Search results for: predictive validity

Authors: A. Gonzalez Aguna, J. M. Santamaria Garcia, J. L. Gomez Gonzalez, R. Barchino Plata, M. Fernandez Batalla, S. Herrero Jaen

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Background: Computer Science is a field that transcends other disciplines of knowledge because it allows to support all kinds of physical and mental tasks. Health centres have a greater number and complexity of technological devices and the population consume and demand services derived from technology. Also, nursing education plans have included competencies related to and, even, courses about new technologies are offered to health professionals. However, nurses still limit their performance to the use and evaluation of products previously built. Objective: Develop a teaching-learning methodology for acquiring skills on designing applications for care. Methodology: Blended learning teaching with a group of graduate nurses through official training within a Master's Degree. The study sample was selected by intentional sampling without exclusion criteria. The study covers from 2015 to 2017. The teaching sessions included a four-hour face-to-face class and between one and three tutorials. The assessment was carried out by written test consisting of the preparation of an IEEE 830 Standard Specification document where the subject chosen by the student had to be a problem in the area of care. Results: The sample is made up of 30 students: 10 men and 20 women. Nine students had a degree in nursing, 20 diploma in nursing and one had a degree in Computer Engineering. Two students had a degree in nursing specialty through residence and two in equivalent recognition by exceptional way. Except for the engineer, no subject had previously received training in this regard. All the sample enrolled in the course received the classroom teaching session, had access to the teaching material through a virtual area and maintained at least one tutoring. The maximum of tutorials were three with an hour in total. Among the material available for consultation was an example of a document drawn up based on the IEEE Standard with an issue not related to care. The test to measure competence was completed by the whole group and evaluated by a multidisciplinary teaching team of two computer engineers and two nurses. Engineers evaluated the correctness of the characteristics of the document and the degree of comprehension in the elaboration of the problem and solution elaborated nurses assessed the relevance of the chosen problem statement, the foundation, originality and correctness of the proposed solution and the validity of the application for clinical practice in care. The results were of an average grade of 8.1 over 10 points, a range between 6 and 10. The selected topic barely coincided among the students. Examples of care areas selected are care plans, family and community health, delivery care, administration and even robotics for care. Conclusion: The applied methodology of learning-teaching for the design of technologies demonstrates the success in the training of nursing professionals. The role of expert is essential to create applications that satisfy the needs of end users. Nursing has the possibility, the competence and the duty to participate in the process of construction of technological tools that are going to impact in care of people, family and community.

Keywords: care, learning, nursing, technology

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: Loha Hashimy, Isabella Castillo

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In today's rapidly evolving technological landscape, the importance of precise navigation and mapping systems cannot be understated. As various sectors undergo transformative changes, the market potential for Advanced Mapping and Management Systems (AMMS) emerges as a critical focus area. The Galileo/GNSS-Based Autonomous Mobile Mapping System (GAMMS) project, specifically targeted toward high-definition mapping (HDM), endeavours to provide insights into this market within the broader context of the geomatics and navigation fields. With the growing integration of Autonomous Vehicles (AVs) into our transportation systems, the relevance and demand for sophisticated mapping solutions like HDM have become increasingly pertinent. The research employed a meticulous, lean, stepwise, and interconnected methodology to ensure a comprehensive assessment. Beginning with the identification of pivotal project results, the study progressed into a systematic market screening. This was complemented by an exhaustive desk research phase that delved into existing literature, data, and trends. To ensure the holistic validity of the findings, extensive consultations were conducted. Academia and industry experts provided invaluable insights through interviews, questionnaires, and surveys. This multi-faceted approach facilitated a layered analysis, juxtaposing secondary data with primary inputs, ensuring that the conclusions were both accurate and actionable. Our investigation unearthed a plethora of drivers steering the HD maps landscape. These ranged from technological leaps, nuanced market demands, and influential economic factors to overarching socio-political shifts. The meteoric rise of Autonomous Vehicles (AVs) and the shift towards app-based transportation solutions, such as Uber, stood out as significant market pull factors. A nuanced PESTEL analysis further enriched our understanding, shedding light on political, economic, social, technological, environmental, and legal facets influencing the HD maps market trajectory. Simultaneously, potential roadblocks were identified. Notable among these were barriers related to high initial costs, concerns around data quality, and the challenges posed by a fragmented and evolving regulatory landscape. The GAMMS project serves as a beacon, illuminating the vast opportunities that lie ahead for the HD mapping sector. It underscores the indispensable role of HDM in enhancing navigation, ensuring safety, and providing pinpoint, accurate location services. As our world becomes more interconnected and reliant on technology, HD maps emerge as a linchpin, bridging gaps and enabling seamless experiences. The research findings accentuate the imperative for stakeholders across industries to recognize and harness the potential of HD mapping, especially as we stand on the cusp of a transportation revolution heralded by Autonomous Vehicles and advanced geomatic solutions.

Keywords: high-definition mapping (HDM), autonomous vehicles, PESTEL analysis, market drivers

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Naghmeh M. Bandari, Javad Dargahi, Muthukumaran Packirisamy

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Robot-assisted minimally invasive surgery (RMIS) has been widely performed around the world during the last two decades. RMIS demonstrates significant advantages over conventional surgery, e.g., improving the accuracy and dexterity of a surgeon, providing 3D vision, motion scaling, hand-eye coordination, decreasing tremor, and reducing x-ray exposure for surgeons. Despite benefits, surgeons cannot touch the surgical site and perceive tactile information. This happens due to the remote control of robots. The literature survey identified the lack of force feedback as the riskiest limitation in the existing technology. Without the perception of tool-tissue contact force, the surgeon might apply an excessive force causing tissue laceration or insufficient force causing tissue slippage. The primary use of force sensors has been to measure the tool-tissue interaction force in real-time in-situ. Design of a tactile sensor is subjected to a set of design requirements, e.g., biocompatibility, electrical-passivity, MRI-compatibility, miniaturization, ability to measure static and dynamic force. In this study, a planar optical fiber-based sensor was proposed to mount at the surgical grasper. It was developed based on the light intensity modulation principle. The deflectable part of the sensor was a beam modeled as a cantilever Euler-Bernoulli beam on rigid substrates. A semi-cylindrical indenter was attached to the bottom surface the beam at the mid-span. An optical fiber was secured at both ends on the same rigid substrates. The indenter was in contact with the fiber. External force on the sensor caused deflection in the beam and optical fiber simultaneously. The micro-bending of the optical fiber would consequently result in light power loss. The sensor was simulated and studied using finite element methods. A laser light beam with 800nm wavelength and 5mW power was used as the input to the optical fiber. The output power was measured using a photodetector. The voltage from photodetector was calibrated to the external force for a chirp input (0.1-5Hz). The range, resolution, and hysteresis of the sensor were studied under monotonic and harmonic external forces of 0-2.0N with 0 and 5Hz, respectively. The results confirmed the validity of proposed sensing principle. Also, the sensor demonstrated an acceptable linearity (R2 > 0.9). A minimum external force was observed below which no power loss was detectable. It is postulated that this phenomenon is attributed to the critical angle of the optical fiber to observe total internal reflection. The experimental results were of negligible hysteresis (R2 > 0.9) and in fair agreement with the simulations. In conclusion, the suggested planar sensor is assessed to be a cost-effective solution, feasible, and easy to use the sensor for being miniaturized and integrated at the tip of robotic graspers. Geometrical and optical factors affecting the minimum sensible force and the working range of the sensor should be studied and optimized. This design is intrinsically scalable and meets all the design requirements. Therefore, it has a significant potential of industrialization and mass production.

