Search results for: prospective scenarios

Authors: Katherine Maurer

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Background and purpose: Violence that occurs within families is a global social problem. Children who are victims or witness to family violence are at risk for many negative effects both proximally and distally. One of the most disconcerting long-term effects occurs when child victims become adult perpetrators: the intergenerational transmission of family violence (ITFV). Early identification of those children most at risk for ITFV is needed to inform interventions to prevent future family violence perpetration and victimization. Only about 25-30% of child family violence victims become perpetrators of adult family violence (either child abuse, partner abuse, or both). Prior research has primarily been conducted using dichotomous measures of exposure (yes; no) to predict ITFV, given the low incidence rate in community samples. It is often assumed that exposure to greater amounts of violence predicts greater risk of ITFV. However, no previous longitudinal study with a community sample has tested a dose-response model of exposure to physical child abuse and parental physical intimate partner violence (IPV) using count data of frequency and severity of violence to predict adult ITFV. The current study used advanced statistical methods to test if increased childhood exposure would predict greater risk of ITFV. Methods: The study utilized 3 panels of prospective data from a cohort of 15 year olds (N=338) from the Project on Human Development in Chicago Neighborhoods longitudinal study. The data were comprised of a stratified probability sample of seven ethnic/racial categories and three socio-economic status levels. Structural equation modeling was employed to test a hurdle regression model of dose-response to predict ITFV. A version of the Conflict Tactics Scale was used to measure physical violence victimization, witnessing parental IPV and young adult IPV perpetration and victimization. Results: Consistent with previous findings, past 12 months incidence rates severity and frequency of interpersonal violence were highly skewed. While rates of parental and young adult IPV were about 40%, an unusually high rate of physical child abuse (57%) was reported. The vast majority of a number of acts of violence, whether minor or severe, were in the 1-3 range in the past 12 months. Reported frequencies of more than 5 times in the past year were rare, with less than 10% of those reporting more than six acts of minor or severe physical violence. As expected, minor acts of violence were much more common than acts of severe violence. Overall, regression analyses were not significant for the dose-response model of ITFV. Conclusions and implications: The results of the dose-response model were not significant due to a lack of power in the final sample (N=338). Nonetheless, the value of the approach was confirmed for the future research given the bi-modal nature of the distributions which suggest that in the context of both child physical abuse and physical IPV, there are at least two classes when frequency of acts is considered. Taking frequency into account in predictive models may help to better understand the relationship of exposure to ITFV outcomes. Further testing using hurdle regression models is suggested.

Keywords: intergenerational transmission of family violence, physical child abuse, intimate partner violence, structural equation modeling

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Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

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Authors: Dino Carpentras, Paul J. Maher, Caoimhe O'Reilly, Michael Quayle

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Opinion dynamics is a sub-field of agent-based modeling that focuses on people’s opinions and their evolutions over time. Despite the rapid increase in the number of publications in this field, it is still not clear how to apply these models to real-world scenarios. Indeed, there is no agreement on how people update their opinion while interacting. Furthermore, it is not clear if different topics will show the same dynamics (e.g., more polarized topics may behave differently). These problems are mostly due to the lack of experimental validation of the models. Some previous studies started bridging this gap in the literature by directly measuring people’s opinions before and after the interaction. However, these experiments force people to express their opinion as a number instead of using natural language (and then, eventually, encoding it as numbers). This is not the way people normally interact, and it may strongly alter the measured dynamics. Another limitation of these studies is that they usually average all the topics together, without checking if different topics may show different dynamics. In our work, we collected data from 200 participants on 5 unpolarized topics. Participants expressed their opinions in natural language (“agree” or “disagree”). We also measured the certainty of their answer, expressed as a number between 1 and 10. However, this value was not shown to other participants to keep the interaction based on natural language. We then showed the opinion (and not the certainty) of another participant and, after a distraction task, we repeated the measurement. To make the data compatible with opinion dynamics models, we multiplied opinion and certainty to obtain a new parameter (here called “continuous opinion”) ranging from -10 to +10 (using agree=1 and disagree=-1). We firstly checked the 5 topics individually, finding that all of them behaved in a similar way despite having different initial opinions distributions. This suggested that the same model could be applied for different unpolarized topics. We also observed that people tend to maintain similar levels of certainty, even when they changed their opinion. This is a strong violation of what is suggested from common models, where people starting at, for example, +8, will first move towards 0 instead of directly jumping to -8. We also observed social influence, meaning that people exposed with “agree” were more likely to move to higher levels of continuous opinion, while people exposed with “disagree” were more likely to move to lower levels. However, we also observed that the effect of influence was smaller than the effect of random fluctuations. Also, this configuration is different from standard models, where noise, when present, is usually much smaller than the effect of social influence. Starting from this, we built an opinion dynamics model that explains more than 80% of data variance. This model was also able to show the natural conversion of polarization from unpolarized states. This experimental approach offers a new way to build models grounded on experimental data. Furthermore, the model offers new insight into the fundamental terms of opinion dynamics models.

Keywords: experimental validation, micro-dynamics rule, opinion dynamics, update rule

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Authors: Maggie Mosher, Adam Carreon, Sean Smith

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A systematic literature review was conducted to explore the evidence base on the use of augmented reality (AR), virtual reality (VR), mixed reality (MR), and extended reality (XR) to present social skill instruction to school-age students with autism spectrum disorder (ASD). Specifically, the systematic review focus was on a. the participants and intervention agents using AR, VR, MR, and XR for social skill acquisition b. the social skills taught through these mediums and c. the social validity measures (i.e., goals, procedures, and outcomes) reported in these studies. Forty-one articles met the inclusion criteria. Researchers in six studies taught social skills to students through AR, in 27 studies through non-immersive VR, and in 10 studies through immersive VR. No studies used MR or XR. The primary targeted social skills were relationship skills, emotion recognition, social awareness, cooperation, and executive functioning. An intervention to improve many social skills was implemented by 73% of researchers, 17% taught a single skill, and 10% did not clearly state the targeted skill. The intervention was considered effective in 26 of the 41 studies (63%), not effective in four studies (10%), and 11 studies (27%) reported mixed results. No researchers reported information for all 17 social validity indicators. The social validity indicators reported by researchers ranged from two to 14. Social validity measures on the feelings toward and use of the technology were provided in 22 studies (54%). Findings indicated both AR and VR are promising platforms for providing social skill instruction to students with ASD. Studies utilizing this technology show a number of social validity indicators. However, the limited information provided on the various interventions, participant characteristics, and validity measures, offers insufficient evidence of the impact of these technologies in teaching social skills to students with ASD. Future research should develop a protocol for training treatment agents to assess the role of different variables (i.e., whether agents are customizing content, monitoring student learning, using intervention specific vocabulary in their day to day instruction). Sustainability may be increased by providing training in the technology to both treatment agents and participants. Providing scripts of instruction occurring within the intervention would provide the needed information to determine the primary method of teaching within the intervention. These variables play a role in maintenance and generalization of the social skills. Understanding the type of feedback provided would help researchers determine if students were able to feel rewarded for progressing through the scenarios or if students require rewarding aspects within the intervention (i.e., badges, trophies). AR has the potential to generalize instruction and VR has the potential for providing a practice environment for performance deficits. Combining these two technologies into a mixed reality intervention may provide a more cohesive and effective intervention.

