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Continuous magnitude production of loudness
No full textContinuous magnitude estimation and continuous cross-modality matching with line length can efficiently track the momentary loudness of time-varying sounds in behavioural experiments. These methods are known to be prone to systematic biases but may be checked for consistency using their counterpart, magnitude production. Thus, in Experiment 1, we performed such an evaluation for time-varying sounds. Twenty participants produced continuous cross-modality matches to assess the momentary loudness of fourteen songs by continuously adjusting the length of a line. In Experiment 2, the resulting temporal line length profile for each excerpt was played back like a video together with the given song and participants were asked to continuously adjust the volume to match the momentary line length. The recorded temporal line length profile, however, was manipulated for segments with durations between 7 to 12 s by eight factors between 0.5 and 2, corresponding to expected differences in adjusted level of -10, -6, -3, -1, 1, 3, 6 and 10 dB according to Stevens’s power law for loudness. The average adjustments 5 s after the onset of the change were -3.3, -2.4, - 1.0, -0.2, 0.2, 1.4, 2.4 and 4.4 dB. Smaller adjustments than predicted by the power law are in line with magnitude-production results by Stevens and co-workers due to ‘regression effects’. Continuous cross-modality matches of line length turned out to be consistent with current loudness models, and by passing the consistency check with cross-modal productions, demonstrate that the method is suited to track the momentary loudness of time-varying sounds
Biohybrid Entities for Environmental Monitoring
No full textIn the wake of climate change and water quality crisis, it is crucial to find novel ways to extensively monitor the environment and to detect ecological changes early. Biomonitoring has been found to be an effective way of observing the aggregate effect of environmental fluctuations. In this paper, we outline the development of biohybrids which will autonomously observe simple organisms (microorganisms, algae, mussels etc.) and draw conclusions about the state of the water body. These biohybrids will be used for continuous environmental monitoring and to detect sudden (anthropologically or ecologically catastrophic) events at an early stage. Our biohybrids are being developed within the framework of project Robocoenosis, where the operational area planned are Austrian lakes. Additionally, we discuss the possible use of various species found in these waters and strategies for biomonitoring. We present early prototypes of devices that are being developed for monitoring of organisms
Using Mendelian Randomization methods to understand whether diurnal preference is causally related to mental health
No full textLate diurnal preference has been linked to poorer mental health outcomes, but the understanding of the causal role of diurnal preference on mental health and wellbeing is currently limited. Late diurnal preference is often associated with circadian misalignment (a mismatch between the timing of the endogenous circadian system and behavioural rhythms), so that evening people live more frequently against their internal clock. This study aims to quantify the causal contribution of diurnal preference on mental health outcomes, including anxiety, depression and general wellbeing and test the hypothesis that more misaligned individuals have poorer mental health and wellbeing using an actigraphy-based measure of circadian misalignment. Multiple Mendelian Randomization (MR) approaches were used to test causal pathways between diurnal preference and seven well-validated mental health and wellbeing outcomes in up to 451,025 individuals. In addition, observational analyses tested the association between a novel, objective measure of behavioural misalignment (Composite Phase Deviation, CPD) and seven mental health and wellbeing outcomes. Using genetic instruments identified in the largest GWAS for diurnal preference, we provide robust evidence that early diurnal preference is protective for depression and improves wellbeing. For example, using one-sample MR, a two-fold higher genetic liability of morningness was associated with lower odds of depressive symptoms (OR: 0.92, 95%CI: 0.88, 0.97). It is possible that behavioural factors including circadian misalignment may contribute in the chronotype depression relationship, but further work is needed to confirm these findings
The interpersonal processes of non-suicidal self-injury: A systematic review and meta-synthesis
No full textBackground: Understanding the processes underlying non-suicidal self-injury (NSSI) is important given the negative consequences of this experience. Qualitative research has the potential to provide an in-depth exploration of this. There has been limited research regarding the interpersonal processes associated with NSSI, therefore, a meta-synthesis was conducted to investigate this. Methods: A search of PsycINFO, Medline, Web of Science and CINAHL electronic databases from date of inception to November 2020 was conducted. In total, 30 papers were included in the final review. A meta-ethnographic approach was utilised to synthesise the data. Results: Two overarching themes were found. Within ‘Powerful relational dynamics’, NSSI was cited as a response to participants becoming stuck in aversive or disempowering relational positions with others. Within the ‘Taking matters into their own hands’ subtheme, NSSI was reported as a way for participants to get essential yet unavailable interpersonal and emotional needs met.Limitations: Several included papers did not comment on the researcher-participant relationship, which may have affected qualitative results. A small number of potentially eligible papers were unavailable for synthesising. Conclusion: Findings provide a more nuanced investigation of the interpersonal processes underlying NSSI. Consistent with relevant theories, NSSI appears to be a way of mitigating difficult interpersonal experiences or meeting essential interpersonal needs. NSSI may be engaged in as an alternative to other, less damaging ways to cope. An argument is made for a more empathetic understanding of NSSI and the use of relational interventions. <br/
Energy efficiency in extrusion-related polymer processing: a review of state of the art and potential efficiency improvements