Keywords: force sensor, minimally invasive surgery, optical sensor, robotic surgery, tactile sensor

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Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

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Authors: Wesley Preßler, Lucie Schmidt

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Digital transformation is playing an increasingly important role in the development of smart residential quarters. In order to accompany and steer this process and ultimately make the success of the transformation efforts measurable, it is helpful to use an appropriate maturity model. However, conventional maturity models for digital transformation focus primarily on the evaluation of processes and neglect the information and power imbalances between the stakeholders, which affects the validity of the results. The Multi-Generation Smart Community (mGeSCo) research project is developing an interdisciplinary maturity model that integrates the dimensions of digital literacy, interpretive patterns, and technology acceptance to address this gap. As part of the mGeSCo project, the technological development of selected dimensions in the Smart Quarter Jena-Lobeda (Germany) is being investigated. A specific maturity model, based on Cohen's Smart Cities Wheel, evaluates the central dimensions Working, Living, Housing and Caring. To improve the reliability and relevance of the maturity assessment, the factors Digital Literacy, Interpretive Patterns and Technology Acceptance are integrated into the developed model. The digital literacy dimension examines stakeholders' skills in using digital technologies, which influence their perception and assessment of technological maturity. Digital literacy is measured by means of surveys, interviews, and participant observation, using the European Commission's Digital Literacy Framework (DigComp) as a basis. Interpretations of digital technologies provide information about how individuals perceive technologies and ascribe meaning to them. However, these are not mere assessments, prejudices, or stereotyped perceptions but collective patterns, rules, attributions of meaning and the cultural repertoire that leads to these opinions and attitudes. Understanding these interpretations helps in assessing the overarching readiness of stakeholders to digitally transform a/their neighborhood. This involves examining people's attitudes, beliefs, and values about technology adoption, as well as their perceptions of the benefits and risks associated with digital tools. These insights provide important data for a holistic view and inform the steps needed to prepare individuals in the neighborhood for a digital transformation. Technology acceptance is another crucial factor for successful digital transformation to examine the willingness of individuals to adopt and use new technologies. Surveys or questionnaires based on Davis' Technology Acceptance Model can be used to complement interpretive patterns to measure neighborhood acceptance of digital technologies. Integrating the dimensions of digital literacy, interpretive patterns and technology acceptance enables the development of a roadmap with clear prerequisites for initiating a digital transformation process in the neighborhood. During the process, maturity is measured at different points in time and compared with changes in the aforementioned dimensions to ensure sustainable transformation. Participation, co-creation, and co-production are essential concepts for a successful and inclusive digital transformation in the neighborhood context. This interdisciplinary maturity model helps to improve the assessment and monitoring of sustainable digital transformation processes in smart residential quarters. It enables a more comprehensive recording of the factors that influence the success of such processes and supports the development of targeted measures to promote digital transformation in the neighborhood context.

Keywords: digital transformation, interdisciplinary, maturity model, neighborhood

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Diana Santiago, Tania Lisboa, Sophie Lee, Alexander P. Demos, Monica C. S. Vasconcelos

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Memorization has long been recognized as an arduous and anxiety-evoking task for musicians, and yet, it is an essential aspect of performance. Research shows that musicians are often not taught how to memorize. While memorization and practice strategies of professionals have been studied, little research has been done to examine how student musicians learn to practice and memorize music in different cultural settings. We present the process of developing and testing a questionnaire of music memorization and musical practice for student musicians in the UK and Brazil. A survey was developed for a cross-cultural research project aiming at examining how young orchestral musicians (aged 7–18 years) in different learning environments and cultures engage in instrumental practice and memorization. The questionnaire development included members of a UK/US/Brazil research team of music educators and performance science researchers. A pool of items was developed for each aspect of practice and memorization identified, based on literature, personal experiences, and adapted from existing questionnaires. Item development took the varying levels of cognitive and social development of the target populations into consideration. It also considered the diverse target learning environments. Items were initially grouped in accordance with a single underlying construct/behavior. The questionnaire comprised three sections: a demographics section, a section on practice (containing 29 items), and a section on memorization (containing 40 items). Next, the response process was considered and a 5-point Likert scale ranging from ‘always’ to ‘never’ with a verbal label and an image assigned to each response option was selected, following effective questionnaire design for children and youths. Finally, a pilot study was conducted with young orchestral musicians from diverse learning environments in Brazil and the United Kingdom. Data collection took place in either one-to-one or group settings to facilitate the participants. Cognitive interviews were utilized to establish response process validity by confirming the readability and accurate comprehension of the questionnaire items or highlighting the need for item revision. Internal reliability was investigated by measuring the consistency of the item groups using the statistical test Cronbach’s alpha. The pilot study successfully relied on the questionnaire to generate data about the engagement of young musicians of different levels and instruments, across different learning and cultural environments, in instrumental practice and memorization. Interaction analysis of the cognitive interviews undertaken with these participants, however, exposed the fact that certain items, and the response scale, could be interpreted in multiple ways. The questionnaire text was, therefore, revised accordingly. The low Cronbach’s Alpha scores of many item groups indicated another issue with the original questionnaire: its low level of internal reliability. Several reasons for each poor reliability can be suggested, including the issues with item interpretation revealed through interaction analysis of the cognitive interviews, the small number of participants (34), and the elusive nature of the construct in question. The revised questionnaire measures 78 specific behaviors or opinions. It can be seen to provide an efficient means of gathering information about the engagement of young musicians in practice and memorization on a large scale.

Keywords: cross-cultural, memorization, practice, questionnaire, young musicians

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Authors: Katarzyna Lubecka, Kirsty Flower, Megan Beetch, Lucinda Kurzava, Hannah Buvala, Samer Gawrieh, Suthat Liangpunsakul, Tracy Gonzalez, George McCabe, Naga Chalasani, James M. Flanagan, Barbara Stefanska