Keywords: autism, augmented reality, social and emotional learning, social skills, virtual reality

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Authors: M. Skibinska, A. Rajewska-Rager, M. Dmitrzak-Weglarz, N. Lepczynska, P. Sibilski, P. Kapelski, J. Pawlak, J. Twarowska-Hauser

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Introduction: Neurotrophic factors have been implicated in neuropsychiatric disorders. Brain-Derived Neurotrophic Factor (BDNF) influences neuron differentiation in development as well as synaptic plasticity and neuron survival in adulthood. BDNF is widely studied in mood disorders and has been proposed as a biomarker for depression. BDNF is synthesized as precursor protein – proBDNF. Both forms are biologically active and exert opposite effects on neurons. Aim: The aim of the study was to examine the serum levels of BDNF and proBDNF in unipolar and bipolar young patients below 24 years old during hypo/manic, depressive episodes and in remission compared to healthy control group. Methods: In a prospective 2 years follow-up study, we investigated alterations in levels of BDNF and proBDNF in 79 patients (23 males, mean age 19.08, SD 3.3 and 56 females, mean age 18.39, SD 3.28) diagnosed with mood disorders: unipolar and bipolar disorder compared with 35 healthy control subjects (7 males, mean age 20.43, SD 4.23 and 28 females, mean age 21.25, SD 2.11). Clinical characteristics including mood, comorbidity, family history, and treatment, were evaluated during control visits and clinical symptoms were rated using the Hamilton Depression Rating Scale and Young Mania Rating Scale. Serum BDNF and proBDNF concentrations were determined by Enzyme-Linked Immunosorbent Assays (ELISA) method. Serum BDNF and proBDNF levels were analysed with covariates: sex, age, age > 18 and < 18 years old, family history of affective disorders, drug-free vs. medicated status. Normality of the data was tested using Shapiro-Wilk test. Levene’s test was used to calculate homogeneity of variance. Non-parametric Tests: Mann-Whitney U test, Kruskal-Wallis ANOVA, Friedman’s ANOVA, Wilcoxon signed rank test, Spearman correlation coefficient were applied in analyses The statistical significance level was set at p < 0.05. Results: BDNF and proBDNF serum levels did not differ between patients at baseline and controls as well as comparing patients in acute episode of depression/hypo/mania at baseline and euthymia (at month 3 or 6). Comparing BDNF and proBDNF levels between patients in euthymia and control group no differences have been found. Increased BDNF level in women compared to men at baseline (p=0.01) have been observed. BDNF level at baseline was negatively correlated with depression and mania occurence at 24 month (p=0.04). BDNF level at 12 month was negatively correlated with depression and mania occurence at 12 month (p=0.01). Correlation of BDNF level with sex have been detected (p=0.01). proBDNF levels at month 3, 6 and 12 negatively correlated with disease status (p=0.02, p=0.008, p=0.009, respectively). No other correlations of BDNF and proBDNF levels with clinical and demographical variables have been detected. Discussion: Our results did not show any differences in BDNF and proBDNF levels between depression, mania, euthymia, and controls. Imbalance in BDNF/proBDNF signalling may be involved in pathogenesis of mood disorders. Further studies on larger groups are recommended. Grant was founded by National Science Center in Poland no 2011/03/D/NZ5/06146.

Keywords: bipolar disorder, Brain-Derived Neurotrophic Factor (BDNF), proBDNF, unipolar depression

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Tan Hui Ying Renee, Stella Rizalina Sasha, Farah Safdar Husain

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Introduction: Menopause Hormone Therapy (MHT) is an effective drug indicated for the treatment of menopausal symptoms and as replacement therapy in women who undergo premature menopause. In 2020, less than 8.8% of perimenopausal Singaporean women are on hormonal therapy, as compared to the Western population, where up to 50% may be on MHT. Factors associated with MHT utilization have been studied from patient characteristics, but the impact of locally prescribing physicians resulting in low MHT utilization has yet to be evaluated. The aim of the study is to determine the level of knowledge physicians in the Obstetrics and Gynaecology specialty have and their attitudes toward MHT. We believe that knowledge of MHT is lacking and that negative attitudes towards MHT may influence its use and undermine the benefits MHT may have for women. This paper is a part of a larger study on Singaporean physicians’ knowledge and attitudes towards MHT. Methods: This is a cross-sectional study intended to assess the knowledge and attitudes of physicians toward Menopausal Hormone Therapy. An anonymous questionnaire was disseminated via institutional internal circulations to optimize reach to physicians who may prescribe MHT, particularly in the fields of Gynaecology, Family Medicine and Endocrinology. Responses were completed voluntarily. Physicians had the option for each question to declare that they were ‘unsure’ or that the question was ‘beyond their expertise’. 21 knowledge questions tested factual recall on indications, contraindications, and risks of MHT. The remaining 6 questions were clinical scenarios crafted with the intention of testing specific principles related to the use of MHT. These questions received face validation from experts in the field. 198 responses were collected, 79 of which were from physicians in the Obstetrics and Gynaecology specialty. The data will be statistically analyzed to investigate areas that can be improved to increase the overall benefits of MHT for the Singaporean population. Results: Preliminary results show that the prevailing factors that limit Singaporean gynecologists and obstetricians from prescribing MHT are a lack of knowledge of MHT and a lack of confidence in prescribing MHT. Risks and indications of MHT were not well known by many physicians, with the majority of the questions having more than 25% incorrect and ‘unsure’ as their reply. The clinical scenario questions revealed significant shortcomings in knowledge on how to navigate real-life challenges in MHT use, with 2 of 6 questions with more than 50% incorrect or ‘beyond their expertise’ as their reply. The portion of the questionnaire that investigated the attitudes of physicians showed that though a large majority believed MHT to be an effective drug, only 40.5% were confident in prescribing it. Conclusion: Physicians in the Obstetrics and Gynaecology specialty lack knowledge and confidence in MHT. Therefore, it is imperative to formulate solutions on both the individual and institutional levels to fill these gaps and ensure that MHT is used appropriately and prescribed to the patients who need it.

Keywords: menopause, menopause hormone therapy, physician factors, obstetrics and gynecology, menopausal symptoms, Singapore

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Authors: Mokhlisur Rahman

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The growing consensus among scientists and world leaders indicates that immediate action should be considered regarding the climate change phenomenon. However, climate change is no more a global issue but a personal one. Thus, individual participation is necessary to address such a significant issue. Studies show that individuals who perceive climate change as a personal issue are more likely to act toward it. This abstract presents augmented reality (A.R.) technology in the social media platform Facebook video advertising. The idea involves creating a video advertisement that enables users to interact with the video by navigating its features and experiencing the result uniquely and engagingly. This advertisement uses A.R. to bring changes, such as people making changes in real-life scenarios by simple clicks on the video and hearing an instant rewarding fact about their choices. The video shows three options: room, lawn, and driveway. Users select one option and engage in interaction based on while holding the camera in their personal spaces: Suppose users select the first option, room, and hold their camera toward spots such as by the windows, balcony, corners, and even walls. In that case, the A.R. offers users different plants appropriate for those unoccupied spaces in the room. Users can change the options of the plants and see which space at their house deserves a plant that makes it more natural. When a user adds a natural element to the video, the video content explains a piece of beneficiary information about how the user contributes to the world more to be livable and why it is necessary. With the help of A.R., if users select the second option, lawn, and hold their camera toward their lawn, the options are various small trees for their lawn to make it more environmentally friendly and decorative. The video plays a beneficiary explanation here too. Suppose users select the third option, driveway, and hold their camera toward their driveway. In that case, the A.R. video option offers unique recycle bin designs using A.I. measurement of spaces. The video plays audio information on anthropogenic contribution to greenhouse gas emission. IoT embeds tracking code in the video ad on Facebook, which stores the exact number of views in the cloud for data analysis. An online survey at the end collects short qualitative answers. This study helps understand the number of users involved and willing to change their behavior; It makes personalized advertising in social media. Considering the current state of climate change, the urgency for action is increasing. This ad increases the chance to make direct connections with individuals and gives a sense of personal responsibility for climate change to act

Keywords: motivations, climate, iot, personalized-advertising, action

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Authors: Anastasia Constantinou, Stephen Morris

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Background: The adoption of synchronous and asynchronous telemedicine modalities for primary care consultations has exponentially increased since the COVID-19 pandemic. However, there is limited understanding of how virtual consultations influence healthcare resource utilization and other quality measures including safety, timeliness, efficiency, patient and provider satisfaction, cost-effectiveness and environmental impact. Aim: Quantify the rate of follow-up visits, emergency department visits, hospitalizations, request for investigations and prescriptions and comment on the effect on different quality measures associated with different telemedicine modalities used for primary care services and primary care referrals to secondary care Design and setting: Systematic review in primary care Methods: A systematic search was carried out across three databases (Medline, PubMed and Scopus) between August and November 2023, using terms related to telemedicine, general practice, electronic referrals, follow-up, use and efficiency and supported by citation searching. This was followed by screening according to pre-defined criteria, data extraction and critical appraisal. Narrative synthesis and metanalysis of quantitative data was used to summarize findings. Results: The search identified 2230 studies; 50 studies are included in this review. There was a prevalence of asynchronous modalities in both primary care services (68%) and referrals from primary care to secondary care (83%), and most of the study participants were females (63.3%), with mean age of 48.2. The average follow-up for virtual consultations in primary care was 28.4% (eVisits: 36.8%, secure messages 18.7%, videoconference 23.5%) with no significant difference between them or F2F consultations. There was an average annual reduction of primary care visits by 0.09/patient, an increase in telephone visits by 0.20/patient, an increase in ED encounters by 0.011/patient, an increase in hospitalizations by 0.02/patient and an increase in out of hours visits by 0.019/patient. Laboratory testing was requested on average for 10.9% of telemedicine patients, imaging or procedures for 5.6% and prescriptions for 58.7% of patients. When looking at referrals to secondary care, on average 36.7% of virtual referrals required follow-up visit, with the average rate of follow-up for electronic referrals being higher than for videoconferencing (39.2% vs 23%, p=0.167). Technical failures were reported on average for 1.4% of virtual consultations to primary care. When using carbon footprint estimates, we calculate that the use of telemedicine in primary care services can potentially provide a net decrease in carbon footprint by 0.592kgCO2/patient/year. When follow-up rates are taken into account, we estimate that virtual consultations reduce carbon footprint for primary care services by 2.3 times, and for secondary care referrals by 2.2 times. No major concerns regarding quality of care, or patient satisfaction were identified. 5/7 studies that addressed cost-effectiveness, reported increased savings. Conclusions: Telemedicine provides quality, cost-effective, and environmentally sustainable care for patients in primary care with inconclusive evidence regarding the rates of subsequent healthcare utilization. The evidence is limited by heterogeneous, small-scale studies and lack of prospective comparative studies. Further research to identify the most appropriate telemedicine modality for different patient populations, clinical presentations, service provision (e.g. used to follow-up patients instead of initial diagnosis) as well as further education for patients and providers alike on how to make best use of this service is expected to improve outcomes and influence practice.

