatments during their final days whenever those may be, 76% prefer to spend their final days at home or in a hospice facility, and 94% wanted their future doctors to consult a supportive and palliative care team. 70% would accept attempted CPR in certain limited circumstances. Most respondents would want an autopsy under certain conditions, and 62% would like to donate their organs. Conclusions and relevance: Analysis of early experience with an all-digital web-based ACP/AD platform demonstrates that individuals from a wide range of ages and conditions can engage in an interrogatory process about values, goals, preferences, and priorities for their medical treatments by developing advance directives and easily make changes to the AD created. Online creation, storage, and retrieval of advance directives has the potential to remove barriers to ACP/AD and, thus, to further improve patient-centered end-of-life care.

Keywords: Advance Care Plan, Advance Decisions, Advance Directives, Consumer; Digital, End of Life Care, Goals, Living Wills, Prefences, Universal Advance Directive, Statements

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Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

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This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

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Authors: Caixia Gao, Chuanrong Li, Lingli Tang, Lingling Ma, Yonggang Qian, Ning Wang

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Knowledge of land surface temperature (LST) is of crucial important in energy balance studies and environment modeling. Satellite thermal infrared (TIR) imagery is the primary source for retrieving LST at the regional and global scales. Due to the combination of atmosphere and land surface of received radiance by TIR sensors, atmospheric effect correction has to be performed to remove the atmospheric transmittance and upwelling radiance. Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) provides measurements every 15 minutes in 12 spectral channels covering from visible to infrared spectrum at fixed view angles with 3km pixel size at nadir, offering new and unique capabilities for LST, LSE measurements. However, due to its high temporal resolution, the atmosphere correction could not be performed with radiosonde profiles or reanalysis data since these profiles are not available at all SEVIRI TIR image acquisition times. To solve this problem, a two-part six-parameter semi-empirical diurnal temperature cycle (DTC) model has been applied to the temporal interpolation of ECMWF reanalysis data. Due to the fact that the DTC model is underdetermined with ECMWF data at four synoptic times (UTC times: 00:00, 06:00, 12:00, 18:00) in one day for each location, some approaches are adopted in this study. It is well known that the atmospheric transmittance and upwelling radiance has a relationship with water vapour content (WVC). With the aid of simulated data, the relationship could be determined under each viewing zenith angle for each SEVIRI TIR channel. Thus, the atmospheric transmittance and upwelling radiance are preliminary removed with the aid of instantaneous WVC, which is retrieved from the brightness temperature in the SEVIRI channels 5, 9 and 10, and a group of the brightness temperatures for surface leaving radiance (Tg) are acquired. Subsequently, a group of the six parameters of the DTC model is fitted with these Tg by a Levenberg-Marquardt least squares algorithm (denoted as DTC model 1). Although the retrieval error of WVC and the approximate relationships between WVC and atmospheric parameters would induce some uncertainties, this would not significantly affect the determination of the three parameters, td, ts and β (β is the angular frequency, td is the time where the Tg reaches its maximum, ts is the starting time of attenuation) in DTC model. Furthermore, due to the large fluctuation in temperature and the inaccuracy of the DTC model around sunrise, SEVIRI measurements from two hours before sunrise to two hours after sunrise are excluded. With the knowledge of td , ts, and β, a new DTC model (denoted as DTC model 2) is accurately fitted again with these Tg at UTC times: 05:57, 11:57, 17:57 and 23:57, which is atmospherically corrected with ECMWF data. And then a new group of the six parameters of the DTC model is generated and subsequently, the Tg at any given times are acquired. Finally, this method is applied to SEVIRI data in channel 9 successfully. The result shows that the proposed method could be performed reasonably without assumption and the Tg derived with the improved method is much more consistent with that from radiosonde measurements.

Keywords: atmosphere correction, diurnal temperature cycle model, land surface temperature, SEVIRI

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Matthew Edmunds, Andrew Hunter, Clare Littlewood, Wisha Gul, Gabriel Heppenstall-Harris, Thomas Humphries

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Background: Revision courses for medical students undertaking their final examinations are commonplace throughout the UK. Traditionally these take the form of a series of lectures over multiple weeks or a single day of intensive lectures. The COVID-19 pandemic, however, has required medical educators to create new teaching formats to ensure they adhere to social distancing requirements. It has provided an unexpected opportunity to accelerate the development of students proficiency in the use of ‘technology-enabled communication platforms’, as mandated in the 2018 GMC Outcomes of Graduates. Recent advances in technology have made distance learning possible, whilst also providing novel and more engaging learning opportunities for students. Foundation Year 2 doctors at Aintree University Hospital developed an online series of videos to help prepare medical students in the North West and byond for their final medical school examinations. Method: Eight hour-long videos covering the key topics in medicine and surgery were posted on the Peer Learning Liverpool Youtube channel. These videos were created using new technology such as the screen and audio recording platform, Loom. Each video compromised at least 20 single best answer (SBA) questions, in keeping with the format in most medical school finals. Explanations of the answers were provided, and additional important material was covered. Students were able to ask questions by commenting on the videos, with the authors replying as soon as possible. Feedback was collated using an online Google form. Results: An average of 327 people viewed each video, with 113 students filling in the feedback form. 65.5% of respondents were within one month of their final medical school examinations. The average rating for how well prepared the students felt for their finals was 6.21/10 prior to the course and 8.01/10 after the course. A paired t-test demonstrated a mean increase of 1.80 (95% CI 1.66-1.93). Overall, 98.2% said the online format worked well or very well, and 99.1% would recommend the course to a peer. Conclusions: Based on the feedback received, the online revision course was successful both in terms of preparing students for their final examinations, and with regards to how well the online format worked. Free-text qualitative feedback highlighted advantages such as; students could learn at their own pace, revisit key concepts important to them, and practice exam style questions via the case-based format. Limitations identified included inconsistent audiovisual quality, and requests for a live online Q&A session following the conclusion of the course. This course will be relaunched later in the year with increased opportunities for students to access live feedback. The success of this online course has shown the roll that technology can play in medical education. As well as providing novel teaching modes, online learning allows students to access resources that otherwise would not be available locally, and ensure that they do not miss out on teaching that was previously provided face to face, in the current climate of social distancing.

