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Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues. Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features. Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues. We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and settings (objective, sample mounting) and available segmentation methods. In addition, we include a computational pipeline alongside custom code to assist replication of the protocol. While we focus on the segmentation of cell outlines from membrane labelling, this protocol applies to a wide variety of samples, and we believe it be valuable for studying other tissues that demand complex analysis in 3D.

Hydrogen is one of the most promising candidates for clean energy production. Photoelectrochemical devices look promising for the decomposition of the water molecule into 2H$_2$ + O$_2$. Oxynitrides, like the solid solution Sr$_{1-x}$Ca$_x$O$_2$N, are good candidates due to the low band gap that lies into the maximum zone of the solar radiation spectrum. A necessary condition for the photoelectrochemical process to work without a bias voltage is that the minimum of the semiconductor conduction band must be more positive than the reduction potential H$^+$ to H$_2$, whereas the maximum of the semiconductor valence band must be more negative than the oxidation potential of H$_2$O to O$_2$. Thus, band alignment studies in interfaces of semiconductors with water become of great importance. They present several subtleties, as different or simplistic modelling will result in few decimes of eV difference that would lead to a wrong prediction in the alignment. So, to find a trustful method is desirable. In this work, first principles calculations based on density functional theory (DFT) in the all electron and the pseudopotential approaches have been performed for the analysis of the band alignment in Sr$_{1-x}$Ca$_x$O$_2$N /H$_2$O interfaces. A detailed study of the modelling of the surface, supercells, and interface with water molecules was done. Experimental data were taken for the structures and photoelectrochemical behaviour and well reproduced by the methodology implemented. The analysis carried out led to theoretical results compatible with the experimental results: the calculations show that the Sr$_{1-x}$Ca$_x$O$_2$N /H$_2$O is suitable for photoelectrochemical applications, and the partial substitution of Sr by Ca enables the gap and alignment tuning thus enhancing the complex performance.

Heat wave (HW) events are becoming more frequent, and they have important consequences because of the negative effects they can have not only on the human population in health terms, but also on biodiversity and agriculture. This motivated a study of the trends in HW events over Extremadura, a region in the southwest of Spain, with much of its area in summer devoted to the production of irrigated crops such as maize and tomatoes. Heat waves were defined for the study as two consecutive days with temperatures above the 95th percentile of the summer (June-August) maximum temperature (Tmax) time series. Two datasets were used: one consisted of 13 daily temperature records uniformly distributed over the Region, and the other was the SPAIN02 gridded observational dataset, extracting just the points corresponding to Extremadura. The trends studied were in the duration, intensity, and frequency of HW events, and in other parameters such as the mean, low (25th percentile), and high (75th percentile) values. In general terms, the results showed significant positive trends in those parameters over the east, the northwest, and a small area in the south of the Region. In order to study changes in HW characteristics (duration, frequency and intensity) considering different subperiods, a stochastic model was used to generate 1000 time series equivalent to the observed ones. The results showed that there were no significant changes in HW duration in the last 10-year subperiod in comparison with the first. But the results were different for warm events (WE), defined with a lower threshold (the 75th percentile), which are also important for agriculture. For several sites, there were significant changes in WE duration, frequency, and intensity., Comment: 36 pages, 11 figures

Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential level is challenging due to the higher uncertainties involved. Recently deep neural networks have been leveraged to address this issue. This paper investigates the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based BiLSTM (CNN-BiLSTM) to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand. Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr. span, given the previous 24 hr. load data. The BiLSTM model achieved the lowest RMSE of 1.4842 for the overall daily forecast. In addition, standard LSTM and CNN-LSTM models are trained and compared with the BiLSTM architecture. The RMSE of BiLSTM is 5.60%, 2.85% and 2.60% lower than the LSTM, CNN-LSTM and CNN-BiLSTM models respectively. The source code of this work is available at https://github.com/Varat7v2/STLF-BiLSTM-CNNBiLSTM.git., Comment: This article has been accepted for publication in 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). This preprint is for personal use - that is solely for the purpose of research, but republication/redistribution requires IEEE permission. Please check IEEE website for more information

