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Journal articleDvorkin V, Ratha A, Pinson P, et al., 2022,
Stochastic Control and Pricing for Natural Gas Networks
, IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, Vol: 9, Pages: 450-462, ISSN: 2325-5870 -
ReportSandwell P, Candelise C, Solomon B, et al., 2022,
The role of mini-grids for electricity access and climate change mitigation in India
, The role of mini-grids for electricity access and climate change mitigation in India -
Journal articleChard I, Van Zalk N, 2022,
Virtual Reality Exposure Therapy for treating social anxiety: A scoping review of treatment designs and adaptation to stuttering
, Frontiers in Digital Health, Vol: 4, ISSN: 2673-253XVirtual Reality Exposure Therapy (VRET) has been shown to be an effective technique for reducing social anxiety. People who stutter are at greater risk of developing heightened social anxiety. Cognitive behavior therapy protocols have shown promise in reducing social anxiety in people who stutter, but no studies have investigated VRET targeting social anxiety associated with stuttering. The aim of the current review is to provide an overview of VRET techniques used to treat social anxiety and insights into how these techniques might be adopted in the case of comorbid stuttering and socialanxiety. Twelve studies were reviewed to understand key distinctions in VRET protocols used to treat social anxiety. Distinctions include exercises targeting public speaking vs. general social anxiety, computer-generated virtual environments vs. 360⁰ video, and therapist guided vs. automatedVRET. Based on the review findings, we propose how certain features could be applied in the case of stuttering. Virtual therapists, inhibitory learning techniques and integration into speech therapy may be suitable ways to tailor VRET. Regardless of these different techniques, VRET should consider thesituations and cognitive-behavioral processes that underlie the experience of social anxiety amongst people who stutter.
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Journal articleBahshwan M, Gee M, Nunn J, et al., 2022,
In situ observation of anisotropic tribological contact evolution in 316L steel formed by selective laser melting
, Wear, Vol: 490-491, Pages: 1-12, ISSN: 0043-1648A consensus on the tribological performance of components by additive-versus conventional manufacturing has not been achieved; mainly because the tribological test set-ups thus far were not suited for investigating the underlying microstructure's influence on the tribological properties. As a result, utilization of additive manufacturing techniques, such as selective laser melting (SLM), for tribological applications remains questionable. Here, we investigate the anisotropic tribological response of SLM 316L stainless steel via in situ SEM reciprocating micro-scratch testing to highlight the microstructure's role. As-built 316L SLM specimens were compared against annealed wire-drawn 316L. We found that: (i) microgeometric conformity was the main driver for achieving steady-state friction, (ii) the anisotropic friction of the additively manufactured components is limited to the break-in and is caused by the lack of conformity, (iii) the cohesive bonds, whose strength is proportional to frictional forces, are stronger in the additively manufactured specimens likely due to the dislocation-dense, cellular structures, (iv) low Taylor-factor grains with large dimension stimulate microcutting in the form of long, thin sheets with serrated edges. These findings uncover some microstructurally driven tribological complexities when comparing additive to conventional manufacturing.
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Journal articleChappell D, Son HW, Clark AB, et al., 2022,
Virtual reality pre-prosthetic hand training with physics simulation and robotic force interaction
, IEEE Robotics and Automation Letters, Vol: 7, Pages: 1-1, ISSN: 2377-3766Virtual reality (VR) rehabilitation systems have been proposed to enable prosthetic hand users to perform training before receiving their prosthesis. Improving pre-prosthetic training to be more representative and better prepare the patient for prosthesis use is a crucial step forwards in rehabilitation. However, existing VR platforms lack realism and accuracy in terms of the virtual hand and the forces produced when interacting with the environment. To address these shortcomings, this work presents a VR training platform based on accurate simulation of an anthropomorphic prosthetic hand, utilising an external robot arm to render realistic forces that the user would feel at the attachment point of their prosthesis. Experimental results with non-disabled participants show that training with this platform leads to a significant improvement in Box and Block scores compared to training in VR alone and a control group with no prior training. Results performing pick-and-place tasks with a wider range of objects demonstrates that training in VR alone negatively impacts performance, whereas the proposed platform has no significant impact on performance. User perception results highlight that the platform is much closer to using a physical prosthesis in terms of physical demand and effort, however frustration is significantly higher during training.