Keywords: COVID-19 pandemic, Medical School, Online learning, Revision course

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Authors: Richard Levy, Peter Dawson

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Ft. Conger, located in the Canadian Arctic was one of the most remote 19th-century scientific stations. Established in 1881 on Ellesmere Island, a wood framed structure established a permanent base from which to conduct scientific research. Under the charge of Lt. Greely, Ft. Conger was one of 14 expeditions conducted during the First International Polar Year (FIPY). Our research project “From Science to Survival: Using Virtual Exhibits to Communicate the Significance of Polar Heritage Sites in the Canadian Arctic” focused on the creation of a virtual museum website dedicated to one of the most important polar heritage site in the Canadian Arctic. This website was developed under a grant from Virtual Museum of Canada and enables visitors to explore the fort’s site from 1875 to the present, http://fortconger.org. Heritage sites are often viewed as static places. A goal of this project was to present the change that occurred over time as each new group of explorers adapted the site to their needs. The site was first visited by British explorer George Nares in 1875 – 76. Only later did the United States government select this site for the Lady Franklin Bay Expedition (1881-84) with research to be conducted under the FIPY (1882 – 83). Still later Robert Peary and Matthew Henson attempted to reach the North Pole from Ft. Conger in 1899, 1905 and 1908. A central focus of this research is on the virtual reconstruction of the Ft. Conger. In the summer of 2010, a Zoller+Fröhlich Imager 5006i and Minolta Vivid 910 laser scanner were used to scan terrain and artifacts. Once the scanning was completed, the point clouds were registered and edited to form the basis of a virtual reconstruction. A goal of this project has been to allow visitors to step back in time and explore the interior of these buildings with all of its artifacts. Links to text, historic documents, animations, panorama images, computer games and virtual labs provide explanations of how science was conducted during the 19th century. A major feature of this virtual world is the timeline. Visitors to the website can begin to explore the site when George Nares, in his ship the HMS Discovery, appeared in the harbor in 1875. With the emergence of Lt Greely’s expedition in 1881, we can track the progress made in establishing a scientific outpost. Still later in 1901, with Peary’s presence, the site is transformed again, with the huts having been built from materials salvaged from Greely’s main building. Still later in 2010, we can visit the site during its present state of deterioration and learn about the laser scanning technology which was used to document the site. The Science and Survival at Fort Conger project represents one of the first attempts to use virtual worlds to communicate the historical and scientific significance of polar heritage sites where opportunities for first-hand visitor experiences are not possible because of remote location.

Keywords: 3D imaging, multimedia, virtual reality, arctic

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Authors: Charlotte Entwistle, Ryan Boyd

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Recent developments in information retrieval techniques and natural language processing have allowed for greater exploration of psychological and social processes. Linguistic analysis methods for understanding behaviour have provided useful insights within the field of mental health. One area within mental health that has received little attention though, is borderline personality disorder (BPD). BPD is a common mental health disorder characterised by instability of interpersonal relationships, self-image and affect. It also manifests through maladaptive behaviours, such as impulsivity and self-harm. Examination of language patterns associated with BPD could allow for a greater understanding of the disorder and its links to maladaptive thoughts and behaviours. Language analysis methods could also be used in a predictive way, such as by identifying indicators of BPD or predicting maladaptive thoughts, emotions and behaviours. Additionally, associations that are uncovered between language and maladaptive thoughts and behaviours could then be applied at a more general level. This study explores linguistic characteristics of BPD, and their links to maladaptive thoughts and behaviours, through the analysis of social media data. Data were collected from a large corpus of posts from the publicly available social media platform Reddit, namely, from the ‘r/BPD’ subreddit whereby people identify as having BPD. Data were collected using the Python Reddit API Wrapper and included all users which had posted within the BPD subreddit. All posts were manually inspected to ensure that they were not posted by someone who clearly did not have BPD, such as people posting about a loved one with BPD. These users were then tracked across all other subreddits of which they had posted in and data from these subreddits were also collected. Additionally, data were collected from a random control group of Reddit users. Disorder-relevant behaviours, such as self-harming or aggression-related behaviours, outlined within Reddit posts were coded to by expert raters. All posts and comments were aggregated by user and split by subreddit. Language data were then analysed using the Linguistic Inquiry and Word Count (LIWC) 2015 software. LIWC is a text analysis program that identifies and categorises words based on linguistic and paralinguistic dimensions, psychological constructs and personal concern categories. Statistical analyses of linguistic features could then be conducted. Findings revealed distinct linguistic features associated with BPD, based on Reddit posts, which differentiated these users from a control group. Language patterns were also found to be associated with the occurrence of maladaptive thoughts and behaviours. Thus, this study demonstrates that there are indeed linguistic markers of BPD present on social media. It also implies that language could be predictive of maladaptive thoughts and behaviours associated with BPD. These findings are of importance as they suggest potential for clinical interventions to be provided based on the language of people with BPD to try to reduce the likelihood of maladaptive thoughts and behaviours occurring. For example, by social media tracking or engaging people with BPD in expressive writing therapy. Overall, this study has provided a greater understanding of the disorder and how it manifests through language and behaviour.