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Hepatocellular carcinoma (HCC), the most prevalent type of primary liver cancer, is the second leading cause of cancer death worldwide. Late onset of clinical symptoms in HCC results in late diagnosis and poor disease outcome. Approximately 85% of individuals with HCC have underlying liver cirrhosis. However, not all cirrhotic patients develop cancer. Reliable early detection biomarkers that can distinguish cirrhotic patients who will develop cancer from those who will not are urgently needed and could increase the cure rate from 5% to 80%. We used Illumina-450K microarray to test whether blood DNA, an easily accessible source of DNA, bear site-specific changes in DNA methylation in response to HCC before diagnosis with conventional tools (pre-diagnostic). Top 11 differentially methylated sites were selected for validation by pyrosequencing. The diagnostic potential of the 11 pyrosequenced probes was tested in blood samples from a prospective cohort of cirrhotic patients. We identified 971 differentially methylated CpG sites in pre-diagnostic HCC cases as compared with healthy controls (P < 0.05, paired Wilcoxon test, ICC ≥ 0.5). Nearly 76% of differentially methylated CpG sites showed lower levels of methylation in cases vs. controls (P = 2.973E-11, Wilcoxon test). Classification of the CpG sites according to their location relative to CpG islands and transcription start site revealed that those hypomethylated loci are located in regulatory regions important for gene transcription such as CpG island shores, promoters, and 5’UTR at higher frequency than hypermethylated sites. Among 735 CpG sites hypomethylated in cases vs. controls, 482 sites were assigned to gene coding regions whereas 236 hypermethylated sites corresponded to 160 genes. Bioinformatics analysis using GO, KEGG and DAVID knowledgebase indicate that differentially methylated CpG sites are located in genes associated with functions that are essential for gene transcription, cell adhesion, cell migration, and regulation of signal transduction pathways. Taking into account the magnitude of the difference, statistical significance, location, and consistency across the majority of matched pairs case-control, we selected 11 CpG loci corresponding to 10 genes for further validation by pyrosequencing. We established that methylation of CpG sites within 5 out of those 10 genes distinguish cirrhotic patients who subsequently developed HCC from those who stayed cancer free (cirrhotic controls), demonstrating potential as biomarkers of early detection in populations at risk. The best predictive value was detected for CpGs located within BARD1 (AUC=0.70, asymptotic significance ˂0.01). Using an additive logistic regression model, we further showed that 9 CpG loci within those 5 genes, that were covered in pyrosequenced probes, constitute a panel with high diagnostic accuracy (AUC=0.887; 95% CI:0.80-0.98). The panel was able to distinguish pre-diagnostic cases from cirrhotic controls free of cancer with 88% sensitivity at 70% specificity. Using blood as a minimally invasive material and pyrosequencing as a straightforward quantitative method, the established biomarker panel has high potential to be developed into a routine clinical test after validation in larger cohorts. This study was supported by Showalter Trust, American Cancer Society (IRG#14-190-56), and Purdue Center for Cancer Research (P30 CA023168) granted to BS.

Keywords: biomarker, DNA methylation, early detection, hepatocellular carcinoma

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Authors: Ichiro Takahashi

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One of the major assumptions of mainstream macroeconomics is the path independence of capital stock. This paper challenges this assumption by employing an agent-based approach. The simulation results showed the existence of multiple "quasi-steady state" equilibria of the capital stock, which may cast serious doubt on the validity of the assumption. The finding would give a better understanding of many phenomena that involve hysteresis, including the causes of poverty. The "market-clearing view" has been widely shared among major schools of macroeconomics. They understand that the capital stock, the labor force, and technology, determine the "full-employment" equilibrium growth path and demand/supply shocks can move the economy away from the path only temporarily: the dichotomy between the short-run business cycles and the long-run equilibrium path. The view then implicitly assumes the long-run capital stock to be independent of how the economy has evolved. In contrast, "Old Keynesians" have recognized fluctuations in output as arising largely from fluctuations in real aggregate demand. It will then be an interesting question to ask if an agent-based macroeconomic model, which is known to have path dependence, can generate multiple full-employment equilibrium trajectories of the capital stock in the super-long run. If the answer is yes, the equilibrium level of capital stock, an important supply-side factor, would no longer be independent of the business cycle phenomenon. This paper attempts to answer the above question by using the agent-based macroeconomic model developed by Takahashi and Okada (2010). The model would serve this purpose well because it has neither population growth nor technology progress. The objective of the paper is twofold: (1) to explore the causes of long-term business cycle, and (2) to examine the super-long behaviors of the capital stock of full-employment economies. (1) The simulated behaviors of the key macroeconomic variables such as output, employment, real wages showed widely diversified macro-economies. They were often remarkably stable but exhibited both short-term and long-term fluctuations. The long-term fluctuations occur through the following two adjustments: the quantity and relative cost adjustments of capital stock. The first one is obvious and assumed by many business cycle theorists. The reduced aggregate demand lowers prices, which raises real wages, thereby decreasing the relative cost of capital stock with respect to labor. (2) The long-term business cycles/fluctuations were synthesized with the hysteresis of real wages, interest rates, and investments. In particular, a sequence of the simulation runs with a super-long simulation period generated a wide range of perfectly stable paths, many of which achieved full employment: all the macroeconomic trajectories, including capital stock, output, and employment, were perfectly horizontal over 100,000 periods. Moreover, the full-employment level of capital stock was influenced by the history of unemployment, which was itself path-dependent. Thus, an experience of severe unemployment in the past kept the real wage low, which discouraged a relatively costly investment in capital stock. Meanwhile, a history of good performance sometimes brought about a low capital stock due to a high-interest rate that was consistent with a strong investment.

Keywords: agent-based macroeconomic model, business cycle, hysteresis, stability

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Authors: Paul Licina, Emma M. Johnston, David Lisle, Mark Young, Chris Brady

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Background: Lumbar pars pathology is a common cause of pain in the growing spine. It can be seen in young athletes participating in at-risk sports and can affect sporting performance and long-term health due to its resistance to traditional management. There is a current lack of consensus of classification and treatment for pars injuries. Previous systems used CT to stage pars defects but could not assess early stress reactions. A modified classification is proposed that considers findings on MRI, significantly improving early treatment guidance. The treatment protocol is designed for patients aged 5 to 19 years. Method: Clinical screening identifies patients with a low, medium, or high index of suspicion for lumbar pars injury using patient age, sport participation and pain characteristics. MRI of the at-risk cohort enables augmentation of existing CT-based classification while avoiding ionising radiation. Patients are classified into five categories based on MRI findings. A type 0 lesion (stress reaction) is present when CT is normal and MRI shows high signal change (HSC) in the pars/pedicle on T2 images. A type 1 lesion represents the ‘early defect’ CT classification. The group previously referred to as a 'progressive stage' defect on CT can be split into 2A and 2B categories. 2As have HSC on MRI, whereas 2Bs do not. This distinction is important with regard to healing potential. Type 3 lesions are terminal stage defects on CT, characterised by pseudarthrosis. MRI shows no HSC. Results: Stress reactions (type 0) and acute fractures (1 and 2a) can heal and are treated in a custom-made hard brace for 12 weeks. It is initially worn 23 hours per day. At three weeks, patients commence basic core rehabilitation. At six weeks, in the absence of pain, the brace is removed for sleeping. Exercises are progressed to positions of daily living. Patients with continued pain remain braced 23 hours per day without exercise progression until becoming symptom-free. At nine weeks, patients commence supervised exercises out of the brace for 30 minutes each day. This allows them to re-learn muscular control without rigid support of the brace. At 12 weeks, bracing ceases and MRI is repeated. For patients with near or complete resolution of bony oedema and healing of any cortical defect, rehabilitation is focused on strength and conditioning and sport-specific exercise for the full return to activity. The length of this final stage is approximately nine weeks but depends on factors such as development and level of sports participation. If significant HSC remains on MRI, CT scan is considered to definitively assess cortical defect healing. For these patients, return to high-risk sports is delayed for up to three months. Chronic defects (2b and 3) cannot heal and are not braced, and rehabilitation follows traditional protocols. Conclusion: Appropriate clinical screening and imaging with MRI can identify pars pathology early. In those with potential for healing, we propose hard bracing and appropriate rehabilitation as part of a multidisciplinary management protocol. The validity of this protocol will be tested in future studies.