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Journal articleBaker C, Martin P, Wilson M, et al., 2022,
The relationship between road traffic collision dynamics and traumatic brain injury pathology
, Brain Communications, Vol: 4, ISSN: 2632-1297Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2,065 collisions from Great Britain’s Road Accident In-depth Studies collision database involving 5,374 subjects (2013-20). 595 subjects sustained a traumatic brain injury (20.2% of 2,940 casualties), including 315 moderate-severe and 133 mild-probable. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1,000,000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ~20,000 traumatic brain injury casualties (~5,000 moderate-severe) annually on Great Britain’s roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change-in-velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8km/h delta-V for pedestrians/cyclists and 19km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet was protective against overall and mild-to-moderate-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of delta-V direction. Car occupants exposed to a higher latera
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Journal articlePetrovskaya E, Deterding S, Zendle D, 2022,
Prevalence and Salience of Problematic Microtransactions in Top-Grossing Mobile and PC Games: A Content Analysis of User Reviews
<p>Microtransactions have become a major monetisation model in digital games, shaping their design, impacting their player experience, and raising ethical concerns. Research in this area has chiefly focused on loot boxes. This begs the question whether other microtransactions might actually be more relevant and problematic for players. We therefore conducted a content analysis of negative player reviews (n=801) of top-grossing mobile and desktop games to determine which problematic microtransactions are most prevalent and salient for players. We found that problematic microtransactions with mobile games featuring more frequent and different techniques compared to desktop games. Across both, players minded issues related to fairness, transparency, and degraded user experience, supporting prior theoretical work, and importantly take issue with monetisation-driven design as such. We identify future research needs on why microtransactions in particular spark this critique, and which player communities it may be more or less representative of.</p>
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Journal articleYao X, Olsson E, Zhao J, et al., 2022,
Voltage plateau variation in a bismuth-potassium battery
, JOURNAL OF MATERIALS CHEMISTRY A, Vol: 10, Pages: 2917-2923, ISSN: 2050-7488- Author Web Link
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- Citations: 5
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Journal articleAllison JT, Cardin MA, McComb C, et al., 2022,
Special Issue: Artificial intelligence and engineering design
, Journal of Mechanical Design, Transactions of the ASME, Vol: 144, ISSN: 1050-0472 -
Journal articleAlAttar A, Chappell D, Kormushev P, 2022,
Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance
, Frontiers in Robotics and AI, Vol: 9, Pages: 1-9, ISSN: 2296-9144Model predictive control is a widely used optimal control method for robot path planning andobstacle avoidance. This control method, however, requires a system model to optimize controlover a finite time horizon and possible trajectories. Certain types of robots, such as softrobots, continuum robots, and transforming robots, can be challenging to model, especiallyin unstructured or unknown environments. Kinematic-model-free control can overcome thesechallenges by learning local linear models online. This paper presents a novel perception-basedrobot motion controller, the kinematic-model-free predictive controller, that is capable of controllingrobot manipulators without any prior knowledge of the robot’s kinematic structure and dynamicparameters and is able to perform end-effector obstacle avoidance. Simulations and physicalexperiments were conducted to demonstrate the ability and adaptability of the controller toperform simultaneous target reaching and obstacle avoidance.
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Journal articleCursi F, Bai W, Yeatman EM, et al., 2022,
GlobDesOpt: a global optimization framework for optimal robot manipulator design
, IEEE Access, Vol: 10, Pages: 5012-5023, ISSN: 2169-3536Robot design is a major component in robotics, as it allows building robots capable of performing properly in given tasks. However, designing a robot with multiple types of parameters and constraints and defining an optimization function analytically for the robot design problem may be intractable or even impossible. Therefore black-box optimization approaches are generally preferred. In this work we propose GlobDesOpt, a simple-to-use open-source optimization framework for robot design based on global optimization methods. The framework allows selecting various design parameters and optimizing for both single and dual-arm robots. The functionalities of the framework are shown here to optimally design a dual-arm surgical robot, comparing the different two optimization strategies.
