Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure–activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics.The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhance  This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms. In this review, we present the latest advances in high-throughput combinatorial workflows aimed to accelerate OPV materials discovery and device optimization beyond classical one-sample-at-a-time experimentation. In parallel, these approaches have fostered the generation of big datasets at unprecedented paces, thus rendering the OPV scene as an ideal application niche for advanced statistical and artificial intelligence (AI) algorithms. In this work, we also advent distinct synergic usages of AI in OPV to extract hidden patterns from computational and experimental datasets, as well as to orchestrate experimental execution in next-generation robotized and self-driven laboratories. Arguably, these are considered as the game-changing material screening routines defining the OPV technology roadmap in the upcoming decade.Organic photovoltaic (OPV) materials discovery of novel small molecular acceptors and low band gap donor polymers has recently propelled power conversion efficiencies to figures approaching 20%. High-throughput screening routines, deployed both 1 Indeed, power conversion efficiencies (PCEs) in excess of 18% have already been demonstrated under 1 sun and even higher under indoor illumination.2–4 This is taking dream traits of OPVs closer to reality, including the promise of a flexible and lightweight photovoltaic technology with tunable degrees of transparency and colour5 at extremely low energy payback times.6Organic photovoltaics (OPVs) are certainly gaining momentum: the 20% efficiency milestone in single-junction devices is nowadays closer than ever.7–10 Here we would like to contextualize the field by summarizing the different historical stages in terms of PCEs, as shown in 11 The initial stage comprises the first decade of the 21st century in which most of the fundamental physical concepts were established, basic geometries explored and the role of the active layer morphology revealed.7 Then, the introduction of low band gap donor polymers to substitute more classical materials such as polythiophenes, took PCEs up to the 10% regime.12 More recently, the vertiginous increase in efficiency over the last five years is the result of the irruption of non-fullerene acceptors (NFAs) in the OPV scene.13,14 Every year, an increased number of NFAs are being synthesized targeting improved light absorption capabilities  as well as fine-tuned energy levels .Many excellent reviews exist that describe the operational principles and progress of the OPV field over the years.cf. fullerenes) relates to their chemical flexibility, which enables proper adjustment of energy levels, band gaps, solubility and crystallinity, resulting in an endless catalogue of potentially high-performing NFA candidates.9,13,14 On the other hand, push–pull donor copolymers are synthetically flexible as well, thus the number of donor:acceptor pairs waking OPV interest up is seemingly infinite. Clearly, the efforts of the community are also increasing from a few hundred papers per year, to over a thousand in recent years, especially after breaking the 10% PCE milestone back in 2012  extrinsic conditions related to the film processing such as casting temperature or ink formulation; and (3) device features such as the active layer thickness or the donor:acceptor blend ratio forming the OPV blend. Due to its large complexity, investigating efficiently and completely the parametric space becomes unaffordable following traditional one-variable-at-a-time methods , in which each of the involved parameters is varied individually. These facts, together with the ever-growing catalogue of OPV materials and combinations thereof  intrinsic material properties such as energy level alignment, miscibility, optical extinction or exciton diffusion length; thereof , result 16 Since then, the development of combinatorial libraries has been progressively extended to other fields within materials science such as polymer characterization,17 including the high-throughput measurement of polymer blend phase behaviour;18 but also to the fields of engineering and physics, with examples of high-throughput measurements of mechanical properties via micro-scale experiments19 and the very recently reported synthesis of multimetallic nanoclusters.20 In biochemistry, combinatorial libraries are usually exploited in microfluidic setups21 targeting cell-based applications,22,23 drug discovery24 and toxicology screening,25 amongst many other applications.The concept of high-throughput experimentation was first introduced in 1995 to drive the discovery of superconducting materials by exploiting combinatorial stoichiometric libraries.26 or making systematic studies of e.g. molecular weight, side chains etc., with statistical relevance. Moreover, larger fractions of the parameter landscape of the systems under study can also be accessed through these methodologies. This opens the possibility to explore the parameter space much more thoroughly, and thus find the absolute maximum, or the existence of several maxima27 as well as critical behaviours (singularities would otherwise remain undetected).In most cases, high-throughput experimentation relies on the efficient and rapid generation of combinatorial parametric libraries coupled to fast characterization tools with the aim of unravelling complex multivariate spaces at high exploratory paces. This objective must be achieved guaranteeing a minimal investment on human (workforce) time and resources, while leading to a rich density of data points in the targeted space for robust conclusions. As a result, with the same amount of time and resources, high-throughput methods may explore a much greater number of systems, enabling truly combinatorial screening of donor:acceptor pairs,big data) where machine-learning (ML) algorithms are found to have an ideal application niche. ML is thought to help in rationalizing the scientific findings, helping in the design and discovery of novel materials and orchestrating the autonomous experimentation in robotized high-throughput laboratories.Such unprecedented pace for data generation enables constructing large and well-structured datasets , whereas screening aims at probing uncharted parametric combinations in a more systematic manner to look for further maxima .– The terms Computational screening deals with performing massive quantum chemical calculations in supercomputing architectures with the objective of identifying and preselecting the most promising molecular candidates, possibly rationalizing the design and discovery of novel materials. These calculations can be run ab initio or biased by human intuition, e.g. including a bias on the ease of synthesis.– design of experiments (DoE) comprises an experimental planning strategy for complex multivariate spaces that aims at discovering the overall best performing optima by executing a rationale and factorial sampling of the parametric space. In other words, DoE can be described as an experimental execution strategy that minimizes experimental effort at maximal information output.29– The concept of artificial intelligence (AI) embraces a toolbox of intelligent computational-based methods that are able to learn from the inputs of an environment and accordingly take actions on that precise environment.30 Within the broad field of AI, machine-learning (ML) refers to all those algorithms which confer computers the ability to learn without being explicitly programmed. When these algorithms incorporate perceptrons or neural networks, they are classified as deep learning (DL) methods. In overall, these are able to extract correlations, trends and models from large experimental datasets or big data through mathematical and statistical algorithms.– The term Molecular descriptors are useful parameters, usually in the form of numbers, that result from a logic and mathematical operation performed using the symbolic representation of a molecule or the result of some standardized experiments/calculations.31 Typically, these are exploited to draw quantitative structure–property relationships (QSPRs) or ML models.– self-driven laboratories leverage high-throughput combinatorial experimentation and AI by combining both processes in a closed-loop pipeline. They are able to take independent decisions and execute autonomous experimentation (i.e. device manufacturing and characterization) until matching certain targets, thus accelerating material discovery.– Finally, 32 In OPV, computational tools are designed to prescreen millions of molecular motifs and select the most promising photovoltaic candidates based on distinct descriptors extracted from quantum chemical calculations, such as HOMO/LUMO energy levels,33 oscillator strengths,34 absorption spectra or exciton diffusion lengths;35 as well as macroscopic figures-of-merit, such as the PCE, according to simplified device models (e.g. Scharber model).36 Furthermore, descriptors based on molecular graphs (which represent connection paths of atoms in a molecule) are exploited in conjunction with generative models and genetic algorithms33 to propose new materials based on the best-performing molecular motifs including as well a bias on the ease of synthesis.37 A thorough mining of molecules and the application of ML algorithms will eventually result in the establishment of predictive models based on such descriptors, but also in the rationalization of some of the findings, as these can serve to identify key molecular fingerprints to guide further materials synthesis and device prototyping. Finally, the computational forecasts can be calibrated using experimental data taken from literature or public repositories to improve the reliability of the calculations (vide infra).High-throughput computational studies in materials science represent an emerging field that leverages supercomputing architectures to guide the discovery of novel materials.in silico screening study in OPV comes from the Harvard Clean Energy Project (CEP),38 which represents the first example of computational virtual screening of molecules with the aim of understanding the structure–property relations in OPV-related materials. The outcomes of the CEP have been evolving since its presentation and launch as a volunteer-driven interconnected calculation platform through the IBM World Community Grid back in 2017. The CEP was initially exploited to screen up to 1.3 million donor materials, as obtained from the combinatorial bonding of 26 different molecular fragments extracted from the literature. A more recent implementation of the CEP algorithm includes generational models biased by the human intuition39 to build a combinatorial library of molecules with potential interest in OPV, thus limiting the exploration space of the screening routine. Interestingly, the fundamental parameters retrieved from the ab initio quantum chemical calculations are publicly available and stored in a repository.40 Some of these datasets, namely the HOPV15 dataset, have already been exploited in training ML models and predicting key properties of OPV devices, such as PCE.41The most extended high-throughput 34 In this work, the lowest energy conformers of the molecules are geometrically optimized via density functional theory (DFT) to obtain their corresponding HOMO and LUMO energies, which are then calibrated against experimental values using a Gaussian process regression (GPR) based on their molecular similarity. These are finally exploited to estimate the PCE of the corresponding blends by means of the Scharber model,36 which is calibrated