Keywords: adolescents, MRI classification, pars interticularis, treatment protocol

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Authors: Laura Butabayeva, Svetlana Ismagulova, Gulbarshin Nogaibayeva, Maiya Temirbayeva, Aidana Zhussip

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Over the years of independence, Kazakhstan has not only ratified international documents regulating the rights of children to Inclusive education, but also developed its own inclusive educational policy. Along with this, the state pays particular attention to high school students' preparedness for professional self-determination. However, a number of problematic issues in this field have been revealed, such as the lack of systemic mechanisms coordinating stakeholders’ actions in preparing schoolchildren for a conscious choice of in-demand profession, meeting their individual capabilities and special educational needs (SEN). The analysis of the state’s current situation indicates school graduates’ adaptation to the labor market does not meet existing demands of the society. According to the Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan, about 70 % of Kazakhstani school graduates find themselves difficult to choose a profession, 87 % of schoolchildren make their career choice under the influence of parents and school teachers, 90 % of schoolchildren and their parents have no idea about the most popular professions on the market. The results of the study conducted by KorlanSyzdykova in 2016 indicated the urgent need of Kazakhstani school graduates in obtaining extensive information about in- demand professions and receiving professional assistance in choosing a profession in accordance with their individual skills, abilities, and preferences. The results of the survey, conducted by Information and Analytical Center among heads of colleges in 2020, showed that despite significant steps in creating conditions for students with SEN, they face challenges in studying because of poor career guidance provided to them in schools. The results of the study, conducted by the Center for Inclusive Education of the National Academy of Education named after Y. Altynsarin in the state’s general education schools in 2021, demonstrated the lack of career guidance, pedagogical and psychological support for children with SEN. To investigate these issues, the further study was conducted to examine the state of students’ career guidance and socialization, taking into account their SEN. The hypothesis of this study proposed that to prepare school graduates for a conscious career choice, school teachers and specialists need to develop their competencies in early identification of students' interests, inclinations, SEN and ensure necessary support for them. The state’s 5 regions were involved in the study according to the geographical location. The triangulation approach was utilized to ensure the credibility and validity of research findings, including both theoretical (analysis of existing statistical data, legal documents, results of previous research) and empirical (school survey for students, interviews with parents, teachers, representatives of school administration) methods. The data were analyzed independently and compared to each other. The survey included questions related to provision of pedagogical support for school students in making their career choice. Ethical principles were observed in the process of developing the methodology, collecting, analyzing the data and distributing the results. Based on the results, methodological recommendations on students’ career guidance for school teachers and specialists were developed, taking into account the former’s individual capabilities and SEN.

Keywords: career guidance, children with special educational needs, inclusive education, Kazakhstan

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Authors: Divya Palaniappan

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Early Childhood assessment tools must measure the quality and the appropriateness of a curriculum with respect to culture and age of the children. Preschool assessment tools lack psychometric properties and were developed to measure only few areas of development such as specific skills in music, art and adaptive behavior. Existing preschool assessment tools in India are predominantly informal and are fraught with judgmental bias of observers. The World of Discovery TM curriculum focuses on accelerating the physical, cognitive, language, social and emotional development of pre-schoolers in India through various activities. The curriculum caters to every child irrespective of their dominant intelligence as per Gardner’s Theory of Multiple Intelligence which concluded "even students as young as four years old present quite distinctive sets and configurations of intelligences". The curriculum introduces a new theme every week where, concepts are explained through various activities so that children with different dominant intelligences could understand it. For example: The ‘Insects’ theme is explained through rhymes, craft and counting corner, and hence children with one of these dominant intelligences: Musical, bodily-kinesthetic and logical-mathematical could grasp the concept. The child’s progress is evaluated using an assessment tool that measures a cluster of inter-dependent developmental areas: physical, cognitive, language, social and emotional development, which for the first time renders a multi-domain approach. The assessment tool is a 5-point rating scale that measures these Developmental aspects: Cognitive, Language, Physical, Social and Emotional. Each activity strengthens one or more of the developmental aspects. During cognitive corner, the child’s perceptual reasoning, pre-math abilities, hand-eye co-ordination and fine motor skills could be observed and evaluated. The tool differs from traditional assessment methodologies by providing a framework that allows teachers to assess a child’s continuous development with respect to specific activities in real time objectively. A pilot study of the tool was done with a sample data of 100 children in the age group 2.5 to 3.5 years. The data was collected over a period of 3 months across 10 centers in Chennai, India, scored by the class teacher once a week. The teachers were trained by psychologists on age-appropriate developmental milestones to minimize observer’s bias. The norms were calculated from the mean and standard deviation of the observed data. The results indicated high internal consistency among parameters and that cognitive development improved with physical development. A significant positive relationship between physical and cognitive development has been observed among children in a study conducted by Sibley and Etnier. In Children, the ‘Comprehension’ ability was found to be greater than ‘Reasoning’ and pre-math abilities as indicated by the preoperational stage of Piaget’s theory of cognitive development. The average scores of various parameters obtained through the tool corroborates the psychological theories on child development, offering strong face validity. The study provides a comprehensive mechanism to assess a child’s development and differentiate high performers from the rest. Based on the average scores, the difficulty level of activities could be increased or decreased to nurture the development of pre-schoolers and also appropriate teaching methodologies could be devised.

Keywords: child development, early childhood assessment, early childhood curriculum, quantitative assessment of preschool curriculum

Procedia PDF Downloads 358

Authors: Yaser Ghasemi Aryan, Firoozeh Moghiminejad, Mohammadreza Shahraki

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Background: The cooperative institution is considered the first and most essential pillar of strengthening social capital, whose sustainability is the main guarantee of survival and continued participation of local communities in natural resource management projects. The Village Development Group and the Microcredit Fund are two important social and economic institutions in the implementation of the International Project for the Restoration of Degraded Forest Lands (RFLDL) in Sarayan City, South Khorasan Province, which has learned positive lessons from the participation of the beneficiaries in the implementation. They have brought more effective projects to deal with desertification. However, the low activity or liquidation of some of these institutions has become one of the important challenges and concerns of project executive experts. The current research was carried out with the aim of explaining the main components of the instability of these institutions. Materials and Methods: This research is descriptive-analytical in terms of method, practical in terms of purpose, and the method of collecting information is two documentary and survey methods. The statistical population of the research included all the members of the village development groups and microcredit funds in the target villages of the RFLDL project of Sarayan city, based on the Kochran formula and matching with the Karjesi and Morgan table. Net people were selected as a statistical sample. After confirming the validity of the expert's opinions, the reliability of the questionnaire was 0.83, which shows the appropriate reliability of the researcher-made questionnaire. Data analysis was done using SPSS software. Results: The results related to the extraction of obstacles to the stability of social and economic networks were classified and prioritized in the form of 5 groups of social-cultural, economic, administrative, educational-promotional and policy-management factors. Based on this, in the socio-cultural factors, the items ‘not paying attention to the structural characteristics and composition of groups’, ‘lack of commitment and moral responsibility in some members of the group,’ and ‘lack of a clear pattern for the preservation and survival of groups’, in the disciplinary factors, The items ‘Irregularity in holding group meetings’ and ‘Irregularity of members to participate in meetings’, in the economic factors of the items "small financial capital of the fund’, ‘the low amount of loans of the fund’ and ‘the fund's inability to conclude contracts and attract capital from other sources’, in the educational-promotional factors of the items ‘non-simultaneity of job training with the granting of loans to create jobs’ and ‘insufficient training for the effective use of loans and job creation’ and in the policy-management factors of the item ‘failure to provide government facilities for support From the funds, they had the highest priority. Conclusion: In general, the results of this research show that policy-management factors and social factors, especially the structure and composition of social and economic institutions, are the most important obstacles to their sustainability. Therefore, it is suggested to form cooperative institutions based on network analysis studies in order to achieve the appropriate composition of members.