Keywords: behaviour analysis, borderline personality disorder, natural language processing, social media data

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

Procedia PDF Downloads 117

Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Kuo-Kai Lin, Po-Lun Chang

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Research Background and Purpose: Following the development of the Internet and multimedia, the Internet and information technology have become crucial avenues of modern communication and knowledge acquisition. The advantages of using mobile devices for learning include making learning borderless and accessible. Mobile learning has become a trend in disease management and health promotion in recent years. End-stage renal disease (ESRD) is an irreversible chronic disease, and patients who do not receive kidney transplants can only rely on hemodialysis or peritoneal dialysis to survive. Due to the complexities in caregiving for patients with ESRD that stem from their advanced age and other comorbidities, the patients’ incapacity of self-care leads to an increase in the need to rely on their families or primary caregivers, although whether the primary caregivers adequately understand and implement patient care is a topic of concern. Therefore, this study explored whether primary caregivers’ health care provisions can be improved through the intervention of an automatic speech intelligent system, thereby improving the objective health outcomes of patients undergoing long-term dialysis. Method: This study developed an automatic speech intelligent system with healthcare functions such as health information voice prompt, two-way feedback, real-time push notification, and health information delivery. Convenience sampling was adopted to recruit eligible patients from a hemodialysis center at a regional teaching hospital as research participants. A one-group pretest-posttest design was adopted. Descriptive and inferential statistics were calculated from the demographic information collected from questionnaires answered by patients and primary caregivers, and from a medical record review, a health care scale (recorded six months before and after the implementation of intervention measures), a subjective health assessment, and a report of objective physiological indicators. The changes in health care behaviors, subjective health status, and physiological indicators before and after the intervention of the proposed automatic speech intelligent system were then compared. Conclusion and Discussion: The preliminary automatic speech intelligent system developed in this study was tested with 20 pretest patients at the recruitment location, and their health care capacity scores improved from 59.1 to 72.8; comparisons through a nonparametric test indicated a significant difference (p < .01). The average score for their subjective health assessment rose from 2.8 to 3.3. A survey of their objective physiological indicators discovered that the compliance rate for the blood potassium level was the most significant indicator; its average compliance rate increased from 81% to 94%. The results demonstrated that this automatic speech intelligent system yielded a higher efficacy for chronic disease care than did conventional health education delivered by nurses. Therefore, future efforts will continue to increase the number of recruited patients and to refine the intelligent system. Future improvements to the intelligent system can be expected to enhance its effectiveness even further.

Keywords: automatic speech intelligent system for health care, primary caregiver, long-term hemodialysis, health care capabilities, health outcomes

Procedia PDF Downloads 88

Authors: Muhammad Waqas Baqai, Noman Shahzad, Rehman Alvi

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Communication among health care givers is the essence of quality patient care and any compromise results in errors and inefficiency leading to cumbersome outcomes. The use of smartphone among health professionals has increased tremendously. Almost every health professional carries it and majority of them uses a third party communication software called whatsApp for work related communications. It gives instant access to the person responsible for any particular query and therefore helps in efficient and timely decision making. It is also an easy way of sharing medical documents, multimedia and provides platform for consensual decision making through group discussions. However clinical communication through whatsApp has some demerits too including reduction in verbal communication, worsening professional relations, unprofessional behavior, risk of confidentiality breach and threats from cyber-attacks. On the other hand the traditional pager device being used in many health care systems is a unidirectional communication that lacks the ability to convey any information other than the number to which the receiver has to respond. Our study focused on these two widely used modalities of communication among doctors of the largest tertiary care center of Pakistan i.e. The Aga Khan University Hospital. Our aim was to note which modality is considered better and has fewer threats to medical data. Approval from ethical review committee of the institute was taken prior to conduction of this study. We submitted an online survey form to all the interns and residents working at our institute and collected their response in a month’s time. 162 submissions were recorded and analyzed using descriptive statistics. Only 20% of them were comfortable with using pagers exclusively, 52% with whatsApp and 28% with both. 65% think that whatsApp is time-saving and quicker than pager. 54% of them considered whatsApp to be causing nuisance from work related notifications in their off-work hours. 60% think that they are more likely to miss information through pager system because of the unidirectional nature. Almost all (96%) of residents and interns found whatsApp to be useful in terms of saving information for future reference. For urgent issues, majority (70%) preferred pager over whatsApp and also pager was considered more valid in terms of hospital policies and legal issues. Among major advantages of whatsApp as listed by them were; easy mass communication, sharing of clinical pictures, universal access and no need of carrying additional device. However the major drawback of using whatsApp for clinical communication that everyone shared was threat to patients’ confidentiality as clinicians usually share pictures of wounds, clinical documents etc. Lastly we asked them if they think there is a need of a separate application for instant communication dedicated to clinical communication only and 90% responded positively. Therefore, we concluded that both modalities have their merits and demerits but the greatest drawback with whatsApp is the risk of breach in patients’ confidentiality and off-work disturbance. Hence, we recommend a more secure, institute-run application for all intra hospital communications where they can share documents, pictures etc. easily under a controlled environment.