as well following GPR. Furthermore, a statistical fingerprint analysis shows fragment ligation patterns that increase the absorption properties of the molecules in the visible range of the solar spectrum, such as those including diketopyrrolopyrrole and quinoidal thiophene moieties  in the so-called industrial figure-of-merit (i-FOM), as termed by C. J. Brabec's group.46 Similarly, Po et al. explored the synthetic complexity (SC) of active layer donor polymers47 and NFAs,48 while Moser et al. introduced a simplified metric termed as scalability factor (SF) accounting for the semiconductor synthetic cost.49 Based on our cost analysis, close to 50% of the overall device cost is solely ascribed to the obtainment of the raw semiconducting materials  when blended with NFAs from the Y-series.49 On the other hand, state-of-the-art low bandgap polymers including PBDB-T and their halogenated derivatives do not exceed the PCE/SF threshold of 0.4. According to this report, ternary blends including low SF semiconductors (such as P3HT) are prominent alternatives from the economic perspective since they have as high PCE/SF values as the PTQ10-based devices. Therefore, a more sophisticated computational pre-screening of OPV semiconductors should look for potential candidates weighted by scalable figures-of-merit and ease of synthesis.From the commercial viability standpoint, factors such as lifetime and cost are as relevant as device performance, thus they also have to be considered as weighting factors in upcoming high-throughput computational screening studies. These needs motivated the introduction of a metric that summarizes all three key factors  while simplifying the computer-suggested synthetic pathway.51 Yet being so far solely applied to small molecules ,52 the extrapolation of computer-aided synthesis planning to distinct fields in materials science is potentially subversive.53To overcome this issue efficiently, AI and organic chemistry are becoming jointly orchestrated in the field of computer-aided synthesis planning.analog and digital worlds  parametric gradients whereas fabrication-intensive workflows rely on the automated fabrication of prototypes by robotic arms in discrete  steps. Gradients intrinsically require the development of fast and high-resolution characterization and data acquisition platforms to spatially resolve the lateral parametric variations and correlate them with accessible device properties such as the photocurrent. On the other hand, robotized setups are intrinsically more compatible with standard characterization setups yet offering by default a larger consumption of raw materials and the use of highly specialized fabrication equipment (robotic arms and sophisticated control loops). In this section, we detail the latest advances and some of the still untackled bottlenecks of high-throughput manufacturing frameworks in OPV.High-throughput experimentation approaches in OPV can be classified into two main branches depending on the actual density of the parametric libraries that are generated, as inspired by the properties of the l worlds . Measurianalog architectures with minimal experimental effort. Thickness gradients serve to locate the optimal active layer thickness that maximizes light harvesting in organic solar cells54 and to tune on demand the resonant wavelength in optical cavities.55 However, lateral gradients in thin films are not restricted to thickness and might include other features such as the blend ratio in multi-component mixtures or the annealing temperature, amongst others. Herein, we discuss the different experimental approaches developed so far to generate such thin film lateral gradients in a controlled manner while showing some of their unique applications in high-throughput OPV screening.Lateral gradients comprise a continuous variation of certain parameter(s) spanning from few mm to several cm in space. In thin film technologies, the most extended application niche of parametric gradients is represented by the film thickness. Thickness gradients are of recurrent use since distinct optical interference phenomena can be discerned using such type of 56 coating techniques (such as slot-die coating)57 have been widely employed in the realization of continuous lateral gradients from solution with outstanding results and thus we focus here on these. Examples of alternative methods will also be mentioned.For the last 20 years of research in organic thin film libraries, blade coating and similar meniscus-guided54,58,59 In these conditions the wet film thickness depends mainly on the coating speed; therefore, a continuous thickness variation in solid-state is achieved by employing a blade applicator actuated at constant acceleration, rather than at constant speed (60 Blade coated thickness gradients can span over large areas with excellent homogeneity (ca. 20 cm2 have been demonstrated),54,61,62 rendering them useful to explore interference phenomena in optoelectronic devices in a high-throughput manner. These gradients were accordingly employed to tailor the optimal active layer thickness in bulk heterojunction, polymer:small molecule solar cells;63 tandem solar cells with their active layers arranged in an orthogonal fashion;62 and to accelerate the screening of the thickness dependence on the photovoltaic performance and stability.61Thickness gradients constitute one of the simplest high-throughput libraries that can be readily produced by blade coating. Linear thickness gradients spanning from a few tens of nm to several hundreds of nm in a single sample have been demonstrated in the Landau-Levich lubrication regime.nt speed .60 Blade64 While in spin-coating a significant fraction (>90%)‡‡−1 solution and 1 cm2 substrates, spin coating at least requires depositing a drop of 50 μL (or 500 μg) to homogeneously cast a film. Assuming that the spin coating procedure leads to a 100 nm-thick film with a density of 1 g cm−3, this results in 10 μg dried-deposited on the film, or 98% of the original solid content wasted. These are conservative numbers, as by spin coating typical concentrations are closer to 20 mg mL−1 and 100 μL, which would lead to 2 mg of material first deposited and 99% of waste. Similarly, when employing 4 cm2 substrates with 10 mg mL−1 solutions and a 50 μL drop, the percentage of solid content wasted lowers to 92%.For a 10 mg mL of the deposited ink is spun out of the substrate, and thus is not an optimal method for high-throughput screening, the fact that it is so widely available may render the technique useful. On the other hand, using movable shadow masks, evaporated devices with thickness gradients can also be obtained.65 Recently, evaporated OPVs with bilayer architecture have been optimized by using orthogonal wedges of donor and acceptor layers.66 In addition to lab-scale research, solution-processed thickness gradients have also been demonstrated to be an efficient way to screen the active layer thickness dependence in roll-to-roll (R2R) setups. Alstrup et al. fabricated flexible solar modules including controlled thickness variations along the web by adjusting the flow rate fed in slot-die coating heads.57While blade coating is probably the most controllable and material-efficient technique to produce thickness gradients, other approaches have been used and are worth mentioning. Interestingly, there is one literature report demonstrating off-center spin coating to generate lateral film thickness gradients in P3HT:PCBM blends with good reproducibility .64 While57 Two years later, Lee et al. reported the fabrication of lateral compositional gradients using spray-assisted deposition methods although showing very limited intermixing of the donor and acceptor materials.67 Since then, and given the unsparingly growing catalogue of light-harvesting organic semiconductors, different high-throughput experimental approaches have been developed to be compatible as well with lab-scale research laboratories.The generation of combinatorial compositional libraries in solid-state is certainly more experimentally challenging yet it renders attractive in those systems in which the mixing ratio strongly influences the device performance, such as the case of OPV and the donor:acceptor ratio. Since composition affects the amount of light harvested and the efficiency of exciton splitting and charge collection, every novel donor:acceptor pair must be subjected to its corresponding optimization. In 2010, F. C. Krebs's group implemented a differentially pumped slot-die coater in a R2R setup to realize a one-dimensional compositional library as an elegant way to accelerate the donor:acceptor ratio optimization process in OPV devices, which they demonstrated for the P3HT:PCBM system.68,69 These were demonstrated to enable a fine-tuning of the mixing profile obtained at the outlet, yet their actual transfer to solid-state film profiles was demonstrated more recently as a result of a synergic combination of microfluidic devices and blade coating  mixture) to the blade ink reservoir during the coating process. Then, the movement of the applicator drives their coalescence at the wetting edge, resulting in smooth compositional profiles in solid-state for polymer:small molecule blends as well as the more challenging all-polymer counterparts. Importantly, less than 50 μL of ink per material are required to perform the experiments, while the original ink vials remain unaltered as well for further experimentation.Current envisioned approaches to process compositional gradients in solid-state directly from solution merge previous knowledge and ideas on liquid-based gradient generation  with meniscus-guided coating techniques such as blade coating or slot-die coating. Compositional libraries in solution were realized in the early century by the prototyping of microfluidic branched arrays containing long zig-zag streams. coating .70 In thet al. for binary bulk heterojunction blends comprising a donor polymer and a fullerene acceptor  along the coating direction by accelerating the applicator. Very recently, our group applied the same methodology to heterogeneous blends of donor polymers and NFAs.26 The possibility to pattern 2D gradients over large areas is unique to blade coating, thus advancing the previous 1D exploration based on differentially-pumped slot-die coating.57In the particular case of polymer:small molecule binary blends, the rheological characteristics of the inks are not as demanding and simpler, yet less controllable processing schemes are possible to realize combinatorial libraries. These were first demonstrated by Sánchez-Díaz acceptor .54 In th71 have also been effectively screened by means of compositional libraries realized by blade coating.27,72 In this case, a layer-by-layer strategy was employed to subsequently deposit, one on top of the other, the different layers of the constituent active layer materials. Depending on the layer ordering and velocity profile employed (either constant speed or acceleration), complementary areas of the ternary diagram are covered, so that less than 5 combinatorial samples are required to explore a significant fraction of the ternary phase diagram.Ternary OPV blends, which offer a series of long-term stability and performance advantages over the classical binary bulk heterojunctions,et al. employed 1D thermal gradients to determine the optimum annealing temperature in P3HT:NFA based devices.61 In the most frequent arrangement, the temperature gradient is oriented perpendicularly to a secondary gradient such as film thickness, thus generating 2D combinatorial libraries in a single substrate  while correlating them with the corresponding photovoltaic performance (device properties). These techniques are, thus, a secondary yet