No full textEnergy saving and industrial pollution have become increasingly important issues,therefore the identification and adoption of more energy efficient machines and industrial processesare now industrial priorities, and worthy topics for furtherdevelopment through academic research. Polymeric materials are amajor raw material, finding widespread application to a range ofcurrent industrial machine components as well as multiple productsand packaging found in our daily life. Polymer extrusion serves asa particular example of polymer processing techniques,representative of others in as much as there are analogousintermediate stages in the processing. Processing techniques whichrequire such intermediate stages includethe manufacture of blown film, blow moulding, thermo-forming, andinjection moulding. Hence, the study of polymer extrusion is arepresentative paradigm for a wider range of processingtechniques. Since polymer processing is an energy intensiveprocess and accounts for a huge share (maybe more than 1/3) of thematerials processing sector, any improvement to the process wouldcontribute significantly to global energy savings. This workpresents a review of studies, which focus on, or appertain to, theenergy consumption of extrusion related polymer processingapplications. Typical energy demand and losses during processingare considered, and possible approaches for improving the processenergy efficiency while maintaining the required end productquality are considered. Overall, this work provides a detaileddiscussion about how and where energy is utilized; how, where andwhy energy losses occur; and sets out approaches for optimizingthe process energy efficiency
Multipliers for nonlinearities with monotone bounds
No full textWe consider Lurye (sometimes written Lur’e) systems whose nonlinear operator is characterised by a possibly multivalued nonlinearity that is bounded above and below by monotone functions. Stability can be established using a subclass of the Zames-Falb multipliers. The result generalises similar approaches in the literature. Appropriate multipliers can be found using convex searches. Because the multipliers can be used for multivalued nonlinearities they can be applied after loop transformation. We illustrate the power of the new multipliers with two examples, one in continuous time and one in discrete time: in the first the approach is shown to outperform available stability tests in the literature; in the second we focus on the special case for asymmetric saturation with important consequences for systems with non-zero steady state exogenous signals
Sparse Spatial Attention Network for Semantic Segmentation
No full textThe spatial attention mechanism captures long-range dependencies by aggregating global contextual information to each query location, which is beneficial for semantic segmentation. In this paper, we present a sparse spatial attention network (SSANet) to improve the efficiency of the spatial attention mechanism without sacrificing the performance. Specifically, a sparse non-local (SNL) block is proposed to sample a subset of key and value elements for each query element to capture long-range relations adaptively and generate a sparse affinity matrix to aggregate contextual information efficiently. Experimental results show that the proposed approach outperforms other context aggregation methods and achieves state-of-the-art performance on the Cityscapes and the PASCAL Context datasets
Many-objective optimization of sustainable drainage systems in urban areas with different surface slopes
No full textSustainable urban drainage systems are multi-functional nature-based solutions that can facilitate flood management in urban catchments while improving stormwater runoff quality. Traditionally, the evaluation of the performance of sustainable drainage infrastructure has been limited to a narrow set of design objectives to simplify their implementation and decision-making process. In this study, the spatial design of sustainable urban drainage systems is optimized considering five objective functions, including minimization of flood volume, flood duration, average peak runoff, total suspended solids, and capital cost. This allows selecting an ensemble of admissible portfolios that best trade-off capital costs and the other important urban drainage services. The impact of the average surface slope of the urban catchment on the optimal design solutions is discussed in terms of spatial distribution of sustainable drainage types. Results show that different subcatchment slopes result in non- uniform distributional designs of sustainable urban drainage systems, with higher capital costs and larger surface areas of green assets associated with steeper slopes. This has two implications. First, urban areas with different surface slopes should not have a one-size-fits-all design policy. Second, spatial equality must be taken into account when applying optimization models to urban subcatchments with different surface slopes to avoid unequal distribution of environmental and human health co-benefits associated with green drainage infrastructure
Screening, diagnostic and prognostic tests for COVID-19: A 2 comprehensive review
No full textWhile molecular testing implementing real-time polymerase chain reaction (RT-PCR) re-22 main the gold-standard test for COVID-19 diagnosis and screening, more rapid or affordable mo-23 lecular and antigen testing options are developed. More affordable, point-of-care antigen testing, 24 despite being less sensitive compared to molecular assays, it might be preferable for wider screening 25 initiatives. Simple laboratory, imaging and clinical parameters could facilitate prognostication and 26 triage. This comprehensive review summarizes current evidence on the diagnostic, screening and 27 prognostic tests for COVID-19
Penalized joint generalized estimating equations for longitudinal binary data
No full textIn statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally can not be expressed in analytically closed form, and standard variable selection methods can not be directly applied. As an alternative, the penalized generalized estimating equation is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Frechet-Hoeffding upper bound. In this paper, we proposed SCAD-based and LASSO-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under differentscenarios are made to assess the performance of the proposed method. Compared to existing penalized generalized estimating equation (PGEE) methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real dataset on Clinical Global Impression is analyzed for illustration