Texas Instruments (Ireland)

We propose integral representations of the Whittaker functions for the classical Lie algebras sp 2ℓ , so 2ℓ and so 2ℓ+1 .These integral representations generalize the integral representation of gl ℓ+1 -Whittaker functions first introduced by Givental.One of the salient features of the Givental representation is its recursive structure with respect to the rank ℓ of the Lie algebra gl ℓ+1 .The proposed generalization of the Givental representation to the classical Lie algebras retains this property.It was shown elsewhere that the integral recursion operator for gl ℓ+1 -Whittaker function in the Givental representation coincides with a degeneration of the Baxter Q-operator for gl ℓ+1 -Toda chains.We construct Q-operator for affine Lie algebras so 2ℓ , so 2ℓ+1 and a twisted form of gl 2ℓ .We demonstrate that the relation between recursion integral operators of the generalized Givental representation and degenerate Q-operators remains valid for all classical Lie algebras.The plan of this paper is as follows.In Part I we formulate the results for the classical Lie algebras sp 2ℓ , so 2ℓ and so 2ℓ+1 .The main results are formulated in the Theorems 2.3, 2.6, 2.10, 2.14 respectively.In Part II we collect the proofs of the results presented in Part I.

OBJECTIVE: Levetiracetam (LEV) is an effective antiseizure medicine, but 10%-20% of people treated with LEV report psychiatric side-effects, and up to 1% may have psychotic episodes. Pharmacogenomic predictors of these adverse drug reactions (ADRs) have yet to be identified. We sought to determine the contribution of both common and rare genetic variation to psychiatric and behavioral ADRs associated with LEV. METHODS: This case-control study compared cases of LEV-associated behavioral disorder (n = 149) or psychotic reaction (n = 37) to LEV-exposed people with no history of psychiatric ADRs (n = 920). All samples were of European ancestry. We performed genome-wide association study (GWAS) analysis comparing those with LEV ADRs to controls. We estimated the polygenic risk scores (PRS) for schizophrenia and compared cases with LEV-associated psychotic reaction to controls. Rare variant burden analysis was performed using exome sequence data of cases with psychotic reactions (n = 18) and controls (n = 122). RESULTS: Univariate GWAS found no significant associations with either LEV-associated behavioural disorder or LEV-psychotic reaction. PRS analysis showed that cases of LEV-associated psychotic reaction had an increased PRS for schizophrenia relative to contr ols (p = .0097, estimate = .4886). The rare-variant analysis found no evidence of an increased burden of rare genetic variants in people who had experienced LEV-associated psychotic reaction relative to controls. SIGNIFICANCE: The polygenic burden for schizophrenia is a risk factor for LEV-associated psychotic reaction. To assess the clinical utility of PRS as a predictor, it should be tested in an independent and ideally prospective cohort. Larger sample sizes are required for the identification of significant univariate common genetic signals or rare genetic signals associated with psychiatric LEV ADRs.

Abstract An ensemble of high‐resolution regional climate model simulation data is used to examine the impacts of climate change on offshore and onshore wind energy generation in Ireland. Two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 8.5) are analysed for the mid‐term (2041–2060) and the long‐term (2081–2100) future. Wind energy is projected to decrease (≤2%) overall in future climate scenarios. Changes are evident by mid‐century and are more pronounced by late 21st century, particularly for RCP 8.5 offshore. Seasonally, wind energy is projected to decrease by less than 6% in summer and to increase slightly in winter (up to 1.1%). The distinct changes in different parts of the power curve, presented here for the first time, show a reversed pattern of duration at certain levels of the power curve. In summer, there is an increase of low‐power and a decrease of high‐power generation, whereas during winter, there is a projected increase in the time spent at high power. This could lead to diverse consequences for system operators depending on the season. The impacts of climate change on the duration and frequency of long periods (longer than 24 h) of low‐/high‐power wind energy events in Ireland are also presented. The frequency of low‐power events is projected to increase slightly, especially during summer. Onshore and offshore events are considered separately, demonstrating the complementarity of developing both onshore and offshore wind farms for future energy systems. Regional analysis highlights the benefit of developing a geographically dispersed wind farm network incorporating different local wind conditions.