fundamental kingpin supporting high-throughput screening using gradients. Therefore, they should ideally be non-destructive and fast in terms of data-acquisition to leverage their unique throughput capabilities.63 A few years later, the same group, led by A. Colsmann, demonstrated the high-throughput optimization of organic tandem solar cells using orthogonally oriented absorber layers.62 Therein, the local current density was accessed using a movable shadow mask with either a slit or a pinhole aperture. An additional probe was required to quantify the lateral film thickness variations  and correlate them with the photocurrent retrieved by means of LBIC mapping. Regarding film thickness, these are typically destructive techniques such as profilometry63 or confocal microscopy,62 which is still largely invasive since the determination of the thickness requires scratching and peeling off several layers from the device stack. Despite these drawbacks, the authors demonstrated a very efficient use of materials and evaluation times that were faster than conventional optimization protocols.In this regard, optical characterization techniques represent the ubiquitous choice in OPV. This is motivated by the fact that shadowed or focused light beams can locally induce the photogeneration of charge carriers throughout the gradients, being the photocurrent an excellent proxy of the overall photovoltaic performance  at an unbeatable pace . Beyond monochromatic light, broadband excitation has also been recently incorporated to the setup albeit doubling the measuring time required to complete the characterization (as it cannot be retrieved on-the-fly during Raman spectra acquisition). This methodology has now been extensively exploited to demonstrate the high-throughput optimization of binary26 and ternary27 OPV blends.Recently, our group introduced a co-local optoelectronic characterization technique combining Raman spectroscopy and LBIC maps in a single confocal setup that embraces both non-destructiveness and fast data acquisition .27,54,6676 For that reason, they are natively fast evaluation techniques compatible with large area devices that keep an excellent sample preservation. When applied to lateral parametric gradients or libraries, these techniques can be leveraged as qualitative imaging tools in high-throughput experiments despite not being able to quantify their lateral distribution, such as thickness or composition (as Raman spectroscopy does). Conversely, these provide reliable and quick qualitative assessments of distinct device-scale properties such as defects, degree of homogeneity or overall device performance in fully operational devices. Likely, it is simply a matter of time for these techniques to be eventually exploited in conjunction with combinatorial parametric libraries in high-throughput experiments based on lateral gradients.In addition to LBIC, other non-destructive imaging methods such as lock-in thermography (LIT) and luminescence imaging  are techniques of frequent use in quality control when upscaling OPV modules .76 For t77–79 Conversely, illuminated lock-in thermography (ILIT) studies the temperature distribution of the devices under optical excitation,80 serving as well to quantify shunt resistances in OPV modules.81 LIT renders useful to study degradation in organic solar cells82 employing broadly accessible pieces of equipment such as a digital lock-in, a thermostat as sample holder and an infrared camera. Since the major contributor to the observed rise in device temperature is the large resistivity of the active layer, DLIT can be exploited as well to image its inhomogeneities and defects77 as demonstrated by Hoppe and co-workers.79 While we are not aware of its use in samples with gradients, this technique could complement LBIC as an indirect method to evaluate local series resistance, and thus, as a proxy of the FF.Dark lock-in thermography (DLIT) serves to visualize thermal losses in operational solar cells under electrical excitation, with the ability to resolve spatially the shunt and contact resistances.83 In this regard, Doll et al. introduced a camera prototype including indium gallium arsenide detectors with integration times as low as 5 ms to acquire EL images in a high-throughput fashion. Their setup is applied outdoors in photovoltaic installations under low-light conditions and even at daytime.84 On the other hand, PL mapping serves to evaluate the degree of phase separation between the donor and acceptor species by looking at the PL intensity quenching and shifting. Moreover, PL imaging has been exploited as a marker of the Voc in ternary OPV libraries27 and to evaluate the efficacy of the laser-patterning process in scalable OPV devices.85Luminescence imaging is exploited to resolve the spatial distribution and quantify the efficiency of the radiative recombination processes taking place in OPV devices. PL imaging evaluates the recombination taking place in the active layer only whereas EL imaging embraces as well the quality of the charge injection and extraction of the full device stack. As a result, EL imaging can easily identify cracks or malfunctioning areas on working photovoltaic modules as being an excellent prior of the short-circuit current distribution.et al. introduced the photovoltaic mapping of gradients (PVMAP) by employing a liquid metal top electrode that reversibly contacts OPV devices without evaporated electrodes  control performance in air-exposed P3HT:PCBM samples. This approach leverages gradients and the movable liquid-metal top electrode to extract local JV curves over mm2 areas yet having the limitation of not reproducing the exact conditions found in functional devices, where a well-defined interlayer and top electrode are included for correct operation.Apart from optical characterization approaches, Savagatrup ectrodes .64 The n86 Accordingly, robots are expected to keep on replacing humans in the most repetitive tasks (including those in science) due to their unbeatable yield and batch-to-batch reproducibility while leaving non-repetitive experiments, as well as the rationalization of results and proposal of groundbreaking ideas and experiments  solely to researchers. These features render robots particularly profitable in those research scenarios designed as a discretized  exploration of multi-dimensional parametric spaces. Following these guidelines, automated experimentation constitutes an emerging field in materials science that inherits most of the previous know-how from the high-throughput pharmaceutical industry87 and combinatorial chemical synthesis.88,89 In the latter, the use of robots extends for several decades now, with the first example of closed-loop automated chemical synthesis dating from 1978.90In the second half of the 20th century, the substitution of the human workforce by robot arms resulted in major breakthroughs in the industrial production models worldwide, where fabrication paces reached unprecedented levels.JV curve extraction) in a specifically designed modular setup, including robotic manipulation, custom electronics and dedicated sample holders  and electrically measure up to 49 fully functional devices in 4 hours , thus leading to a screening rate close to 5 minutes per sample. This figure is very similar to that attained in more modern robotized manufacturing setups,93 thus may indicate the existence of an intrinsic limitation in the automatization of thin-film fabrication protocols. One important bottleneck of this first automated approach in organic electronics lies in the spin casting process since a single spin coating already takes on the order of 1 minute, and several layers are deposited per device (active and transport layers). Moreover, at that particular stage more than 90% of the initially pipetted ink is wasted.In the field of organic electronics, Kiy et al. introduced a platform based on inkjet printing to demonstrate the combinatorial screening of polymer:fullerene94 and polymer:polymer:fullerene95 blends  experimentation setup to evaluate the effect that small amounts of additives have in increasing the long-term stability and lifetime of quaternary high performing OPV blends.93 In this study, inks were drop-casted to form films, and the photodegradation was evaluated by looking at the evolution of the absorption spectra under continuous illumination , which are compatible only with drop-cast films, reduces the use of raw semiconducting materials to 15 μg per sample. For a typical high-throughput automated exploration of 500–1000 samples, the total amount of raw material required is close to 10–15 mg, which are truly competitive figures and similar to those attained using lateral gradients .98 In a first demonstration, they evaluated the efficiency and photostability of the workhorse donor:acceptor OPV blend PM6:Y6.99 The 10-dimensional multivariate space therein screened required 24 hours to be completed, and close to 72 hours including a 50 h photostability test performed with the help of Gaussian Process Regression (GPR) algorithms to identify the key process parameters. GPR predictions of photovoltaic parameters were drawn as well by incorporating an in-line measurement of absorption and its spectral decomposition, which further guides the decision-making and, thus, the screening procedure.The same group led by C. J. Brabec and J. Hauch recently deployed an automated solar cell fabrication and characterization platform labeled as AMANDA Line One  by making use of the ChemOS orchestrating software package.108,109 The raw material requirements were also downscaled proportionally: from 10–15 mg following conventionally-automated high-throughput experimentation to less than 1 mg per compound in the best self-driven scenario. While very promising, their high-throughput experimental screening procedure is thus far limited to drop cast films only, hence the assessment of the photovoltaic performance itself is not encompassed in their study and it is restricted to stability evaluations on bare films.In the OPV scene, Langner et al. optimized the hole mobility of amorphous spiro-OMeTAD using a flexible and modular self-driving laboratory known as Ada, which incorporates ChemOS as controlling software108 and Phoenics as Bayesian optimization algorithm.110 Their approach includes as well the fabrication of samples by spin coating, their thermal annealing and optical characterization in terms of reflection and transmission via UV-vis-NIR spectroscopy and four-point probe conductance measurements to get a proxy of the pseudomobility  can be exploited to significantly reduce the number of samples needed to explore complex multivariate spaces.112 In a first optimization round of their PCDTBT:PC71BM solar cells, a four-variable parametric space consisting of donor weight ratio, solution concentration, spin speed and concentration of processing additive is screened according to a Latin square factorial sampling approach followed by an analysis of variance (ANOVA). Other variables such as the temperature and duration of the annealing treatments have been recently optimized as well for NFA-based devices following the same analysis.113 In this first round,112 one of the parameters is dropped (presence of additive) and the remaining data is fitted using a ML algorithm (a support vector machine (SVM) model regressor) to obtain a complex hypersurface describing the PCE. In a subsequent optimization round, the range of values experimentally screened is narrowed to fine-tune the experimental conditions that maximize the PCE. This approach renders useful to reduce the number of samples fabricated when optimizing multi-parametric spaces of correlated features in parallel, and it also increases the probability to discover the