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Journal articleBurge TA, Jeffers JRT, Myant CW, 2022,
Development of an automated mass-customization pipeline for knee replacement surgery using biplanar X-Rays
, Journal of Mechanical Design, Vol: 144, Pages: 1-11, ISSN: 1050-0472For standard “off-the-shelf” knee replacement procedures, surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Customized solutions have been shown to improve results but require increased preoperative assessment (Computed Tomography or Magnetic Resonance Imaging), longer lead times, and higher costs which have prevented widespread adoption. To attain the benefits of custom implants, whilst avoiding the limitations of currently available solutions, a fully automated mass-customization pipeline, capable of developing customized implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The proof-of-concept pipeline uses convolutional neural networks to extract information from biplanar X-ray images, point depth, and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customized implant designs. The pipeline was trained using data from the Korea Institute of Science and Technology Information. Thirty subjects were used to test the accuracy of the anatomical reconstruction, ten from this data set, and a further 20 independent subjects obtained from the Osteoarthritis Initiative. An average root-mean-squared error of 1.00 mm was found for the femur test cases and 1.07 mm for the tibia. Three-dimensional (3D) distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customization of knee replacement implants is feasible from 2D medical imaging.
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Journal articleWang K, Fei H, Kormushev P, 2022,
Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model
, Frontiers in Robotics and AI, Vol: 9, Pages: 1-11, ISSN: 2296-9144We present an online optimization algorithm which enables bipedal robots to blindly walk overvarious kinds of uneven terrains while resisting pushes. The proposed optimization algorithmperforms high level motion planning of footstep locations and center-of-mass height variationsusing the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupledaSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics inhorizontal states and spring dynamics in the vertical state. The motion planning can beformulated as a discrete-time Model Predictive Control (MPC) problem and solved at a frequencyof 1 kHz. The output of the motion planner is fed into an inverse-dynamics based whole bodycontroller for execution on the robot. A key result of this controller is that the feet of the robot arecompliant, which further extends the robot’s ability to be robust to unobserved terrain variations.We evaluate our method in simulation with the bipedal robot SLIDER. Results show the robotcan blindly walk over various uneven terrains including slopes, wave fields and stairs. It can alsoresist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrain.
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Journal articleCaputo C, Cardin MA, 2022,
Analyzing Real Options and Flexibility in Engineering Systems Design Using Decision Rules and Deep Reinforcement Learning
, Journal of Mechanical Design, Vol: 144, ISSN: 1050-0472Engineering systems provide essential services to society, e.g., power generation, transportation. Their performance, however, is directly affected by their ability to cope with uncertainty, especially given the realities of climate change and pandemics. Standard design methods often fail to recognize uncertainty in early conceptual activities, leading to rigid systems that are vulnerable to change. Real options and flexibility in design are important paradigms to improve a system’s ability to adapt and respond to unforeseen conditions. Existing approaches to analyze flexibility, however, do not leverage sufficiently recent developments in machine learning enabling deeper exploration of the computational design space. There is untapped potential for new solutions that are not readily accessible using existing methods. Here, a novel approach to analyze flexibility is proposed based on deep reinforcement learning (DRL). It explores available datasets systematically and considers a wider range of adaptability strategies. The methodology is evaluated on an example waste-to-energy (WTE) system. Low and high flexibility DRL models are compared against stochastically optimal inflexible and flexible solutions using decision rules. The results show highly dynamic solutions, with action space parametrized via artificial neural network (ANN). They show improved expected economic value up to 69% compared with previous solutions. Combining information from action space probability distributions along expert insights and risk tolerance helps make better decisions in real-world design and system operations. Out of sample testing shows that the policies are generalizable, but subject to tradeoffs between flexibility and inherent limitations of the learning process.
