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In this exploratory study, we studied and qualitatively evaluated a prototype video data collection system to capture and analyze fish behavior in a small-scale indoor aquaculture operation. The research objective was to design and develop a hardware / software system that would have the potential to capture meaningful data from which to extract fish size, swim trajectory, and swim velocity, ultimately as information toward an assessment of fish health. The initial work presented in this paper discusses the development choices of the prototype system, including various combinations of lighting and camera positions both inside and outside of the aquaculture tanks, and several post-processing techniques to isolate fish in video, calibrate the distance from camera to fish through water, and infer fish trajectories and swim velocities. Preliminary results provided a qualitative assessment of such a system. Specific results on the system’s ability to detect fishes’ positions, trajectories, and velocities are presently limited to observational outcomes and descriptive statistics rather than large-scale quantitative analysis. The present work lays a foundation for a future commercially hardened system that would be required for the collection of larger datasets, which would in turn facilitate the future development of machine learning (ML) algorithms to begin to statistically correlate data to fish conditions and behaviors in near-real time.

Implications for the academic and interpersonal development of children and adolescents underpin a global political consensus to maintain in-classroom teaching during the ongoing COVID-19 pandemic. In support of this aim, the WHO and UNICEF have called for schools around the globe to be made safer from the risk of COVID-19 transmission. Detailed guidance is needed on how this goal can be successfully implemented in a wide variety of educational settings in order to effectively mitigate impacts on the health of students, staff, their families, and society. This review provides a comprehensive synthesis of current scientific evidence and emerging standards in relation to the use of layered prevention strategies (involving masks, distancing, and ventilation), setting out the basis for their implementation in the school environment. In the presence of increasingly infectious SARS-Cov-2 variants, in-classroom teaching can only be safely maintained through a layered strategy combining multiple protective measures. The precise measures that are needed at any point in time depend upon a number of dynamic factors, including the specific threat-level posed by the circulating variant, the level of community infection, and the political acceptability of the resultant risk. By consistently implementing appropriate prophylaxis measures, evidence shows that the risk of infection from in-classroom teaching can be dramatically reduced. Current studies indicate that wearing high-quality masks and regular testing are amongst the most important measures in preventing infection transmission; whilst effective natural and mechanical ventilation systems have been shown to reduce infection risks in classrooms by over 80%.

The electric potential, electric field, and charge density of a monolayer of MoS2 have been quantitatively measured at atomic-scale resolution. This has been performed by off-axis electron holography using a double aberration-corrected transmission electron microscope operated at 80 kV and a low electron beam current density. Using this low dose rate and acceleration voltage, the specimen damage is limited during imaging. In order to improve the sensitivity of the measurement, a series of holograms have been acquired. Instabilities of the microscope such as the drifts of the specimen, biprism, and optical aberrations during the acquisition have been corrected by data processing. Phase images of the MoS2 monolayer have been acquired with a sensitivity of 2π/698 rad associated with a spatial resolution of 2.4 A. The improvement in the signal-to-noise ratio allows the charge density to be directly calculated from the phase images using Poisson's equation. Density functional theory simulations of the potential and charge density of this MoS2 monolayer were performed for comparison to the experiment. The experimental measurements and simulations are consistent with each other, and notably, the charge density in a sulfur monovacancy (VS) site is shown.

3D printing is transforming traditional processing methods for applications ranging from tissue engineering to optics. To fulfill its maximum potential, 3D printing requires a robust technique for producing structures with precise three-dimensional (x, y and z) control of mechanical properties. Previous efforts to realize such spatial control of modulus within 3D printed parts have largely focused on low-resolution (mm to cm scale) multi-material processes and grayscale approaches that spatially vary the modulus in the x-y plane and energy dose-based (E = I(0) t(exp)) models that do not account for the resin’s sub-linear response to irradiation intensity. Here, we demonstrate a novel approach for through-thickness (z) voxelated control of mechanical properties within a single-material, monolithic part. Control over the local modulus is enabled by a predictive model that incorporates the observed non-reciprocal dose response of the material. The model is validated by an application of atomic force microscopy to map the through-thickness modulus on multi-layered 3D parts. Overall, both smooth gradations (30 MPa change over ≈75 μm) and sharp step-changes (30 MPa change over ≈5 μm) in modulus are realized in poly(ethylene glycol) diacrylate based 3D constructs, paving the way for advancements in tissue engineering, stimuli-responsive 4D printing and graded metamaterials.