true optimum. However, this testbed optimization procedure for a four-dimensional space of a polymer:fullerene blend still required up to 235 samples to be prototyped (distributed in 29 different exploratory steps) due to the large number of replicates required to build robust statistics. This results in a raw material consumption close to 470 mg of semiconducting materials only (100 μL per sample at an average concentration of 20 mg mL−1). Therefore, the investments in terms of time, cost and raw materials might be inadequate to meet the demands of high-throughput experimentation in OPV. Likely, DoE has a broader potential to decrease time and resources requirements when applied in combination with robotized and self-driven laboratories, where the higher reproducibility of the manufacturing process could be exploited to reduce the number of replicates and control samples to a few units only. Still, factorial design is typically limited to the simultaneous optimization of five parameters at two levels in the best case scenario.29 Beyond that degree of experimental complexity, human intuition ought to be exploited to prescreen the parameter space. Conversely, A. Aspuru-Guzik's group has very recently introduced Golem in the DoE scene as a novel process and experimental optimization algorithm.114Golem is able to minimize the impact of noise in the existing experimental conditions while providing robust solutions and decision-making on-the-fly using the outcomes of past experiments.The application of DoE strategies such as factorial design in OPV was first introduced by J. M. Buriak's group, who replaced Edisonian experimentation by a statistically-based rationale in the selection of devices to be fabricated while optimizing the use of human workforce time and resources.i.e. automated experimentation/characterization or fully self-driven laboratories. We next perform a one-to-one comparison in terms of experimental time required and raw material consumptions in each of them, in order to benchmark the distinct methodologies available nowadays. Accordingly, we summarize in At this stage, two main protocols for realizing high-throughput experimentation in complex multi-parametric scenarios have been described: gradients, spanning either one or two dimensions; and robotized laboratories in any of their variants, 2) of their devices, our estimations indicate a consumption of raw active layer materials close to 40 mg to complete a 49-devices batch (i.e. 50 μL per device at a concentration of 17 mg mL−1), thus leading to a raw material consumption rate close to 0.85 mg per device (i.e. close to the standard figures in spin coated devices) while accessing to relevant device figures-of-merit (luminance efficiency in the case of LEDs). These numbers, despite the automatization of the manufacturing process and the unbeatable device fabrication yield, might still be prohibitively high for the exploration of complex multi-dimensional landscapes when only small synthetic batches of the raw materials are readily available. Conversely, their synergic implementation with AI algorithms in an integrated workflow can lower the consumption of materials to figures below 5 mg (and even 500 μg) in high-dimensional optimization studies.93 However, the access to photovoltaic figures such as the Jsc must yet to be accomplished and accordingly included in the time and material spent per experimental data point. In this regard, that same research group recently tackled a 10-dimensional parametric space including access to the photovoltaic figures-of-merit  while increasing by one order of magnitude the semiconductor material requirements up to ca. 50 mg.99Regarding automated experimentation, the pioneer work by Kiy and co-workers already set the figures-of-merit of automated approaches. Given the small active area .Comparatively, gradient-based screening methodologies arise as one of the most cost-effective and straightforward approaches toward the fast screening of multivariate spaces. 1D gradients comprising discrete devices offer throughput figures comparable to those of state-of-the-art robotized laboratories as well as a very efficient consumption of raw materials (of a few tens of μg per data point). Furthermore, these allow measuring Beyond the use of raw semiconducting materials, a more detailed material cost analysis illustrates that in those cases where transparent conductive oxides such as ITO are commercially obtained and employed as substrate, it is the actual cost of the substrates what acts as limiting factor in the implementation of a cost-effective screening procedure. In particular, when employing commercially-available OPV materials, our calculations show that the cost of the ITO substrates typically covers 25 to 50% of the total price of the devices .123 In OPV, public consortia such as the recently launched emerging-pv.org platform10,124 will be very useful in organizing and automatically updating a state-of-the-art database freely exploited by data scientists. In this case, top-performing photovoltaic devices of emerging technologies are carefully indexed, including as well the experimental details required to reproduce the corresponding devices and performance figures. These suites of classified individual experiments will complement very well the even richer computational screening compilations of materials such as that of the Harvard CEP.40For this reason, initiatives promoting a careful indexation of the most prominent results in public repositories are highly valuable for the development of the corresponding technology. In thin film inorganic photovoltaics, the publicly open High Throughput Experimental Materials (HTEM) database currently contains more than 140 000 classified entries.descriptors: they might include intrinsic optoelectronic properties such as frontier molecular orbitals energy levels and energy band gaps, as well as others related with chemical graphs such as molecular connectivity indices or number of heteroatoms. An extensive catalogue of computational tools are currently available to retrieve thousands of molecular descriptors from any molecule right from their simplified molecular-input line-entry system (SMILES) string.118 This particular discipline serves to establish quantitative structure–property relationships (QSPR) or quantitative structure–activity relationships (QSAR), and in the case of OPV we encounter several examples of successful correlation detection. Since the visual inspection of the databases to extract dependencies with molecular properties is far from being straightforward, ML models are employed to retrieve potential correlations as well. However, special attention must be paid to the curation of the indexed data when dealing with molecular modeling investigations,125 as the structural errors can have a detrimental effect on the predictive ability of the obtained models.126One of the first and most important usages of data mining is to search for potential correlations between features of the photoactive molecules employed in the active layer of the devices and their ultimate performance. As previously mentioned, such features are known as ca. 300 small-molecule OPV devices retrieved from literature and applied ML models to draw potential QSPR correlations.127,128 In their data analysis, they included 13 different geometrical descriptors and optoelectronic magnitudes computed via DFT calculations as well as the experimentally measured device characteristics, such as Voc, Jsc, FF and PCE, as target properties. For data interpretation, they tested up to five different ML techniques and found that random forest (RF) ensembles and gradient boosting regression trees (GBRT) performed significantly better when training predictive PCE models.127 Their results indicate that in small-molecule OPV devices containing either -phenyl-C61-butyric acid methyl ester (PC61BM) or -phenyl-C71-butyric acid methyl ester (PC71BM) as acceptors, the degeneracy of the frontier molecular orbitals,127 the LUMO and LUMO+1 levels of the donor, their alignment with those of the acceptor and the change in the dipole moment from the ground state to the first excited state of the donor molecule might have more importance than thought so far in determining the PCE.128Sahu, Ma and co-workers built a database consisting of 129 In this work, the predictive accuracy in terms of PCE of up to 5 different regression ML models and 6 classifiers was compared until identifying that RF model ensembles outperformed amongst the tested approaches. Interestingly, the LUMO level of the donor materials was found to be the most important parameter in determining the photovoltaic performance, in good agreement with previous data mining studies. That same year, a similar study was reported exploiting a database consisting of 135 donor:NFA pairs showing that, in this particular type of blends, the band gap of the NFA and the HOMO level of the donor are the most important parameters in drawing PCE predictions.130 In this case, a RF regressor was demonstrated to retrieve a coefficient of determination (R2) of 0.80 in the testing set.In the case of ternary blends, including as well fullerene derivatives as acceptor species, Min-Hsuan Lee constructed a photovoltaic performance dataset of 124 material combinations together with their corresponding frontier energy levels retrieved experimentally in the same publications.et al. collected the photovoltaic performance  of 565 donor:acceptor pairs from the literature and tested the predictive accuracy for the PCE of a variety of ML algorithms.131 The study shows once again that RF models and GBRT outperform the investigation of the performance in polymer:NFA devices. However, in this study Wu et al. introduced an advanced segmentation of the input molecules into fragments, which were then combinatorially screened to propose new high-performing candidates and donor:acceptor pairs  band gaps of the materials under study. Similarly, Du, Lüer et al. fed a GPR algorithm with data consistently retrieved from their AMANDA automated research line and identified powerful predictive models in terms of efficiency and photostability for faster optimization of the blend under study.99 Given these results, the implementation of AI algorithms in conjunction with consistent experimental datasets is largely encouraged in both gradient-based and robotized high-throughput combinatorial studies.Very recently, our group implemented a consistent pairing between high-throughput experimentation based on gradients and AI algorithms to extract predictive models for the photocurrent-composition dependence in organic solar cells.deep learning is gaining increased attention to draw predictive PCE models in combination with data mining,119 as well as to overcome the computational limitations ascribed to fitting complex analytical models in organic solar cells.118 Majeed et al. trained a dense neural network using synthetic data (20 000 sample points) generated by the Schockley–Read–Hall based drift-diffusion model.134,135 Their output layer of nodes included relevant microstructural parameters of the devices such as charge carrier mobilities, Urbach energies, trap densities, recombination time constants and contact and shunt resistances  workflows . These render useful to extract statistically meaningful trends from the experiments, draw consistent rationale and promote further material discovery through synergy with generative and predictive ML models.Gradient-based approaches rely on the fabrication of continuous parametric libraries, either from evaporation sources or directly from solution, to explore the multivariate space in an All authors wrote and discussed about the manuscript.There are no conflicts to declare.EE-014-D1EE00559F-s001