Keywords: WhatsApp, pager, clinical communication, confidentiality

Procedia PDF Downloads 117

Authors: Begona del Pino, Beatriz Prieto, Alberto Prieto

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Two opposite approaches to teaching can be considered: in-class learning (teacher-oriented) versus virtual learning (student-oriented). The most known example of the latter is Massive Online Open Courses (MOOCs). Both methodologies have pros and cons. Nowadays there is an increasing trend towards combining both of them. Blending learning is considered a valuable tool for improving learning since it combines student-centred interactive e-learning and face to face instruction. The aim of this contribution is to exchange and share the experience and research results of a blended-learning project that took place in the University of Granada (Spain). The research objective was to prove how combining didactic resources of a MOOC with in-class teaching, interacting directly with students, can substantially improve academic results, as well as student acceptance. The proposed methodology is based on the use of flipped learning technics applied to the subject ‘Fundamentals of Computer Science’ of the first course of two degrees: Telecommunications Engineering, and Industrial Electronics. In this proposal, students acquire the theoretical knowledges at home through a MOOC platform, where they watch video-lectures, do self-evaluation tests, and use other academic multimedia online resources. Afterwards, they have to attend to in-class teaching where they do other activities in order to interact with teachers and the rest of students (discussing of the videos, solving of doubts and practical exercises, etc.), trying to overcome the disadvantages of self-regulated learning. The results are obtained through the grades of the students and their assessment of the blended experience, based on an opinion survey conducted at the end of the course. The major findings of the study are the following: The percentage of students passing the subject has grown from 53% (average from 2011 to 2014 using traditional learning methodology) to 76% (average from 2015 to 2018 using blended methodology). The average grade has improved from 5.20±1.99 to 6.38±1.66. The results of the opinion survey indicate that most students preferred blended methodology to traditional approaches, and positively valued both courses. In fact, 69% of students felt ‘quite’ or ‘very’ satisfied with the classroom activities; 65% of students preferred the flipped classroom methodology to traditional in-class lectures, and finally, 79% said they were ‘quite’ or ‘very’ satisfied with the course in general. The main conclusions of the experience are the improvement in academic results, as well as the highly satisfactory assessments obtained in the opinion surveys. The results confirm the huge potential of combining MOOCs in formal undergraduate studies with on-campus learning activities. Nevertheless, the results in terms of students’ participation and follow-up have a wide margin for improvement. The method is highly demanding for both students and teachers. As a recommendation, students must perform the assigned tasks with perseverance, every week, in order to take advantage of the face-to-face classes. This perseverance is precisely what needs to be promoted among students because it clearly brings about an improvement in learning.

Keywords: blended learning, educational paradigm, flipped classroom, flipped learning technologies, lessons learned, massive online open course, MOOC, teacher roles through technology

Procedia PDF Downloads 155

Authors: Orit Wolf

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Classical music performance is undergoing a profound transformation, marked by a confluence of technological advancements and evolving cultural dynamics. This academic paper explores the multifaceted changes and challenges faced by classical music performance, considering the impact of artificial intelligence (AI) along with other vital factors shaping this evolution. In the contemporary era, classical music is experiencing shifts in performance practices. This paper delves into these changes, emphasizing the need for adaptability within the classical music world. From repertoire selection and concert formats to artistic expression, performers and institutions navigate a delicate balance between tradition and innovation. We explore how these changes impact the authenticity and vitality of classical music performances. Furthermore, the influence of AI in the classical music concert world cannot be underestimated. AI technologies are making inroads into various aspects, from composition assistance to rehearsal and live performances. This paper examines the transformative effects of AI, considering how it enhances precision, adaptability, and creative exploration for musicians. We explore the implications for composers, performers, and the overall concert experience while addressing ethical concerns and creative opportunities. In addition to AI, there is the importance of cross-genre interactions within the classical music sphere. Mash-ups and collaborations with artists from diverse musical backgrounds are redefining the boundaries of classical music and creating works that resonate with a wider and more diverse audience. The benefits of cross-pollination in classical music seem crucial, offering a fresh perspective to listeners. As an active concert artist, Orit Wolf will share how the expectations of classical music audiences are evolving. Modern concertgoers seek not only exceptional musical performances but also immersive experiences that may involve technology, multimedia, and interactive elements. This paper examines how classical musicians and institutions are adapting to these changing expectations, using technology and innovative concert formats to deliver a unique and enriched experience to their audiences. As these changes and challenges reshape the classical music world, the need for a harmonious coexistence of tradition, technology, and innovation becomes evident. Musicians, composers, and institutions are striving to find a balance that ensures classical music remains relevant in a rapidly changing cultural landscape while maintaining the value it brings to compositions and audiences. This paper, therefore, aims to explore the evolving trends in classical music performance. It considers the influence of AI as one element within the broader context of change, highlighting the necessity of adaptability, cross-genre interactions, and a response to evolving audience expectations. By doing so, the classical music world can navigate this transformative period while preserving its timeless traditions and adding value to both performers and listeners. Orit Wolf, an international concert pianist, fulfils her vision to bring this music in new ways to mass audiences and will share her personal and professional experience as an artist who goes on stage and makes disruptive concerts.