Implantable electrophoretic drug delivery devices have shown promise for applications ranging from treating pathologies such as epilepsy and cancer to regulating plant physiology. Upon applying a voltage, the devices electrophoretically transport charged drug molecules across an ion‐conducting membrane out to the local implanted area. This solvent‐flow‐free “dry” delivery enables controlled drug release with minimal pressure increase at the outlet. However, a major challenge these devices face is limiting drug leakage in their idle state. Here, a method of reducing passive drug leakage through the choice of the drug co‐ion is presented. By switching acetylcholine's associated co‐ion from chloride to carboxylate co‐ions as well as sulfopropyl acrylate‐based polyanions, steady‐state drug leakage rate is reduced up to sevenfold with minimal effect on the active drug delivery rate. Numerical simulations further illustrate the potential of this method and offer guidance for new material systems to suppress passive drug leakage in electrophoretic drug delivery devices., Drug leakage is a safety concern for long‐term implantable electrophoretic drug delivery devices. In this work, co‐ion engineering is proposed as a clinically viable method to drastically reduce drug leakage rate, without any sacrifices on the device active performance. The co‐ion engineering method is drug‐independent and can be readily applied to existing electrophoretic drug delivery devices.

Photopolymers formed from the family of thiol-X “click” reactions notably form sulfur-containing thioether linkages. While sulfur-containing materials are conventionally expected to result in high ...

A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that whilst GAMs are capable of improving the forecasting accuracy, the improvements are significant only up to 3–6 h ahead. During heatwaves and over longer forecasting horizons, GAMs were found to be less reliable and accurate than ARX models. The marginal improvement in forecasting accuracy at shorter horizons did not justify the additional computational time and risk of instability associated with more complex GAMs, at longer forecasting horizons. Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions.

Organic electrochemical transistors (OECTs) are thin-film transistors that have shown great promise in a range of applications including biosensing, logic circuits, and neuromorphic engineering. The device physics of OECTs are determined by the interaction between ionic and electronic charge carriers. This interaction sets OECTs apart from conventional transistor technologies and has necessitated the development of device models for the unique behavior of OECTs. In this Review, we discuss existing models for OECTs and provide a framework for understanding these models. Moreover, we show how the insight from these models inform device optimization. Finally, we discuss details of OECT operation that are not well-understood and that provide exciting opportunities for future research.

With high contrast optical images, it is possible to write sub-diffraction limited features over large areas. The dimensions of these super-resolved lithographic features are a function of optical contrast and total dose. Writing uniform subwavelength features requires uniform dose, and therefore uniform recording intensity. Patterns written with Gaussian beams exhibit large linewidth variation within each exposure field, and we mitigate this by incorporating a refractive beam shaper. A beam shaper does improve uniformity; however, intensity noise remains an issue. To mitigate intensity noise, we show that it is possible to scan the beam while recording without washing out the interference pattern. This is accomplished with an interferometer that incorporated a diffraction grating as a beam splitter. To first order, this design is insensitive to beam pointing. That is, as pointing angle changes, the interference fringe positions remain fixed while the beam position moves. Scanning over the resist averages out intensity noise, dramatically improving uniformity. We demonstrate both experimentally and with models, linewidth variation across a field written with different recording strategies. Linewidth from a Gaussian recording beam varies by 32% center to beam waist. With the inclusion of refractive beam shaping, linewidth uniformity can be brought down to 13%. By incorporating scanning with beam shaping, linewidth uniformity can be further reduced to as low as 4%.

