Queen's University Belfast

Non-intrusive load monitoring (NILM) disaggregates energy consumption data collected from a single measurement point into appliance-level data. This process facilitates energy savings. Most studies treat NILM as a residential task with few considering its application in industry. By chronologically reviewing existing literature, this paper presents a review of the latest research in NILM, focusing on its potential employment within a utility company, Northern Ireland Water. A practical example of NILM is also provided using data collected by a pilot IoT device where the benefits of NILM are exhibited via a cost analysis. Results from the literature review show deep learning models to be the most recent preferred disaggregation approach. Furthermore, the standardization of evaluation metrics is deemed essential to facilitate the comparison of different disaggregation models. Finally, the NILM tool kit is outlined as a useful platform for Northern Ireland Water to practically implement NILM.

Plastic and biomass waste pose a serious environmental risk; thus, herein, we mixed biomass waste with plastic bottle waste (PET) to produce char composite materials for producing a magnetic char composite for better separation when used in water treatment applications. This study also calculated the life cycle environmental impacts of the preparation of adsorbent material for 11 different indicator categories. For 1 functional unit (1 kg of pomace leaves as feedstock), abiotic depletion of fossil fuels and global warming potential were quantified as 7.17 MJ and 0.63 kg CO2 equiv for production of magnetic char composite materials. The magnetic char composite material (MPBC) was then used to remove crystal violet dye from its aqueous solution under various operational parameters. The kinetics and isotherm statistical theories showed that the sorption of CV dye onto MPBC was governed by pseudo-second-order, and Langmuir models, respectively. The quantitative assessment of sorption capacity clarifies that the produced MPBC exhibited an admirable ability of 256.41 mg g-1. Meanwhile, the recyclability of 92.4% of MPBC was demonstrated after 5 adsorption/desorption cycles. Findings from this study will inspire more sustainable and cost-effective production of magnetic sorbents, including those derived from combined plastic and biomass waste streams.

Efficiently grasping and releasing objects using robotic grippers is an essential step in robotic assembly. This paper presents a low-cost four-finger adaptive gripper capable of performing stable and reliable grasping operations on irregular-shaped flat objects. Unlike other grasping systems in the fixture-to-fixture robotic assembly, the assembly process using the proposed gripper does not rely on any vision systems or six-axis force/torque sensors. Specifically, assuming that the relative position $\bm {\Lambda }$ between the two fixtures is known, such assembly tasks can be accomplished simply by performing a predefined motion of a robot arm only related to $\bm {\Lambda }$ . This is mainly because the developed gripper not only adapts to the shape and size of the grasped object, but also keeps its position and posture relative to the gripper unchanged throughout the grasping process. A novel underactuated grasping mechanism consisting of an X-shaped differential mechanism and two seesaw ones is proposed to perform adaptive and stable fingertip grasps with more contact points. The kinematics and statics of the gripper-object system are derived for the analysis and design of the gripper. The proposed gripper is fabricated, and a grasping system is built using a commercial robot arm (UR5) to verify its capability for adaptive grasps and assembly of difficult-to-handle flat objects. Experimental results show that it is effective to grasp objects with uncertain shape/size and position, control the grasping force, and accomplish the fixture-to-fixture assembly, etc.

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.

Deep neural networks (DNNs) are shown to be vulnerable to adversarial attacks; carefully crafted additive noise that undermines DNNs integrity. Previously proposed defenses against these attacks require substantial overheads, making it challenging to deploy these solutions in power and computational resource-constrained devices, such as embedded systems and the Edge. In this paper, we explore the use of voltage over-scaling (VOS) as a lightweight and efficient defense against adversarial attacks. Specifically, we exploit the stochastic timing violations of VOS within computing elements to implement a moving-target defense for DNNs. Our experimental results demonstrate that VOS guarantees effective defense against different attack methods, does not require any software/hardware modifications, and offers a by-product reduction in power consumption. We propose a space exploration to identify a possible trade-off between robustness, accuracy and power gains. Furthermore, we observe the behavior of models’ epistemic uncertainty under variable undervolting aggressiveness. Our experiments show that model uncertainty analysis is coherent with the observation in our robustness/accuracy exploration.

CRYSTALS-Kyber (Kyber) was recently chosen as the first quantum resistant Key Encapsulation Mechanism (KEM) scheme for standardisation, after three rounds of the National Institute of Standards and Technology (NIST) initiated PQC competition which begin in 2016 and search of the best quantum resistant KEMs and digital signatures. Kyber is based on the Module-Learning with Errors (M-LWE) class of Lattice-based Cryptography, that is known to manifest efficiently on FPGAs. This work explores several architectural optimizations and proposes a high-performance and area-time (AT) product efficient hardware accelerator for Kyber. The proposed architectural optimizations include inter-module and intra-module pipelining, that are designed and balanced via FIFO based buffering to ensure maximum parallelisation. The implementation results show that compared to state-of-the-art designs, the proposed architecture delivers 25-51% speedups for Kyber’s three different security levels on Artix-7 and Zynq UltraScale+ devices, and a 50-75% reduction in DSPs at comparable security level. Consequently, the proposed design achieve higher AT product efficiencies of 19-33%.