Keywords: cross culture collaboration, music performance and ai, classical music in the digital age, classical concerts, innovation and technology, performance innovation, audience engagement in classical concerts

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

Procedia PDF Downloads 227

Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

Procedia PDF Downloads 234

Authors: Rahaba Marima, Clement Penny

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The Health-related quality of life (HRQoL) for HIV positive patients has improved since the introduction of the highly active antiretroviral treatment (HAART). However, in the present HAART era, HIV co-morbidities such as lung cancer, a non-AIDS (NAIDS) defining cancer have been documented to be on the rise. Under normal physiological conditions, cells grow, repair and proliferate through the cell-cycle as cellular homeostasis is important in the maintenance and proper regulation of tissues and organs. Contrarily, the deregulation of the cell-cycle is a hallmark of cancer, including lung cancer. The association between lung cancer and the use of HAART components such as Efavirenz (EFV) is poorly understood. This study aimed at elucidating the effects of EFV on the cell-cycle genes’ expression in lung cancer. For this purpose, the human cell-cycle gene array composed of 84 genes was evaluated on both normal lung fibroblasts (MRC-5) cells and adenocarcinoma (A549) lung cells, in response to 13µM EFV or 0.01% vehicle. The ±2 up or down fold change was used as a basis of target selection, with p < 0.05. Additionally, RT-qPCR was done to validate the gene array results. Next, In-silico bio-informatics tools, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Reactome, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Ingenuity Pathway Analysis (IPA) were used for gene/gene interaction studies as well as to map the molecular and biological pathways influenced by the identified targets. Interestingly, the DNA damage response (DDR) pathway genes such as p53, Ataxia telangiectasia mutated and Rad3 related (ATR), Growth arrest and DNA damage inducible alpha (GADD45A), HUS1 checkpoint homolog (HUS1) and Role of radiation (RAD) genes were shown to be upregulated following EFV treatment, as revealed by STRING analysis. Additionally, functional enrichment analysis by the KEGG pathway revealed that most of the differentially expressed gene targets function at the cell-cycle checkpoint such as p21, Aurora kinase B (AURKB) and Mitotic Arrest Deficient-Like 2 (MAD2L2). Core analysis by IPA revealed that p53 downstream targets such as survivin, Bcl2, and cyclin/cyclin dependent kinases (CDKs) complexes are down-regulated, following exposure to EFV. Furthermore, Reactome analysis showed a significant increase in cellular response to stress genes, DNA repair genes, and apoptosis genes, as observed in both normal and cancerous cells. These findings implicate the genotoxic effects of EFV on lung cells, provoking the DDR pathway. Notably, the constitutive expression of this pathway (DDR) often leads to uncontrolled cell proliferation and eventually tumourigenesis, which could be the attribute of HAART components’ (such as EFV) effect on human cancers. Targeting the cell-cycle and its regulation holds a promising therapeutic intervention to the potential HAART associated carcinogenesis, particularly lung cancer.

Keywords: cell-cycle, DNA damage response, Efavirenz, lung cancer

Procedia PDF Downloads 119

Authors: Hollie Ashworth, Sarah Heath, Nick Bryan, Liam Abrahamsen, Simon Kellet

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Sellafield has a number of fuel storage ponds, some of which have been open to the air for a number of decades. This has caused corrosion of the fuel resulting in a release of some activity into solution, reduced water clarity, and accumulation of sludge at the bottom of the pond consisting of brucite (Mg(OH)2) and other uranium corrosion products. Both of these phases are also present as colloidal material. 90Sr and 137Cs are known to constitute a small volume of the radionuclides present in the pond, but a large fraction of the activity, thus they are most at risk of challenging effluent discharge limits. Organic molecules are known to be present also, due to the ponds being open to the air, with occasional algal blooms restricting visibility further. The contents of the pond need to be retrieved and safely stored, but dealing with such a complex, undefined inventory poses a unique challenge. This work aims to determine and understand the sorption-desorption interactions of 90Sr and 137Cs to brucite and uranium phases, with and without the presence of organic molecules from chemical degradation and bio-organisms. The influence of organics on these interactions has not been widely studied. Partitioning of these radionuclides and organic molecules has been determined through LSC, ICP-AES/MS, and UV-vis spectrophotometry coupled with ultrafiltration in both binary and ternary systems. Further detailed analysis into the surface and bonding environment of these components is being investigated through XAS techniques and PHREEQC modelling. Experiments were conducted in CO2-free or N2 atmosphere across a high pH range in order to best simulate conditions in the pond. Humic acid used in brucite systems demonstrated strong competition against 90Sr for the brucite surface regardless of the order of addition of components. Variance of pH did have a small effect, however this range (10.5-11.5) is close to the pHpzc of brucite, causing the surface to buffer the solution pH towards that value over the course of the experiment. Sorption of 90Sr to UO2 obeyed Ho’s rate equation and demonstrated a slow second-order reaction with respect to the sharing of valence electrons from the strontium atom, with the initial rate clearly dependent on pH, with the equilibrium concentration calculated at close to 100% sorption. There was no influence of humic acid seen when introduced to these systems. Sorption of 137Cs to UO3 was significant, with more than 95% sorbed in just over 24 hours. Again, humic acid showed no influence when introduced into this system. Both brucite and uranium based systems will be studied with the incorporation of cyanobacterial cultures harvested at different stages of growth. Investigation of these systems provides insight into, and understanding of, the effect of organics on radionuclide partitioning to brucite and uranium phases at high pH. The majority of sorption-desorption work for radionuclides has been conducted at neutral to acidic pH values, and mostly without organics. These studies are particularly important for the characterisation of legacy wastes at Sellafield, with a view to their safe retrieval and storage.

