Search

Strategy process has been widely publicised during the last three decades, but what has been accomplished by strategy management literature in manufacturing small to medium sized enterprises (SMEs)? The application of strategy management in manufacturing SME practices can be seen as posing particular challenges. It is argued in literature that there is a need to understand whether and how managers in manufacturing SMEs have taken up the language and practice of planning, strategic analysis and execution. This research suggests a process and activity based approach to look at the practice of strategy management in SMEs in order to tackle this challenge. This exploratory study based on four comprehensive case studies investigates the strategy stories via exploring key strategic initiatives and activities, how they link together and which strategy tools, methods and techniques are used. This research concludes that a process based approach is useful and valid to understand strategy in SMEs because this view decomposes the process phases into activities which managers are more comfortable to talk through. However, we need to understand SME managers' language around strategising. There is an indication that if we change the language of SME managers, the findings of this study would map onto main stream strategy management theory clearly. It is found that the dynamics of the manufacturing SME strategy process have both emergent and planned dimensions. SME managers execute the strategy process mainly from an informal fashion by holding multiple functions and with limited application of strategy management methods and techniques. At an activity level, SMEs seem to be putting more emphasis on external environmental scanning (customers, suppliers, competitors, universities and lenders) and defining grand strategy and goals. This implies that SME strategy process is characterised by market based orientation, opportunity seeking and strategic awareness rather than resources or core competencies/ capabilities. Although this study's findings may be criticised because they are grounded on four companies, robust dimensions and insights into dynamics of the strategy process in manufacturing SMEs are achieved through saturation among emergent themes in data.

Self-amplifying RNA (saRNA) is a next-generation vaccine platform, but like all nucleic acids, requires a delivery vehicle to promote cellular uptake and protect the saRNA from degradation. To date, delivery platforms for saRNA have included lipid nanoparticles (LNP), polyplexes and cationic nanoemulsions; of these LNP are the most clinically advanced with the recent FDA approval of COVID-19 based-modified mRNA vaccines. While the effect of RNA on vaccine immunogenicity is well studied, the role of biomaterials in saRNA vaccine effectiveness is under investigated. Here, we tested saRNA formulated with either pABOL, a bioreducible polymer, or LNP, and characterized the protein expression and vaccine immunogenicity of both platforms. We observed that pABOL-formulated saRNA resulted in a higher magnitude of protein expression, but that the LNP formulations were overall more immunogenic. Furthermore, we observed that both the helper phospholipid and route of administration (intramuscular versus intranasal) of LNP impacted the vaccine immunogenicity of two model antigens (influenza hemagglutinin and SARS-CoV-2 spike protein). We observed that LNP administered intramuscularly, but not pABOL or LNP administered intranasally, resulted in increased acute interleukin-6 expression after vaccination. Overall, these results indicate that delivery systems and routes of administration may fulfill different delivery niches within the field of saRNA genetic medicines., Graphical abstract Unlabelled Image

We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backpropagation and the variational inference by backpropagation frameworks, in order to efficiently draw samples from the posterior distribution of transformation parameters. To address the modelling issues, we formulate a Bayesian model for image registration that overcomes the existing barriers when using a dense, high-dimensional, and diffeomorphic transformation parametrisation. This results in improved calibration of uncertainty estimates. We compare the model in terms of both image registration accuracy and uncertainty quantification to VoxelMorph, a state-of-the-art image registration model based on deep learning.

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model., Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org

International audience; The electrochemical reduction of CO2 to produce fuels and value-added organic chemicals is of great potential, providing a mechanism to convert and store renewable energy within a carbon-neutral energy circle. Currently the majority of studies report C-1 products such as carbon monoxide and formate as the major CO2 reduction products. A particularly challenging goal within CO2 electrochemical reduction is the pursuit of multi-carbon (C2+) products which have been proposed to enable a more economically viable value chain. This review summaries recent development across electro-, photoelectro- and bioelectro-catalyst developments. It also explores the role of device design and operating conditions in enabling C-C bond generation.

International audience; This paper applies metric-based mesh adaptation methods to advection-dominated tracer transport modelling problems in two and three dimensions, using the finite element package Firedrake. In particular, the mesh adaptation methods considered are built upon goal-oriented estimates for the error incurred in evaluating a diagnostic quantity of interest (QoI). In the motivating example of modelling to support desalination plant outfall design, such a QoI could be the salinity at the plant inlet, which could be negatively impacted by the transport of brine from the plant’s outfall. Four approaches are considered, one of which yields isotropic meshes. The focus on advection-dominated problems means that flows are often anisotropic; thus, three anisotropic approaches are also considered. Meshes resulting from each of the four approaches yield solutions to the tracer transport problem which give better approximations to QoI values than uniform meshing, for a given mesh size. The methodology is validated using an existing 2D tracer transport test case with a known analytical solution. Goal-oriented meshes for an idealised time-dependent desalination outfall scenario are also presented.