This work presents a modelling and simulation study of academic hiring policies to address equity, diversity, and inclusion (EDI) in an engineering program. An agent-based modelling approach is used to investigate the comparative impacts of various EDI hiring interventions on outputs including the time associated with achieving target representation of underrepresented groups, average value (‘qualification score’) of new hires, and number of positions that the best qualified overrepresented group applicant applies for before being hired. The simulation results demonstrate that the time constants for cultural change are long even with proposals that may be considered radical. Also, the simulation results do not support a common argument that EDI initiatives will sacrifice excellence in faculty hiring.

This work presents a redeveloped introductory course in Digital Systems Design, often considered a course on embedded systems and IoT in an undergraduate computer engineering program at a Canadian university. The course was moved from a theory-driven course to a team project-based course built on a framework of sustainable design, design for accessibility, and equity principles for deaf curling athletes. Students were guided through a modified design cycle, from conceptual design to a functional prototype and physical prototype. In addition, the course included a strong emphasis on allied topics for knowledge transfer, including business development, technology marketing, intellectual property protection, and moving toward commercialization.

Volumetric additive manufacturing (VAM) enables rapid printing into a wide range of materials, offering significant advantages over other printing technologies, with a lack of inherent layering of particular note. However, VAM suffers from striations, similar in appearance to layers, and similarly limiting applications due to mechanical and refractive index inhomogeneity, surface roughness, etc. We hypothesize that these striations are caused by a self-written waveguide effect, driven by the gelation material nonlinearity upon which VAM relies, and that they are not a direct recording of non-uniform patterning beams. We demonstrate a simple and effective method of mitigating striations via a uniform optical exposure added to the end of any VAM printing process. We show this step to additionally shorten the period from initial gelation to print completion, mitigating the problem of partially gelled parts sinking before print completion, and expanding the range of resins printable in any VAM printer.

This work introduces a rapid and facile approach to predictably control integration between two materials with divergent properties. Programmed integration between photopolymerizable soft and stiff hydrogels was investigated for their promise in applications such as tissue engineering where heterogeneous properties are often desired. Spatial control afforded by grayscale 3D printing was leveraged to define regions at the interface that permit diffusive transport of a second material in-filled into the 3D printed part. The printing parameters (i.e., effective exposure dose) for the resin were correlated directly to mesh size to achieve controlled diffusion. Applying this information to grayscale exposures led to a range of distances over which integration was achieved with high fidelity. A prescribed finite distance of integration between soft and stiff hydrogels led to a 33% increase in strain to failure under tensile testing and eliminated failure at the interface. The feasibility of this approach was demonstrated in a layer-by-layer 3D printed part fabricated by stereolithography, which was subsequently infilled with a soft hydrogel containing osteoblastic cells. In summary, this approach holds promise for applications where integration of multiple materials and living cells is needed by allowing precise control over integration and reducing mechanical failure at contrasting material interfaces.

Ptychography is a coherent diffractive imaging technique that can determine how an electron wave is transmitted through an object by probing it in many small overlapping regions and processing the diffraction data obtained at each point. The resulting electron transmission model describes both phase and amplitude changes to the electron wave. Ptychography has been adopted in transmission electron microscopy in recent years following advances in high-speed direct electron detectors and computer algorithms which now make the technique suitable for practical applications. Its ability to retrieve quantitative phase information at high spatial resolution makes it a plausible alternative or complement to electron holography. Furthermore, unlike off-axis electron holography, it can provide phase information without an electron bi-prism assembly or the requirement of a minimally structured region adjacent to the region of interest in the object. However, it does require a well-calibrated scanning transmission electron microscope and a well-managed workflow to manage the calibration, data acquisition and reconstruction process to yield a practical technique. Here we detail this workflow and highlight how this is greatly assisted by acquisition management software. Through experimental data and modelling we also explore the similarities and differences between high-resolution ptychography and electron holography. Both techniques show a dependence of the recovered phase on the crystalline orientation of the material which is attributable to dynamical scattering. However, the exact nature of the variation differs reflecting fundamental expectations, but nonetheless equally useful information is obtained from electron holography and the ptychographically determined object transmission function.