In the search for new renewable energy to replace fossil fuels, Hydrogen is one of the most promising candidates for clean energy production. But cheap Hydrogen separation and storage is still a big challenge. Photoelectrochemical devices look promising for the decomposition of the water molecule into $2H_2+O_2$. Every day new materials and combinations are discovered or invented to improve the efficiency of the complex total process. A necessary condition for the photoelectrochemical process to work without a bias voltage is that the minimum of the semiconductor conduction band (CBM) must be more positive than the reduction potential $H^{+}$ to $H_2$, whereas the highest value of the semiconductor valence band (VBM) must be more negative than the oxidation potential of $H_2O$ to $O_2$. Thus, band alignment studies in interfaces of semiconductors with water become of vital importance. In this work, first principles calculations based on density functional theory (DFT) in the all electron and the pseudo potential approaches have been performed for the analysis of the band alignment in $SrTaO_2N/H_2O$ interfaces. Different surface terminations were analyzed, together with the dependence of the gap and band alignment with lattice constants for systems grown on mismatched substrates. Water structures were built from classical molecular dynamics and its electronic structure calculated using DFT. The calculations show that the $SrTaO_2N$ (001) is suitable for photoelectrochemical applications on a wide range of lattice constant a, except for a compression/elongation of -2%, -1% and 3%, while STN (110) results suitable for photoelectrochemical devices over a wider range of lattice constants from -1% to 3%., Comment: 13 pages, 7 figures

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines data-driven methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. To solve mixed-integer bilinear programs online with data-driven methods, several formulations exist including mathematical programming with complementary constraints (MPCC), mixed-integer programming (MIP). In this work, we benchmark the performances of those data-driven schemes on a bookshelf organization problem that has discrete mode switch and collision avoidance constraints. The success rate, optimal cost and solving time are compared along with non-data-driven methods. Our proposed methods are demonstrated as a high level planner for a robotic arm for the bookshelf problem., Comment: arXiv admin note: substantial text overlap with arXiv:2110.00666

The logistics of transporting a package from a storage facility to the consumer's front door usually employs highly specialized robots often times splitting sub-tasks up to different systems, e.g., manipulator arms to sort and wheeled vehicles to deliver. More recent endeavors attempt to have a unified approach with legged and humanoid robots. These solutions, however, occupy large amounts of space thus reducing the number of packages that can fit into a delivery vehicle. As a result, these bulky robotic systems often reduce the potential for scalability and task parallelization. In this paper, we introduce LIMMS (Latching Intelligent Modular Mobility System) to address both the manipulation and delivery portion of a typical last-mile delivery while maintaining a minimal spatial footprint. LIMMS is a symmetrically designed, 6 degree of freedom (DoF) appendage-like robot with wheels and latching mechanisms at both ends. By latching onto a surface and anchoring at one end, LIMMS can function as a traditional 6-DoF manipulator arm. On the other hand, multiple LIMMS can latch onto a single box and behave like a legged robotic system where the package is the body. During transit, LIMMS folds up compactly and takes up much less space compared to traditional robotic systems. A large group of LIMMS units can fit inside of a single delivery vehicle, opening the potential for new delivery optimization and hybrid planning methods never done before. In this paper, the feasibility of LIMMS is studied and presented using a hardware prototype as well as simulation results for a range of sub-tasks in a typical last-mile delivery.

Designing objects with predefined optical properties is a task of fundamental importance for nanophotonics, and chirality is a prototypical example of such a property, with applications ranging from photochemistry to nonlinear photonics. A measure of electromagnetic chirality with a well-defined upper bound has recently been proposed. Here, we optimize the shape of silver helices at discrete frequencies ranging from the far infrared to the optical band. Gaussian process optimization, taking into account also shape derivative information of the helices scattering response, is used to maximize the electromagnetic chirality. We show that the theoretical designs achieve more than 90 percent of the upper bound of em-chirality for wavelenghts \SI{3}{\micro\meter} or larger, while their performance decreases towards the optical band. We fabricate and characterize helices for operation at \SI{800}{\nano\meter}, and identify some of the imperfections that affect the performance. Our work motivates further research both on the theoretical and fabrication sides to unlock potential applications of objects with large electromagnetic chirality at optical frequencies, such as helicity filtering glasses. We show that, at \SI{3}{\micro\meter}, a thin slab of randomly oriented helices can absorb 99 percent of the light of one helicity while absorbing only 9 percent of the opposite helicity.

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. Gathering sufficient training data to employ these methods still present a challenge since getting data from traditional solvers are slow and newer learning approaches still require large amounts of data. In order to scale up and make these hybrid learning approaches more manageable we propose ReDUCE, a method that exploits structure within small to medium size datasets. We also introduce the bookshelf organization problem as an MINLP as a way to measure performance of solvers with ReDUCE. Results show that existing algorithms with ReDUCE can solve this problem within a few seconds, a significant improvement over the original formulation. ReDUCE is demonstrated as a high level planner for a robotic arm for the bookshelf problem.