Keywords: caesium, legacy wastes, organics, sorption-desorption, strontium, uranium

Procedia PDF Downloads 248

Authors: M. Isabel Santos, Ana Breda, Ana Margarida Almeida

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Research on academic abilities of individuals with autism spectrum disorder (ASD) underlines the importance of mathematics interventions. Yet the proposal of digital applications for children and youth with ASD continues to attract little attention, namely, regarding the development of mathematical reasoning, being the use of the digital technologies an area of great interest for individuals with this disorder and its use is certainly a facilitative strategy in the development of their mathematical abilities. The use of digital technologies can be an effective way to create innovative learning opportunities to these students and to develop creative, personalized and constructive environments, where they can develop differentiated abilities. The children with ASD often respond well to learning activities involving information presented visually. In this context, we present the digital Learning Environment on Mathematics for Autistic children (LEMA) that was a research project conducive to a PhD in Multimedia in Education and was developed by the Thematic Line Geometrix, located in the Department of Mathematics, in a collaboration effort with DigiMedia Research Center, of the Department of Communication and Art (University of Aveiro, Portugal). LEMA is a digital mathematical learning environment which activities are dynamically adapted to the user’s profile, towards the development of mathematical abilities of children aged 6–12 years diagnosed with ASD. LEMA has already been evaluated with end-users (both students and teacher’s experts) and based on the analysis of the collected data readjustments were made, enabling the continuous improvement of the prototype, namely considering the integration of universal design for learning (UDL) approaches, which are of most importance in ASD, due to its heterogeneity. The learning strategies incorporated in LEMA are: (i) provide options to custom choice of math activities, according to user’s profile; (ii) integrates simple interfaces with few elements, presenting only the features and content needed for the ongoing task; (iii) uses a simple visual and textual language; (iv) uses of different types of feedbacks (auditory, visual, positive/negative reinforcement, hints with helpful instructions including math concept definitions, solved math activities using split and easier tasks and, finally, the use of videos/animations that show a solution to the proposed activity); (v) provides information in multiple representation, such as text, video, audio and image for better content and vocabulary understanding in order to stimulate, motivate and engage users to mathematical learning, also helping users to focus on content; (vi) avoids using elements that distract or interfere with focus and attention; (vii) provides clear instructions and orientation about tasks to ease the user understanding of the content and the content language, in order to stimulate, motivate and engage the user; and (viii) uses buttons, familiarly icons and contrast between font and background. Since these children may experience little sensory tolerance and may have an impaired motor skill, besides the user to have the possibility to interact with LEMA through the mouse (point and click with a single button), the user has the possibility to interact with LEMA through Kinect device (using simple gesture moves).

Keywords: autism spectrum disorder, digital technologies, inclusion, mathematical abilities, mathematical learning activities

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Authors: Ogunmefun Folorunsho Muyideen, Aluko Tolulope Evelyn

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One of the trending menaces faced in the world today is centered on the porosity of borders and proliferation of illegal weapons among the state members without the state authorizations. The proliferation of weapons along porous borders remains a germane and unsolvable question among developed and developing nations due to crisis degenerated from the menace (loss of lives, properties, traumatization, civil unrest and retrogressive economic development). A mixed method was adopted while the survey method was used for communities’ selection (Oke-Odan, Ajilete, Illaise, Lanlate) at Ipokia Local Government as a sample frame. Multi-stage sampling was employed to break down the site into wards, streets, and different house numbers before randomizing administration of the questionnaires using face to face method, while purposive sampling was used for collecting verbal information through an in-depth interviews method. The population size for the site is 150.398, while 399 was the sample size derived from the use of Yamane sample size formula. After retrieval of structured questionnaires, 346 were found useful, while 10 percent (399) of the quantitative instruments was summed to 30 participants that were interviewed using the in-depth interviews technique. The result of the first hypothesis shows a composite relationship between the variables tested (independents and dependent). The result indicated that the porosity of the border, illegal possession of guns, and limited security staff jointly predispose insecurity among the residents of the selected study site. The result of the second hypothesis deciphers that the illegal gun possession (independent) variable predict business outcome among the residents of the study site because sporadic gun shoot will regress the business activities in the study area. The result of third result indicated that the independent (porosity of borders) variable predict social bonding network because a high level of insecurity will destroy the level of trust in the communication among the residents of the study area. The last questions give comprehensive meaning to one of the recommendations derived using content systematic analysis, which explains that out of 30 participants interviewed, 18 submitted individual involvement in monitoring communities will solve the problem, 7 out of 30 opines that governmental agents are to be trained for effective combat, 3 participants out 30 submits that the fight is for both government and the citizens while 2 participants out of 30 claimed that there must be an agreement between Nigerian and neighbouring countries on border security. International donors must totally control the sales of weapons to unauthorized personalities. Criminal cases must be treated with deterrence measures and target hardened procedures through decoying and blending, stakeout, and sting tactics.