This study aimed to compare the differences in motivation to receive a COVID-19 vaccination between frontline physicians and nurses and the Taiwanese public. The associations of threat and coping appraisals, as described in Protection Motivation Theory (PMT), with motivation to receive COVID-19 vaccination were compared between these groups, too. We recruited 279 frontline physicians and nurses and 768 members of the public by a Facebook advertisement. Participants’ motivation to receive COVID-19 vaccination, perceived severity of and vulnerability to COVID-19, self-efficacy and response efficacy of COVID-19 vaccination, response cost of COVID-19 vaccination, and knowledge about the mechanism of COVID-19 vaccination in light of PMT were determined. The results demonstrated that frontline health workers had higher motivation to receive COVID-19 vaccination than the public. Response efficacy and knowledge of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination in both frontline health workers and the public, whereas perceived vulnerability, perceived severity, and response cost of COVID-19 vaccination were positively associated with motivation in the public but not in frontline physicians and nurses. The factors related to motivation to receive COVID-19 vaccination should be considered when designing programs to increase motivation to receive COVID-19 vaccination among frontline health workers and the public. Frontline health care workers are a group at high risk of contracting COVID-19. A prospective cohort study showed that the incidence rate of COVID-19 infection in 99,795 frontline health care workers (HCWs) in the United Kingdom and United States was 3.96% from March to April 2020, a higher rate than that of the overall community . Many inWith proactive containment efforts and comprehensive contact tracing, the number of COVID-19 cases in Taiwan has been low compared with other countries . As of 1Vaccination is a primary measure to stop the spreading of COVID-19 . Taiwan Examining and understanding the factors predicting the motivation to receive COVID-19 vaccination among frontline HCWs is critical for designing an effective intervention to increase their acceptance of vaccination . ProtectThe present study aimed to examine two research questions. First, is the level of motivation to receive COVID-19 vaccination in frontline HCWs different from that among the public? Second, are the associations of threat appraisal  and coping appraisal  with motivation to receive COVID-19 vaccination different between frontline HCWs and the public? Accordingly, the specific hypotheses are as follows:Hypothesis 1 (H1).Frontline HCWs have higher motivation to receive COVID-19 vaccination than do members of the public.Hypothesis 2 (H2).The associations of threat and coping appraisals with motivation to receive COVID-19 vaccination are different between frontline HCWs and members of the public.Participants were recruited by using a Facebook advertisement on 15 October 2020 and 21 December 2020. Facebook  users were eligible for this study if they were ≥20 years old and living in Taiwan. The Regulations on Human Trials in Taiwan stipulate that academic research recruiting minors younger than 20 years old shall obtain consent not only from the minors but also from their guardians. There were difficulties in obtaining consent from the guardians in online surveys; therefore, we only recruited individuals who were 20 or older into this study. The Facebook advertisement included a headline, main text, pop-up banner, and weblink to the research questionnaire website. We designed the advertisement to appear in the “News Feed” of Facebook, which is a streaming list of updates from the user’s connections  and advertisers. We focused solely on News Feed advertisements (Facebook), as opposed to other Facebook advertising locations , because News Feed advertisements are more effective in terms of recruitment metrics for research studies. We targeted the advertisement to Facebook users by location (Taiwan) and by language (Mandarin Chinese), such that a given advertisement appeared on a user’s news feed as determined by a Facebook algorithm.To ensure that frontline HCWs were recruited, we also posted the link of the Facebook advertisement to LINE  and to Facebook groups joined by HCWs. The headline and main text of the advertisement introduced the purpose and procedure of this study. Facebook users could press the button “Agree to participate” and go to the research questionnaire website or press the button “Disagree to participate” and leave the advertisement. In total, 1047 respondents pressed “Agree” to participate in this study, and 35 respondents pressed “Disagree” and did not participate. The Institutional Review Board of Kaohsiung Medical University Hospital approved this study  20,200,019 and date of approval: 13 October 2020). At the end of the online questionnaire, the participants were provided with web links to pages with information about COVID-19 on the websites of the Taiwan Centers for Disease Control, Kaohsiung Medical University Hospital, and the Medical College of National Cheng Kung University.We defined “frontline HCWs” as physicians and nurses responsible for providing direct care to patients with suspected or confirmed COVID-19. All other participants were classified as part of the public.The level of motivation to receive COVID-19 vaccination was assessed using one item: “Please rate your current willingness to receive a COVID-19 vaccine: 1 (very low) to 10 (very high)”  Table 1Table 1.The items for the Constructs of PMT are listed in Participants’ sex and age were collected.2 test and t test. Motivation to receive COVID-19 vaccination and PMT constructs related to COVID-19 vaccination were compared between frontline HCWs and the public by using analysis of covariance (ANCOVA) and controlling for the effects of sex and age. Due to the multiple comparisons, a two-tailed p value of <0.007 (0.05/7) indicated statistical significance.Data analysis was performed using SPSS 24.0 . Sex and age were compared between frontline HCWs and the public by using the χp value of <0.05 indicated statistical significance.The associations of occupational classification (frontline HCWs vs. the public) and of PMT constructs related to COVID-19 vaccination with motivation to receive COVID-19 vaccination were examined by using a multiple regression analysis. To examine the moderating effect of occupational classification, the interactions between occupational classification and the constructs of PMT were further investigated by using a multiple regression model to understand their associations with motivation to receive COVID-19 vaccination. A two-tailed In total, 279 frontline HCWs and 768 members of the public participated in this study. The results of comparing sex, age, motivation to receive COVID-19 vaccination, and PMT constructs related to COVID-19 vaccination between frontline HCWs and the public are shown in Differences in motivation to receive COVID-19 vaccination between frontline HCWs and the public were further examined using multiple regression analysis (Model I in The interactions between frontline HCWs and PMT constructs related to COVID-19 vaccination were further selected for use with the multiple regression model to examine their associations with motivation to receive COVID-19 vaccination (Model II). The results demonstrated that the perceived vulnerability, perceived severity, and response efficacy cost of COVID-19 vaccination among frontline HCWs were significantly associated with motivation to receive COVID-19 vaccination, indicating that frontline HCWs and members of the public had different associations between these factors and vaccination motivation. This result only partially supported H2.The associations of PMT constructs related to the motivation to receive COVID-19 vaccination were further examined in frontline HCWs and in the public separately . The resThe results revealed that after controlling for the effects of sex, age, and the PMT constructs related to COVID-19 vaccination, frontline HCWs had higher motivation to receive COVID-19 vaccination than did the public. Both response efficacy and knowledge of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination in both groups, whereas perceived vulnerability, perceived severity, and response cost of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination among the public but not among frontline HCWs.The data supported the hypothesis that frontline HCWs have higher motivation to receive COVID-19 vaccination than do the public. The WHO declared that HCWs at high risk of exposure to COVID-19 should be prioritized for vaccination . A high This study revealed various associations between PMT threat and coping appraisals with the motivation to receive COVID-19 vaccination for frontline HCWs and for the public. First, both response efficacy and knowledge of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination in both groups. In Taiwan, seasonal influenza mass vaccination campaigns are conducted annually. These campaigns both mitigate the effects of seasonal influenza and serve as functional exercises for mass vaccination operations during a pandemic [Second, perceived vulnerability, perceived severity, and response cost of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination among the public but not among frontline HCWs. A study also found that perceived severity of COVID-19 was positively associated with motivation to receive COVID-19 vaccination among university students in China , indicatThird, the present study did not reveal a significant association between self-efficacy and motivation for COVID-19 vaccination in either group. Self-efficacy of COVID-19 vaccination indicates an individual’s perception of their ability to receive COVID-19 vaccination. During the period of study, COVID-19 vaccines were unavailable in Taiwan, and participants may have had difficulty judging whether they could receive the COVID-19 vaccination.The present study had some limitations. First, this study has a limitation in terms of its representativeness. Recruiting participants using social media such as Facebook advertisement can deliver large numbers of participants quickly  and is aThe present study found that response efficacy and knowledge of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination in both frontline HCWs and the public and could serve as the basis of programs for enhancing motivation to have COVID-19 vaccination regardless of the groups. It is recommended that government health departments invite medical professionals to introduce the efficacy and knowledge of COVID-19 vaccination in a convincing way to enhance the public’s appraisal of COVID-19 vaccination. Programs of enhancing frontline HCWs’ motivation to have COVID-19 vaccination may introduce the results of scientific studies examining the efficacy and knowledge of COVID-19 vaccination and the coverage rates of COVID-19 vaccination among frontline HCWs in other countries. The present study also found that some PMT factors such as perceived vulnerability, perceived severity, and response cost of COVID-19 vaccination were positively associated with motivation to receive COVID-19 vaccination among the public but not among frontline HCWs. It is recommended that government health departments develop group-specific programs for enhancing motivation to get vaccinated. For example, it is recommended that government health departments invite medical personnel to introduce COVID-19 threats to promote the public’s awareness; however, the introduction should not evoke a COVID-19 scare. Making COVID-19 vaccination easily available for the public is also important.