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- Citations: 8
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Working paperCullen A, Ferraro P, Sanders W, et al., 2022,
Access Control for Distributed Ledgers in the Internet of Things: A Networking Approach
, Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC- Author Web Link
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- Citations: 4
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Journal articleDeady M, Glozier N, Calvo R, et al., 2022,
Preventing depression using a smartphone app: a randomized controlled trial
, Psychological Medicine, Vol: 52, Pages: 457-466, ISSN: 0033-2917BackgroundThere is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.MethodsA randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.ResultsThose assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p = 0.001, 95% CI 0.26–0.70).ConclusionsThis trial demonstrates that a smartphone app can reduce depression symptoms and potentially prevent incident depression caseness and such interventions may have a role in improving working population mental health. Some caution in interpretation is needed regarding the clinical significance due to small effect size and trial attrition.Trial Registration Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au/) ACTRN12617000548336
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Journal articleOVerko R, Ordonez-Hurtado R, Zhuk S, et al., 2022,
Spatial positioning token (SPToken) for smart mobility
, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 1529-1542, ISSN: 1524-9050We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture is also suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by ``jumping'' from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach.
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Journal articleCullen A, Ferraro P, Sanders W, et al., 2022,
Access control for distributed ledgers in the internet of things: a networking approach
, IEEE Internet of Things Journal, Vol: 9, Pages: 2277-2292, ISSN: 2327-4662In the Internet of Things (IoT) domain, devices need a platform to transact seamlessly without a trusted intermediary. Although Distributed Ledger Technologies (DLTs) could provide such a platform, blockchains, such as Bitcoin, were not designed with IoT networks in mind, hence are often unsuitable for such applications: they offer poor transaction throughput and confirmation times, put stress on constrained computing and storage resources, and require high transaction fees. In this work, we consider a class of IoT-friendly DLTs based on directed acyclic graphs, rather than a blockchain, and with a reputation system in the place of Proof of Work (PoW). However, without PoW, implementation of these DLTs requires an access control algorithm to manage the rate at which nodes can add new transactions to the ledger. We model the access control problem and present an algorithm that is fair, efficient and secure. Our algorithm represents a new design paradigm for DLTs in which concepts from networking are applied to the DLT setting for the first time. For example, our algorithm uses distributed rate setting which is similar in nature to transmission control used in the Internet. However, our solution features novel adaptations to cope with the adversarial environment of DLTs in which no individual agent can be trusted. Our algorithm guarantees utilisation of resources, consistency, fairness, and resilience against attackers. All of this is achieved efficiently and with regard for the limitations of IoT devices. We perform extensive simulations to validate these claims.
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Journal articleSteinhardt M, Barreras JV, Ruan H, et al., 2022,
Meta-analysis of experimental results for heat capacity and thermal conductivity in lithium-ion batteries: A critical review
, Journal of Power Sources, Vol: 522, Pages: 1-25, ISSN: 0378-7753Scenarios with rapid energy conversion for lithium-ion batteries are increasingly relevant, due to the desire for more powerful electric tools or faster charging electric vehicles. However, higher power means higher cooling requirements, affecting the battery temperature and its thermal gradients. In turn, temperature is a key quantity influencing battery performance, safety and lifetime. Therefore, thermal models are increasingly important for the design and operation of battery systems. Key parameters are specific heat capacity and thermal conductivity. For these parameters, this paper presents a comprehensive review of the experimental results in the literature, where the median values and corresponding uncertainties are summarized. Whenever available, data is analyzed from component to cell level with the discussion of dependencies on temperature, state of charge (SOC) and state of health (SOH). This meta-analysis reveals gaps in knowledge and research needs. For instance, we uncover inconsistencies between the specific heat capacity of electrode-separator stacks and full-cells. For the thermal conductivity, we found that thermal contact resistance and dependencies on battery states have been poorly studied. There is also a lack of measurements at high temperatures, which are required for safety studies. Overall, this study serves as a valuable reference material for both modellers and experimenters.