Primary-Side-Regulation (PSR) indirectly senses output voltage, to eliminate the secondary-side error amplifier and feedback opto-coupler - saving cost and standby power. PSR is commonly used in low-power Discontinuous-Conduction-Mode (DCM) flyback converters, but rarely at high power, or in Continuous-Conduction-mode (CCM). A new generation PSR flyback control scheme can achieve better than 1% load/line regulation and CCM operation at power levels to 100 W and beyond. The scheme uses a fixed-sample-point algorithm and a novel compensation scheme, to adjust for errors, dramatically improving static regulation, and extending PSR operation to much higher power.

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.

In order for autonomously navigating Unmanned Air Vehicles(UAVs) to be implemented in day-to-day life, proof of safe operation will be necessary for all realistic navigation scenarios. For Deep Learning powered navigation protocols, this requirement is challenging to fulfil as the performance of a network is impacted by how much the test case deviates from data that the network was trained on. Though networks can generalise to manage multiple scenarios in the same task, they require additional data representing those cases which can be costly to gather. In this work, a solution to this data acquisition problem is suggested by way of the implementation of a visually realistic, yet artificial, simulated dataset. A method is presented for the creation of a “Digital Twin Area” inside of a modern high fidelity game engine using 3D scanned models of physical locations, and a realistic dataset of each area is created to showcase this concept.

Collagen-glycosaminoglycan (CG) scaffolds have been widely developed for a range of regenerative medicine applications. To enhance their efficacy, CG scaffolds have previously been prevascularised in vitro using human umbilical vein endothelial cells and human mesenchymal stromal cells (hMSCs); however, at later timepoints, a regression of vascularisation is observed. This is undesirable for longer preculture periods (e.g., for partial/full organ regeneration) and for in vitro vascularised tissue model systems (e.g., for drug testing/modelling). We hypothesised that delayed platelet-derived growth factor (PDGF)-BB addition could stabilise vessels, preventing their regression. In 2D, we identified 25 ng/ml as a suitable dose that enhanced hMSC metabolic activity and proliferation, without affecting endothelial cells, or migration in either cell type. In our 3D model of CG scaffold vascularisation, early addition of PDGF (Day 3) behaved similarly to no PDGF controls. However, PDGF addition at later timepoints (i.e., Days 4 and 5), with a second addition on Day 10, prevented vascular regression. In quantifying our observations, we identified a need for a tool to measure in vitro vascularisation in porous scaffolds. This was a second key objective of this work. A novel ImageJ macro was developed, which allowed us to analyse vessel-like structures, evaluating their number and morphology, and confirmed our qualitative observations. Finally, upregulation of angiogenic genes (ANG1, KDR, and TEK2) involved in vessel maturation illustrated how PDGF addition contributed to vascular stability. Taken together, the results suggest that addition of PDGF at specific timepoints can be used to stabilise vasculature in CG scaffolds.

Primary-side sensing is an observer-based approach to estimate the output voltage of flyback converters from a primary winding (or an auxiliary winding). Various observer-laws have been recently developed for flyback converters operating in discontinuous conduction mode (DCM). The extension to continuous conduction mode (CCM), however, has not been considered due to the difficulties in compensating for the voltage drop in the secondary winding. From the possibility to predict the winding voltage drop using the magnetizing current and the transformer model, this paper presents a new observer method that can work accurately and smoothly in both CCM and DCM. The methodology can be combined with any controller to provide either output voltage regulation or output current regulation. The proposed sensing technique is verified by simulation. (6 pages)