Background: Ultrasound (US) imaging is characterised by high levels of operator subjectivity and variability. Recent advances in artificial intelligence (AI) have demonstrated the potential to reduce both factors. This study pilots the end-to-end automation of multiple elements of the mid-trimester obstetric screening US scan using AI-enabled tools. Methods: A single centre, prospective method comparison study was conducted. Participants had both standard manual and AI-assisted US scans performed independently by different blinded sonographers. The AI-tools automated the acquisition of standard plane images, measurement of fetal biometrics and the production of a written clinical report with saved images available for review. A feedback survey captured the sonographers’ perception of scanning with both methods. Findings: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) when using the AI-assisted method (p

Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single `4 Chamber Heart' view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing)., Comment: MICCAI'21 Main Conference

Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals., Comment: Medical Image Analysis Volume 71 2021 https://doi.org/10.1016/j.media.2021.102062

Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo. Currently, it is still challenging to achieve high accuracy in localisation and tracking particularly with limited imaging frame rates and in the presence of high bubble concentrations. This study introduces microbubble image features into a Kalman tracking framework, and makes the framework compatible with sparsity-based deconvolution to address these key challenges. The performance of the method is evaluated on both simulations using individual bubble signals segmented from in vivo data and experiments on a mouse brain and a human lymph node. The simulation results show that the deconvolution not only significantly improves the accuracy of isolating overlapping bubbles, but also preserves some image features of the bubbles. The combination of such features with Kalman motion model can achieve a significant improvement in tracking precision at a low frame rate over that using the distance measure, while the improvement is not significant at the highest frame rate. The in vivo results show that the proposed framework generates SR images that are significantly different from the current methods with visual improvement, and is more robust to high bubble concentrations and low frame rates.

International audience; Sodium emissions during pulverized-coal combustion (PCC) are known to result in severeash-related operating issues of coal furnaces, e.g., fouling, slagging and corrosion. To relieve these issues and advance the clean utilization technologies of coal, a better understanding of thefundamental mechanisms driving the formation and transformation of the sodium species is required.In the present study, sodium emissions have been simulated in both one-dimensional (1D) premixed/diffusion flames of the coal volatile and an early-stage two-dimensional (2D)pulverized-coal flame. The properties of Loy Yang brown coal are used. The DRM22 skeletal mechanism is employed for volatile-gas combustion, and the reaction of sodium species is modeled by a detailed mechanism encompassing the elements Na, C, H, O, S and Cl. The compositions of the volatile fuels are obtained from the chemical percolation devolatilization (CPD) model, including CH4, C2H2, CO, H2, CO2 and H2O. The initial species of Na, Cl and S in the volatile gas is set to be NaOH, HCl and SO2, respectively. The transformation characteristics of 12 sodium species are investigated in both the 1D volatile flames and the 2D pulverized-coal flame. The response of the sodium chemistry to volatile-gas combustion is analyzed under fuel-lean, stoichiometric and fuel-rich conditions. Na, NaOH and NaCl are found to be the major sodium species during the combustion. Parametric studies with HCl, SO2 or both species removed from the volatile are then performed to investigate their effects on the sodium transformation characteristics in both the 1D and 2D flames. The results show that HCl has a much stronger ability to react with sodium species than SO2.

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multiscale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4 − 5 times.

International audience; We present a crystallographic and optoelectronic study of the double perovskite Cs2AgBi1-xInxBr6. From structural characterization we determine that the indium cation shrinks the lattice and shifts the cubic-to-tetragonal phase transition point to lower temperatures. The absorption onset is shifted to shorter wavelengths upon increasing the indium content, leading to wider band gaps, which we rationalize through first-principles band structure calculations. Despite the unfavorable band gap shift, we observe an enhancement in the steadystate photoluminescence intensity, and n-i-p photovoltaic devices present short-circuit current greater than that of neat Cs2AgBiBr6 devices. In order to evaluate the prospects of this material as a solar absorber, we combine accurate absorption measurements with thermodynamic modeling and identify the fundamental limitations of this system. Provided radiative efficiency can be increased and the choice of charge extraction layers are specifically improved, this material could prove to be a useful wide band gap solar absorber.

Rapid-response vaccine production platform technologies, including RNA vaccines, are being developed to combat viral epidemics and pandemics. A key enabler of rapid response is having quality-oriented disease-agnostic manufacturing protocols ready ahead of outbreaks. We are the first to apply the Quality by Design (QbD) framework to enhance rapid-response RNA vaccine manufacturing against known and future viral pathogens. This QbD framework aims to support the development and consistent production of safe and efficacious RNA vaccines, integrating a novel qualitative methodology and a quantitative bioprocess model. The qualitative methodology identifies and assesses the direction, magnitude and shape of the impact of critical process parameters (CPPs) on critical quality attributes (CQAs). The mechanistic bioprocess model quantifies and maps the effect of four CPPs on the CQA of effective yield of RNA drug substance. Consequently, the first design space of an RNA vaccine synthesis bioreactor is obtained. The cost-yield optimization together with the probabilistic design space contribute towards automation of rapid-response, high-quality RNA vaccine production.