Keywords: telemedicine, healthcare utilisation, digital interventions, environmental impact, sustainable healthcare

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Authors: Xiaoqing Peng, Aijing Luo, Yang Chen

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Background: Cyberchondria, as an "emerging risk" accompanied by the information era, is a new abnormal pattern characterized by excessive or repeated online searches for health-related information and escalating health anxiety, which endangers people's physical and mental health and poses a huge threat to public health. Objective: To explore and discuss the research status, hotspots and trends of Cyberchondria. Methods: Based on a total of 77 articles regarding "Cyberchondria" extracted from Web of Science from the beginning till October 2019, the literature trends, countries, institutions, hotspots are analyzed by bibliometric analysis, the concept definition of Cyberchondria, instruments, relevant factors, treatment and intervention are discussed as well. Results: Since "Cyberchondria" was put forward for the first time in 2001, the last two decades witnessed a noticeable increase in the amount of literature, especially during 2014-2019, it quadrupled dramatically at 62 compared with that before 2014 only at 15, which shows that Cyberchondria has become a new theme and hot topic in recent years. The United States was the most active contributor with the largest publication (23), followed by England (11) and Australia (11), while the leading institutions were Baylor University(7) and University of Sydney(7), followed by Florida State University(4) and University of Manchester(4). The WoS categories "Psychiatry/Psychology " and "Computer/ Information Science "were the areas of greatest influence. The concept definition of Cyberchondria is not completely unified in the world, but it is generally considered as an abnormal behavioral pattern and emotional state and has been invoked to refer to the anxiety-amplifying effects of online health-related searches. The first and the most frequently cited scale for measuring the severity of Cyberchondria called “The Cyberchondria Severity Scale (CSS) ”was developed in 2014, which conceptualized Cyberchondria as a multidimensional construct consisting of compulsion, distress, excessiveness, reassurance, and mistrust of medical professionals which was proved to be not necessary for this construct later. Since then, the Brazilian, German, Turkish, Polish and Chinese versions were subsequently developed, improved and culturally adjusted, while CSS was optimized to a simplified version (CSS-12) in 2019, all of which should be worthy of further verification. The hotspots of Cyberchondria mainly focuses on relevant factors as follows: intolerance of uncertainty, anxiety sensitivity, obsessive-compulsive disorder, internet addition, abnormal illness behavior, Whiteley index, problematic internet use, trying to make clear the role played by “associated factors” and “anxiety-amplifying factors” in the development of Cyberchondria, to better understand the aetiological links and pathways in the relationships between hypochondriasis, health anxiety and online health-related searches. Although the treatment and intervention of Cyberchondria are still in the initial stage of exploration, there are kinds of meaningful attempts to seek effective strategies from different aspects such as online psychological treatment, network technology management, health information literacy improvement and public health service. Conclusion: Research on Cyberchondria is in its infancy but should be deserved more attention. A conceptual consensus on Cyberchondria, a refined assessment tool, prospective studies conducted in various populations, targeted treatments for it would be the main research direction in the near future.

Keywords: cyberchondria, hypochondriasis, health anxiety, online health-related searches

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Authors: Maryam Mohammadi, Mohammadjavad Mahdavinejad, Mojtaba Ansari

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The main driving force for increasing movement towards the design of High-Performance Buildings (HPB) are building codes and rating systems that address the various components of the building and their impact on the environment and energy conservation through various methods like prescriptive methods or simulation-based approaches. The methods and tools developed to meet these needs, which are often based on building performance simulation tools (BPST), have limitations in terms of compatibility with the integrated design process (IDP) and HPB design, as well as use by architects in the early stages of design (when the most important decisions are made). To overcome these limitations in recent years, efforts have been made to develop Design Decision Support Systems, which are often based on artificial intelligence. Numerous needs and steps for designing and developing a Decision Support System (DSS), which complies with the early stages of energy-efficient architecture design -consisting of combinations of different methods in an integrated package- have been listed in the literature. While various review studies have been conducted in connection with each of these techniques (such as optimizations, sensitivity and uncertainty analysis, etc.) and their integration of them with specific targets; this article is a critical and holistic review of the researches which leads to the development of applicable systems or introduction of a comprehensive framework for developing models complies with the IDP. Information resources such as Science Direct and Google Scholar are searched using specific keywords and the results are divided into two main categories: Simulation-based DSSs and Meta-simulation-based DSSs. The strengths and limitations of different models are highlighted, two general conceptual models are introduced for each category and the degree of compliance of these models with the IDP Framework is discussed. The research shows movement towards Multi-Level of Development (MOD) models, well combined with early stages of integrated design (schematic design stage and design development stage), which are heuristic, hybrid and Meta-simulation-based, relies on Big-real Data (like Building Energy Management Systems Data or Web data). Obtaining, using and combining of these data with simulation data to create models with higher uncertainty, more dynamic and more sensitive to context and culture models, as well as models that can generate economy-energy-efficient design scenarios using local data (to be more harmonized with circular economy principles), are important research areas in this field. The results of this study are a roadmap for researchers and developers of these tools.

Keywords: integrated design process, design decision support system, meta-simulation based, early stage, big data, energy efficiency

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Authors: Robert Pellerin, Michel Gamache, Remi Trudeau, Nathalie Perrier

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The context of large engineering projects is particularly favorable to the appearance of engineering changes and contractual modifications. These elements are potential causes for claims. In this paper, we investigate one of the critical components of the claim management process: the calculation of the impacts of changes in terms of losses of productivity due to the need to accelerate some project activities. When project changes are initiated, delays can arise. Indeed, project activities are often executed in fast-tracking in an attempt to respect the completion date. But the acceleration of project execution and the resulting rework can entail important costs as well as induce productivity losses. In the past, numerous methods have been proposed to quantify the duration of delays, the gains achieved by project acceleration, and the loss of productivity. The calculation related to those changes can be divided into two categories: direct cost and indirect cost. The direct cost is easily quantifiable as opposed to indirect costs which are rarely taken into account during the calculation of the cost of an engineering change or contract modification despite several research projects have been made on this subject. However, proposed models have not been accepted by companies yet, nor they have been accepted in court. Those models require extensive data and are often seen as too specific to be used for all projects. These techniques are also ignoring the resource constraints and the interdependencies between the causes of delays and the delays themselves. To resolve this issue, this research proposes a simulation model that mimics how major engineering changes or contract modifications are handled in large construction projects. The model replicates the use of overtime in a reactive scheduling mode in order to simulate the loss of productivity present when a project change occurs. Multiple tests were conducted to compare the results of the proposed simulation model with statistical analysis conducted by other researchers. Different scenarios were also conducted in order to determine the impact the number of activities, the time of occurrence of the change, the availability of resources, and the type of project changes on productivity loss. Our results demonstrate that the number of activities in the project is a critical variable influencing the productivity of a project. When changes occur, the presence of a large number of activities leads to a much lower productivity loss than a small number of activities. The speed of reducing productivity for 30-job projects is about 25 percent faster than the reduction speed for 120-job projects. The moment of occurrence of a change also shows a significant impact on productivity. Indeed, the sooner the change occurs, the lower the productivity of the labor force. The availability of resources also impacts the productivity of a project when a change is implemented. There is a higher loss of productivity when the amount of resources is restricted.