For flyback converter applications, an accurate model of the transformer is necessary for simulation studies, as well as a basis for model-based controller design. In general, transformer modeling has either focused on the winding model, using frequency-domain methods, or on the nonlinear core model, using time-domain methods. Nonlinear modeling is confined to the time domain and certain difficulties have precluded the use of time-domain methods for winding model estimation, resulting in the lack of integrated modeling approaches. This paper focuses on identifying a complete nonlinear dynamic model of a 3-winding transformer using time-domain system identification approaches. Our study demonstrates a possible way to handle the difficulties of working in the time domain and provides a model at least as accurate as that obtained with the frequency response data. In addition to the parameters of the Jiles-Atherton model, which is used to describe the nonlinear core behavior, the air-gap length is also computed from the experimental data to enhance the core model accuracy. The obtained transformer winding model, core model, and full model are experimentally verified.

The last ten years the world has experienced a revival in computing education with countries transiting to or adopting a compulsory computing education curriculum, which has created a resurgence in undergraduate students studying computing. Coinciding with this movement, the decolonization of curriculum began to collect momentum. This paper explores these two movements in education based on Freire’s Pedagogy of the Oppressed, and the influence on the design of computing curriculum, and instructional techniques for grades 9-16 computing modules. This review will inform computing curriculum designers and computing educators lessons learned in the application of social science learning theories in computing education curriculum design and delivery.

The concept of self-healing asphalt has been developed to implement an extrinsic crack repair system, reduce maintenance efforts, and extend the service life of asphalt pavements. Various self-healing asphalt methods have been proposed and demonstrated, however, it is difficult to compare and finalize an optimum self-healing design for an upscaled application. To provide a better understanding of the prospects of each self-healing technology, this study investigates the physical properties and ranks the healing efficiency of each self-healing asphalt technology. Four self-healing systems were investigated, including alginate capsule system, conductive alginate capsule system, induction system, and a hybrid system (alginate capsule & induction). Laboratory tests, including Indirect Tensile test (ITT), Water Sensitivity test (WS), Binder Drainage test (BD), Triaxial test, and Semi-circular Bending test (SCB), were conducted to assess the physical performance of the asphalt mixtures. The healing efficiency of each mix was evaluated with a SCB bending and healing program. The results indicate that the addition of self-healing additives affects the physical properties of the SMA mix. The capsules reduce the mixture strength, stiffness and high-temperature stability, while the steel fibers have the opposite effect. The healing efficiency results show that the capsule healing system and conductive capsule healing system can be repeated twice, while the induction system and hybrid healing system showed a healing index above 60 % in all eight bending-healing cycles, demonstrating a promising and durable healing effect for the SMA mix. • Four up-to-date asphalt self-healing methods, including alginate capsules, conductive capsules, induction heating and hybrid system were investigated. • An upscaled alginate capsule production line was designed to meet the requirements for trial section testing. • Tests on water sensitivity, binder drainage, triaxial stress, and others were conducted to ensure the workability of the asphalt mix. • Semi-circular bending and healing tests were repeated nine times to evaluate the healing efficiency of each mix. • The induction heating method and hybrid healing method are recommended for further investigation on real road applications.

In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy. This paper showcases two illustrative scenarios that highlight the potential of federated learning (FL) as a key to delivering efficient and privacy-preserving machine learning within IoT networks. We first give the mathematical foundations for key aggregation algorithms in federated learning, i.e., FedAvg and FedProx. Then, we conduct simulations, using Flower Framework, to show the efficiency of these algorithms by training deep neural networks on common datasets and show a comparison between the accuracy and loss metrics of FedAvg and FedProx. Then, we present the results highlighting the trade-off between maintaining privacy versus accuracy via simulations - involving the implementation of the differential privacy (DP) method - in Pytorch and Opacus ML frameworks on common FL datasets and data distributions for both FedAvg and FedProx strategies.