MAX phases have garnered considerable research attention due to their unusual combination of metallic and ceramic properties that make them desirable materials especially in applications requiring extreme operating conditions. Zr-Al-C MAX phases specifically, are of particular interest in the nuclear industry where their low neutron absorption make them compelling candidates for fuel cladding materials. The synthesis of Zr-Al-C MAX phases, however, has been challenging, with the presence of impurities suggested as necessary to stabilise them [1, 2, 3] and secondary phases considered unavoidable in the reported successful synthesis of Zr$_2$AlC [4] and Zr$_3$AlC$_2$ [5]. This has led to questions as to whether the composition of MAX phases in this system is likely to change when in service. Addressing these uncertainties has been the main objective of this thesis, making the theoretical study of the thermodynamic stability of Zr$_{n+1}$AlC$_n$ of central importance. The stability of the Zr$_{n+1}$AlC$_n$ and closely related Ti$_{n+1}$AlC$_n$ MAX phases in the context of the M-A-X ternary phase diagrams and competing binary and ternary compounds, as a function of temperature, was calculated by applying density functional theory (DFT) within the quasiharmonic approximation. We found that the Zr-based MAX phases are thermodynamically unstable at room temperature, although Zr$_3$AlC$_2$ becomes stable above 500 K. Ti-based MAX phases on the other hand, show higher thermodynamic stability, with Ti$_2$AlC in particular, having the lowest formation energy of the MAX phases on the Ti-Al-C convex hull and appearing stable at all temperatures, in agreement with its reported success in synthesis. In the course of this work we also attempted to identify trends and similarities in predicted structural, elastic, thermophysical and electronic properties as well as the chemical bonding within the MAX phases in the two systems. Chemical bonding differences between the two systems, though, were not found to explain their differences in stability. Based on phonon calculations, Raman-active mode frequencies of Zr-based MAX phases and their most competing phases were also predicted, to assist in identifying phases present in a Zr$_3$AlC$_2$ synthesised sample [6]. Our predicted Zr$_3$AlC$_2$ frequencies of Raman-active modes were within 2% of peaks in the experimental Raman spectra recently measured by Lyons [6]. Open Access

International audience; A novel compact realization of the XNOR/XOR function is demonstrated with multimodal transistors (MMTs). The multimodal thin-film transistors (MMT's) structure allows efficient use of layout area in an implementation optimized for unipolar thin-film transistor (TFT) technologies, which may serve as a multipurpose element for conventional and emerging large-area electronics. Microcrystalline silicon device fabrication is complemented by physical simulations.

International audience; The importance of carbon capture and storage in mitigating climate change has emerged from the results of techno-economic or integrated assessment modeling, in which scenarios of future energy systems are developed subject to constraints from economic growth and climate change targets. These models rarely include limits imposed by injectivity, ultimate amounts, or the geographic distribution of storage resources. However, they could if a sufficiently simple model were available. We develop a methodology for the fast assessment of the dynamic storage resource of a reservoir under different scenarios of well numbers and interwell distance. The approach combines the use of a single-well multiphase analytical solution and the superposition of pressure responses to evaluate the pressure buildup in a multiwell scenario. The injectivity is directly estimated by means of a nonlinear relationship between flow-rate and overpressure and by imposing a limiting overpressure, which is evaluated on the basis of the mechanical parameters for failure. The methodology is implemented within a tool, named CO2BLOCK, which can optimise site design for the numbers of wells and spacing between wells. Given its small computational expense, the methodology can be applied to a large number of sites within a region. We apply this to analyse the storage potential in the offshore of the UK. We estimate that 25–250 GtCO2 can be safely stored over an injection time interval of 30 years. We also demonstrate the use of the tool in evaluating tradeoffs between infrastructure costs and maximising injectivity at two specific sites in the offshore UK.

Supporting Information is available online at https://pubs.acs.org/doi/10.1021/acs.langmuir.2c00201#_i26 . Copyright . The evolution of the liquid–vapor interface of a Lennard-Jones fluid is examined with molecular dynamics simulations using the intrinsic sampling method. Results suggest clear damping of the intrinsic profiles with increasing temperature. Investigating the surface stress distribution, we have identified a linear variation of the space-filling nature (fractal dimension) of the stress clusters at the intrinsic surface with increasing surface tension or, equivalently, with decreasing temperature. A percolation analysis of these stress networks indicates that the stress field is more disjointed at higher temperatures. This leads to more fragile (or poorly connected) interfaces which result in a reduction in surface tension. M.R.R. acknowledges Ph.D. studentship funding from Shell via the University Technology Centre for Fuels and Lubricants and the Beit Trust for the Beit Fellowship for Scientific Research. L.S. thanks the Engineering and Physical Sciences Research Council (EPSRC) for a Postdoctoral Fellowship (EP/V005073/1). J.P.E. was supported by the Royal Academy of Engineering through a Research Fellowship. D.D. thanks the EPSRC for an Established Career Fellowship (EP/N025954/1).

International audience; CRISPR guide RNAs (gRNAs) can be programmed with relative ease to allow the genetic editing of nearly any DNA or RNA sequence. Here, we propose novel molecular architectures to achieve RNA-dependent modulation of CRISPR activity in response to specific RNA molecules. We designed and tested, in both living Escherichia coli cells and cell-free assays for rapid prototyping, cis-repressed RNA-interacting guide RNA (igRNA) that switch to their active state only upon interaction with small RNA fragments or long RNA transcripts, including pathogen-derived mRNAs of medical relevance such as the human immunodeficiency virus infectivity factor. The proposed CRISPR-igRNAs are fully customizable and easily adaptable to the majority if not all the available CRISPR-Cas variants to modulate a variety of genetic functions in response to specific cellular conditions, providing orthogonal activation and increased specificity. We thereby foresee a large scope of application for therapeutic, diagnostic, and biotech applications in both prokaryotic and eukaryotic systems.