Keywords: cooperative institution, social capital, network analysis, participation, Sarayan.

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Authors: Sonam Behl, Raju, Ginu Rajan, Paul Farrar, B. Gangadhara Prusty

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Reinforcing dental composites with micro-sized fibres can significantly improve the physio-mechanical properties of dental composites. The short fibres can be oriented randomly within dental composites, thus providing quasi-isotropic reinforcing efficiency unlike unidirectional/bidirectional fibre reinforced composites enhancing anisotropic properties. Thus, short fibres reinforced dental composites are getting popular among practitioners. However, despite their popularity, resin-based dental composites are prone to failure on account of shrinkage during photo polymerisation. The shrinkage in the structure may lead to marginal gap formation, causing secondary caries, thus ultimately inducing failure of the restoration. The traditional methods to evaluate polymerisation shrinkage using strain gauges, density-based measurements, dilatometer, or bonded-disk focuses on average value of volumetric shrinkage. Moreover, the results obtained from traditional methods are sensitive to the specimen geometry. The present research aims to evaluate the real-time shrinkage strain at selected locations in the material with the help of optical fibre Bragg grating (FBG) sensors. Due to the miniature size (diameter 250 µm) of FBG sensors, they can be easily embedded into small samples of dental composites. Furthermore, an FBG array into the system can map the real-time shrinkage strain at different regions of the composite. The evaluation of real-time monitoring of shrinkage values may help to optimise the physio-mechanical properties of composites. Previously, FBG sensors have been able to rightfully measure polymerisation strains of anisotropic (unidirectional or bidirectional) reinforced dental composites. However, very limited study exists to establish the validity of FBG based sensors to evaluate volumetric shrinkage for randomly oriented fibres reinforced composites. The present study aims to fill this research gap and is focussed on establishing the usage of FBG based sensors for evaluating the shrinkage of dental composites reinforced with randomly oriented fibres. Three groups of specimens were prepared by mixing the resin (80% UDMA/20% TEGDMA) with 55% of silane treated BaAlSiO₂ particulate fillers or by adding 5% of micro-sized fibres of diameter 5 µm, and length 250/350 µm along with 50% of silane treated BaAlSiO₂ particulate fillers into the resin. For measurement of polymerisation shrinkage strain, an array of three fibre Bragg grating sensors was embedded at a depth of 1 mm into a circular Teflon mould of diameter 15 mm and depth 2 mm. The results obtained are compared with the traditional method for evaluation of the volumetric shrinkage using density-based measurements. Degree of conversion was measured using FTIR spectroscopy (Spotlight 400 FT-IR from PerkinElmer). It is expected that the average polymerisation shrinkage strain values for dental composites reinforced with micro-sized fibres can directly correlate with the measured degree of conversion values, implying that more C=C double bond conversion to C-C single bond values also leads to higher shrinkage strain within the composite. Moreover, it could be established the photonics approach could help assess the shrinkage at any point of interest in the material, suggesting that fibre-Bragg grating sensors are a suitable means for measuring real-time polymerisation shrinkage strain for randomly fibre reinforced dental composites as well.

Keywords: dental composite, glass fibre, polymerisation shrinkage strain, fibre-Bragg grating sensors

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Eman Ghoneim

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The Arabian deserts include some of the driest areas on Earth. Yet, its landforms reserved a record of past wet climates. During humid phases, the desert was green and contained permanent rivers, inland deltas and lakes. Some of their water would have seeped and replenished the groundwater aquifers. When the wet periods came to an end, several thousand years ago, the entire region transformed into an extended band of desert and its original fluvial surface was totally covered by windblown sand. In this work, radar and thermal infrared images were used to reveal numerous hidden surface/subsurface features. Radar long wavelength has the unique ability to penetrate surface dry sands and uncover buried subsurface terrain. Thermal infrared also proven to be capable of spotting cooler moist areas particularly in hot dry surfaces. Integrating Radarsat images and GIS revealed several previously unknown paleoriver and lake basins in the region. One of these systems, known as the Kufrah, is the largest yet identified river basin in the Eastern Sahara. This river basin, which straddles the border between Egypt and Libya, flowed north parallel to the adjacent Nile River with an extensive drainage area of 235,500 km2 and massive valley width of 30 km in some parts. This river was most probably served as a spillway for an overflow from Megalake Chad to the Mediterranean Sea and, thus, may have acted as a natural water corridor used by human ancestors to migrate northward across the Sahara. The Gilf-Kebir is another large paleoriver system located just east of Kufrah and emanates from the Gilf Plateau in Egypt. Both river systems terminate with vast inland deltas at the southern margin of the Great Sand Sea. The trends of their distributary channels indicate that both rivers drained to a topographic depression that was periodically occupied by a massive lake. During dry climates, the lake dried up and roofed by sand deposits, which is today forming the Great Sand Sea. The enormity of the lake basin provides explanation as to why continuous extraction of groundwater in this area is possible. A similar lake basin, delimited by former shorelines, was detected by radar space data just across the border of Sudan. This lake, called the Northern Darfur Megalake, has a massive size of 30,750 km2. These former lakes and rivers could potentially hold vast reservoirs of groundwater, oil and natural gas at depth. Similar to radar data, thermal infrared images were proven to be useful in detecting potential locations of subsurface water accumulation in desert regions. Analysis of both Aster and daily MODIS thermal channels reveal several subsurface cool moist patches in the sandy desert of the Arabian Peninsula. Analysis indicated that such evaporative cooling anomalies were resulted from the subsurface transmission of the Monsoonal rainfall from the mountains to the adjacent plain. Drilling a number of wells in several locations proved the presence of productive water aquifers confirming the validity of the used data and the adopted approaches for water exploration in dry regions.