Keywords: human security, illegal weapons, porous borders, development

Procedia PDF Downloads 141

Authors: Mohkammad Nur Cahyadi, Imam Wahyu Farid, Ronny Mardianto, Agung Budi Cahyono, Eko Yuli Handoko, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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Unmanned aerial vehicle (UAV) technology has cost efficiency and data retrieval time advantages. Using technologies such as UAV, GNSS, and LiDAR will later be combined into one of the newest technologies to cover each other's deficiencies. This integration system aims to increase the accuracy of calculating the volume of the land stockpile of PT. Garam (Salt Company). The use of UAV applications to obtain geometric data and capture textures that characterize the structure of objects. This study uses the Taror 650 Iron Man drone with four propellers, which can fly for 15 minutes. LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetry and structural science principles from Motion point cloud technology. LiDAR can perform data acquisition that enables the creation of point clouds, three-dimensional models, Digital Surface Models, Contours, and orthomosaics with high accuracy. LiDAR has a drawback in the form of coordinate data positions that have local references. Therefore, researchers use GNSS, LiDAR, and drone multi-sensor technology to map the stockpile of salt on open land and warehouses every year, carried out by PT. Garam twice, where the previous process used terrestrial methods and manual calculations with sacks. Research with LiDAR needs to be combined with UAV to overcome data acquisition limitations because it only passes through the right and left sides of the object, mainly when applied to a salt stockpile. The UAV is flown to assist data acquisition with a wide coverage with the help of integration of the 200-gram LiDAR system so that the flying angle taken can be optimal during the flight process. Using LiDAR for low-cost mapping surveys will make it easier for surveyors and academics to obtain pretty accurate data at a more economical price. As a survey tool, LiDAR is included in a tool with a low price, around 999 USD; this device can produce detailed data. Therefore, to minimize the operational costs of using LiDAR, surveyors can use Low-Cost LiDAR, GNSS, and UAV at a price of around 638 USD. The data generated by this sensor is in the form of a visualization of an object shape made in three dimensions. This study aims to combine Low-Cost GPS measurements with Low-Cost LiDAR, which are processed using free user software. GPS Low Cost generates data in the form of position-determining latitude and longitude coordinates. The data generates X, Y, and Z values to help georeferencing process the detected object. This research will also produce LiDAR, which can detect objects, including the height of the entire environment in that location. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours. This study conducted an experimental process on the roof of a building with a radius of approximately 30 meters.

Keywords: LiDAR, unmanned aerial vehicle, low-cost GNSS, contour

Procedia PDF Downloads 52

Authors: Grant Bifolchi

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As global environmental problems continue to have highly adverse effects, finding long-term, sustainable solutions to combat ecological distress are of growing paramount concern. Ghost Gear—also known as abandoned, lost or otherwise discarded fishing gear (ALDFG) and derelict fishing gear (DFG)—represents one of the greatest threats to the world’s oceans, posing a significant hazard to human health, livelihoods, and global food security. In fact, according to the UN Food and Agriculture Organization (FAO), abandoned, lost and discarded fishing gear represents approximately 10% of marine debris by volume. Around the world, many governments, governmental and non-profit organizations are doing their best to manage the reporting and retrieval of nets, lines, ropes, traps, floats and more from their respective bodies of water. However, these organizations’ ability to effectively manage files and documents about the environmental problem further complicates matters. In Ghost Gear monitoring and management, organizations face additional complexities. Whether it’s data ingest, industry regulations and standards, garnering actionable insights into the location, security, and management of data, or the application of enforcement due to disparate data—all of these factors are placing massive strains on organizations struggling to save the planet from the dangers of Ghost Gear. In this 90-minute educational session, globally recognized Ghost Gear technology expert Grant Bifolchi CET, BBA, Bcom, will provide real-world insight into how governments currently manage Ghost Gear and the technology that can accelerate success in combatting ALDFG and DFG. In this session, attendees will learn how to: • Identify specific technologies to solve the ingest and management of Ghost Gear data categories, including type, geo-location, size, ownership, regional assignment, collection and disposal. • Provide enhanced access to authorities, fisheries, independent fishing vessels, individuals, etc., while securely controlling confidential and privileged data to globally recognized standards. • Create and maintain processing accuracy to effectively track ALDFG/DFG reporting progress—including acknowledging receipt of the report and sharing it with all pertinent stakeholders to ensure approvals are secured. • Enable and utilize Business Intelligence (BI) and Analytics to store and analyze data to optimize organizational performance, maintain anytime-visibility of report status, user accountability, scheduling, management, and foster governmental transparency. • Maintain Compliance Reporting through highly defined, detailed and automated reports—enabling all stakeholders to share critical insights with internal colleagues, regulatory agencies, and national and international partners.