Layered or Tensor light field 3D display has attracted greater attention these days. Using only a few light-attenuating pixelized layers , it supports many views from different viewing directions that can be displayed simultaneously with a high resolution. This paper presents a novel flexible scheme for efficient layer-based representation and lossy compression of light fields on layered displays. The proposed scheme learns stacked multiplicative layers optimized using a convolutional neural network (CNN). The intrinsic redundancy in light field data is efficiently removed by analyzing the hidden low-rank structure of multiplicative layers on a Krylov subspace. Factorization derived from Block Krylov singular value decomposition (BK-SVD) exploits the spatial correlation in layer patterns for multiplicative layers with varying low ranks. Further, encoding with HEVC eliminates inter-frame and intra-frame redundancies in the low-rank approximated representation of layers and improves the compression efficiency. The scheme is flexible to realize multiple bitrates at the decoder by adjusting the ranks of BK-SVD representation and HEVC quantization. Thus, it would complement the generality and flexibility of a data-driven CNN-based method for coding with multiple bitrates within a single training framework for practical display applications. Extensive experiments demonstrate that the proposed coding scheme achieves substantial bitrate savings compared with pseudo-sequence-based light field compression approaches and state-of-the-art JPEG and HEVC coders.To create a realistic 3D perception on glasses-free displays, it is critical to support continuous motion parallax, greater depths of field, and wider fields of view. A new type of  Realistic presentation of a three-dimensional world on displays has been a long-standing challenge for researchers in the areas of plenoptics, light field, and full parallax imaging ,2,3. GlaDesigns based on a single display panel attached with a parallax barrier or special lens  usually suffer from inherent resolution limitations. The resolution for each view decreases with an increase in multiple viewing directions. Thus, supporting a full parallax visualization of the 3D scene is impractical . On the Multi-layered or tensor light field displays offer an optimized solution to support direction-dependent outputs simultaneously, without sacrificing the resolution in reproducing dense light fields. This is deemed as a critical characteristic of glasses-free multi-view displays ,15,16,17In this paper, we address the problem of light field dimensionality reduction for existing multi-layer or tensor 3D displays. The CNN is employed to optimize multiplicative layers obtained from light field data. CNN-based methods are proven to be better in terms of balance between computation time and accuracy than the previous analytical optimization approaches based on non-negative tensor factorization ,14,17,18The majority of existing light field coding approaches are not directly applicable for multi-layered displays. Several coding approaches extract the SAIs of the light field and interpret them as a pseudo video sequence ,26,27,28The algorithms that exploit low rank structure in light field data follow disparity based models ,41. JianMethods that explicitly consider the content of light field data for compression ,46 also A novel scheme for efficient layer-based representation and lossy compression of light fields on multi-layered displays. The essential factor for efficient coding is redundancy in multiplicative layers patterns, which has been deeply analyzed in the proposed mathematical formulation. The source of intrinsic redundancy in light field data is analyzed on a Krylov subspace by approximating hidden low-rank structure of multiplicative layers considering different ranks in factorization derived from Block Krylov singular value decomposition (BK-SVD). The scheme efficiently exploits the spatial correlation in multiplicative layers with varying low ranks. Further, encoding with HEVC eliminates inter-frame and intra-frame redundancies in the approximated multiplicative layers and improves compression efficiency.The proposed scheme is flexible to realize a range of multiple bitrates within an integrated system trained using a single CNN by adjusting the ranks of BK-SVD representation and HEVC quantization parameters. This critical characteristic of the proposed scheme sets it apart from other existing light field coding methods, which train a system (or network) to support only specific bitrates during the compression. It can complement existing light-field coding schemes, which employ different networks to encode light field images at different bit rates.The proposed scheme could flexibly work with different light-ray operations (multiplication and addition) and analytical or data-driven CNN-based methods. It is adaptable for a variety of holographic and multi-view light field displays. This would enable deploying the concept of layered displays on a variety of computational or multi-view auto-stereoscopic platforms, head-mounted displays, table-top or mobile platforms by optimizing the bandwidth for a given display target bitrate and desired light-field output. Further, the study is important to understand the kinds of 3-D contents that can be displayed on a layered light-field display with limited hardware resources.The proposed low-rank coding scheme compresses the optimal multiplicative layered representation of the light field, rather than the entire light field views. Since the proposed scheme uses just three multiplicative layers, it is advantageous in terms of computation speed, bytes written to file during compression, bitrate savings, and PSNR/SSIM performance. We believe that this study triggers new research that will lead to a profound understanding in applying mathematically valid tensor-based factorization models with data-driven CNNs for practical compressive light field synthesis and formal analysis of layered displays.Different from existing approaches, our proposed Block Krylov SVD based lossy compression scheme works for layered light-field displays with light-ray operations regulated using stacked multiplicative layers and a CNN-based method. The concept of “one network, multiple bitrates” motivates us to achieve the goal of covering a range of bitrates, leveraging the generality of low-rank models and data-driven CNNs for different types of display devices. The proposed coding model does not just support multi-view/light field displays; it can also complement existing light-field coding schemes, which employ different networks to encode light field images at different bit rates. Our experiments with real light field data exhibit very competitive results. The main contributions of the proposed scheme are:The rest of this article is organized into three major sections. The proposed mathematically valid representation and coding scheme for multi-layered displays is described in BLOCK I) represents a convolutional neural network that converts the input light field views into three multiplicative layers. In the second component (BLOCK II), the intrinsic redundancy in light field data is efficiently removed by analyzing the hidden low-rank structure of multiplicative layers on a Krylov subspace by varying BK-SVD ranks. This followed by the encoding of low-rank approximated multiplicative layers using HEVC effectively eliminates inter-frame and intra-frame redundancies. In the last component (BLOCK III), the light field is reconstructed from the decoded layers, as depicted in The workflow of our proposed representation and coding scheme is illustrated in z, the ray will have coordinates In the first component, the proposed scheme generates optimized multiplicative layers from real light field data. The four-dimensional light field is parameterized by the coordinates of the intersections of light rays with two planes ,48. The nd plane c. In thiMultiplicative layers  are lighz corresponds to the disparity among the directional views, rather than the physical length [Here, we assume that a light-field display is composed of three layers located at l length . The mulWe utilize a CNN to generate optimal multiplicative layers from the input light field images. The CNN-based optimization method proves to be better in terms of the balance between computation time and accuracy than the previous analytical optimization approach based on non-negative tensor factorization (NMF) methods ,14,17,18Similarly, the mappings from the layers patterns to the light field f of matrices ,51. Thisv method , which clgorithm ,54 and oz are denoted as In our proposed scheme, we denote each multiplicative layer pattern produced by the CNN as B in the following mathematical formulation.The intrinsic redundancies in multiplicative layers of the light field can be effectively removed by following low-rank BK-SVD approximation in a Krylov subspace for each ∈{r,g,b} . For simr, the SVD can be performed as B are the orthonormal columns of B. Conventional SVD methods are computationally expensive. Thus, there is substantial research done on randomized techniques to achieve optimal low-rank approximation [B’s top singular vectors and avoids the gap dependence in singular values. We target spectral norm low-rank approximation error of B, which is intuitively stronger. It is defined asW is a rank k matrix with orthonormal columns k approximation, only the top k singular vectors of B are considered relevant and the spectral norm guarantee ensures that B up to a threshold Given a matrix ximation ,51,52. TTraditional Simultaneous Power Iteration algorithms for SVD initialized with random start vectors achieve the spectral norm error (7) in nearly eference , a randoeference ,54, guarB and improve performance with fewer iterations. We construct q by working with Krylov subspace K. The approximation of matrix B done by projecting it onto the span of k rank approximation of B lying in the span of the Krylov space K. Thus, the nearly optimal low-rank approximation of B can be achieved by finding this best rank k approximation. Further, to ensure that the best spectral norm error low-rank approximation lies in a specific subspace , we orthonormalize the columns of