Keywords: radarsat, SRTM, MODIS, thermal infrared, near-surface water, ancient rivers, desert, Sahara, Arabian peninsula

Procedia PDF Downloads 245

Authors: Alejandra Martin-Dominguez, Javier Canto-Rios, Velitchko Tzatchkov

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Electrocoagulation is a water treatment technology that consists of generating coagulant species in situ by electrolytic oxidation of sacrificial anode materials triggered by electric current. It removes suspended solids, heavy metals, emulsified oils, bacteria, colloidal solids and particles, soluble inorganic pollutants and other contaminants from water, offering an alternative to the use of metal salts or polymers and polyelectrolyte addition for breaking stable emulsions and suspensions. The method essentially consists of passing the water being treated through pairs of consumable conductive metal plates in parallel, which act as monopolar electrodes, commonly known as ‘sacrificial electrodes’. Physicochemical, electrochemical and hydraulic processes are involved in the efficiency of this type of treatment. While the physicochemical and electrochemical aspects of the technology have been extensively studied, little is known about the influence of the hydraulics. However, the hydraulic process is fundamental for the reactions that take place at the electrode boundary layers and for the coagulant mixing. Electrocoagulation reactors can be open (with free water surface) and closed (pressurized). Independently of the type of rector, hydraulic head loss is an important factor for its design. The present work focuses on the study of the total hydraulic head loss and flow velocity and pressure distribution in electrocoagulation reactors with single or multiple concentric annular cross sections. An analysis of the head loss produced by hydraulic wall shear friction and accessories (minor head losses) is presented, and compared to the head loss measured on a semi-pilot scale laboratory model for different flow rates through the reactor. The tests included laminar, transitional and turbulent flow. The observed head loss was compared also to the head loss predicted by several known conceptual theoretical and empirical equations, specific for flow in concentric annular pipes. Four single concentric annular cross section and one multiple concentric annular cross section reactor configuration were studied. The theoretical head loss resulted higher than the observed in the laboratory model in some of the tests, and lower in others of them, depending also on the assumed value for the wall roughness. Most of the theoretical models assume that the fluid elements in all annular sections have the same velocity, and that flow is steady, uniform and one-dimensional, with the same pressure and velocity profiles in all reactor sections. To check the validity of such assumptions, a computational fluid dynamics (CFD) model of the concentric annular pipe reactor was implemented using the ANSYS Fluent software, demonstrating that pressure and flow velocity distribution inside the reactor actually is not uniform. Based on the analysis, the equations that predict better the head loss in single and multiple annular sections were obtained. Other factors that may impact the head loss, such as the generation of coagulants and gases during the electrochemical reaction, the accumulation of hydroxides inside the reactor, and the change of the electrode material with time, are also discussed. The results can be used as tools for design and scale-up of electrocoagulation reactors, to be integrated into new or existing water treatment plants.

Keywords: electrocoagulation reactors, hydraulic head loss, concentric annular pipes, computational fluid dynamics model

Procedia PDF Downloads 216

Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

Procedia PDF Downloads 157

Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

Procedia PDF Downloads 54

Authors: Pierre-Andre Mudry, Jean Decaix, Jeremy Schmid, Cesar Papilloud, Cecile Munch-Alligne

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In most of the Swiss Alpine regions, the availability of water resources is usually adequate even in times of drought, as evidenced by the 2003 and 2018 summers. Indeed, important natural stocks are for the moment available in the form of snow and ice, but the situation is likely to change in the future due to global and regional climate change. In addition, alpine mountain regions are areas where climate change will be felt very rapidly and with high intensity. For instance, the ice regime of these regions has already been affected in recent years with a modification of the monthly availability and extreme events of precipitations. The current research, focusing on the municipality of Val de Bagnes, located in the canton of Valais, Switzerland, is part of a project led by the Altis company and achieved in collaboration with WSL, BlueArk Entremont, and HES-SO Valais-Wallis. In this region, water occupies a key position notably for winter and summer tourism. Thus, multiple actors want to apprehend the future needs and availabilities of water, on both the 2050 and 2100 horizons, in order to plan the modifications to the water supply and distribution networks. For those changes to be salient and efficient, a good knowledge of the current water distribution networks is of most importance. In the current case, the water drinking network is well documented, but this is not the case for the irrigation one. Since the water consumption for irrigation is ten times higher than for drinking water, data acquisition on the irrigation network is a major point to determine future scenarios. This paper first presents the instrumentation and simulation of the irrigation network using custom-designed IoT devices, which are coupled with a digital clone simulated to reduce the number of measuring locations. The developed IoT ad-hoc devices are energy-autonomous and can measure flows and pressures using industrial sensors such as calorimetric water flow meters. Measurements are periodically transmitted using the LoRaWAN protocol over a dedicated infrastructure deployed in the municipality. The gathered values can then be visualized in real-time on a dashboard, which also provides historical data for analysis. In a second phase, a digital clone of the irrigation network was modeled using EPANET, a software for water distribution systems that performs extended-period simulations of flows and pressures in pressurized networks composed of reservoirs, pipes, junctions, and sinks. As a preliminary work, only a part of the irrigation network was modelled and validated by comparisons with the measurements. The simulations are carried out by imposing the consumption of water at several locations. The validation is performed by comparing the simulated pressures are different nodes with the measured ones. An accuracy of +/- 15% is observed on most of the nodes, which is acceptable for the operator of the network and demonstrates the validity of the approach. Future steps will focus on the deployment of the measurement devices on the whole network and the complete modelling of the network. Then, scenarios of future consumption will be investigated. Acknowledgment— The authors would like to thank the Swiss Federal Office for Environment (FOEN), the Swiss Federal Office for Agriculture (OFAG) for their financial supports, and ALTIS for the technical support, this project being part of the Swiss Pilot program 'Adaptation aux changements climatiques'.

Keywords: hydraulic digital clone, IoT water monitoring, LoRaWAN water measurements, EPANET, irrigation network

Procedia PDF Downloads 141

Authors: Kimberly Winnie Achieng Otieno

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The Nairobi Hospital plays a pivotal role in healthcare provision in East and Central Africa, yet it faces challenges in providing accessible dialysis care. This paper explores strategic interventions to enhance dialysis care, improve access and streamline health information management, with an aim of fostering an integrated and patient-centered healthcare system in our region. Challenges at The Nairobi Hospital The Nairobi Hospital currently grapples with insufficient dialysis machines which results in extended turn around times. This issue stems from both staffing bottle necks and infrastructural limitations given our growing demand for renal care services. Our Paper-based record keeping system and fragmented flow of information downstream hinders the hospital’s ability to manage health data effectively. There is also a need for investment in expanding The Nairobi Hospital dialysis facilities to far reaching communities. Setting up satellite clinics that are closer to people who live in areas far from the main hospital will ensure better access to underserved areas. Community Outreach and Education Implementing education programs on kidney health within local communities is vital for early detection and prevention. Collaborating with local leaders and organizations can establish a proactive approach to renal health hence reducing the demand for acute dialysis interventions. We can amplify this effort by expanding The Nairobi Hospital’s corporate social responsibility outreach program with weekend engagement activities such as walks, awareness classes and fund drives. Enhancing Efficiency in Dialysis Care Demand for dialysis services continues to rise due to an aging Kenyan population and the increasing prevalence of chronic kidney disease (CKD). Present at this years International Nursing Conference are a diverse group of caregivers from around the world who can share with us their process optimization strategies, patient engagement techniques and resource utilization efficiencies to catapult The Nairobi Hospital to the 21st century and beyond. Plans are underway to offer ongoing education opportunities to keep staff updated on best practices and emerging technologies in addition to utilizing a patient feedback mechanisms to identify areas for improvement and enhance satisfaction. Staff empowerment and suggestion boxes address The Nairobi Hospital’s organizational challenges. Current financial constraints may limit a leapfrog in technology integration such as the acquisition of new dialysis machines and an investment in predictive analytics to forecast patient needs and optimize resource allocation. Streamlining Health Information Management Fully embracing a shift to 100% Electronic Health Records (EHRs) is a transformative step toward efficient health information management. Shared information promotes a holistic understanding of patients’ medical history, minimizing redundancies and enhancing overall care quality. To manage the transition to community-based care and EHRs effectively, a phased implementation approach is recommended. Conclusion By strategically enhancing dialysis care access and streamlining health information management, The Nairobi Hospital can strengthen its position as a leading healthcare institution in both East and Central Africa. This comprehensive approach aligns with the hospital’s commitment to providing high-quality, accessible, and patient-centered care in an evolving landscape of healthcare delivery.