Keywords: ghost gear, ALDFG, DFG, abandoned, lost or otherwise discarded fishing gear, data, technology

Procedia PDF Downloads 71

Authors: Kenneth A. Rostowsky, Alexander S. Maher, Nahian F. Chowdhury, Andrei Irimia

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The clinical significance of cerebral microbleeds (CMBs) due to mild traumatic brain injury (mTBI) remains unclear. Here we use magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and connectomic analysis to investigate the statistical association between mTBI-related CMBs, post-TBI changes to the human connectome and neurological/cognitive deficits. This study was undertaken in agreement with US federal law (45 CFR 46) and was approved by the Institutional Review Board (IRB) of the University of Southern California (USC). Two groups, one consisting of 26 (13 females) mTBI victims and another comprising 26 (13 females) healthy control (HC) volunteers were recruited through IRB-approved procedures. The acute Glasgow Coma Scale (GCS) score was available for each mTBI victim (mean µ = 13.2; standard deviation σ = 0.4). Each HC volunteer was assigned a GCS of 15 to indicate the absence of head trauma at the time of enrollment in our study. Volunteers in the HC and mTBI groups were matched according to their sex and age (HC: µ = 67.2 years, σ = 5.62 years; mTBI: µ = 66.8 years, σ = 5.93 years). MRI [including T1- and T2-weighted volumes, gradient recalled echo (GRE)/susceptibility weighted imaging (SWI)] and gradient echo (GE) DWI volumes were acquired using the same MRI scanner type (Trio TIM, Siemens Corp.). Skull-stripping and eddy current correction were implemented. DWI volumes were processed in TrackVis (http://trackvis.org) and 3D Slicer (http://www.slicer.org). Tensors were fit to DWI data to perform DTI, and tractography streamlines were then reconstructed using deterministic tractography. A voxel classifier was used to identify image features as CMB candidates using Microbleed Anatomic Rating Scale (MARS) guidelines. For each peri-lesional DTI streamline bundle, the null hypothesis was formulated as the statement that there was no neurological or cognitive deficit associated with between-scan differences in the mean FA of DTI streamlines within each bundle. The statistical significance of each hypothesis test was calculated at the α = 0.05 level, subject to the family-wise error rate (FWER) correction for multiple comparisons. Results: In HC volunteers, the along-track analysis failed to identify statistically significant differences in the mean FA of DTI streamline bundles. In the mTBI group, significant differences in the mean FA of peri-lesional streamline bundles were found in 21 out of 26 volunteers. In those volunteers where significant differences had been found, these differences were associated with an average of ~47% of all identified CMBs (σ = 21%). In 12 out of the 21 volunteers exhibiting significant FA changes, cognitive functions (memory acquisition and retrieval, top-down control of attention, planning, judgment, cognitive aspects of decision-making) were found to have deteriorated over the six months following injury (r = -0.32, p < 0.001). Our preliminary results suggest that acute post-TBI CMBs may be associated with cognitive decline in some mTBI patients. Future research should attempt to identify mTBI patients at high risk for cognitive sequelae.

Keywords: traumatic brain injury, magnetic resonance imaging, diffusion tensor imaging, connectomics

Procedia PDF Downloads 147

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

Procedia PDF Downloads 371

Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

Procedia PDF Downloads 91

Authors: Laith F. Gulli, Nicole M. Mallory

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The undertaking to develop a psychometric instrument is monumental. Understanding the relationship between variables and events is important in structural and exploratory design of psychometric instruments. Considering this, we describe a method used to group, pair and combine multiple Philosophical Assumption statements that assisted in development of a 13 item psychometric screening instrument. We abbreviated our Philosophical Assumptions (PA)s and added parameters, which were then condensed and mathematically modeled in a specific process. This model produced clusters of combinatorics which was utilized in design and development for 1) information retrieval and categorization 2) item development and 3) estimation of interactions among variables and likelihood of events. The psychometric screening instrument measured Knowledge, Assessment (education) and Beliefs (KAB) of New Addictions Research (NAR), which we called KABNAR. We obtained an overall internal consistency for the seven Likert belief items as measured by Cronbach’s α of .81 in the final study of 40 Clinicians, calculated by SPSS 14.0.1 for Windows. We constructed the instrument to begin with demographic items (degree/addictions certifications) for identification of target populations that practiced within Outpatient Substance Abuse Counseling (OSAC) settings. We then devised education items, beliefs items (seven items) and a modifiable “barrier from learning” item that consisted of six “choose any” choices. We also conceptualized a close relationship between identifying various degrees and certifications held by Outpatient Substance Abuse Therapists (OSAT) (the demographics domain) and all aspects of their education related to EB-NAR (past and present education and desired future training). We placed a descriptive (PA)1tx in both demographic and education domains to trace relationships of therapist education within these two domains. The two perceptions domains B1/b1 and B2/b2 represented different but interrelated perceptions from the therapist perspective. The belief items measured therapist perceptions concerning EB-NAR and therapist perceptions using EB-NAR during the beginning of outpatient addictions counseling. The (PA)s were written in simple words and descriptively accurate and concise. We then devised a list of parameters and appropriately matched them to each PA and devised descriptive parametric (PA)s in a domain categorized information grid. Descriptive parametric (PA)s were reduced to simple mathematical symbols. This made it easy to utilize parametric (PA)s into algorithms, combinatorics and clusters to develop larger information grids. By using matching combinatorics we took paired demographic and education domains with a subscript of 1 and matched them to the column with each B domain with subscript 1. Our algorithmic matching formed larger information grids with organized clusters in columns and rows. We repeated the process using different demographic, education and belief domains and devised multiple information grids with different parametric clusters and geometric arrays. We found benefit combining clusters by different geometric arrays, which enabled us to trace parametric variables and concepts. We were able to understand potential differences between dependent and independent variables and trace relationships of maximum likelihoods.

Keywords: psychometric, parametric, domains, grids, therapists

Procedia PDF Downloads 247