Sport provides a significant role in the lives of athletes; however, both positive and negative mental health effects may occur from sporting experiences, including burnout and/or well-being. A cross-sectional survey was conducted including 685 athletes ( M age = 23.39, SD = 6.22, 71% = male), testing multiple, complementary, self-determination theory hypotheses linked to well-being, and burnout. A multistage modeling approach encompassing confirmatory factor and path analysis was utilized, with results showing significant variance explained for well-being ( R ² = .30) and burnout ( R ² = .35). Several direct effects were found in line with self-determination theory, including between; needs-support and needs-satisfaction (β = 0.48), and needs-control and needs-frustration (β = 0.44); needs-satisfaction and motivational orientation (β = 0.25); needs-satisfaction and well-being (β = 0.37), and needs frustration and burnout (β = 0.25); and motivational orientation and burnout (β = −0.27), and motivational orientation and well-being (β = 0.18). Indirect effects were found for well-being and burnout via coach needs-support, needs-satisfaction, and motivational orientation in sequence (β = 0.24 and β = −0.22, respectively), in addition to burnout via coach needs-control, needs frustration, and motivational orientation in sequence (β = −0.12). To conclude, coach-based, sporting mental health interventions that promote the utilization of needs-supportive behaviors, while also highlighting the need to minimize needs-controlling behaviors, are recommended for the prevention of burnout and the promotion of well-being in athletes.

Among nematodes, the free-living model organism Caenorhabditis elegans boasts the most advanced portfolio of high-quality omics data. The resources available for parasitic nematodes, including Strongyloides spp., however, are lagging behind. While C. elegans remains the most tractable nematode and has significantly advanced our understanding of many facets of nematode biology, C. elegans is not suitable as a surrogate system for the study of parasitism and it is important that we improve the omics resources available for parasitic nematode species. Here, we review the omics data available for Strongyloides spp. and compare the available resources to those for C. elegans and other parasitic nematodes. The advancements in C. elegans omics offer a blueprint for improving omics-led research in Strongyloides. We suggest areas of priority for future research that will pave the way for expansions in omics resources and technologies. This article is part of the Theo Murphy meeting issue ‘Strongyloides: omics to worm-free populations’.

The Strongyloides genus of parasitic nematodes have a fascinating life cycle and biology, but are also important pathogens of people and a World Health Organization-defined neglected tropical disease. Here, a community of Strongyloides researchers have posed thirteen major questions about Strongyloides biology and infection that sets a Strongyloides research agenda for the future. This article is part of the Theo Murphy meeting issue ‘Strongyloides: omics to worm-free populations’.

John Owen (1616-83) was one of the foremost English Puritans of the seventeenth century. His story has been largely limited to events in Britain. The letters examined in this article, translated from the French, reveal Owen's reputation and activity among Huguenots at the end of Oliver Cromwell's Protectorate. Responding to critics of English religion like Moïse Amyraut, they highlight the largely neglected internationality of Interregnum religion and politics in which Owen participated through epistolary and print culture. They display the apocalyptic themes behind attempts at international Protestant union where ecclesiological debates over the nature of synods, toleration, political sovereignty and Church-State relations were decisive.

Plio-Pleistocene records of ice-rafted detritus suggest northwest European ice sheets regularly reached coastlines. However, these records provide limited insight on the frequency, extent, and dynamics of ice sheets delivering the detritus. Three-dimensional reflection seismic data of the northwest European glaciated margin have previously documented buried landforms that inform us on these uncertainties. This paper reviews and combines these existing records with new seismic geomorphological observations to catalogue landform occurrence along the European glaciated margin and considers how they relate to ice sheet history. The compilation shows Early Pleistocene ice sheets regularly advanced across the continental shelves. Early Pleistocene sea level reconstructions demonstrate lower magnitude fluctuations compared to the Middle-Late Pleistocene, and more extensive/frequent Early Pleistocene glaciation provides a possible mismatch with sea level reconstructions. This evidence is discussed with global records of glaciation to consider possible impacts on our wider understanding of Plio-Pleistocene climate changes, in particular how well Early Pleistocene sea level records capture ice sheet volume changes. Resolving such issues relies on how well landforms are dated, whether they can be correlated with other proxy datasets, and how accurately these proxies reconstruct the magnitudes of past climatic changes. Many questions about Pleistocene glaciation in Europe and elsewhere remain.

Research shows that the search for healthier foods and concern for sustainability are driving the purchase of organic products. However, consumer expectations for quality attributes and sustainability often go beyond the parameters for organic certification, even after the latest revision (EU Regulation 2018/848). This article aims to explore Italian consumers’ expectations for attributes beyond the current organic certification and to highlight the importance of introducing an additional certification for this added value. Nine focus groups (59 participants) were conducted and analyzed with the Template Analysis approach using Atlas.ti 6.0. Participants expressed expectations beyond current organic certification: i) “for the consumer” (excellent product quality); ii) “for the people and the planet” (ethical aspects of production and distribution); iii) information about organic labels. The development of an “organic plus sustainability certification” seems important: it could allow consumers to make more conscious and ethical purchasing decisions in mature but still changing organic markets such as Italy.