Keywords: engineering changes, indirect costs overtime, productivity, scheduling, simulation

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Authors: Antonina A. Shumakova, Sergey A. Khotimchenko

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The relevance of research is associated, on the one hand, with an ever-increasing volume of production and the expansion of the scope of application of engineered nanomaterials (ENMs), and on the other hand, with the lack of sufficient scientific information on the nature of the interactions of nanoparticles (NPs) with components of biogenic and abiogenic origin. In particular, studying the effect of ENMs (TiO2 NPs, SiO2 NPs, Al2O3 NPs, fullerenol) on the toxicometric characteristics of common contaminants such as lead and cadmium is an important hygienic task, given the high probability of their joint presence in food products. Data were obtained characterizing a multidirectional change in the toxicity of model toxicants when they are co-administered with various types of ENMs. One explanation for this fact is the difference in the adsorption capacity of ENMs, which was further studied in in vitro studies. For this, a method was proposed based on in vitro modeling of conditions simulating the environment of the small intestine. It should be noted that the obtained data are in good agreement with the results of in vivo experiments: - with the combined administration of lead and TiO2 NPs, there were no significant changes in the accumulation of lead in rat liver; in other organs (kidneys, spleen, testes and brain), the lead content was lower than in animals of the control group; - studying the combined effect of lead and Al2O3 NPs, a multiple and significant increase in the accumulation of lead in rat liver was observed with an increase in the dose of Al2O3 NPs. For other organs, the introduction of various doses of Al2O3 NPs did not significantly affect the bioaccumulation of lead; - with the combined administration of lead and SiO2 NPs in different doses, there was no increase in lead accumulation in all studied organs. Based on the data obtained, it can be assumed that at least three scenarios of the combined effects of ENMs and chemical contaminants on the body: - ENMs quite firmly bind contaminants in the gastrointestinal tract and such a complex becomes inaccessible (or inaccessible) for absorption; in this case, it can be expected that the toxicity of both ENMs and contaminants will decrease; - the complex formed in the gastrointestinal tract has partial solubility and can penetrate biological membranes and / or physiological barriers of the body; in this case, ENMs can play the role of a kind of conductor for contaminants and, thus, their penetration into the internal environment of the body increases, thereby increasing the toxicity of contaminants; - ENMs and contaminants do not interact with each other in any way, therefore the toxicity of each of them is determined only by its quantity and does not depend on the quantity of another component. Authors hypothesized that the degree of adsorption of various elements on the surface of ENMs may be a unique characteristic of their action, allowing a more accurate understanding of the processes occurring in a living organism.

Keywords: absorption, cadmium, engineered nanomaterials, lead

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Authors: Pedro Llanos, Diego García

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Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

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Authors: Ali Hashemifarzad, Jens Zum Hingst

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The structure of the world’s energy systems has changed significantly over the past years. One of the most important challenges in the 21st century in Germany (and also worldwide) is the energy transition. This transition aims to comply with the recent international climate agreements from the United Nations Climate Change Conference (COP21) to ensure sustainable energy supply with minimal use of fossil fuels. Germany aims for complete decarbonization of the energy sector by 2050 according to the federal climate protection plan. One of the stipulations of the Renewable Energy Sources Act 2017 for the expansion of energy production from renewable sources in Germany is that they cover at least 80% of the electricity requirement in 2050; The Gross end energy consumption is targeted for at least 60%. This means that by 2050, the energy supply system would have to be almost completely converted to renewable energy. An essential basis for the development of such a sustainable energy supply from 100% renewable energies is to predict the energy requirement by 2050. This study presents two scenarios for the final energy demand in Germany in 2050. In the first scenario, the targets for energy efficiency increase and demand reduction are set very ambitiously. To build a comparison basis, the second scenario provides results with less ambitious assumptions. For this purpose, first, the relevant framework conditions (following CUTEC 2016) were examined, such as the predicted population development and economic growth, which were in the past a significant driver for the increase in energy demand. Also, the potential for energy demand reduction and efficiency increase (on the demand side) was investigated. In particular, current and future technological developments in energy consumption sectors and possible options for energy substitution (namely the electrification rate in the transport sector and the building renovation rate) were included. Here, in addition to the traditional electricity sector, the areas of heat, and fuel-based consumptions in different sectors such as households, commercial, industrial and transport are taken into account, supporting the idea that for a 100% supply from renewable energies, the areas currently based on (fossil) fuels must be almost completely be electricity-based by 2050. The results show that in the very ambitious scenario a final energy demand of 1,362 TWh/a is required, which is composed of 818 TWh/a electricity, 229 TWh/a ambient heat for electric heat pumps and approx. 315 TWh/a non-electric energy (raw materials for non-electrifiable processes). In the less ambitious scenario, in which the targets are not fully achieved by 2050, the final energy demand will need a higher electricity part of almost 1,138 TWh/a (from the total: 1,682 TWh/a). It has also been estimated that 50% of the electricity revenue must be saved to compensate for fluctuations in the daily and annual flows. Due to conversion and storage losses (about 50%), this would mean that the electricity requirement for the very ambitious scenario would increase to 1,227 TWh / a.

Keywords: energy demand, energy transition, German Energiewende, 100% renewable energy production

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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The utilization of Large Eddy Simulation (LES) has been extensive in turbulence research. LES concentrates on resolving the significant grid-scale motions while representing smaller scales through subfilter-scale (SFS) models. The deconvolution model, among the available SFS models, has proven successful in LES of engineering and geophysical flows. Nevertheless, the thorough investigation of how sub-filter scale dynamics and filter anisotropy affect SFS modeling accuracy remains lacking. The outcomes of LES are significantly influenced by filter selection and grid anisotropy, factors that have not been adequately addressed in earlier studies. This study examines two crucial aspects of LES: Firstly, the accuracy of direct deconvolution models (DDM) is evaluated concerning sub-filter scale (SFS) dynamics across varying filter-to-grid ratios (FGR) in isotropic turbulence. Various invertible filters are employed, including Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The importance of FGR becomes evident as it plays a critical role in controlling errors for precise SFS stress prediction. When FGR is set to 1, the DDM models struggle to faithfully reconstruct SFS stress due to inadequate resolution of SFS dynamics. Notably, prediction accuracy improves when FGR is set to 2, leading to accurate reconstruction of SFS stress, except for cases involving Helmholtz I and II filters. Remarkably high precision, nearly 100%, is achieved at an FGR of 4 for all DDM models. Furthermore, the study extends to filter anisotropy and its impact on SFS dynamics and LES accuracy. By utilizing the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with anisotropic filters, aspect ratios (AR) ranging from 1 to 16 are examined in LES filters. The results emphasize the DDM’s proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. Notably high correlation coefficients exceeding 90% are observed in the a priori study for the DDM’s reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as filter anisotropy increases. In the a posteriori analysis, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, including velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strainrate tensors, and SFS stress. It is evident that as filter anisotropy intensifies, the results of DSM and DMM deteriorate, while the DDM consistently delivers satisfactory outcomes across all filter-anisotropy scenarios. These findings underscore the potential of the DDM framework as a valuable tool for advancing the development of sophisticated SFS models for LES in turbulence research.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Rachana Tank, Donald. M. Lyall, Kristin Flegal, Joey Ward, Jonathan Cavanagh