Transition metal dichalcogenides (TMDs) have shown outstanding semiconducting properties which make them promising materials for next-generation optoelectronic and electronic devices. These properties are imparted by fundamental carrier–carrier and carrier–phonon interactions that are foundational to hot carrier cooling. Recent transient absorption studies have reported ultrafast time scales for carrier cooling in TMDs that can be slowed at high excitation densities via a hot-phonon bottleneck (HPB) and discussed these findings in the light of optoelectronic applications. However, quantitative descriptions of the HPB in TMDs, including details of the electron–lattice coupling and how cooling is affected by the redistribution of energy between carriers, are still lacking. Here, we use femtosecond pump–push–probe spectroscopy as a single approach to systematically characterize the scattering of hot carriers with optical phonons, cold carriers, and defects in a benchmark TMD monolayer of polycrystalline WS2. By controlling the interband pump and intraband push excitations, we observe, in real-time (i) an extremely rapid “intrinsic” cooling rate of ∼18 ± 2.7 eV/ps, which can be slowed with increasing hot carrier density, (ii) the deprecation of this HPB at elevated cold carrier densities, exposing a previously undisclosed role of the carrier–carrier interactions in mediating cooling, and (iii) the interception of high energy hot carriers on the subpicosecond time scale by lattice defects, which may account for the lower photoluminescence yield of TMDs when excited above band gap.

Semiconducting polymer nanoparticles (SPNs) are explored for applications in cancer theranostics because of their high absorption coefficients, photostability, and biocompatibility. However, SPNs are susceptible to aggregation and protein fouling in physiological conditions, which can be detrimental for in vivo applications. Here, a method for achieving colloidally stable and low-fouling SPNs is described by grafting poly(ethylene glycol) (PEG) onto the backbone of the fluorescent semiconducting polymer, poly(9,9′-dioctylfluorene-5-fluoro-2,1,3-benzothiadiazole), in a simple one-step substitution reaction, postpolymerization. Further, by utilizing azide-functionalized PEG, anti-human epidermal growth factor receptor 2 (HER2) antibodies, antibody fragments, or affibodies are site-specifically “clicked” onto the SPN surface, which allows the functionalized SPNs to specifically target HER2-positive cancer cells. In vivo, the PEGylated SPNs are found to have excellent circulation efficiencies in zebrafish embryos for up to seven days postinjection. SPNs functionalized with affibodies are then shown to be able to target HER2 expressing cancer cells in a zebrafish xenograft model. The covalent PEGylated SPN system described herein shows great potential for cancer theranostics.

This paper summarizes the submissions to a recently announced contact-mechanics modeling challenge. The task was to solve a typical, albeit mathematically fully defined problem on the adhesion between nominally flat surfaces. The surface topography of the rough, rigid substrate, the elastic properties of the indenter, as well as the short-range adhesion between indenter and substrate, were specified so that diverse quantities of interest, e.g., the distribution of interfacial stresses at a given load or the mean gap as a function of load, could be computed and compared to a reference solution. Many different solution strategies were pursued, ranging from traditional asperity-based models via Persson theory and brute-force computational approaches, to real-laboratory experiments and all-atom molecular dynamics simulations of a model, in which the original assignment was scaled down to the atomistic scale. While each submission contained satisfying answers for at least a subset of the posed questions, efficiency, versatility, and accuracy differed between methods, the more precise methods being, in general, computationally more complex. The aim of this paper is to provide both theorists and experimentalists with benchmarks to decide which method is the most appropriate for a particular application and to gauge the errors associated with each one.

Summary This paper is concerned with nonparametric estimation of the intensity function of a point process on a Riemannian manifold. It provides a first-order asymptotic analysis of the proposed kernel estimator for Poisson processes, supplemented by empirical work to probe the behaviour in finite samples and under other generative regimes. The investigation highlights the scope for finite-sample improvements by allowing the bandwidth to adapt to local curvature.

We demonstrate a novel and versatile sensing platform, based on electrolyte-gated graphene field-effect transistors, for easy, low-cost and scalable production of chemical sensor test strips. The Lab-on-PCB platform is enabled by low-boiling, low-surface-tension sprayable graphene ink deposited on a substrate manufactured using a commercial printed circuit board process. We demonstrate the versatility of the platform by sensing pH and Na+ concentrations in an aqueous solution, achieving a sensitivity of 143 ± 4 μA per pH and 131 ± 5 μA per log10Na+, respectively, in line with state-of-the-art graphene chemical sensing performance.

Peer reviewed: True, Funder: Leverhulme Trust, Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ± 62.8 μgm−3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ± 20.4 μgm−3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm−3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ± 61 ppm) and the pub (bar) (744 ± 136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.

We study several Fokker-Planck equations arising from a stochastic chemical kinetic system modeling a gene regulatory network in biology. The densities solving the Fokker-Planck equations describe the joint distribution of the messenger RNA and micro RNA content in a cell. We provide theoretical and numerical evidences that the robustness of the gene expression is increased in the presence of micro RNA. At the mathematical level, increased robustness shows in a smaller coefficient of variation of the marginal density of the messenger RNA in the presence of micro RNA. These results follow from explicit formulas for solutions. Moreover, thanks to dimensional analyses and numerical simulations we provide qualitative insight into the role of each parameter in the model. As the increase of gene expression level comes from the underlying stochasticity in the models, we eventually discuss the choice of noise in our models and its influence on our results., Comment: Minor revisions

The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

A new method for industrial radiography is presented to assess image quality objectively. The assessment is performed by a modelled observer developed to interpret radiographic images in order to rate the detectability of structural defects. For the purpose of qualifying radiographic NDE procedures, computational tools simulate the image, but should additionally automatically assess the associated image quality instead of relying on human interpretation. The Channelized Hotelling Model Observer (CHO) approach, originally developed for medical imaging, is here developed for industrial NDE applications to measure objectively the defect's detectability. A validation study based on a comparison of the model's efficiency of observing circular and elongated flaws shows that the CHO outperforms other detectability models used by industry. Furthermore, the model's reliability was verified by comparing it to psychophysical data.