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Journal articleZahedmanesh A, Muttaqi KM, Islam MR, et al., 2022,
Consensus-based decision making approach for techno-economic operation of largescale battery energy storage in industrial microgrids
, JOURNAL OF ENERGY STORAGE, Vol: 46, ISSN: 2352-152X -
Journal articleEngel Alonso Martinez J, Goodman D, Picinali L, 2022,
Assessing HRTF preprocessing methods for Ambisonics rendering through perceptual models
, Acta Acustica -Peking-, Vol: 6, ISSN: 0371-0025Binaural rendering of Ambisonics signals is a common way to reproduce spatial audio content. Processing Ambisonics signals at low spatial orders is desirable in order to reduce complexity, although it may degrade the perceived quality, in part due to the mismatch that occurs when a low-order Ambisonics signal is paired with a spatially dense head-related transfer function (HRTF). In order to alleviate this issue, the HRTF may be preprocessed so its spatial order is reduced. Several preprocessing methods have been proposed, but they have not been thoroughly compared yet. In this study, nine HRTF preprocessing methods were used to render anechoic binaural signals from Ambisonics representations of orders 1 to 44, and these were compared through perceptual hearing models in terms of localisation performance, externalisation and speech reception. This assessment was supported by numerical analyses of HRTF interpolation errors, interaural differences, perceptually-relevant spectral differences, and loudness stability. Models predicted that the binaural renderings’ accuracy increased with spatial order, as expected. A notable effect of the preprocessing method was observed: whereas all methods performed similarly at the highest spatial orders, some were considerably better at lower orders. A newly proposed method, BiMagLS, displayed the best performance overall and is recommended for the rendering of bilateral Ambisonics signals. The results, which were in line with previous literature, indirectly validate the perceptual models’ ability to predict listeners’ responses in a consistent and explicable manner.
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Journal articlePinson P, 2022,
Editorial: Epidemics and forecasting with a focus on COVID-19
, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 38, Pages: 407-409, ISSN: 0169-2070 -
Journal articleAhmadpour N, Ludden G, Peters D, et al., 2022,
Editorial: Responsible Digital Health
, FRONTIERS IN DIGITAL HEALTH, Vol: 3- Author Web Link
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- Citations: 3
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Journal articleAbdin AF, Caunhye A, Zio E, et al., 2022,
Optimizing generation expansion planning with operational uncertainty: A multistage adaptive robust approach
, Applied Energy, Vol: 306, Pages: 1-18, ISSN: 0306-2619This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable fo
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Journal articleFrolke L, Sousa T, Pinson P, 2022,
A network-aware market mechanism for decentralized district heating systems
, APPLIED ENERGY, Vol: 306, ISSN: 0306-2619 -
Journal articleGeng L, Wang X, Han K, et al., 2022,
Eutectic Electrolytes in Advanced Metal-Ion Batteries
, ACS ENERGY LETTERS, Vol: 7, Pages: 247-260, ISSN: 2380-8195- Author Web Link
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- Citations: 45
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Journal articleDeady M, Collins DAJ, Johnston DA, et al., 2022,
The impact of depression, anxiety and comorbidity on occupational outcomes.
, Occup Med (Lond), Vol: 72, Pages: 17-24BACKGROUND: Anxiety and depression account for considerable cost to organizations, driven by both presenteeism (reduced performance due to attending work while ill) and absenteeism. Most research has focused on the impact of depression, with less attention given to anxiety and comorbid presentations. AIMS: To explore the cross-sectional relationship between depression and anxiety (individually and comorbidly) on workplace performance and sickness absence. METHODS: As part of a larger study to evaluate a mental health app, 4953 working Australians were recruited. Participants completed in-app assessment including demographic questions, the Patient Health Questionnaire-9, two-item Generalized Anxiety Disorder and questions from the World Health Organization Health and Work Performance Questionnaire. Cut-off scores were used to establish probable cases of depression alone, anxiety alone and comorbidity. RESULTS: Of the total sample, 7% met cut-off for depression only, 13% anxiety only, while 16% were comorbid. Those with comorbidity reported greater symptom severity, poorer work performance and more sickness absence compared to all other groups. Presenteeism and absenteeism were significantly worse in those with depression only and anxiety only compared to those with non-clinical symptom levels. Although those with depression alone tended to have poorer outcomes than the anxiety-only group, when sample prevalence rates were considered, the impact on presenteeism was comparable. CONCLUSIONS: Workplace functioning is heavily impacted by depression and anxiety both independently and where they co-occur. While comorbidity and more severe depression presentations stand out as impairing, workplace interventions should also prioritize targeting of anxiety disorders (and associated presenteeism) given their high population prevalence.