BACKGROUND: Legislative reform in Ireland and Europe, including the introduction of a Health Information Bill in Ireland and the European Health Data Space (EHDS) Regulation, promote strong governance of health data, including control over how health data is used for different purposes, such as individual care, research, planning and policy-making. The aim of this study was to explore key stakeholders’ perspectives on the role of patients in enabling inclusive and ethical use of health data for primary and secondary purposes in Ireland. METHODS: This was a cross-sectional qualitative study with focus group design. Thirty-five participants were evenly distributed across five groups: Academics and Researchers; Data Controllers, Data Protection Officers and Ethics Experts; Patients and Public; Healthcare Professionals and the Industry Group. A semi-structured approach guided by a topic guide was used, and thematic data analysis was conducted. RESULTS: This study identified strong support for increased patient involvement. However, contradictions in participants’ views within and across groups were found particularly around patient control over health data and data ownership and embedding Patient and Public Involvement (PPI) in research. Most of the participants agreed that patient autonomy over health data is of ‘vital’ importance; yet they advocated for staged and delayed patient access. Similarly, the participants believed that PPI was required to drive the direction of research and funding allocation; however, patients’ lack of understanding of research areas was a challenge. Concerns were expressed around informed consent required for sharing of patient data, particularly with the industry, and around the timing of consent when patients are at their most vulnerable. CONCLUSION: Participants expressed strong support for increased patient involvement in the use of health data in Ireland; however, there were contrasting views in relation to data control and ownership, consent processes and PPI. These findings have implications for policy development in the implementation of the EHDS in Europe, and the establishment of the Health Data Access Body in Ireland. This study emphasises the importance of patient involvement to support successful implementation of new health information systems and data access infrastructure.

BACKGROUND: Tobacco use is closely linked to social and health inequalities, including economic vulnerability, morbidity, and premature death. Young adults with disabilities experience significant social and material hardships, which may be exacerbated by tobacco use. Limited research exists on smoking and e-cigarette use in this population. This study examines the prevalence of disability among young adults in Ireland, compares smoking and e-cigarette use between those with and without disabilities, identifies protective and risk factors, explores shared risk factors, and evaluates disability as an independent risk factor for smoking and e-cigarette use. METHODS: We analysed weighted data from 4,729 20-year-olds in the Growing Up in Ireland Cohort '98 study who were present in Waves 1 (2008), 3 (2016), and 4 (2019). Current smoking, e-cigarette use, disability (excluding mental ill-health) and all other variables were assessed at Wave 4, while peer smoking data were drawn from Wave 3. Analyses were conducted using SPSS version 27. RESULTS: 18.1% of participants reported a disability, which was associated with significantly higher smoking (41.8% vs. 36.7%) and e-cigarette use (16.1% vs. 12.9%). Protective factors against both behaviours included being female (OR 0.87 for smoking, OR 0.57 for e-cigarettes), later smoking initiation (OR 0.35, OR 0.62), living in two-parent families (OR 0.83, OR 0.70), and physical activity (smoking only). Risk factors included having peers who smoked (OR 3.67 for smoking; OR 2.36 for e-cigarette use) and caregivers who smoked (OR 1.48, OR 1.48), being employed at age 20 (OR 1.58, OR 1.48), and social media engagement (smoking only). Young adults with disabilities were significantly more likely to experience risk factors (e.g., earlier smoking initiation, caregivers who smoked, one-parent families, employment) but were less likely to have peers who smoked or engage with social media. Disability was independently associated with a higher likelihood of smoking (by 54%) and e-cigarette use (by 36%) after adjusting for protective and risk factors. CONCLUSION: Higher smoking and e-cigarette use in 20-year-olds with disabilities adds further inequality to their lives. Increased awareness, targeted surveys and focused prevention and therapeutic interventions are required to reduce inequalities in this population and hasten the tobacco endgame.