Generating 2D noise with local, space-varying spectral characteristics is vital where random noise fields with spatially heterogeneous statistical proper-ties are observed and need to be simulated. A realistic, non-stationary noise generator relying on experimental data is presented. That generator is desired in areas such as photography and radiography. For example, before performing actual X-ray imaging in practice, output imag-es are simulated to assess and improve setups. For that purpose, realistic film noise modelling is crucial because noise downgrades the detectability of visual signals. The presented film noise synthesiser improves the realism and value of radiographic simulations significantly, allowing more realistic assessments of radiographic test setups. The method respects space-varying spectral characteristics and probability distributions, locally simulating noise with re-alistic granularity and contrast. The benefits of this ap-proach are to respect the correlation between noise and image as well as internal correlation, the fast generation of any number of unique noise samples, the exploitation of real experimental data, and its statistical non-stationarity. The combination of these benefits is not available in exist-ing work. Validation of the new technique was undertaken in the field of industrial radiography. While applied to that field here, the technique is general and can also be utilised in any other field where the generation of 2D noise with local, space-varying statistical properties is necessary.

In a recent article, we showed how the properties of the density-density correlation function and its integral, the local structure factor, in the fluid interfacial region, in systems with short-ranged forces, can be understood microscopically by considering the resonances of the local structure factor [Nat.Phys.~{\bf 15}, 287 (2019)]. Here we illustrate, using mean-field square-gradient theory and the more microscopic Sullivan density functional model, how this approach generalises when there is liquid-gas asymmetry, i.e. when the bulk correlation lengths of the coexisting liquid and gas phases are different. In particular, we are able to express the correlation function \textit{exactly} as a simple average of contributions arising from two effective Ising-symmetric systems referred to as the symmetric gas and symmetric liquid. When combined with our earlier results, this generates analytical approximations for the correlation function and the local structure factor, which are near indistinguishable from the numerical solution to the Ornstein-Zernike equations over the whole range of wave-vectors. Our results highlight how asymmetry affects the correlation function structure, and describes the crossover from a long-ranged Goldstone mode to short-ranged properties determined by the local density, as the wave-vector increases., Comment: 12 pages, 7 figures

Mechanochemistry is known to play a key role in the function of some lubricant additives, such as the tribofilm growth of zinc dialkyldithiophosphate (ZDDP). This raises the intriguing possibility of tailoring the mechanochemical response of additives by modifying their alkyl substituents. Here, we study the tribofilm formation rate of ZDDPs containing several different alkyl groups on steel surfaces from a high-friction base oil. We use macroscale tribometer experiments under full-film elastohydrodynamic lubrication conditions to enable careful control of the temperature and stress during tribofilm growth. We show how the chain length and the presence of branches or bulky cycloaliphatic groups can lead to large differences in the temperature- and stress-dependencies of the tribofilm formation rate, which can be explained through variations in packing density, steric hindrance, and stress transmission efficiency. Our rate data are successfully fitted using the Bell model; a simple modification of the Arrhenius equation that is commonly employed to model the kinetics of mechanochemical processes. Using this model, we observe large differences in the activation energy, pre-exponential factor, and activation volume for the various ZDDPs. Our findings show how structure–performance relationships can be identified for lubricant additives, which may be useful to optimise their molecular structure.

We present a coarse-grained molecular model of the surface of human hair, which consists of a supported lipid monolayer, in the MARTINI framework. Using coarse-grained molecular dynamics (MD) simulations, we identify a lipid grafting distance that yields a monolayer thickness consistent with both atomistic MD simulations and experimental measurements of the hair surface. Coarse-grained models for fully-functionalised, partially damaged, and fully damaged hair surfaces are created by randomly replacing neutral thioesters with anionic sulfonate groups. This mimics the progressive removal of fatty acids from the hair surface by bleaching and leads to chemically heterogeneous surfaces. Using molecular dynamics (MD) simulations, we study the island structures formed by the lipid monolayers at different degrees of damage in vacuum and in the presence of polar (water) and non-polar (n-hexadecane) solvents. We also use MD simulations to compare the wetting behaviour of water and n-hexadecane droplets on the model surfaces through contact angle measurements, which are compared to experiments using virgin and bleached hair. The model surfaces capture the experimentally-observed transition of the hair surface from hydrophobic (and oleophilic) to hydrophilic (and oleophobic) as the level of bleaching damage increases. By selecting surfaces with specific damage ratios, we obtain contact angles from the MD simulations that are in good agreement with experiments for both solvents on virgin and bleached human hairs. To negate the possible effects of microscale curvature and roughness of real hairs on wetting, we also conduct additional experiments using biomimetic surfaces that are co-functionalised with fatty acids and sulfonate groups. In both the MD simulations and experiments, the cosine of the water contact angle increases linearly with the sulfonate group surface coverage with a similar slope. We expect that the proposed systems will be useful for future molecular dynamics simulations of the adsorption and tribological behaviour of hair, as well as other chemically heterogeneous surfaces.