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Alzheimer’s research UK estimates by 2050, 2 million individuals will be living with Late Onset Alzheimer’s disease (LOAD). However, individuals experience considerable cognitive deficits and brain pathology over decades before reaching clinically diagnosable LOAD and studies have utilised gene candidate studies such as genome wide association studies (GWAS) and polygenic risk (PGR) scores to identify high risk individuals and potential pathways. This investigation aims to determine whether high genetic risk of LOAD is associated with worse brain MRI and cognitive performance in healthy older adults within the UK Biobank cohort. Previous studies investigating associations of PGR for LOAD and measures of MRI or cognitive functioning have focused on specific aspects of hippocampal structure, in relatively small sample sizes and with poor ‘controlling’ for confounders such as smoking. Both the sample size of this study and the discovery GWAS sample are bigger than previous studies to our knowledge. Genetic interaction between loci showing largest effects in GWAS have not been extensively studied and it is known that APOE e4 poses the largest genetic risk of LOAD with potential gene-gene and gene-environment interactions of e4, for this reason we  also analyse genetic interactions of PGR with the APOE e4 genotype. High genetic loading based on a polygenic risk score of 21 SNPs for LOAD is associated with worse brain MRI and cognitive outcomes in healthy individuals within the UK Biobank cohort. Summary statistics from Kunkle et al., GWAS meta-analyses (case: n=30,344, control: n=52,427) will be used to create polygenic risk scores based on 21 SNPs and analyses will be carried out in N=37,000 participants in the UK Biobank. This will be the largest study to date investigating PGR of LOAD in relation to MRI. MRI outcome measures include WM tracts, structural volumes. Cognitive function measures include reaction time, pairs matching, trail making, digit symbol substitution and prospective memory. Interaction of the APOE e4 alleles and PGR will be analysed by including APOE status as an interaction term coded as either 0, 1 or 2 e4 alleles. Models will be adjusted partially for adjusted for age, BMI, sex, genotyping chip, smoking, depression and social deprivation. Preliminary results suggest PGR score for LOAD is associated with decreased hippocampal volumes including hippocampal body (standardised beta = -0.04, P = 0.022) and tail (standardised beta = -0.037, P = 0.030), but not with hippocampal head. There were also associations of genetic risk with decreased cognitive performance including fluid intelligence (standardised beta = -0.08, P<0.01) and reaction time (standardised beta = 2.04, P<0.01). No genetic interactions were found between APOE e4 dose and PGR score for MRI or cognitive measures. The generalisability of these results is limited by selection bias within the UK Biobank as participants are less likely to be obese, smoke, be socioeconomically deprived and have fewer self-reported health conditions when compared to the general population. Lack of a unified approach or standardised method for calculating genetic risk scores may also be a limitation of these analyses. Further discussion and results are pending.

Keywords: Alzheimer's dementia, cognition, polygenic risk, MRI

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Authors: Yury S. Shpanskiy, Boris V. Kuteev

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Development of a fusion-fission hybrid facility based on superconducting conventional tokamak DEMO-FNS runs in Russia since 2013. The main design goal is to reach the technical feasibility and outline prospects of industrial hybrid technologies providing the production of neutrons, fuel nuclides, tritium, high-temperature heat, electricity and subcritical transmutation in Fusion-Fission Hybrid Systems. The facility should operate in a steady-state mode at the fusion power of 40 MW and fission reactions of 400 MW. Major tokamak parameters are the following: major radius R=3.2 m, minor radius a=1.0 m, elongation 2.1, triangularity 0.5. The design provides the neutron wall loading of ~0.2 MW/m², the lifetime neutron fluence of ~2 MWa/m², with the surface area of the active cores and tritium breeding blanket ~100 m². Core plasma modelling showed that the neutron yield ~10¹⁹ n/s is maximal if the tritium/deuterium density ratio is 1.5-2.3. The design of the electromagnetic system (EMS) defined its basic parameters, accounting for the coils strength and stability, and identified the most problematic nodes in the toroidal field coils and the central solenoid. The EMS generates toroidal, poloidal and correcting magnetic fields necessary for the plasma shaping and confinement inside the vacuum vessel. EMC consists of eighteen superconducting toroidal field coils, eight poloidal field coils, five sections of a central solenoid, correction coils, in-vessel coils for vertical plasma control. Supporting structures, the thermal shield, and the cryostat maintain its operation. EMS operates with the pulse duration of up to 5000 hours at the plasma current up to 5 MA. The vacuum vessel (VV) is an all-welded two-layer toroidal shell placed inside the EMS. The free space between the vessel shells is filled with water and boron steel plates, which form the neutron protection of the EMS. The VV-volume is 265 m³, its mass with manifolds is 1800 tons. The nuclear blanket of DEMO-FNS facility was designed to provide functions of minor actinides transmutation, tritium production and enrichment of spent nuclear fuel. The vertical overloading of the subcritical active cores with MA was chosen as prospective. Analysis of the device neutronics and the hybrid blanket thermal-hydraulic characteristics has been performed for the system with functions covering transmutation of minor actinides, production of tritium and enrichment of spent nuclear fuel. A study of FNS facilities role in the Russian closed nuclear fuel cycle was performed. It showed that during ~100 years of operation three FNS facilities with fission power of 3 GW controlled by fusion neutron source with power of 40 MW can burn 98 tons of minor actinides and 198 tons of Pu-239 can be produced for startup loading of 20 fast reactors. Instead of Pu-239, up to 25 kg of tritium per year may be produced for startup of fusion reactors using blocks with lithium orthosilicate instead of fissile breeder blankets.

Keywords: fusion-fission hybrid system, conventional tokamak, superconducting electromagnetic system, two-layer vacuum vessel, subcritical active cores, nuclear fuel cycle

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Authors: Marina Nechayeva, Malgorzata Marciniak, Vladimir Przhebelskiy, A. Dragutan, S. Lamichhane, S. Oikawa

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This presentation is a progress report on a faculty-student research collaboration at CUNY LaGuardia Community College (LaGCC) aimed at designing a small horizontal axis wind turbine optimized for the wind patterns on the roof of our campus. Our project combines statistical and engineering research. Our wind modeling protocol is based upon a recent wind study by a faculty-student research group at MIT, and some of our blade design methods are adopted from a senior engineering project at CUNY City College. Our use of genetic algorithms has been inspired by the work on small wind turbines’ design by David Wood. We combine these diverse approaches in our interdisciplinary project in a way that has not been done before and improve upon certain techniques used by our predecessors. We employ several estimation methods to determine the best fitting parametric probability distribution model for the local wind speed data obtained through correlating short-term on-site measurements with a long-term time series at the nearby airport. The model serves as a foundation for engineering research that focuses on adapting and implementing genetic algorithms (GAs) to engineering optimization of the wind turbine design using Blade Element Momentum Theory. GAs are used to create new airfoils with desirable aerodynamic specifications. Small scale models of best performing designs are 3D printed and tested in the wind tunnel to verify the accuracy of relevant calculations. Genetic algorithms are applied to selected airfoils to determine the blade design (radial cord and pitch distribution) that would optimize the coefficient of power profile of the turbine. Our approach improves upon the traditional blade design methods in that it lets us dispense with assumptions necessary to simplify the system of Blade Element Momentum Theory equations, thus resulting in more accurate aerodynamic performance calculations. Furthermore, it enables us to design blades optimized for a whole range of wind speeds rather than a single value. Lastly, we improve upon known GA-based methods in that our algorithms are constructed to work with XFoil generated airfoils data which enables us to optimize blades using our own high glide ratio airfoil designs, without having to rely upon available empirical data from existing airfoils, such as NACA series. Beyond its immediate goal, this ongoing project serves as a training and selection platform for CUNY Research Scholars Program (CRSP) through its annual Aerodynamics and Wind Energy Research Seminar (AWERS), an undergraduate summer research boot camp, designed to introduce prospective researchers to the relevant theoretical background and methodology, get them up to speed with the current state of our research, and test their abilities and commitment to the program. Furthermore, several aspects of the research (e.g., writing code for 3D printing of airfoils) are adapted in the form of classroom research activities to enhance Calculus sequence instruction at LaGCC.

Keywords: engineering design optimization, genetic algorithms, horizontal axis wind turbine, wind modeling

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper discusses the recent advances and future prospects of spacecraft position and attitude control using Model Predictive Control (MPC). First, the challenges of the space missions are summarized, in particular, taking into account the errors, uncertainties, and constraints imposed by the mission, spacecraft and, onboard processing capabilities. The summary of space mission errors and uncertainties provided in categories; initial condition errors, unmodeled disturbances, sensor, and actuator errors. These previous constraints are classified into two categories: physical and geometric constraints. Last, real-time implementation capability is discussed regarding the required computation time and the impact of sensor and actuator errors based on the Hardware-In-The-Loop (HIL) experiments. The rationales behind the scenarios’ are also presented in the scope of space applications as formation flying, attitude control, rendezvous and docking, rover steering, and precision landing. The objectives of these missions are explained, and the generic constrained MPC problem formulations are summarized. Three key design elements used in MPC design: the prediction model, the constraints formulation and the objective cost function are discussed. The prediction models can be linear time invariant or time varying depending on the geometry of the orbit, whether it is circular or elliptic. The constraints can be given as linear inequalities for input or output constraints, which can be written in the same form. Moreover, the recent convexification techniques for the non-convex geometrical constraints (i.e., plume impingement, Field-of-View (FOV)) are presented in detail. Next, different objectives are provided in a mathematical framework and explained accordingly. Thirdly, because MPC implementation relies on finding in real-time the solution to constrained optimization problems, computational aspects are also examined. In particular, high-speed implementation capabilities and HIL challenges are presented towards representative space avionics. This covers an analysis of future space processors as well as the requirements of sensors and actuators on the HIL experiments outputs. The HIL tests are investigated for kinematic and dynamic tests where robotic arms and floating robots are used respectively. Eventually, the proposed algorithms and experimental setups are introduced and compared with the authors' previous work and future plans. The paper concludes with a conjecture that MPC paradigm is a promising framework at the crossroads of space applications while could be further advanced based on the challenges mentioned throughout the paper and the unaddressed gap.

