Applications of Machine Learning in Content Generation for Educational Video Games

Over the past few years, students have become increasingly unmotivated to read their assigned textbooks as an accompaniment to classroom lectures and activities. Reading the textbook is known to improve comprehension and overall student performance in classrooms. If reading the textbook was reformatted into a more engaging experience, perhaps it would improve student motivation and knowledge retention. Teaching students the importance of learning while also motivating them to do well in class will help them gain the knowledge and grades needed to land competitive jobs after they graduate college. Game-Based Learning (GBL) is an emerging field of study that attempts to use video games to create interactive educational experiences. Game-Based Learning has been shown to have educational merit, being well-known for providing intrinsic motivation for students to learn (most often, as a supplement to traditional coursework). With GBL in mind, is it possible to generate interactive game content from textbooks using machine learning (ML) and artificial intelligence (AI) that can replace or supplement the source material in terms of educational content in a traditional classroom setting? Our team proposes to lay the groundwork for future research in Game-Based Learning and Machine Learning at the LIVE Lab undergraduate research lab (Texas A&M University, College of Architecture, Dept. of Visualization) by attempting to reformat school textbooks into interactive chatbot AIs with the assistance of knowledge compilation & fact-retrieval systems designed for generating educational video game content

Graphene-Si CMOS oscillators

Graphene field-effect transistors (GFETs) offer a possibility of exploiting unique physical properties of graphene in realizing novel electronic circuits. However, graphene circuits often lack the voltage swing and switchability of Si complementary metal-oxide-semiconductor (CMOS) circuits, which are the main building block of modern electronics. Here we introduce graphene in Si CMOS circuits to exploit favorable electronic properties of both technologies and realize a new class of simple oscillators using only a GFET, Si CMOS D latch, and timing RC circuit. The operation of the two types of realized oscillators is based on the ambipolarity of graphene, i.e., the symmetry of the transfer curve of GFETs around the Dirac point. The ambipolarity of graphene also allowed to turn the oscillators into pulse-width modulators (with a duty cycle ratio ∼1 : 4) and voltage-controlled oscillators (with a frequency ratio ∼1 : 8) without any circuit modifications. The oscillation frequency was in the range from 4 kHz to 4 MHz and limited only by the external circuit connections, rather than components themselves. The demonstrated graphene-Si CMOS hybrid circuits pave the way to the more widespread adoption of graphene in electronics

Psychological distress, depression, anxiety and life satisfaction following COVID-19 infection: Evidence from 11 UK longitudinal population studies

Background: Evidence on associations between COVID-19 illness and mental health is mixed. We aimed to examine whether COVID-19 is associated with deterioration in mental health while considering pre-pandemic mental health, time since infection, subgroup differences, and confirmation of infection via self-reported test and serology data. Methods: We obtained data from 11 UK longitudinal studies with repeated measures of mental health (psychological distress, depression, anxiety, and life satisfaction; mental health scales were standardised within each study across time) and COVID-19 status between April, 2020, and April, 2021. We included participants with information available on at least one mental health outcome measure and self-reported COVID-19 status (suspected or test-confirmed) during the pandemic, and a subset with serology-confirmed COVID-19. Furthermore, only participants who had available data on a minimum set of covariates, including age, sex, and pre-pandemic mental health were included. We investigated associations between having ever had COVID-19 and mental health outcomes using generalised estimating equations. We examined whether associations varied by age, sex, ethnicity, education, and pre-pandemic mental health, whether the strength of the association varied according to time since infection, and whether associations differed between self-reported versus confirmed (by test or serology) infection. Findings: Between 21 Dec, 2021, and July 11, 2022, we analysed data from 54 442 participants (ranging from a minimum age of 16 years in one study to a maximum category of 90 years and older in another; including 33 200 [61·0%] women and 21 242 [39·0%] men) from 11 longitudinal UK studies. Of 40 819 participants with available ethnicity data, 36 802 (90·2%) were White. Pooled estimates of standardised differences in outcomes suggested associations between COVID-19 and subsequent psychological distress (0·10 [95% CI 0·06 to 0·13], I2=42·8%), depression (0·08 [0·05 to 0·10], I2=20·8%), anxiety (0·08 [0·05 to 0·10], I2=0·0%), and lower life satisfaction (–0·06 [–0·08 to –0·04], I2=29·2%). We found no evidence of interactions between COVID-19 and sex, education, ethnicity, or pre-pandemic mental health. Associations did not vary substantially between time since infection of less than 4 weeks, 4–12 weeks, and more than 12 weeks, and were present in all age groups, with some evidence of stronger effects in those aged 50 years and older. Participants who self-reported COVID-19 but had negative serology had worse mental health outcomes for all measures than those without COVID-19 based on serology and self-report. Participants who had positive serology but did not self-report COVID-19 did not show association with mental health outcomes. Interpretation: Self-reporting COVID-19 was longitudinally associated with deterioration in mental health and life satisfaction. Our findings emphasise the need for greater post-infection mental health service provision, given the substantial prevalence of COVID-19 in the UK and worldwide. Funding: UK Medical Research Council and UK National Institute for Health and Care Research