This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system. The grey-box enables participation analysis in impedance models, which is popular in power electronics and increasingly accepted in power systems for stability analysis. The Impedance participation factor is proposed and defined in terms of the residue of the whole-system admittance matrix. It is proved that, the so defined impedance participation factor equals the sensitivity of the whole-system eigenvalue with respect to apparatus impedance. The classic state participation factor is related to the impedance participation factor via a chain-rule. Based on the chain-rule, a three-layer grey-box approach, with three degrees of transparency, is proposed for root-cause tracing to different depths, i.e. apparatus, states, and parameters, according to the available information. The association of impedance participation factor with eigenvalue sensitivity points to the re-tuning that would stabilize the system. The impedance participation factor can be measured in the field or calculated from the black-box impedance spectra with little prior knowledge required.

Self-doping organic semiconductors provide a promising route to avoid instabilities and morphological issues associated with molecular n-type dopants. Structural characterization of a naphthalenetetracarboxylic diimide (NDI) semiconductor covalently bound to an ammonium hydroxide group is presented. The dopant precursor was found to be the product of an unexpected base catalyzed hydrolysis, which was reversible. The reversible hydrolysis had profound consequences on the chemical composition, morphology, and electronic performance of the doped films. In addition, we investigated the degradation mechanism of the quaternary ammonium group and the subsequent doping of NDI. These findings reveal that the products of more than one chemical reaction during processing of films must be considered when utilizing this promising class of water-soluble semiconductors.

Social network-based information campaigns can be used for promoting beneficial health behaviours and mitigating polarization (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals’ attributes (e.g. age, income), which are jointly informative of an individual’s opinions and their social network position. We investigate strategies for influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data-based examples we illustrate the advantages of implementing coarse-grained influence strategies on Ising models with modular structure in the presence of external fields. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of efficiently computing and implementing public information campaigns using insights from social network theory without costly or invasive levels of data collection.

Printed electronics using inks based on graphene and other two-dimensional materials can be used to create large-scale, flexible, and wearable devices. However, the complexity of the ink formulations, and the polycrystalline nature of the resulting thin films, have made it difficult to examine charge transport in such devices. Here we report the charge transport mechanisms of surfactant- and solvent-free inkjet-printed thin-film devices based on few-layer graphene (semi-metal), molybdenum disulfide (MoS2, semiconductor) and titanium carbide MXene (Ti3C2, metal) by investigating the temperature, gate and magnetic field dependencies of their electrical conductivity. We find that charge transport in printed few-layer MXene and MoS2 devices is dominated by the intrinsic transport mechanism of the constituent flakes: MXene exhibits a weakly-localized 2D metallic behaviour at any temperature, whereas MoS2 behaves as an insulator with a crossover from 3D-Mott variable-range hopping to nearest-neighbour hopping around 200 K. Charge transport in printed few-layer graphene devices is dominated by the transport mechanism between different flakes, which exhibit 3D-Mott variable range hopping conduction at any temperature.

We present Hipernetch, a novel FPGA-based design for performing high-bandwidth network switching. FPGAs have recently become more popular in data centers due to their promising capabilities for a wide range of applications. With the recent surge in transceiver bandwidth, they could further benefit the implementation and refinement of network switches used in data centers. Hipernetch replaces the crossbar with a “combined parallel round-robin arbiter”. Unlike a crossbar, the combined parallel round-robin arbiter is easy to pipeline, and does not require centralised iterative scheduling algorithms that try to fit too many steps in a single or a few FPGA cycles. The result is a network switch implementation on FPGAs operating at a high frequency and with a low port-to-port latency. Our proposed Hipernetch architecture additionally provides a competitive switching performance approaching output-queued crossbar switches. Our implemented Hipernetch designs exhibit a throughput that exceeds 100 Gbps per port for switches of up to 16 ports, reaching an aggregate throughput of around 1.7 Tbps.

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance.

This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats each regression coefficient in turn as the interest parameter, the remaining coefficients being nuisance parameters, and seeks an optimal interest-respecting transformation, inducing sparsity on the relevant blocks of the notional Fisher information matrix. The induced sparsity is exploited through a marginal least-squares analysis for each variable, as in a factorial experiment, thereby avoiding penalization. One parameterization of the problem is found to be particularly convenient, both computationally and mathematically. In particular, it permits an analytic solution to the optimal transformation problem, facilitating theoretical analysis and comparison to other work. In contrast to regularized regression, such as the lasso and its extensions, neither adjustment for selection nor rescaling of the explanatory variables is needed, ensuring the physical interpretation of regression coefficients is retained. Recommended usage is within a broader set of inferential statements, so as to reflect uncertainty over the model as well as over the parameters. The considerations involved in extending the work to other regression models are briefly discussed.

Ni-Mn-Ga based alloys are widely studied due to their potential for practical applications, making use of their martensitic transformations. However, hysteresis is a long-standing drawback that reduces the chance of transferring these alloys from the laboratory to industry. In this work, we studied a Cr-doped Ni2.15Mn0.70Cr0.15Ga alloy. We arrived at this composition by integrating data obtained from the previous phase and hysteresis diagrams taken from the literature. The compound presents a magnetostructural transition at room temperature, with a ferromagnetic martensite phase, and moderate thermal hysteresis of approximately 4 K. The magnetocaloric and ferromagnetic shape memory effects were explored, showing a reversible entropy change of approximately 9 Jkg-1K-1 under 0–5 T field change, and a cyclical magnetic-field-induced deformation close to 0.81% under 0–9 T, an advance towards high reversibility for this family alloys.