In this study, we present a novel approach for efficient resource allocation in a digital twin (DT) framework for task offloading in a UAV-aided Internet-of-Vehicles (IoV) network. Our approach incorporates a hybrid machine learning approach that combines asynchronous federated learning (AFL) and multi-agent deep reinforcement learning (DRL) to jointly optimize task completion rate, energy consumption, and delay parameters, enhancing overall system efficiency. We instantiate a DT infrastructure within a UAV-assisted IoV network for V2V and V2I task offloading with three task processing modes and three types of tasks. The DT network is composed of three distinct DTs: task vehicles, service vehicles, and roadside units. Subsequently, we formulate an optimization problem aimed at maximizing the system efficiency while concurrently minimizing delay and total energy consumption. To address this challenging non-convex problem, we introduce a multi-agent DRL algorithm named MARS for resource allocation within the DT-assisted IoV network. This innovative algorithm, MARS, is trained to utilize a hybrid AFL approach referred to as HAFL. MARS optimizes the allocation of resources across various modes of computation, striving to maximize the system's overall utility. Finally, our proposed approach's effectiveness is validated through comprehensive simulation results, where it is compared against various benchmark schemes for evaluation.

Background Kidney transplantation is the optimal treatment option for most patients with end-stage kidney disease given the significantly lower morbidity and mortality rates compared to remaining on dialysis. Rejection and graft failure remain common in transplant recipients with limited improvement in long-term transplant outcomes despite therapeutic advances. There is an unmet need in the development of non-invasive biomarkers that specifically monitor graft function and predict transplant pathologies that affect outcomes. Despite the potential of proteomic investigatory approaches, up to now, no candidate biomarkers of sufficient sensitivity or specificity have translated into clinical use. The aim of this review was to collate and summarise protein findings and protein pathways implicated in the literature to date, and potentially flag putative biomarkers worth validating in independent patient cohorts. Methods This review followed the Joanna Briggs’ Institute Methodology for a scoping review. MedlineALL, Embase, Web of Science Core Collection, Scopus and Google Scholar databases were searched from inception until December 2022. Abstract and full text review were undertaken independently by two reviewers. Data was collated using a pre-designed data extraction tool. Results One hundred one articles met the inclusion criteria. The majority were single-centre retrospective studies of small sample size. Mass spectrometry was the most used technique to evaluate differentially expressed proteins between diagnostic groups and studies identified various candidate biomarkers such as immune or structural proteins. Discussion Putative immune or structural protein candidate biomarkers have been identified using proteomic techniques in multiple sample types including urine, serum and fluid used to perfuse donor kidneys. The most consistent findings implicated proteins associated with tubular dysfunction and immunological regulatory pathways such as leukocyte trafficking. However, clinical translation and adoption of candidate biomarkers is limited, and these will require comprehensive evaluation in larger prospective, multicentre trials.

Background The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding various topics have been developed. Objective We performed this study to assess the quality of guidelines, consensus statements, and standards in the field of AI for medicine and to provide a foundation for recommendations about the future development of AI guidelines. Methods We searched 7 electronic databases from database establishment to April 6, 2022, and screened articles involving AI guidelines, consensus statements, and standards for eligibility. The AGREE II (Appraisal of Guidelines for Research & Evaluation II) and RIGHT (Reporting Items for Practice Guidelines in Healthcare) tools were used to assess the methodological and reporting quality of the included articles. Results This systematic review included 19 guideline articles, 14 consensus statement articles, and 3 standard articles published between 2019 and 2022. Their content involved disease screening, diagnosis, and treatment; AI intervention trial reporting; AI imaging development and collaboration; AI data application; and AI ethics governance and applications. Our quality assessment revealed that the average overall AGREE II score was 4.0 (range 2.2-5.5; 7-point Likert scale) and the mean overall reporting rate of the RIGHT tool was 49.4% (range 25.7%-77.1%). Conclusions The results indicated important differences in the quality of different AI guidelines, consensus statements, and standards. We made recommendations for improving their methodological and reporting quality. Trial Registration PROSPERO International Prospective Register of Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360

Over the last few decades, neoliberal, managerial, reforms have dominated the university sector in a number of different countries. Calculative practices, including performance measures and indicators, have spread and targets, in terms of research outputs and increasing student numbers in order to generate “profit”, have become the norm, despite the many voices seeing this as often clashing with the mission of universities to create knowledge and contribute to social development. The paper provides an overview of the changing focus on how universities have been managed over time and, at the same time, of the emergence and measurementof Public Value themes in the university sector. A future research agenda is proposed for those interested in the study of the university sector, together with possibleresearch questions to further this area.