Psychological Distress Before and During the COVID-19 Pandemic Among Adults in the United Kingdom Based on Coordinated Analyses of 11 Longitudinal Studies

Importance: How population mental health has evolved across the COVID-19 pandemic under varied lockdown measures is poorly understood, and the consequences for health inequalities are unclear. Objective: To investigate changes in mental health and sociodemographic inequalities from before and across the first year of the COVID-19 pandemic in 11 longitudinal studies. Design, Setting, and Participants: This cohort study included adult participants from 11 UK longitudinal population-based studies with prepandemic measures of psychological distress. Analyses were coordinated across these studies, and estimates were pooled. Data were collected from 2006 to 2021. Exposures: Trends in the prevalence of poor mental health were assessed in the prepandemic period (time period 0 [TP 0]) and at 3 pandemic TPs: 1, initial lockdown (March to June 2020); 2, easing of restrictions (July to October 2020); and 3, a subsequent lockdown (November 2020 to March 2021). Analyses were stratified by sex, race and ethnicity, education, age, and UK country. Main Outcomes and Measures: Multilevel regression was used to examine changes in psychological distress from the prepandemic period across the first year of the COVID-19 pandemic. Psychological distress was assessed using the 12-item General Health Questionnaire, the Kessler 6, the 9-item Malaise Inventory, the Short Mood and Feelings Questionnaire, the 8-item or 9-item Patient Health Questionnaire, the Hospital Anxiety and Depression Scale, and the Centre for Epidemiological Studies–Depression across different studies. Results: In total, 49 993 adult participants (12 323 [24.6%] aged 55-64 years; 32 741 [61.2%] women; 4960 [8.7%] racial and ethnic minority) were analyzed. Across the 11 studies, mental health deteriorated from prepandemic scores across all 3 pandemic periods, but there was considerable heterogeneity across the study-specific estimated effect sizes (pooled estimate for TP 1: standardized mean difference [SMD], 0.15; 95% CI, 0.06-0.25; TP 2: SMD, 0.18; 95% CI, 0.09-0.27; TP 3: SMD, 0.21; 95% CI, 0.10-0.32). Changes in psychological distress across the pandemic were higher in women (TP 3: SMD, 0.23; 95% CI, 0.11, 0.35) than men (TP 3: SMD, 0.16; 95% CI, 0.06-0.26) and lower in individuals with below–degree level education at TP 3 (SMD, 0.18; 95% CI, 0.06-0.30) compared with those who held degrees (SMD, 0.26; 95% CI, 0.14-0.38). Increased psychological distress was most prominent among adults aged 25 to 34 years (SMD, 0.49; 95% CI, 0.14-0.84) and 35 to 44 years (SMD, 0.35; 95% CI, 0.10-0.60) compared with other age groups. No evidence of changes in distress differing by race and ethnicity or UK country were observed. Conclusions and Relevance: In this study, the substantial deterioration in mental health seen in the UK during the first lockdown did not reverse when lockdown lifted, and a sustained worsening was observed across the pandemic period. Mental health declines have been unequal across the population, with women, those with higher degrees, and those aged 25 to 44 years more affected than other groups