Keywords: convex optimization, model predictive control, rendezvous and docking, spacecraft autonomy

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Karisma Karisma, Pardis Moslemzadeh Tehrani

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This study aims to conduct a high-level strategic dialogue on the lack of consensus, consistency, and legal certainty regarding blockchain-based energy prosumerism so that appropriate institutional and governance structures can be put in place to address the inadequacies and gaps in the legal and regulatory framework. The drive to achieve national and global decarbonization targets is a driving force behind climate goals and policies under the Paris Agreement. In recent years, efforts to ‘demonopolize’ and ‘decentralize’ energy generation and distribution have driven the energy transition toward decentralized systems, invoking concepts such as ownership, sovereignty, and autonomy of RE sources. The emergence of individual and collective forms of prosumerism and the rapid diffusion of blockchain is expected to play a critical role in the decarbonization and democratization of energy systems. However, there is a ‘regulatory void’ relating to individual and collective forms of prosumerism that could prevent the rapid deployment of blockchain systems and potentially stagnate the operationalization of blockchain-enabled energy sharing and trading activities. The application of broad and facile regulatory fixes may be insufficient to address the major regulatory gaps. First, to the authors’ best knowledge, the concepts and elements circumjacent to individual and collective forms of prosumerism have not been adequately described in the legal frameworks of many countries. Second, there is a lack of legal certainty regarding the creation and adaptation of business models in a highly regulated and centralized energy system, which inhibits the emergence of prosumer-driven niche markets. There are also current and prospective challenges relating to the legal status of blockchain-based platforms for facilitating energy transactions, anticipated with the diffusion of blockchain technology. With the rise of prosumerism in the energy sector, the areas of (a) network charges, (b) energy market access, (c) incentive schemes, (d) taxes and levies, and (e) licensing requirements are still uncharted territories in many countries. The uncertainties emanating from this area pose a significant hurdle to the widespread adoption of blockchain technology, a complementary technology that offers added value and competitive advantages for energy systems. The authors undertake a conceptual and theoretical investigation to elucidate the lack of consensus, consistency, and legal certainty in the study of blockchain-based prosumerism. In addition, the authors set an exploratory tone to the discussion by taking an analytically eclectic approach that builds on multiple sources and theories to delve deeper into this topic. As an interdisciplinary study, this research accounts for the convergence of regulation, technology, and the energy sector. The study primarily adopts desk research, which examines regulatory frameworks and conceptual models for crucial policies at the international level to foster an all-inclusive discussion. With their reflections and insights into the interaction of blockchain and prosumerism in the energy sector, the authors do not aim to develop definitive regulatory models or instrument designs, but to contribute to the theoretical dialogue to navigate seminal issues and explore different nuances and pathways. Given the emergence of blockchain-based energy prosumerism, identifying the challenges, gaps and fragmentation of governance regimes is key to facilitating global regulatory transitions.

Keywords: blockchain technology, energy sector, prosumer, legal and regulatory.

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Authors: Karen L. Lindsay, Sonja Entringer, Claudia Buss, Pathik D. Wadhwa

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Maternal nutrition and stress are independently recognized as among the most important factors that influence prenatal biology, with implications for fetal development and poor pregnancy outcomes. While there is substantial evidence from non-pregnancy human and animal studies that a complex, bi-directional relationship exists between nutrition and stress, to the author’s best knowledge, their interaction in the context of pregnancy has been significantly understudied. The aim of this study is to assess the interaction between maternal psychological stress and diet quality across pregnancy and its effects on biomarkers of prenatal insulin resistance and inflammation. This is a prospective longitudinal study of N=235 women carrying a healthy, singleton pregnancy, recruited from prenatal clinics of the University of California, Irvine Medical Center. Participants completed a 4-day ambulatory assessment in early, middle and late pregnancy, which included multiple daily electronic diary entries using Ecological Momentary Assessment (EMA) technology on a dedicated study smartphone. The EMA diaries gathered moment-level data on maternal perceived stress, negative mood, positive mood and quality of social interactions. The numerical scores for these variables were averaged across each study time-point and converted to Z-scores. A single composite variable for 'STRESS' was computed as follows: (Negative mood+Perceived stress)–(Positive mood+Social interaction quality). Dietary intakes were assessed by three 24-hour dietary recalls conducted within two weeks of each 4-day assessment. Daily nutrient and food group intakes were averaged across each study time-point. The Alternative Healthy Eating Index adapted for pregnancy (AHEI-P) was computed for early, middle and late pregnancy as a validated summary measure of diet quality. At the end of each 4-day ambulatory assessment, women provided a fasting blood sample, which was assayed for levels of glucose, insulin, Interleukin (IL)-6 and Tumor Necrosis Factor (TNF)-α. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was computed. Pearson’s correlation was used to explore the relationship between maternal STRESS and AHEI-P within and between each study time-point. Linear regression was employed to test the association of the stress-diet interaction (STRESS*AHEI-P) with the biological markers HOMA-IR, IL-6 and TNF-α at each study time-point, adjusting for key covariates (pre-pregnancy body mass index, maternal education level, race/ethnicity). Maternal STRESS and AHEI-P were significantly inversely correlated in early (r=-0.164, p=0.018) and mid-pregnancy (-0.160, p=0.019), and AHEI-P from earlier gestational time-points correlated with later STRESS (early AHEI-P x mid STRESS: r=-0.168, p=0.017; mid AHEI-P x late STRESS: r=-0.142, p=0.041). In regression models, the interaction term was not associated with HOMA-IR or IL-6 at any gestational time-point. The stress-diet interaction term was significantly associated with TNF-α according to the following patterns: early AHEI-P*early STRESS vs early TNF-α (p=0.005); early AHEI-P*early STRESS vs mid TNF-α (p=0.002); early AHEI-P*mid STRESS vs mid TNF-α (p=0.005); mid AHEI-P*mid STRESS vs mid TNF-α (p=0.070); mid AHEI-P*late STRESS vs late TNF-α (p=0.011). Poor diet quality is significantly related to higher psychosocial stress levels in pregnant women across gestation, which may promote inflammation via TNF-α. Future prenatal studies should consider the combined effects of maternal stress and diet when evaluating either one of these factors on pregnancy or infant outcomes.

Keywords: diet quality, inflammation, insulin resistance, nutrition, pregnancy, stress, tumor necrosis factor-alpha

Procedia PDF Downloads 199

Authors: Silvia D. Vaca, Benjamin J. Kuo, Joao Ricardo Nickenig Vissoci, Catherine A. Staton, Linda W. Xu, Michael Muhumuza, Hussein Ssenyonjo, John Mukasa, Joel Kiryabwire, Henry E. Rice, Gerald A. Grant, Michael M. Haglund

Abstract:

Background: Patients with traumatic brain injury (TBI) can develop rapid neurological deterioration from swelling and intracranial hematomas, which can result in focal tissue ischemia, brain compression, and herniation. Moreover, delays in management increase the risk of secondary brain injury from hypoxemia and hypotension. Therefore, in TBI patients with subdural hematomas (SDHs) and epidural hematomas (EDHs), surgical intervention is both necessary and time sensitive. Significant delays are seen along the care continuum in low- and middle-income countries (LMICs) largely due to limited healthcare capacity to address the disproportional rates of TBI in Sub Saharan Africa (SSA). While many LMICs have subsidized systems to offset surgical costs, the burden of securing funds by the patients for medications, supplies, and CT diagnostics poses a significant challenge to timely surgical interventions. In Kampala Uganda, the challenge of obtaining timely CT scans is twofold: logistical and financial barriers. These bottlenecks contribute significantly to the care continuum delays and are associated with poor TBI outcomes. Objective: The objectives of this study are to 1) describe the temporal delays through a modified three delays model that fits the context of neurosurgical interventions for TBI patients in Kampala and 2) investigate the association between delays and mortality. Methods: Prospective data were collected for 563 TBI patients presenting to a tertiary hospital in Kampala from 1 June – 30 November 2016. Four time intervals were constructed along five time points: injury, hospital arrival, neurosurgical evaluation, CT results, and definitive surgery. Time interval differences among mild, moderate and severe TBI and their association with mortality were analyzed. Results: The mortality rate of all TBI patients presenting to MNRH was 9.6%, which ranged from 4.7% for mild and moderate TBI patients receiving surgery to 81.8% for severe TBI patients who failed to receive surgery. The duration from injury to surgery varied considerably across TBI severity with the largest gap seen between mild TBI (174 hours) and severe TBI (69 hours) patients. Further analysis revealed care continuum differences for interval 3 (neurosurgical evaluation to CT result) and 4 (CT result to surgery) between severe TBI patients (7 hours for interval 3 and 24 hours for interval 4) and mild TBI patients (19 hours for interval 3, and 96 hours for interval 4). These post-arrival delays were associated with mortality for mild (p=0.05) and moderate TBI (p=0.03) patients. Conclusions: To our knowledge, this is the first analysis using a modified 'three delays' framework to analyze the care continuum of TBI patients in Uganda from injury to surgery. We found significant associations between delays and mortality for mild and moderate TBI patients. As it currently stands, poorer outcomes were observed for these mild and moderate TBI patients who were managed non-operatively or failed to receive surgery while surgical services were shunted to more severely ill patients. While well intentioned, high mortality rates were still observed for the severe TBI patients managed surgically. These results suggest the need for future research to optimize triage practices, understand delay contributors, and improve pre-hospital logistical referral systems.

Keywords: care continuum, global neurosurgery, Kampala Uganda, LMIC, Mulago, traumatic brain injury

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Authors: Samira Hamiditehrani

Abstract:

Automated vehicles (AVs) are emerging technologies that could potentially offer a wide range of opportunities and challenges for the transportation sector. The advent of AV technology has also resulted in new business models in shared mobility services where many ride hailing and car sharing companies are developing on-demand AVs including shared automated vehicles (SAVs) and pooled automated vehicles (Pooled AVs). SAVs and Pooled AVs could provide alternative shared mobility services which encourage sustainable transport systems, mitigate traffic congestion, and reduce automobile dependency. However, the success of on-demand AVs in addressing major transportation policy issues depends on whether and how the public adopts them as regular travel modes. To identify conditions under which individuals may adopt on-demand AVs, previous studies have applied human behavior and technology acceptance theories, where Theory of Planned Behavior (TPB) has been validated and is among the most tested in on-demand AV research. In this respect, this study has three objectives: (a) to propose and validate a theoretical model for behavioral intention to use SAVs and Pooled AVs by extending the original TPB model; (b) to identify the characteristics of early adopters of SAVs, who prefer to have a shorter and private ride, versus prospective users of Pooled AVs, who choose more affordable but longer and shared trips; and (c) to investigate Canadians’ intentions to adopt on-demand AVs for regular trips. Toward this end, this study uses data from an online survey (n = 3,622) of workers or adult students (18 to 75 years old) conducted in October and November 2021 for six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa, Montreal, Calgary, and Hamilton. To accomplish the goals of this study, a base bivariate ordered probit model, in which both SAV and Pooled AV adoptions are estimated as ordered dependent variables, alongside a full structural equation modeling (SEM) system are estimated. The findings of this study indicate that affective motivations such as attitude towards AV technology, perceived privacy, and subjective norms, matter more than sociodemographic and travel behavior characteristic in adopting on-demand AVs. Also, the results of second objective provide evidence that although there are a few affective motivations, such as subjective norms and having ample knowledge, that are common between early adopters of SAVs and PooledAVs, many examined motivations differ among SAV and Pooled AV adoption factors. In other words, motivations influencing intention to use on-demand AVs differ among the service types. Likewise, depending on the types of on-demand AVs, the sociodemographic characteristics of early adopters differ significantly. In general, findings paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Findings from the final objective suggest that policymakers, planners, the vehicle and technology industries, and the public at large should moderate their expectations that on-demand AVs may suddenly transform the entire transportation sector. Instead, this study suggests that SAVs and Pooled AVs (when they entire the Canadian market) are likely to be adopted as supplementary mobility tools rather than substitutions for current travel modes

Keywords: automated vehicles, Canadian perception, theory of planned behavior, on-demand AVs

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Authors: A. C. Facin, M. C. Maronezi , M. P. Menezes, G. L. Montanhim, L. Pavan, M. A. R. Feliciano, R. P. Nociti, R. A. R. Uscategui, P. C. Moraes

Abstract:

The incidence of brachycephalic syndrome (BS) in the clinical routine of small animals has increased significantly giving the higher proportion of brachycephalic pets in the last years and has been considered as an animal welfare problem. The treatment of BS is surgical and the clinical signs related can be considerably attenuated. Nevertheless, the systemic effects of the BS are still poorly reported and little is known about these when the surgical correction is not performed early. Affected dogs are more likely to develop cardiopulmonary, gastrointestinal and sleep disorders in which the chronic hypoxemia plays a major role. This syndrome is compared with the obstructive sleep apnea (OSA) in humans, both considered as causes of systemic and metabolic dysfunction. Among the several consequences of the BS little is known if the syndrome also affects the hepatic tissue of brachycephalic patients. Elastography is a promising ultrasound technique that evaluates tissue elasticity and has been recently used with the purpose of diagnosis of liver fibrosis. In medicine, it is a growing concern regarding the hepatic injury of patients affected by OSA. This prospective study hypothesizes if there is any consequence of BS in the hepatic parenchyma of brachycephalic dogs that don’t receive any surgical treatment. This study was conducted following the approval of the Animal Ethics and Welfare Committee of the Faculdade de Ciências Agrárias e Veterinárias, UNESP, Campus Jaboticabal, Brazil (protocol no 17944/2017) and funded by Sao Paulo Research Foundation (FAPESP, process no 2017/24809-4). The methodology was based in ARFI elastography using the ACUSON S2000/SIEMENS device, with convex multifrequential transducer and specific software as well as clinical evaluation of the syndrome, in order to determine if they can be used as a prognostic non-invasive tool. On quantitative elastography, it was collected three measures of shear wave velocity (meters per second) and depth in centimeters in the left lateral, left medial, right lateral, right medial and caudate lobe of the liver. The brachycephalic patients, 16 pugs and 30 french bulldogs, were classified using a previously established 4-point functional grading system based on clinical evaluation before and after a 3-minute exercise tolerance test already established and validated. The control group was based on the same features collected in 22 beagles. The software R version 3.3.0 was used for the analysis and the significance level was set at 0.05. The data were analysed for normality of residuals and homogeneity of variances by Shapiro-Wilks test. Comparisons of parametric continuous variables between breeds were performed by using ANOVA with a post hoc test for pair wise comparison. The preliminary results show significant statistic differences between the brachycephalic groups and the control group in all lobes analysed (p ≤ 0,05), with higher values of shear wave velocities in the hepatic tissue of brachycephalic dogs. In this context, the results obtained in this study contributes to the understanding of BS as well as its consequences in our patients, reflecting in evidence that one more systemic consequence of the syndrome may occur in brachycephalic patients, which was not related in the veterinary literature yet.

Keywords: airway obstruction, brachycephalic airway obstructive syndrome, hepatic injury, obstructive sleep apnea

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Authors: In-Tsang Lin, Chiching Wei

Abstract:

Reading order is an important task in many digitization scenarios involving the preservation of the logical structure of a document. From the paper survey, it finds that the state-of-the-art algorithm could not fulfill to get the accurate reading order in the portable document format (PDF) files with rich formats, diverse layout arrangement. In recent years, most of the studies on the analysis of reading order have targeted the specific problem of associating layout components with logical labels, while less attention has been paid to the problem of extracting relationships the problem of detecting the reading order relationship between logical components, such as cross-references. Over 3 years of development, the company Foxit has demonstrated the layout recognition (LR) engine in revision 20601 to eager for the accuracy of the reading order. The bounding box of each paragraph can be obtained correctly by the Foxit LR engine, but the result of reading-order is not always correct for single-column, and two-column layout format due to the table issue, formula issue, and multiple mini separated bounding box and footer issue. Thus, the algorithm is developed to improve the accuracy of the reading order based on the Foxit LR structure. In this paper, a creative observation method (Here called the MESH method) is provided here to open a new chance in the research of the reading-order field. Here two important parameters are introduced, one parameter is the number of the bounding box on the right side of the present bounding box (NRight), and another parameter is the number of the bounding box under the present bounding box (Nunder). And the normalized x-value (x/the whole width), the normalized y-value (y/the whole height) of each bounding box, the x-, and y- position of each bounding box were also put into consideration. Initial experimental results of single column layout format demonstrate a 19.33% absolute improvement in accuracy of the reading-order over 7 PDF files (total 150 pages) using our proposed method based on the LR structure over the baseline method using the LR structure in 20601 revision, which its accuracy of the reading-order is 72%. And for two-column layout format, the preliminary results demonstrate a 44.44% absolute improvement in accuracy of the reading-order over 2 PDF files (total 18 pages) using our proposed method based on the LR structure over the baseline method using the LR structure in 20601 revision, which its accuracy of the reading-order is 0%. Until now, the footer issue and a part of multiple mini separated bounding box issue can be solved by using the MESH method. However, there are still three issues that cannot be solved, such as the table issue, formula issue, and the random multiple mini separated bounding boxes. But the detection of the table position and the recognition of the table structure are out of the scope in this paper, and there is needed another research. In the future, the tasks are chosen- how to detect the table position in the page and to extract the content of the table.