Abstract Background Prior to COVID‐19, >90% of caregivers of people with brain health challenges (dementia, mental ill health, intellectual disability) experienced high levels of distress, burden, loneliness and social isolation. The COVID‐19 pandemic has significantly increased these impacts, particularly since these caregivers are often older and physically vulnerable themselves. The aim of this cross‐sectional study is to explore coping and caregiver burden, loneliness and social isolation in caregivers of people with brain health challenges during the COVID‐19 pandemic. Method CLIC‐Caregiver was a cross‐sectional, online, and global survey (June 2 nd ‐ November 15 th , 2020) using self‐administered questionnaires directed at informal caregivers of people with long‐term brain health challenges. The study was embedded within a larger survey of loneliness and social isolation for general public (‘Comparing Loneliness and Isolation in COVID‐19’ (CLIC)), including validated loneliness and isolation tools. Translated into ten different languages such as Arabic, French, Romanian, etc, the survey was disseminated over 100 countries. Respondents were included in the CLIC‐caregiver sub‐study if they answered yes to the question ‘ Do you provide care and support to a family member or friend with a long‐term or life‐limiting health problem or disability (including mental health) ’. The CLIC project received the initial global ethical approval from Ulster University. The data were fully anonymized. Result From the CLIC main study, 5243 (25%) identified themselves as caregivers. This proportion varied in different countries, from 12 % in Romania to 65% in France. 2323 (44%) had care recipients with dementia, 1761 with physical conditions (disability or long‐term illness), 832 with enduring mental health problems, and 404 with intellectual disability. Measures of caregiver burden, loneliness and social isolation will be compared across geographic regions, sociodemographic factors, and risk factors for poor outcomes sought. Findings will be distributed to relevant stakeholders in the form of a project report, with region and country‐specific outcomes. This will support recommendations and actions supporting caregivers of people with brain health challenges. Conclusion This represents the largest, most widespread survey on the impact of the COVID‐19 pandemic on caregivers of people with long‐term conditions to date. It will be an important resource for support agencies and to inform policy.

Seeding spectral datasets by augmenting the data matrix with either the full spectrum or selected spectral features in order to bias multivariate analysis towards the solution of interest is explored. It is demonstrated that such seeding can have a profound effect on the endpoint of the analysis. Using Raman spectroscopic data of human lung adenocarcinoma cells (A549) in vitro, systematic perturbations to the spectra are introduced to simulate dose dependent exposure to a drug (cisplatin), and/or cellular response, representing reduced viability. Taking Principal Components Analysis (PCA) as the first example, seeding with the known spectral profiles of the drug exposure is demonstrated to greatly increase the ability of the algorithm to differentiate two distinct data subsets, representing control and exposed. The improved differentiation is quantified by further Linear Discriminant Analysis of the PCA data. Other examples of where seeding may be applied include, simulated datasets consisting of simultaneous changes in the spectral markers of exposure dose and cellular response, which are used for Multivariate Curve Resolution - Alternating Least Squares analysis (MCR-ALS). In the example presented, adding pure components to the dataset improves the ability of the algorithm to both model the systematic variation of concentration dependent data and extract the component spectra more accurately than the unseeded dataset. The seeded approach thus provides improved performance for differential analysis of datasets, as well as spectral unmixing analyses, to monitor, for example, the kinetic evolution of a reaction mixture, or metabolic pathway.

This paper studies the effect of variable sampling frequency on the dynamic of the fixed-parameter digital compensator in switched-mode power supplies. Based on the resulting analysis, we propose a simple technique to design a computationally efficient adaptive predictive functional controller (PFC) which can be implemented in a low-cost micro-controller. While the approach should have general applicability to systems where the sampling/switching frequency is varied, in this paper we use the example of a flyback power converter operating in discontinuous conduction mode (DCM). The performance of the proposed controller is verified with both simulation and experimental results.