This paper investigates the effects of different inlet flow profiles on thermal mixing within a T-junction using CFD simulations with the IDDES-SST turbulence model. The different combinations of inlet flow profiles are related to different stage in the flow entry region. The effects of the inlet flow profile on the mean and transient flow behaviour are assessed, while a spectral proper orthogonal decomposition and power spectral density analysis are performed to assess the underlying flow structures and the predominant frequency modes. It is found that the vortical structures associated with the horseshoe and hovering vortex systems consist of a single roll-up vortex for cases with uniformly distributed boundary conditions (BCs) at the branch inlet whereas a double roll-up vortex is observed for the other cases. The double roll-up vortex enhances the mixing locally due to the entrainment of fluid from the branch pipe in these vortical structures, which then results in a lower mean temperature distribution. The appearance of the secondary vortex pair and the nested vortices is delayed for cases with uniformly distributed BCs at the branch inlet, which again results in lower thermal mixing and consequently higher values of mean temperature when compared with the other cases. It is also found that the vorticity related to the counter-rotating vortex pair as well as to the second pair of vortices rotating in the opposite direction is higher for cases with uniformly distributed BCs at the branch inlet. Lastly, the combinations of inlet flow profiles lead to different coherent structures, and the dominant frequencies are of a Strouhal number of around 0.7 for uniformly distributed profiles at the branch inlet and in the range 0.4–0.5 for the other cases.

The main objective of this study is to compare and optimize two power-to-gas energy storage systems from a thermo-economic perspective. The first system is based on a solid oxide electrolyzer cell (SOEC) combined with a methanation reactor, and the second is based on a polymer electrolyte membrane electrolyzer cell (PEMEC) integrated into a Sabatier reactor. The first system relies on the co-electrolysis of steam and carbon dioxide followed by methanation, whereas the basis of the second system is hydrogen production and conversion into methane via a Sabatier reaction. The systems are also analyzed for being applied in different countries and being fed by different renewable and non- renewable power sources. Simulation results of both systems were compared with similar studies from the literature; the errors were negligible, acknowledging the reliability and accuracy of the simulations. The results reveal that for the same carbon dioxide availability (i.e., flow rate), the SOEC-based system has higher exergy and power-to-gas efficiencies, and lower electricity consumption compared to the PEMEC-based system. However, the PEMEC-based system produces 1.2 % more methane, also with a lower heating value (LHV) of the generated gas mixture that is 7.6 % higher than that of the SOEC-based system. Additionally, the levelized cost of energy (based on the LHV) of the SOEC-based system is found to be 11 % lower. A lifecycle analysis indicates that the lowest lifecycle cost is attained when solar PV systems are employed as the electricity supply option. Eventually, the SOEC-based system is found to be more attractive for power-to-gas purposes from a thermo-economic standpoint.

There is an increased need and focus to understand how local brain microstructure affects the transport of drug molecules directly administered to the brain tissue, for example in convection-enhanced delivery procedures. This study reports a systematic attempt to characterize the cytoarchitecture of commissural, long association and projection fibres, namely the corpus callosum, the fornix and the corona radiata, with the specific aim to map different regions of the tissue and provide essential information for the development of accurate models of brain biomechanics. Ovine samples are imaged using scanning electron microscopy combined with focused ion beam milling to generate 3D volume reconstructions of the tissue at subcellular spatial resolution. Focus is placed on the characteristic cytological feature of the white matter: the axons and their alignment in the tissue. For each tract, a 3D reconstruction of relatively large volumes, including a significant number of axons, is performed and outer axonal ellipticity, outer axonal cross-sectional area and their relative perimeter are measured. The study of well-resolved microstructural features provides useful insight into the fibrous organization of the tissue, whose micromechanical behaviour is that of a composite material presenting elliptical tortuous tubular axonal structures embedded in the extra-cellular matrix. Drug flow can be captured through microstructurally-based models using 3D volumes, either reconstructed directly from images or generated in silico using parameters extracted from the database of images, leading to a workflow to enable physically-accurate simulations of drug delivery to the targeted tissue.

This paper investigates the model-based nonlinear control of a class of soft continuum pneumatic manipulators that bend due to pressurization of their internal chambers and that operate in the presence of disturbances. A port-Hamiltonian formulation is employed to describe the closed loop system dynamics, which includes the pressure dynamics of the pneumatic actuation, and new nonlinear control laws are constructed with an energy-based approach. In particular, a multi-step design procedure is outlined for soft continuum manipulators operating on a plane and in 3D space. The resulting nonlinear control laws are combined with adaptive observers to compensate the effect of unknown disturbances and model uncertainties. Stability conditions are investigated with a Lyapunov approach, and the effect of the tuning parameters is discussed. For comparison purposes, a different control law constructed with a backstepping procedure is also presented. The effectiveness of the control strategy is demonstrated with simulations and with experiments on a prototype. To this end, a needle valve operated by a servo motor is employed instead of more sophisticated digital pressure regulators. The proposed controllers effectively regulate the tip rotation of the prototype, while preventing vibrations and compensating the effects of disturbances, and demonstrate improved performance compared to the backstepping alternative and to a PID algorithm.