K to obtain k approximation of B is matrix W, which is obtained ask singular vectors of S. Consequently, the rank k block Krylov approximation of matrices We take advantage of low degree polynomials that allow us to compute fewer powers of n method . We takeWe sectioned out the rows uniformly asThe red, green, and, blue color channels are then combined to form each approximated layer, The High Efficiency Video Coding (HEVC)  is the lThe encoding algorithm of HEVC flexibly divides each frame of the video sequence into square or rectangular blocks. This block partitioning information is relayed to the decoder. The first video frame is coded using only intra prediction , and succeeding frames are predicted from one, two, or more reference frames using inter and intra prediction. Motion-compensated prediction is used to remove the temporal redundancy during inter prediction.In our proposed scheme, we converted the block Krylov approximated layers into the YUV420 format. We then used HEVC encoder for various QPs to remove inter and intra redundancies of the low-rank approximated layers and compress them into a bitstream. A linear spatial transform is applied to the residual signal information between the intra or inter predictions. The transform coefficients are then scaled and quantized. The HEVC performs quantization of the coefficients using QP values in the range of 0–51. To make the mapping of QP values to the step sizes approximately logarithmic, the quantizer doubles step size each time the QP value increases by 6. The scaled and quantized transform coefficients are entropy coded and transmitted to the decoder along with the prediction information. The workflow of the entire encoding process is depicted in BLOCK III in This section reports the decoding procedure of compressed layers and the reconstruction of light field : 0.001, 0.0001, 0.00001;Batch size (BS): 15, 30;Number of epochs (E): 5, 10, 15, 20.In the first component of the proposed coding scheme  and HEVC step . Bunnies are shown in The multiplicative layers can further be fed into BLOCK II (BK-SVD step + HEVC) for compression. The proposed coding scheme achieves flexible bitrates for the various ranks of BK-SVD of multiplicative layers and varying HEVC quantization parameters. Thus, it realizes the goal of covering a range of multiple bitrates using a single CNN model.Bikes, Fountain-Vincent2, and Stone-Pillars Outside were optimized using the chosen CNN with 20 2-D convolutional layers stacked in a sequence. The network was trained  with 30 training images generated from light fields Friends 1, Poppies, University, Desktop, and Flowers, from the EPFL Lightfield JPEG Pleno database [We implemented the proposed coding scheme on a single high-end HP OMEN X 15-DG0018TX Gaming laptop with 9th Gen i7-9750H, 16 GB RAM, RTX 2080 8 GB Graphics, and Windows 10 operating system. The multiplicative layer patterns for input light fields database . Each trBLOCK I of Bikes, Fountain-Vincent2, and Stone-Pillars Outside light field images, obtained from the trained CNN model are presented in During training, the CNN  to compress all the YUV files of different approximated ranks. We choose seven quantization parameters, QP 2, 6, 10, 14, 20, 26, and 38, to test both high and low bitrate cases. The encoder compression produces a bitstream for a fixed rank and QP, that can be stored or transmitted. We followed the reverse procedure on the compressed bitstream to reconstruct the 13 × 13 views of the light fields. The HM decoder is used to recover the files in the YUV420 format, which are then converted back to the RGB color space. The three decoded layers are used to reconstruct the entire sub-aperture images of the light field. A comparison of the original central view of the The performance of our proposed coding scheme is compared to previous pseudo-sequence-based compression schemes ,25, the Bikes, Fountain-Vincent2, and Stone-Pillars Outside light field images to evaluate this multi-view compression algorithm. The bitrate versus PSNR results of Ahmad et al. is shown in Ahmad et al.  interpreBikes, we analyzed their compression performance [Bikes only. The total number of bytes used in compression using this approach is given in Liu et al.  formulatformance  on the 1To evaluate the HEVC  performaThe total number of bytes taken by our approach is comparatively much less than other anchors  for bothFurther, we performed an objective assessment using the Bjontegaard  metric. Bikes, Fountain-Vincent2, and the Stone-Pillars Outside datasets are shown in Bikes data, the proposed scheme achieves Fountain-Vincent2 data, we noticed Stone-Pillars Outside data, we achieve A comparison of the percentage of bitrate savings of our proposed coding scheme with respect to the anchor methods for the ectively . On Founectively . On Stonectively .Bikes light field into BLOCK II of the proposed coding scheme (AV(169)). These results were then compared with usage of just 3 multiplicative layers described in the previous sections (ML(3)). Block-Krylov low rank approximation was done for ranks 20 and 60, followed by HEVC encoding for quantization parameters 2, 6, 10, 14, 20, and 38.The proposed scheme generates three optimal multiplicative layers of input light field for layered light field displays. There is a clear advantage to approximate and encode just the three multiplicative layers rather than the entire set of light field views in terms of computation speed, bytes written to file during compression, bitrate savings with respect to state-of-the-art coding methods, as well as SSIM performance. To analyze this, we experimented by directly feeding all 13 × 13 views of the BLOCK II. AV(169) can be observed. The bitrate reduction of ML(3) and AV(169) with respect to Liu et al. [We noted the computation time for each step in u et al. , HEVC cou et al. , and Ahmu et al.  (estimat(BLOCK III), we used the SSIM metric to compare decoded views of experiment AV(169) and ML(3). Mean SSIM over the decoded views was calculated for each QP result and each rank of Bikes. These SSIM comparison graphs are illustrated in AV(169) is larger in size. The approximation quality deteriorates when all 13 × 13 views are simultaneously considered.To evaluate the decoding module of the proposed scheme Bunnies dataset [In the first component of the proposed coding scheme, the multiplicative layered representation of the light field is generated. This can be done analytically one layer at a time, with repetition until the solution of Equation  converge dataset  to compaIn case of the analytical approach, accuracy gradually increases with computation time. The inference time for the CNN in BLOCK I reproduced Bunnies light field using analytical and CNN methods, along with their error images, in BLOCK I of the proposed coding scheme as it strikes a better balance between computation speed and accuracy.We have also shown view 19 of the We proposed an efficient representation and novel lossy compression scheme for layered light-field displays with light-ray operations regulated using multiplicative layers and a CNN-based method. A single CNN model is employed to learn the optimized multiplicative layer patterns. A light field is compactly (compressed) represented on a Krylov subspace, considering the transmittance patterns of such a few layers and approximation derived by Block Krylov SVD. Factorization derived from BK-SVD exploits spatial correlation in multiplicative layer patterns with varying low ranks. Further, encoding with HEVC eliminates the inter-layer and intra-layer redundancies in the low-rank approximated representation. By choosing varying ranks and quantization parameters, the scheme is flexible to adjust the bit rates in the layered representation with a single CNN. Consequently, it realizes the goal of covering a range of multiple bitrates within a single trained CNN model. The experiments with benchmark light field datasets exhibit very competitive results.This critical characteristic of the proposed scheme sets it apart from other existing light field coding methods which train a system (or network) to support only specific bitrates during the compression. Moreover, current solutions are not specifically designed to target layered displays. Broadly, compression approaches are classified to work for lenslet-based formats or sub-aperture images based pseudo-sequence representation. Our proposed scheme could flexibly work with different light-ray operations (multiplication and addition) and analytical or data-driven CNN-based methods, targeting multi-layered displays. It is adaptable for a variety of multi-view/light field displays. In addition, it can complement existing light-field coding schemes, which employ different networks to encode light field images at different bit rates. This would enable deploying the concept of layered displays on different auto-stereoscopic platforms. One can optimize the bandwidth for a given target bit-rate and deliver the 3D contents with limited hardware resources to best meet the viewers’ preferences.Our future work span in several directions. We will further extend the proposed idea to light field displays, such as ones with more than three light attenuating layers, projection-based, and holographic displays with optical elements constructed using additive layers. Proof-of-concept experiments with our scheme pave way to form a deeper understanding in the rank-analysis of a light field using other mathematically valid tensor-based models and efficient layered representations for 3D displays. Another interesting direction is to extend the proposed mathematical formulation for coded mask cameras useful in compression of dynamic light field contents or a focal stack instead of processing multi-view images in layered displays. We aim to verify our scheme with not only computer simulations, but with a physical light field display hardware. In addition, we will consider the aspect of human perception by performing a subjective analysis of perceptual quality on a light field display. The proposed algorithm uses RGB space in which all channels are equally important. For image perception, there are better color spaces, like HSV/HSL or Lab. Analyzing other color spaces would be worth considering in further work.