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Journal articleSalorio-Corbetto M, Williges B, Lamping W, et al., 2022,
Evaluating spatial hearing using a dual-task approach in a virtual-acoustics environment
, Frontiers in Neuroscience, Vol: 16, Pages: 1-17, ISSN: 1662-453XSpatial hearing is critical for communication in everyday sound-rich environments. It is important to gain an understanding of how well users of bilateral hearing devices function in these conditions. The purpose of this work was to evaluate a Virtual Acoustics (VA) version of the Spatial Speech in Noise (SSiN) test, the SSiN-VA. This implementation uses relatively inexpensive equipment and can be performed outside the clinic, allowing for regular monitoring of spatial-hearing performance. The SSiN-VA simultaneously assesses speech discrimination and relative localization with changing source locations in the presence of noise. The use of simultaneous tasks increases the cognitive load to better represent the difficulties faced by listeners in noisy real-world environments. Current clinical assessments may require costly equipment which has a large footprint. Consequently, spatial-hearing assessments may not be conducted at all. Additionally, as patients take greater control of their healthcare outcomes and a greater number of clinical appointments are conducted remotely, outcome measures that allow patients to carry out assessments at home are becoming more relevant. The SSiN-VA was implemented using the 3D Tune-In Toolkit, simulating seven loudspeaker locations spaced at 30° intervals with azimuths between −90° and +90°, and rendered for headphone playback using the binaural spatialization technique. Twelve normal-hearing participants were assessed to evaluate if SSiN-VA produced patterns of responses for relative localization and speech discrimination as a function of azimuth similar to those previously obtained using loudspeaker arrays. Additionally, the effect of the signal-to-noise ratio (SNR), the direction of the shift from target to reference, and the target phonetic contrast on performance were investigated. SSiN-VA led to similar patterns of performance as a function of spatial location compared to loudspeaker setups for both relative lo
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Conference paperCursi F, Chappell D, Kormushev P, 2022,
Augmenting loss functions of feedforward neural networks with differential relationships for robot kinematic modelling
, Ljubljana, Slovenia, 20th International Conference on Advanced Robotics (ICAR), Publisher: IEEE, Pages: 201-207Model learning is a crucial aspect of robotics as it enables the use of traditional and consolidated model-based controllers to perform desired motion tasks. However, due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build, particularly for redundant robots. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, inverse kinematics is learned, directly obtaining the desired control commands given a desired task. However, learning forward kinematics is simpler and allows the computation of the robot Jacobian and enables the exploitation of the optimality of controllers. Nevertheless, typical learning methods have no knowledge about the differential relationship between the position and velocity mappings. In this work, we present two novel loss functions to train feedforward Artificial Neural network (ANN) which incorporate this information in learning the forward kinematic model of robotic structures, and carry out a comparison with standard ANN training using position data only. Simulation results show that incorporating the knowledge of the velocity mapping improves the suitability of the learnt model for control tasks.
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Conference paperZhou Y, Zhang C, Myant C, et al., 2022,
Knitted Graphene Supercapacitor and Pressure-Sensing Fabric †
This research utilizes a simple and effective dip coating/ultrasonication method to prepare porous graphene-coated sensing fabrics made with commercially produced acrylic/spandex yarn with multifunctional performance. We examine the electrochemical performance of graphene-coated fabrics and explore their potential in applications involving pressure sensors. The results show that our graphene-coated fabric demonstrates a maximum specific capacitance value of 17.4 F/g. When applied as a pressure sensor, the capacitance change rate of our sensor increases linearly with the increase in pressure applied to the fabrics. Our sensor also shows a fast response in a pressure loading–unloading test, which indicates an outstanding sensing property and shows promising capabilities as a supercapacitor.
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