Aims In the native hip, the hip capsular ligaments tighten at the limits of range of hip motion and may provide a passive stabilizing force to protect the hip against edge loading. In this study we quantified the stabilizing force vectors generated by capsular ligaments at extreme range of motion (ROM), and examined their ability to prevent edge loading. Methods Torque-rotation curves were obtained from nine cadaveric hips to define the rotational restraint contributions of the capsular ligaments in 36 positions. A ligament model was developed to determine the line-of-action and effective moment arms of the medial/lateral iliofemoral, ischiofemoral, and pubofemoral ligaments in all positions. The functioning ligament forces and stiffness were determined at 5 Nm rotational restraint. In each position, the contribution of engaged capsular ligaments to the joint reaction force was used to evaluate the net force vector generated by the capsule. Results The medial and lateral arms of the iliofemoral ligament generated the highest inbound force vector in positions combining extension and adduction providing anterior stability. The ischiofemoral ligament generated the highest inbound force in flexion with adduction and internal rotation (FADIR), reducing the risk of posterior dislocation. In this position the hip joint reaction force moved 0.8° inbound per Nm of internal capsular restraint, preventing edge loading. Conclusion The capsular ligaments contribute to keep the joint force vector inbound from the edge of the acetabulum at extreme ROM. Preservation and appropriate tensioning of these structures following any type of hip surgery may be crucial to minimizing complications related to joint instability. Cite this article: Bone Joint Res 2021;10(9):594–601.

This work investigates the tracking control problem for underactuated mechanical systems. To this end, we develop an extension of the dynamic tube Model Predictive Control (MPC) approach by combining an MPC design, an ancillary energy shaping controller constructed with the Interconnection and Damping Assignment Passivity-Based Control methodology, and an analytical expression of the dynamic tube. In addition, we extend the proposed approach by including the adaptive compensation of a class of unknown disturbances. The stability analysis is presented by employing a Lyapunov approach. The effectiveness of the proposed controller is demonstrated with simulations on two underactuated systems: a two-mass-spring-damper system with uncertain damping and either linear or nonlinear spring; an inertia-wheel-pendulum with unmodeled disturbances.

Understanding friction at diamond–rock interfaces is crucial to increase the energy efficiency of drilling operations. Harder rocks usually are usually more difficult to drill; however, poor performance is often observed for polycrystalline diamond compact (PDC) bits on soft calcitecontaining rocks, such as limestone. Using macroscale tribometer experiments with a diamond tip, we show that soft limestone rock (mostly calcite) gives much higher friction coefficients compared to hard granite (mostly quartz) in both humid air and aqueous environments. To uncover the physicochemical mechanisms that lead to higher kinetic friction at the diamond–calcite interface, we employ nonequilibrium molecular dynamics simulations (NEMD) with newly developed Reactive Force Field (ReaxFF) parameters. In the NEMD simulations, higher friction coefficients are observed for calcite than quartz when water molecules are included at the diamond–rock interface. We show that the higher friction in water-lubricated diamond–calcite than diamond–quartz interfaces is due to increased interfacial bonding in the former. For diamond–calcite, the interfacial bonds mostly form through chemisorbed water molecules trapped between the tip and the substrate, while mainly direct tip-surface bonds form inside diamond–quartz contacts. For both rock types, the rate of interfacial bond formation increases exponentially with pressure, which is indicative of a stress-augmented thermally activated process. The mean friction force is shown to be linearly dependant on the mean number of interfacial bonds during steady-state sliding. The agreement between the friction behaviour observed in the NEMD simulations and tribometer experiments suggests that interfacial bonding also controls diamond–rock friction at the macroscale. We anticipate that the improved fundamental understanding provided by this study will assist in the development of bit materials and coatings to minimise friction by reducing diamond–rock interfacial bonding

Concentrating fluid-based spectral-splitting hybrid PV-thermal (SSPVT) collectors are capable of high electrical and thermal efficiencies, as well as high-temperature thermal outputs. However, the optimal optical filter and the maximum potential of such collectors remain unclear. In this study, we develop a comprehensive two-dimensional model of a fluid-based SSPVT collector. The temperature distributions reveal that these designs are effective in thermally decoupling the PV module from the high-temperature filter flow-channel, improving the electrical performance of the module. For a Si solar cell-based SSPVT collector with optical filter #Si400-1100, the filter channel is able to produce high-temperature thermal energy (400 °C) with an efficiency of 19.5%, low-temperature thermal energy (70 °C) with an efficiency of 49.5%, and electricity with an efficiency 17.5%. Of note is that the relative fraction of high-temperature thermal energy, low-temperature thermal energy and electricity generated by such a SSPVT collector can be adjusted by shifting the upper- and lower-bound cut-off wavelengths of the optical filter, which are found to strongly affect the spectral and energy distributions through the collector. The optimal upper-bound cut-off always equals the bandgap wavelength of the solar cell material (e.g., 1100 nm for Si, and 850 nm for CdTe), while the optimal lower-bound cut-off follows more complex selection criteria. The SSPVT collector with the optimal filter has a significantly higher total effective efficiency than an equivalent conventional solar-thermal collector when the relative value of the high-temperature heat to that of electricity is lower than 0.5. Detailed guidance for selecting optimal filters and their role in controlling SSPVT collector performance under different conditions is provided.