Transparent substrates introduce challenges in optical metrology, recording, and microscopy. Backside reflections reduce signal to noise, are recorded as artifacts, or introduce spurious signals. These reflections often need to be suppressed, but large angular and spectral bandwidths preclude the use of anti-reflection (AR) coatings. Using elastomeric materials doped with optical absorbers, we detail a method and a materials set for temporary suppression of Fresnel reflections for multiple substrates spanning wide spectral and angular bandwidths. Tuning the refractive index of the elastomer to match a substrate minimizes reflection and the addition of different absorptive dopants allow for either broadband or wavelength-selective reflection suppression. As performance is limited only by the index mismatch, both spectral and angular performances significantly exceed those of AR coatings. We demonstrate reflection suppression in excess of 30 dB spanning a bandwidth over 500 nm. After use, these light traps may be removed and reused without damaging the substrate.

Volumetric additive manufacturing (VAM) forms complete 3D objects in a single photocuring operation without layering defects, enabling 3D printed polymer parts with mechanical properties similar to their bulk material counterparts. This study presents the first report of VAM-printed thiol-ene resins. With well-ordered molecular networks, thiol-ene chemistry accesses polymer materials with a wide range of mechanical properties, moving VAM beyond the limitations of commonly used acrylate formulations. Since free-radical thiol-ene polymerization is not inhibited by oxygen, the nonlinear threshold response required in VAM is introduced by incorporating 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) as a radical scavenger. Tuning of the reaction kinetics is accomplished by balancing inhibitor and initiator content. Coupling this with quantitative measurements of the absorbed volumetric optical dose allows control of polymer conversion and gelation during printing. Importantly, this work thereby establishes the first comprehensive framework for spatial-temporal control over volumetric energy distribution, demonstrating structures 3D printed in thiol-ene resin by means of tomographic volumetric VAM. Mechanical characterization of this thiol-ene system, with varied ratios of isocyanurate and triethylene glycol monomers, reveals highly tunable mechanical response far more versatile than identical acrylate-based resins. This broadens the range of materials and properties available for VAM, taking another step toward high-performance printed polymers.

Understanding the three-dimensional (3D) mechanical and chemical properties of distinctly different, adjacent biological tissues is crucial to mimicking their complex properties with materials. 3D printing is a technique often employed to spatially control the distribution of the biomaterials, such as hydrogels, of interest, but it is difficult to print both mechanically robust (high modulus and toughness) and biocompatible (low modulus) hydrogels in a single structure. Moreover, due to the fast diffusion of mobile species during printing and nonequilibrium swelling conditions of low-solids-content hydrogels, it is challenging to form the high-fidelity structures required to mimic tissues. Here a predictive transport and swelling model is presented to model these effects and then is used to compensate for these effects during printing. This model is validated experimentally by photopatterning spatially distinct hydrogel elastic moduli using a single photo-tunable poly(ethylene glycol) (PEG) pre-polymer solution by sequentially patterning and in-diffusing fresh pre-polymer for further polymerization.

The thermal radiative properties of glazing materials are fundamental inputs for evaluating the energetic performance and thermal behaviour of buildings. These properties are required for many practical applications ranging from infra-red thermography to building performance simulation and thermal comfort evaluations. Soda–lime–silica glass is the most widely used glass, worldwide, for glazing and the derivation of its thermal and optical properties are the subject of this work. Based on experimentally acquired spectral reference measurement data for soda-lime glass, the complex-valued Fresnel equations are solved and Monte-Carlo integration is carried out over the black-body spectrum, as well as over the hemisphere. These calculations are performed for various radiative temperatures to establish their temperature dependencies, and additionally the temperature-dependent transmittance of soda-lime glass was analysed. By applying a non-linear optimisation algorithm, computationally efficient practical fitting formulae for the angular reflectivity, emissivity and absorptivity are derived. All equations are numerically solved with a high degree of accuracy. The determination of the hemispherical total emissivity for this type of glass confirms the current accepted value (∼0.2%) originally derived by Rubin (1985). A simple yet robust experimental design, based on thermography, is used to validate the results of the numerical reflectivity model. Additionally, we provide angular functions, fitting functions and additional constants that were hitherto unavailable. The practical benefits of the fitting formulae provided are considerable in relation to modelling the reflection of thermal radiation on glazed surfaces, and are demonstrated in two case-study applications in the appendix.