Keywords: Africa, urology, diaylsis, healthcare

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Authors: Emily Simone Doffing, Danielle Kohfeldt

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Disabled students are underrepresented in graduate-level degree enrollment and completion. There is limited research on disabled students' progression during the pandemic. Disabled graduate students (DGS) face unique interpersonal and institutional barriers, yet, limited research explores these barriers, buffering facilitators, and aids to academic persistence. This study adopts an asset-based, embodied disability approach using the critical pedagogy theoretical framework instead of the deficit research approach. The Participatory Action Research (PAR) paradigm, the critical pedagogy theoretical framework, and emancipatory disability research share the same purpose -creating a socially just world through reciprocal learning. This study is one of few, if not the first, to center solely on DGS’ lived understanding using a Participatory Action Research (PAR) epistemology. With a PAR paradigm, participants and investigators work as a research team democratically at every stage of the research process. PAR has individual and systemic outcomes. PAR lessens the researcher-participant power gap and elevates a marginalized community’s knowledge as expertise for local change. PAR and critical pedagogy work toward enriching everyone involved with empowerment, civic engagement, knowledge proliferation, socio-cultural reflection, skills development, and active meaning-making. The PAR process unveils the tensions between disability and graduate school in policy and practice during the pandemic. Likewise, institutional and ideological tensions influence the PAR process. This project is recruiting 10 DGS until September through purposive and snowball sampling. DGS will collectively practice praxis during four monthly focus groups in the fall 2023 semester. Participant researchers can attend a focus group or an interview, both with field notes. September will be our orientation and first monthly meeting. It will include access needs check-ins, ice breakers, consent form review, a group agreement, PAR introduction, research ethics discussion, research goals, and potential research topics. October and November will be available for meetings for dialogues about lived experiences during our collaborative data collection. Our sessions can be semi-structured with “framing questions,” which would be revised together. Field notes include observations that cannot be captured through audio. December will focus on local social action planning and dissemination. Finally, in January, there will be a post-study focus group for students' reflections on their experiences of PAR. Iterative analysis methods include transcribed audio, reflexivity, memos, thematic coding, analytic triangulation, and member checking. This research follows qualitative rigor and quality criteria: credibility, transferability, confirmability, and psychopolitical validity. Results include potential tension points, social action, individual outcomes, and recommendations for conducting PAR. Tension points have three components: dubious practices, contestable knowledge, and conflict. The dissemination of PAR recommendations will aid and encourage researchers to conduct future PAR projects with the disabled community. Identified stakeholders will be informed of DGS’ insider knowledge to drive social sustainability.

Keywords: participatory action research, graduate school, disability, higher education

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Authors: Benjamin Arazi

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Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

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Authors: Susana Barreto

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This paper explores a pedagogical exercise that employs family photos as catalysts for teaching visual analysis and inspiring academic writing among MA students. The study aimed to achieve two primary objectives: to impart students with the skills of analyzing images or artifacts and to ignite their writing for research purposes. Conducted at Viana Polytechnic in Portugal, the exercise involved two classes on Arts Management and Art Education Master course comprising approximately twenty students from diverse academic backgrounds, including Economics, Design, Fine Arts, and Sociology, among others. The exploratory exercise involved selecting an old family photo, analyzing its content and context, and deconstructing the chosen images in an intuitive and systematic manner. Students were encouraged to engage in photo elicitation, seeking insights from family/friends to gain multigenerational perspectives on the images. The feedback received from this exercise was consistently positive, largely due to the personal connection students felt with the objects of analysis. Family photos, with their emotional significance, fostered deeper engagement and motivation in the learning process. Furthermore, visual analysing family photos stimulated critical thinking as students interpreted the composition, subject matter, and potential meanings embedded in the images. This practice enhanced their ability to comprehend complex visual representations and construct compelling visual narratives, thereby facilitating the writing process. The exercise also facilitated the identification of patterns, similarities, and differences by comparing different family photos, leading to a more comprehensive analysis of visual elements and themes. Throughout the exercise, students found analyzing their own photographs both enjoyable and insightful. They progressed through preliminary analysis, explored content and context, and artfully interwove these components. Additionally, students experimented with various techniques such as converting photos to black and white, altering framing angles, and adjusting sizes to unveil hidden meanings.The methodology employed included observation, documental analysis of written reports, and student interviews. By including students from diverse academic backgrounds, the study enhanced its external validity, enabling a broader range of perspectives and insights during the exercise. Furthermore, encouraging students to seek multigenerational perspectives from family and friends added depth to the analysis, enriching the learning experience and broadening the understanding of the cultural and historical context associated with the family photos Highlighting the emotional significance of these family photos and the personal connection students felt with the objects of analysis fosters a deeper connection to the subject matter. Moreover, the emphasis on stimulating critical thinking through the analysis of composition, subject matter, and potential meanings in family photos suggests a targeted approach to developing analytical skills. This improvement focuses specifically on critical thinking and visual analysis, enhancing the overall quality of the exercise. Additionally, the inclusion of a step where students compare different family photos to identify patterns, similarities, and differences further enhances the depth of the analysis. This comparative approach adds a layer of complexity to the exercise, ultimately leading to a more comprehensive understanding of visual elements and themes. The expected results of this study will culminate in a set of practical recommendations for implementing this exercise in academic settings.

Keywords: visual analysis, academic writing, pedagogical exercise, family photos

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Authors: Elizabeth R. LaFave, Grayson Sink, Anna Vassallo, Samantha Mills, Eli G. Hvastkovs