The onset of the COVID-19 pandemic posed an eminent challenge for medical teachers worldwide. Face-to-face lectures and seminars were no longer possible, and alternatives had to be found. E-learning concepts quickly emerged as the only practicable solutions and also offered the opportunity to evaluate whether traditional face-to-face lectures could be translated into an online format, independent of the COVID-19 pandemic.We offered an e-learning program consisting of lecture notes, screencasts with audio narration, and online webinars that covered topics normally taught in traditional lectures and seminars. To evaluate the learning behavior and quality of our e-learning program, we drafted a questionnaire that students completed at the end of the 2020 summer semester that had been designed to enable a comparative analysis of the different e-learning modules.Voluntary participation in the online courses was high. Survey analysis revealed high satisfaction with and a distinctive preference for the format, even under regular, COVID-19-independent conditions. In general, a positive appraisal of e-learning—especially as a substitute for regular lectures—was found. Students also reported higher studying efficiency. Exam results were equal to those of previous semesters.Both acceptance of and satisfaction with our e-learning modules were high, and students displayed increased demand for this kind of e-learning format. We, therefore, conclude that e-learning offerings could serve as reasonable, efficient, student-orientated substitutes for certain medical courses, especially lectures. These curricular adaptations would correlate with the high digitalization seen in students’ everyday lives. This correlation may also hold true independent of the ongoing COVID-19 pandemic. Before the COVID-19 pandemic, digitalization in medical studies had only slowly gained relevance in Germany. Digital media and e-learning concepts had functioned as an addendum rather than an integral part of the regular curriculum, especially with regard to the organization of teaching and the provision of complementary teaching resources . Most meFace-to-face lecture-style teaching is an efficient and well-established method of disseminating core information to a large audience . NonetheThe COVID-19 pandemic has posed new and unprecedented challenges for medical students and medical education in general . The comThe COVID-19 pandemic has provided an opportunity to test different online teaching resources as a substitute for lectures. Our course offered online alternatives to traditional face-to-face lectures and seminars in the format of lecture notes, screencasts with audio narration, or online live webinars. The extent to which this massive boost of e-learning during the pandemic should continue and influence future medical education remains to be decided. We hypothesized that medical students value e-learning favorably and would endorse its broader implementation in the standard curriculum, because it reflects the students’ natural use of media in their everyday lives . TherefoThe COVID-19 pandemic offered the opportunity to test and evaluate new online teaching concepts within the compulsory curricular course of obstetrics and gynecology. Students enrolled in their 8th semester of medical studies in the 2020 summer semester at the medical faculty of the Technical University Munich participated in the course. Clinics were given autonomy to conceptualize and implement the teaching concepts to account for the individual character of each subject.I.Lecture notesThe main goal of our study was to evaluate the extent to which a concise transfer of knowledge from a clinical expert to students—as is traditionally carried out via lectures—could be substituted by potentially more-time-efficient and student-orientated online-learning resources. Our learning modules did not cover other collaborative or more-interactive teaching resources, because their main intent was to focus on the transfer of knowledge in a passive lecture-style teaching format. We offered three e-learning modules that covered the topics of the course in obstetrics and gynecology that is normally taught in a series of face-to-face lectures (for approx. 150 students) and seminars (for 20–30 students). Bedside teaching and practical-skills training were not part of this year’s curriculum as they usually take place one semester later. All topics of the lectures and seminars were randomly transformed into lecture notes, screencasts with audio narration, or online webinars. The online live webinars were available on a given schedule, but all other learning resources could be downloaded from the central university education platform and worked on individually throughout the entire semester. No special order for completing the modules was stipulated. The three modules differed with regard to the extent of verbal illustration and narration, the possibility of interacting with the lecturer, and the opportunity for open discussion:II.Screencasts with audio narrationThe lecture notes took the form of PowerPoint slides, which are usually shown during presentations in face-to-face lectures and frequently downloaded for exam preparation, regardless of whether the students have attended the lecture. PowerPoint is a presentation program that is universally applied in medical studies for lectures and large-group teaching . This moIII.Online live webinarsScreencasts are digital recordings of computer-screen output. In our case, they included PowerPoint presentations with simultaneous audio narration from experts in the field that covered eight comprehensive topics , each of which lasted ca. 45–90 min. Screencasts have been found in the literature to be effective online-teaching instruments in operative dentistry studies , anatomy. Online meetings were planned as classical 90-min lectures at a fixed date and following a specific timetable during the semester and were delivered online for remote learning. Interactions with the audience and the lecturer were possible via chat messages and open questions. All meetings except one were recorded and could be downloaded without restriction. A record of attendance was not conducted. The following topics were covered: pregnancy and its physiologically adaptive changes; fetal surveillance and the detection of fetal distress; hypertensive disease during pregnancy, HELLP, pre-eclampsia; and regular birth- and intrapartum complications.We used the software ZOOM  for our webinarsstrongly disagree (–), 2 = disagree (-), 3 = neither agree nor disagree (-/ +), 4 = agree ( +), 5 = strongly agree (+ +)). Other questions were either dichotomous or classification questions. Results of the multiple-choice examinations from the obstetrics and gynecology course from the past 5 years were anonymously analyzed and compared with the current results. Examination questions were newly issued for each semester.The 61-item questionnaire was disseminated following the multiple-choice exam at the end of the obstetrics and gynecology course in the 2020 summer semester. Study participation was voluntary and independent of the exam. All students provided written consent for participation. The questionnaire included 31 items with 5-point Likert-scale ratings that the participants used to indicate their agreement or disagreement with the statement for each item . Tables and figures were generated in Word (Microsoft®) and GraphPad Prism (GraphPad® Software). In total, 121 students  participated in the study. Our cohort represents the typical distribution of medical students in Germany in terms of sex  and age (86.8% between 20 and 25 years) . More thStudents’ satisfaction with the provided e-learning courses in terms of organization, technical application, relevance, learning success, and didactics was high, with median Likert scales for each item ranging from 3.6 to 3.9 Table .Half of the students attended less than 50% of non-compulsory lectures prior to the COVID-19 pandemic. Students reported diverse reasons for attending lectures and often indicated that receiving condensed and relevant content from a clinical expert as well as having personal interactions with fellow students were relevant factors. A minority 21.5%) considered posing questions to the lecturer to be important. Aside from lectures and seminars, students particularly preferred online self-study  (71.9%) over analogous textbooks (38.8%). Condensed studying before the exam was favored over continuous studying throughout the semester (64.5%. vs. 34.6%). Forty percent of the students considered the optimal time of the day for learning to be the afternoon or night  of students used more than half of them. Students especially liked the flexibility of the e-learning modules; however, they also missed being present at the university and having direct interaction with fellow students or the lecturer. Again, posing questions was not relevant from the students’ point of view Table .Table 3Lp value < 0.001) of the online meetings and screencasts compared with the lecture notes in terms of improved studying, productivity, and motivation for further studies . By contrast, seminars  and especially bedside teaching  were considered less appropriate for e-learning  of them reported potentially clinically relevant depressive symptoms during the COVID-19 pandemic. However, especially medical students demonstrated a higher degree of mental and physical resilience compared to students in other fields, such as in humanities or natural sciences. Therefore, it is not clear how much these social problems have potentially affected the perception of our course . StudentOur data demonstrate that the traditional teaching format of face-to-face lectures does not meet the flexibility demanded by today’s generation of medical students. E-learning, on the contrary, is practical and appreciated by our students and leads to equivalent test results compared with regular teaching methods at a German university hospital. Apart from possible setbacks in overcoming COVID-19 in the near future, various e-learning formats might be feasible tools for making medical education more student-orientated. Students highly value the teaching quality of lectures as a means of transferring knowledge; however, the face-to-face format also frequently deters them from participating. E-learning formats—such as screencasts with audio narration or online webinars—might serve as time-efficient alternatives and thus leave more time for small-group teaching and bedside learning, thereby ultimately improving future patient care. Building on the experiences gained during the COVID-19 pandemic, we want to expand our online offerings in the nearer future. Possible implications could be the regular transformation of face-to-face lectures into online or hybrid live webinars, problem-based-learning via e-learning for better preparation of practical skill modules, or new and innovative mobile app-based teaching formats.


Similarly, the epidemic of MERS in Korea during 2015 was accompanied by high levels of anxiety and anger that declined substantially after 4 to 6 months. A recent report of Czech adults indicates increased levels of mental disorders, including anxiety and major depression, during the COVID-19 pandemic.The COVID-19 disease, caused by a coronavirus known as SARS-CoV-2, was declared a pandemic by World Health Organization on March 11, 2020. The Canadian provinces and territories subsequently declared a state of public health emergency . Public health disasters such as viral pandemics are known to result in long-lasting deleterious effects on mental health of both health-care workers and the general population. Regarding the general public, those exposed to SARS during 2003 in Hong Kong showed highly elevated symptoms of anxiety during the epidemic that declined to pre-epidemic levels after about 3 months.Canadian Perspectives Survey Series 2 (CPSS 2)—Monitoring the effects of COVID-19, 2020 . We both extended previous work and added comparison data from a pre-COVID-19 referent sample. Specifically, we examined data from both available mental health variables: self-perceived mental health and symptoms of generalized anxiety disorder . To estimate the relative impact of COVID-19 on mental health, we used a referent sample from the probability-based Canadian Community Health Survey (CCHS) of 2017/2018 . Use of the same variable  from the 2 surveys provides a useful comparison of Canadian’s mental health before and during the pandemic. The GAD-7 has not been used in Canadian national surveys, so a similar approach is not possible for this measure. For the GAD-7, we used the customary cut-off score of 10, as indicated in a psychometric validation, to indicate moderate or severe symptoms, that is, as evidence of a need for further assessment or treatment. Self-perceived mental health was dichotomized as fair/poor or excellent/very good/good.We used data from the Statistics Canada’s probability-based survey known as We calculated age-dependent frequencies of fair/poor self-perceived mental health and moderate/severe anxiety symptoms . RegardiOf those that reported fair/poor mental health, 49.4%  had elevated GAD-7 scores. Of those who reported excellent/very good/good self-perceived mental health, elevated GAD-7 scores were seen only in 9.4% . The high prevalence of anxiety symptoms in those reporting fair or poor mental health is notable. Further research is needed to provide (a) a long-term perspective of mental health during and after the COVID-19 pandemic and (b) to relate elevated symptoms to the occurrence of diagnosed mental disorders. Our findings indicate a considerable burden to the mental health services and the need for innovative approaches such as Employment Assistance Programs support for psychotherapy.In conclusion, the COVID-19 pandemic is associated with a substantial increase of poor mental health as estimated by both self-reported mental health and assessment of GAD symptoms, but is only evident in those under age 65. A report from early in the pandemic indicated that risk factors for fair/poor mental health at this stage included having a physical condition and family stress.Perceived mental health is difficult to interpret as it relates to perceptions rather than symptoms. The results presented here clarify that such perceptions are strongly related to symptom levels of anxiety and support the use of this measure in future studies.Canadian Perspectives Survey is difficult to interpret in isolation, given the absence of a reference group. Fortunately, the CCHS provides a referent. A striking change in the pattern of fair or poor mental health is seen, with a larger worsening of perceived mental health in younger respondents with no effect on 65+ respondents. It may be that the social disruption of the public health response, which may disproportionally affect youth, is a greater determinant of anxiety symptoms than medical fears. The latter are probably more prominent in the elderly.A high prevalence of negative perceptions of mental health in the