BACKGROUND: Secondary use of health data is important for public and individual health due to its potential to drive research and healthcare improvement; however, there are challenges to be managed from a socio-ethical, legal and technological perspective. The aim of this qualitative study was to explore knowledge, experiences and perspectives of key stakeholders towards secondary use of health data in Ireland, with a specific focus on the challenges with secondary use. METHODS: The study employed a qualitative cross-sectional approach in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. Thirty-five people participated in the study, with seven participants in each of the five focus groups: academics and researchers; healthcare professionals; data controllers, ethics and privacy experts; industry group; and patients and public. Two thirds of the sample were female, and over half of participants were between 35 and 54 years of age. Participants were recruited through purposive and snowballing method. Data was collected through focus group discussions, transcribed and analysed thematically. RESULTS: The participants across all study groups were supportive of secondary use of health data; however, significant challenges were identified. The four main categories of challenges were related to (1) health data use, (2) ethics, (3) health data ecosystem and (4) social inequalities. Specifically, insufficient collection and low quality of health data, alongside issues regarding access, linking and sharing are a significant barrier to effective secondary use. This is further complicated by complex ethical approval processes and requirements around data protection. The fragmented national Information Technology (IT) and data infrastructure and limited resources further hamper secondary use, and concerns about low health literacy among the public and negative experiences with the healthcare system influence patients' willingness to share data for secondary use. CONCLUSIONS: This study identified the multi-layered and intersecting challenges in the Irish health data ecosystem around secondary use, and highlighted the need for structural improvements, reform of ethical processes, integration of disadvantaged communities, and education and awareness-raising among the public. A careful consideration of these challenges on a national level is required to enable effective secondary use of health data.

Abstract This activity report explores the integration of Education for Sustainable Development (ESD) in language pedagogy, focusing on its potential to equip students with both linguistic proficiency and the competencies needed to address complex global challenges. Rooted in the context of TU (Technical University) Dublin’s commitment to embedding sustainability in Higher Education curricula, the activity report builds on insights from a 2023 roundtable discussion convened by two of the authors in collaboration with CercleS and the United Nations Institute for Training and Research (UNITAR) on the occasion of its first Research conference. Featuring perspectives from scholars in sustainability and language education, the event explored innovative pedagogical approaches, challenges, and opportunities in the field of Education for Sustainable Development (ESD) in second/foreign language pedagogy. Drawing on examples from various language programs, the report discusses innovative pedagogical approaches and highlights specific models that integrate sustainability literacy into the teaching of languages. It also addresses challenges in embedding sustainability content into language curricula, including institutional resistance, teacher readiness, and the need for interdisciplinary collaboration. The activity report concludes by advocating for language educators to actively incorporate sustainability topics into their teaching, leveraging familiar themes and drawing on global networks to enhance learning outcomes and contribute to sustainable development.

Contemporary public infrastructure projects emphasise sustainable options that integrate pedestrian routes, leisure facilities and convenient access to public transport systems. It is important to understand the effectiveness of these contemporary designs. In the age of the General Data Protection Regulation (GDPR), there is a need to develop technology-based solutions that collect information about the behaviour of pedestrians in public spaces as they commute and engage in leisure pursuits while simultaneously preserving the privacy of these citizens. Bluetooth Low Energy (BLE), has privacy-preserving features that make it worth considering as part of a technology solution for studies of this type. This work presents the preliminary results of a multi-stakeholder study that collected data via BLE from 28 volunteer pedestrians who regularly used the public domain of the newly developed Grangegorman campus in Dublin's north inner city. Before the commencement of the data collection, each volunteer completed a short questionnaire about their intended movements on the campus, and for the next three weeks, they each carried a small keyring-sized BLE beacon with them as they passed through the campus. Bluetooth received signal strength indication from these beacons was collected at 17 points around the campus over the study period. The data for the volunteers were anonymised at the point of capture by hash encoding the MAC address of the beacons. The results of the work show that BLE can be used to monitor the approximate movements of volunteer pedestrians and so provide valuable privacy-preserving data on the utilisation of public infrastructure.