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Throughout history and into modern times, the continuation of male influence over female healthcare has created inadequacies in availability and access to treatments, often further limited in rural communities. The historical plight of women in healthcare can be understood by studying the advancements made by women in the field, both through their career arcs and by delving into the treatments they offer. An early example is the case of Martha Ballard (1735-1812), a midwife in New York who practiced when female practitioners were dismissed in favor of less educated male physicians, which was a well-accepted practice into the twentieth century. In order to overcome these setbacks, a strategy used by some female practitioners was to develop and market their own remedies in an attempt to better serve female patients. By highlighting the compromises and social manipulation of female entrepreneurs, in comparison with the medicines they developed and used, we can map their ability to carve a specific niche for themselves and their targeted customers. The application of modern chemical approaches in a historical context serves to enhance a variety of perspectives within the museum sphere necessary for the comprehension and understanding of the female plight in both medical care and service. In order to further examine the overall bias and scrutiny for women in the medical field, specifically those undertaking entrepreneurial roles, examples of alternative remedies from female founders will be analyzed utilizing these approaches. Modern analytical chemistry techniques, specifically mass spectrometry (MS), have been successful in offering compositional analyses for both labeled and unlabeled ingredients in old medicines. Previously, we have analyzed two forms of alternative treatment options created by male medical professionals to address lingering historical questions of purity and validity. Although primarily sugar based, both Humphreys’ Specifics and Boericke & Tafel remedies also contained unique ingredients, albeit in small quantities, with medicinal properties. Here, we applied the same methodology to study another highly politicized 19th-century debate surrounding the contribution and role of women in the medical profession through analyzing three remedies, each from a different female-led manufacturing company; Mrs. Joe Persons, Lydia Pinkham, and Winslow’s Syrups. Following MS analyses for both labeled and unlabeled ingredients, both Winslow’s and Pinkham’s remedies were similar to their male counterparts in advertisement strategy, targeted customer base, and overall composition of remedy (primarily sugar-based with small amounts of unique ingredients). In effect, these unbiased chemical assessments are used to dissect the rationality of both market and physician criticism for each individual manufacturer through assessment of authenticity, benefaction, and comparison among female entrepreneurs and their aims to enter the medical community (i.e., geographic location, market size). Our work aims to increase collaboration between STEM (Science, Technology, Engineering, Mathematics)-based fields and historical museum studies on a larger scale while also answering questions of potential bias towards females in the medical community as means of comparison to their male counterparts and in-depth historical analyses to unravel individual strategies to overcome the setback.

Keywords: nineteenth-century medicine, alternative remedies, female healthcare, chemical analyses, mass spectrometry

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Authors: V. Howard, R. Maguire, S. Corrigan

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Background: Type 1 Diabetes (T1D) is a chronic autoimmune disease, typically diagnosed in childhood. T1D puts an enormous strain on families; controlling blood-glucose in children is difficult and the consequences of poor control for patient health are significant. Successful illness management and better health outcomes can be dependent on quality of caregiving. On diagnosis, parent-caregivers face a steep learning curve as T1D care requires a significant level of knowledge to inform complex decision making throughout the day. The majority of illness management is carried out in the home setting, independent of clinical health providers. Parent-caregivers vary in their level of knowledge and their level of engagement in applying this knowledge in the practice of illness management. Enabling researchers to quantify these aspects of the caregiver experience is key to identifying targets for psychosocial support interventions, which are desirable for reducing stress and anxiety in this highly burdened cohort, and supporting better health outcomes in children. Currently, there are limited tools available that are designed to capture this information. Where tools do exist, they are not comprehensive and do not adequately capture the lived experience. Objectives: Development of quantitative tools, informed by lived experience, to enable researchers gather data on parent-caregiver knowledge and engagement, which accurately represents the experience/cohort and enables exploration of questions that are of real-world value to the cohort themselves. Methods: This research employed an engaged approach to address the problem of quantifying two key aspects of caregiver diabetes management: Knowledge and engagement. The research process was multi-staged and iterative. Stage 1: Working from a constructivist standpoint, literature was reviewed to identify relevant questionnaires, scales and single-item measures of T1D caregiver knowledge and engagement, and harvest candidate questionnaire items. Stage 2: Aggregated findings from the review were circulated among a PPI (patient and public involvement) expert panel of caregivers (n=6), for discussion and feedback. Stage 3: In collaboration with the expert panel, data were interpreted through the lens of lived experience to create a long-list of candidate items for novel questionnaires. Items were categorized as either ‘knowledge’ or ‘engagement’. Stage 4: A Delphi-method process (iterative surveys) was used to prioritize question items and generate novel questions that further captured the lived experience. Stage 5: Both questionnaires were piloted to refine wording of text to increase accessibility and limit socially desirable responding. Stage 6: Tools were piloted using an online survey that was deployed using an online peer-support group for caregivers for Juveniles with T1D. Ongoing Research: 123 parent-caregivers completed the survey. Data analysis is ongoing to establish face and content validity qualitatively and through exploratory factor analysis. Reliability will be established using an alternative-form method and Cronbach’s alpha will assess internal consistency. Work will be completed by early 2024. Conclusion: These tools will enable researchers to gain deeper insights into caregiving practices among parents of juveniles with T1D. Development was driven by lived experience, illustrating the value of engaged research at all levels of the research process.

Keywords: caregiving, engaged research, juvenile type 1 diabetes, quantified engagement and knowledge

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Authors: Sujeeva Sebastian Pereira

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Language Anxiety (LA) – a distinct psychological construct of self-perceptions and behaviors related to classroom language learning – is perceived as a significant variable highly correlated with Second Language Acquisition (SLA). However, the existing scholarship has inadequately explored the nuances of LA in relation to South Asia, especially in terms of Sri Lankan higher education contexts. Thus, the current study, situated within the broad areas of Psychology of SLA and Applied Linguistics, investigates the impact of competency-based LA and identity-based LA on learner achievement among undergraduates of Sri Lanka. Employing a case study approach to explore the impact of LA, 750 undergraduates of the University of Sri Jayewardenepura, Sri Lanka, thus covering 25% of the student population from all seven faculties of the university, were selected as participants using stratified proportionate sampling in terms of ethnicity, gender, and disciplines. The qualitative and quantitative research inquiry utilized for data collection include a questionnaire consisting a set of structured and unstructured questions, and semi-structured interviews as research instruments. Data analysis includes both descriptive and statistical measures. As per the quantitative measures of data analysis, the study employed Pearson Correlation Coefficient test, Chi-Square test, and Multiple Correspondence Analysis; it used LA as the dependent variable, and two types of independent variables were used: direct and indirect variables. Direct variables encompass the four main language skills- reading, writing, speaking and listening- and test anxiety. These variables were further explored through classroom activities on grammar, vocabulary and individual and group presentations. Indirect variables are identity, gender and cultural stereotypes, discipline, social background, income level, ethnicity, religion and parents’ education level. Learner achievement was measured through final scores the participants have obtained for Compulsory English- a common first-year course unit mandatory for all undergraduates. LA was measured using the FLCAS. In order to increase the validity and reliability of the study, data collected were triangulated through descriptive content analysis. Clearly evident through both the statistical analysis and the qualitative analysis of the results is the significant linear negative correlation between LA and learner achievement, and the significant negative correlation between LA and culturally-operated gender stereotypes which create identity disparities in learners. The study also found that both competency-based LA and identity-based LA are experienced primarily and inescapably due to the apprehensions regarding speaking in English. Most participants who reported high levels of LA were from an urban socio-economic background of lower income families. Findings exemplify the linguistic inequality prevalent in the socio-cultural milieu in Sri Lankan society. This inequality makes learning English a dire need, yet, very much an anxiety provoking process because of many sociolinguistic, cultural and ideological factors related to English as a Second Language (ESL) in Sri Lanka. The findings bring out the intricate interrelatedness of both the dependent variable (LA) and the independent variables stated above, emphasizing that the significant linear negative correlation between LA and learner achievement is connected to the affective, cognitive and sociolinguistic domains of SLA. Thus, the study highlights the promise in linguistic practices such as code-switching, crossing and accommodating hybrid identities as strategies in minimizing LA and maximizing the experience of ESL.

Keywords: language anxiety, identity-based anxiety, competence-based anxiety, TESL, Sri Lanka

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