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

BACKGROUND: People who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic. OBJECTIVE: To describe the mental health gap between those who live alone and with others in the UK prior to and during the COVID-19 pandemic. METHODS: Self-reported psychological distress and life satisfaction in 10 prospective longitudinal population surveys (LPSs) assessed in the nearest pre-pandemic sweep and three periods during the pandemic. Recorded diagnosis of common and severe mental illnesses between March 2018 and January 2022 in electronic healthcare records (EHRs) within the OpenSAFELY-TPP. FINDINGS: In 37 544 LPS participants, pooled models showed greater psychological distress (standardised mean difference (SMD): 0.09 (95% CI: 0.04; 0.14); relative risk: 1.25 (95% CI: 1.12; 1.39)) and lower life satisfaction (SMD: −0.22 (95% CI: −0.30; −0.15)) for those living alone pre-pandemic. This gap did not change during the pandemic. In the EHR analysis of c.16 million records, mental health conditions were more common in those who lived alone (eg, depression 26 (95% CI: 18 to 33) and severe mental illness 58 (95% CI: 54 to 62) more cases more per 100 000). For common mental health disorders, the gap in recorded cases in EHRs narrowed during the pandemic. CONCLUSIONS: People living alone have poorer mental health and lower life satisfaction. During the pandemic, this gap in self-reported distress remained; however, there was a narrowing of the gap in service use. CLINICAL IMPLICATIONS: Greater mental health need and potentially greater barriers to mental healthcare access for those who live alone need to be considered in healthcare planning

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

Background: People who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic. Objective: To describe the mental health gap between those who live alone and with others in the UK prior to and during the COVID-19 pandemic. Methods: Self-reported psychological distress and life satisfaction in 10 prospective longitudinal population surveys (LPSs) assessed in the nearest pre-pandemic sweep and three periods during the pandemic. Recorded diagnosis of common and severe mental illnesses between March 2018 and January 2022 in electronic healthcare records (EHRs) within the OpenSAFELY-TPP. Findings: In 37 544 LPS participants, pooled models showed greater psychological distress (standardised mean difference (SMD): 0.09 (95% CI: 0.04; 0.14); relative risk: 1.25 (95% CI: 1.12; 1.39)) and lower life satisfaction (SMD: −0.22 (95% CI: −0.30; −0.15)) for those living alone pre-pandemic. This gap did not change during the pandemic. In the EHR analysis of c.16 million records, mental health conditions were more common in those who lived alone (eg, depression 26 (95% CI: 18 to 33) and severe mental illness 58 (95% CI: 54 to 62) more cases more per 100 000). For common mental health disorders, the gap in recorded cases in EHRs narrowed during the pandemic. Conclusions: People living alone have poorer mental health and lower life satisfaction. During the pandemic, this gap in self-reported distress remained; however, there was a narrowing of the gap in service use. Clinical implications: Greater mental health need and potentially greater barriers to mental healthcare access for those who live alone need to be considered in healthcare planning

Aspergillosis in poultry

<p>Avian Aspergillosis is the major mycotic non-contagious disease of birds. It is also known as brooder pneumonia. It is mainly the disease of respiratory system affecting domestic poultry, wild birds and zoo birds. This is considered as disease of typical mishandling of birds mainly backyard and commercial poultry. The aspergillosis is mainly caused by Aspergillus fumigatus fungus, however other species such as A. flavus, A. niger, A. nidulans, and A. terreus may also be isolated from cases of aspergillosis in birds (occasionally in mixed infections).</p&gt

Direction Finding System using an N-Channel Software Defined Radio Implemented with a Phase Interferometry Algorithm

Providing portable and accurate radio frequency direction finding capability remains non-trivial, as estimating the direction of signals at a distance becomes very difficult when both systems are not stationary. Improvements in this area could provide significant advantages for both commercial and military systems. This paper presents a traditional radio signal direction finder implemented with a four-channel coherent receiver, GNU Radio software for the signal processing and a four-dipole antenna element array. The signals are received by the four-channel software defined radio (SDR) using a four-antenna dipole antenna array and processed through GNU Radio software. Within GNU radio software we are implementing an interferometry algorithm, through which we present the ability to determine the arrival direction of a signal based on a quadrant (45°, 135°, 225°, 315°). We further resolve the ability to visualize the signals by providing a compass which displays arrow directed to the quadrant of the signal. We will expand on our work by providing potential future improvements that would continuing to make the device more accurate and portable