In this paper we discuss developments for Lorentz mode or “medium resolution” off-axis electron holography such that it is now routinely possible obtain very high sensitivity phase maps with high spatial resolution whilst maintaining a large field of view. Modifications of the usual Fourier reconstruction procedure have been used to combine series of holograms for sensitivity improvement with a phase-shifting method for doubling the spatial resolution. In the frame of these developments, specific attention is given to the phase standard deviation description and its interaction with the spatial resolution as well as the processing of reference holograms. An experimental study based on Dark-Field Electron Holography (DFEH), using a SiGe/Si multilayer epitaxy sample is compared with theory. The method’s efficiency of removing the autocorrelation term during hologram reconstruction is discussed. Software has been written in DigitalMicrograph that can be used to routinely perform these tasks. To illustrate the real improvements made using these methods we show that a strain measurement sensitivity of ± 0.025 % can be achieved with a spatial resolution of 2 nm and ± 0.13 % with a spatial resolution of 1 nm whilst maintaining a useful field of view of 300 nm. In the frame of these measurements a model of strain noise for DFEH has also been developed.

Early prediction of impending high temperatures in buildings could play a vital role in reducing heat-related morbidity and mortality. A recursive, AutoRegressive time series model using eXogenous inputs (ARX) and a rolling forecasting origin has been developed to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions, especially heatwaves. The model was tested using monitored data from three case study dwellings recorded during the 2015 heatwave. The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. Comparison between ARX and AutoRegressive Moving Average models with eXogenous inputs (ARMAX) models showed that the ARX models provided consistently more reliable multi-step-ahead predictions. Prediction intervals, at the 95% probability level, were adopted to define a credible interval for the forecast temperatures at different prediction horizons. The results point to the potential for using time series forecasting as part of an overheating early-warning system in buildings housing vulnerable occupants or contents.

In the field of supply chain simulation, transport relations are often modeled as transport times using distributions. Considering long-distance transport relations, this is usually a suitable approach. But, for short-distance transports within large cities, delays depend on specific roads and the time of day. Some simulation tools offer geographical data for modeling actual roads. However, in order to model time-dependent transport times, additional data are needed. In this paper, we present an approach to tackle this problem. Road networks are derived from OpenStreetMap data (including traffic signals). In order to obtain the average speed of vehicles on an hourly basis, we conduct pre-simulation runs modeling the entire inner-city traffic. The respective vehicle rides are derived from trajectory data of cell phone users, where the assignment of users to cell phone tower sections is given for each hour of the day. First results for the city of Winnipeg are presented.

peer-reviewed The introduction of fast CMOS detectors is moving the field of transmission electron microscopy into the computer science field of big data. Automated data pipelines control the instrument and initial processing steps which imposes more onerous data transfer and archiving requirements. Here we conduct a technical demonstration whereby storage and read/write times are improved 10× at a dose rate of 1 e−/pix/frame for data from a Gatan K2 direct-detection device by combination of integer decimation and lossless compression. The example project is hosted at github.com/em-MRCZ and released under the BSD license.

This preliminary paper discusses the creation of an agent based model for a signalized traffic intersection derived from a previously developed mathematical vehicle-following micro-simulation model. The results of the agent based model are compared to the results from the mathematical model for verification purposes. The agent based model is then used to show the effects of inclement weather on vehicle throughput at a traffic intersection and to show how increasing the signal intervals in these conditions can help to partially restore traffic throughput to normal condition levels. This effort is just part of the many simulation and modeling efforts that will be required as autonomous vehicles as well as person controlled vehicles begin to share the roadway. The role of IoT will become extremely important and even more so, as driving conditions are exacerbated by unforeseen and environmentally hazardous roadway conditions making intercommunication between vehicles and infrastructure even more critical.