Keywords: document processing, reading order, observation method, layout recognition

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Authors: Ping-Hsiung Wang, Kuo-Chun Chang

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There has been a common trend worldwide in the seismic design and evaluation of bridges towards the performance-based method where the lateral displacement or the displacement ductility of bridge column is regarded as an important indicator for performance assessment. However, the seismic response of a bridge to an earthquake is a combined result of cyclic displacements and accumulated energy dissipation, causing damage to the bridge, and hence the lateral displacement (ductility) alone is insufficient to tell its actual seismic performance. This study aims to propose a damage-based seismic design and evaluation method for reinforced concrete bridges on the basis of the newly developed capacity-based inelastic displacement spectra. The capacity-based inelastic displacement spectra that comprise an inelastic displacement ratio spectrum and a corresponding damage state spectrum was constructed by using a series of nonlinear time history analyses and a versatile, smooth hysteresis model. The smooth model could take into account the effects of various design parameters of RC bridge columns and correlates the column’s strength deterioration with the Park and Ang’s damage index. It was proved that the damage index not only can be used to accurately predict the onset of strength deterioration, but also can be a good indicator for assessing the actual visible damage condition of column regardless of its loading history (i.e., similar damage index corresponds to similar actual damage condition for the same designed columns subjected to very different cyclic loading protocols as well as earthquake loading), providing a better insight into the seismic performance of bridges. Besides, the computed spectra show that the inelastic displacement ratio for far-field ground motions approximately conforms to the equal displacement rule when structural period is larger than around 0.8 s, but that for near-fault ground motions departs from the rule in the whole considered spectral regions. Furthermore, the near-fault ground motions would lead to significantly greater inelastic displacement ratio and damage index than far-field ground motions and most of the practical design scenarios cannot survive the considered near-fault ground motion when the strength reduction factor of bridge is not less than 5.0. Finally, the spectrum formula is presented as a function of structural period, strength reduction factor, and various column design parameters for far-field and near-fault ground motions by means of the regression analysis of the computed spectra. And based on the developed spectrum formula, a design example of a bridge is presented to illustrate the proposed damage-based seismic design and evaluation method where the damage state of the bridge is used as the performance objective.

Keywords: damage index, far-field, near-fault, reinforced concrete bridge, seismic design and evaluation

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Authors: Michael Sonne Kristensen, Frank Loesche, James Foster, Elif Ozcan, Judy Edworthy

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Auditory alarms play an integral role in intensive care nurses’ daily work. Most medical devices in the intensive care unit (ICU) are designed to produce alarm sounds in order to make nurses aware of immediate or prospective safety risks. The utilisation of sound as a carrier of crucial patient information is highly dependent on nurses’ presence - both physically and mentally. For ICU nurses, especially the ones who work with stationary alarm devices at the patient bed space, it is a challenge to display ‘appropriate’ alarm responses at all times as they have to navigate with great flexibility in a complex work environment. While being primarily responsible for a small number of allocated patients they are often required to engage with other nurses’ patients, relatives, and colleagues at different locations inside and outside the unit. This work explores the social strategies used by a team of nurses to comprehend and react to the information conveyed by the alarms in the ICU. Two main research questions guide the study: To what extent do alarms from a patient bed space reach the relevant responsible nurse by direct auditory exposure? By which means do responsible nurses get informed about their patients’ alarms when not directly exposed to the alarms? A comprehensive video-ethnographic field study was carried out to capture and evaluate alarm-related events in an ICU. The study involved close collaboration with four nurses who wore eye-level cameras and ear-level binaural audio recorders during several work shifts. At all time the entire unit was monitored by multiple video and audio recorders. From a data set of hundreds of hours of recorded material information about the nurses’ location, social interaction, and alarm exposure at any point in time was coded in a multi-channel replay-interface. The data shows that responsible nurses’ direct exposure and awareness of the alarms of their allocated patients vary significantly depending on work load, social relationships, and the location of the patient’s bed space. Distributed listening is deliberately employed by the nursing team as a social strategy to respond adequately to alarms, but the patterns of information flow prompted by alarm-related events are not uniform. Auditory Spatiotemporal Trajectory (AST) is proposed as a methodological label to designate the integration of temporal, spatial and auditory load information. As a mixed-method metrics it provides tangible evidence of how nurses’ individual alarm-related experiences differ from one another and from stationary points in the ICU. Furthermore, it is used to demonstrate how alarm-related information reaches the individual nurse through principles of social and distributed cognition, and how that information relates to the actual alarm event. Thereby it bridges a long-standing gap in the literature on medical alarm utilisation between, on the one hand, initiatives to measure objective data of the medical sound environment without consideration for any human experience, and, on the other hand, initiatives to study subjective experiences of the medical sound environment without detailed evidence of the objective characteristics of the environment.

Keywords: auditory spatiotemporal trajectory, medical alarms, social cognition, video-ethography

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Authors: Elzbieta Sikorska-Simmons

Abstract:

Advanced dementia is a progressive terminal brain disease that is accompanied by a syndrome of difficult to manage symptoms and complications that eventually lead to death. The management of advanced dementia poses major challenges to family caregivers who act as patient health care proxies in making medical treatment decisions. Little is known, however, about how they manage advanced dementia and how their treatment choices influence the quality of patient life. This prospective qualitative study examines the key medical treatment decisions that family caregivers make while managing advanced dementia. The term ‘family caregiver’ refers to a relative or a friend who is primarily responsible for managing patient’s medical care needs and legally authorized to give informed consent for medical treatments. Medical decision-making implies a process of choosing between treatment options in response to patient’s medical care needs (e.g., worsening comorbid conditions, pain, infections, acute medical events). Family caregivers engage in this process when they actively seek treatments or follow recommendations by healthcare professionals. Better understanding of medical decision-making from the family caregiver perspective is needed to design interventions that maximize the quality of patient life and limit inappropriate treatments. Data were collected in three waves of semi-structured interviews with 20 family caregivers for patients with advanced dementia. A purposive sample of 20 family caregivers was recruited from a senior care center in Central Florida. The qualitative personal interviews were conducted by the author in 4-5 months intervals. The ethical approval for the study was obtained prior to the data collection. Advanced dementia was operationalized as stage five or higher on the Global Deterioration Scale (GDS) (i.e., starting with the GDS score of five, patients are no longer able survive without assistance due to major cognitive and functional impairments). Information about patients’ GDS scores was obtained from the Center’s Medical Director, who had an in-depth knowledge of each patient’s health and medical treatment history. All interviews were audiotaped and transcribed verbatim. The qualitative data analysis was conducted to answer the following research questions: 1) what treatment decisions do family caregivers make while managing the symptoms of advanced dementia and 2) how do these treatment decisions influence the quality of patient life? To validate the results, the author asked each participating family caregiver if the summarized findings accurately captured his/her experiences. The identified medical decisions ranged from seeking specialist medical care to end-of-life care. The most common decisions were related to arranging medical appointments, medication management, seeking treatments for pain and other symptoms, nursing home placement, and accessing community-based healthcare services. The most challenging and consequential decisions were related to the management of acute complications, hospitalizations, and discontinuation of treatments. Decisions that had the greatest impact on the quality of patient life and survival were triggered by traumatic falls, worsening psychiatric symptoms, and aspiration pneumonia. The study findings have important implications for geriatric nurses in the context of patient/caregiver-centered dementia care. Innovative nursing approaches are needed to support family caregivers to effectively manage medical care needs of patients with advanced dementia.

Keywords: advanced dementia, family caregiver, medical decision-making, symptom management

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