The microstructure, mechanical properties and thermal stability of AlTiN and AlTiBN coatings grown by reactive high-power impulse magnetron sputtering (HiPIMS) have been analyzed as a function of Al/(Al+Ti) ratio, x, between 0.5 and 0.8. The coatings were predominantly formed by a face-centered cubic Ti(Al)N crystalline phase, both with and without B, even for x ratios as high as 0.6, which is higher than the ratio typically encountered for AlTiN coatings deposited by reactive magnetron sputtering. B doping, in combination with the highly energetic deposition conditions offered by HiPIMS, results in the suppression of the columnar grain morphology typically encountered in AlTiN coatings. On the contrary, the AlTiBN coatings grown by HiPIMS present a dense nanocomposite type microstructure, formed by nanocrystalline Ti(Al)N domains and amorphous regions composed of Ti(Al)B2 and BN. As a result, high-Al content (x>0.6) AlTiBN coatings grown by HiPIMS offer higher hardness, elasticity and fracture toughness than AlTiN coatings. Moreover, the thermal stability and the hot hardness are substantially enhanced, delaying the onset of formation of the detrimental hexagonal AlN phase from 850 C in the case of AlTiN, to 1000 C in the case of AlTiBN.

Practitioners often rely on compute-intensive domain randomization to ensure reinforcement learning policies trained in simulation can robustly transfer to the real world. Due to unmodeled nonlinearities in the real system, however, even such simulated policies can still fail to perform stably enough to acquire experience in real environments. In this paper we propose a novel method that guarantees a stable region of attraction for the output of a policy trained in simulation, even for highly nonlinear systems. Our core technique is to use "bias-shifted" neural networks for constructing the controller and training the network in the simulator. The modified neural networks not only capture the nonlinearities of the system but also provably preserve linearity in a certain region of the state space and thus can be tuned to resemble a linear quadratic regulator that is known to be stable for the real system. We have tested our new method by transferring simulated policies for a swing-up inverted pendulum to real systems and demonstrated its efficacy.

Recent studies have shown that even in the absence of extrinsic stress, the morphologically symmetrically dividing model bacteria Escherichia coli do not generate offspring with equal reproductive fitness. Instead, daughter cells exhibit asymmetric division times that converge to two distinct growth states. This represents a limited senescence / rejuvenation process derived from asymmetric division that is stable for hundreds of generations. It remains unclear why the bacteria do not continue the senescence beyond this asymptote. Although there are inherent fitness benefits for heterogeneity in population growth rates, the two growth equilibria are surprisingly similar, differing by a few percent. In this work we derive an explicit model for the growth of a bacterial population with two growth equilibria, based on a generalized Fibonacci recurrence, in order to quantify the fitness benefit of a limited senescence process and examine costs associated with asymmetry that could generate the observed behavior. We find that with simple saturating effects of asymmetric partitioning of subcellular components, two distinct but similar growth states may be optimal while providing evolutionarily significant fitness advantages., Comment: 29 pages (manuscript + supplemental materials). This work grew out of arXiv:1901.04080 v1, which previously discussed both combinatorics and biology. The updated arXiv:1901.04080 v2 now focuses solely on the combinatorial aspects, while this manuscript focuses on the biological application

First-principles calculations based on density-functional theory in the pseudo-potential approach have been performed for the total energy, crystal structure and cell polarization for SrTaO$_2$N/SrTiO$_3$ heterostructures. Different heterojunctions were analyzed in terms of the termination atoms at the interface plane, and periodic or non-periodic stacking in the perpendicular direction. The calculations show that the SrTaO$_2$N layer is compressed along the $ab$-plane, while the SrTiO$_3$ is elongated, thus favoring the formation of P4mm local environment on both sides of the interface, leading to net macroscopic polarization. The analysis of the local polarization as a function of the distance to the interface, for each individual unit cell was found to depend on the presence of a N or an O atom at the interface, and also on the asymmetric and not uniform $c$-axis deformation due to the induced strain in the $ab$-plane. The resulting total polarization in the periodic array was $ \approx 0.54$ C/m$^2$, which makes this type of arrangement suitable for microelectronic applications.

We present an electromagnetic chirality measure for 2D arrays of subwavelength periodicities under normal illumination. The calculation of the measure uses only the complex reflection and transmission coefficients from the array. The measure allows the ordering of arrays according to their electromagnetic chirality, which further allows a quantitative comparison of different design strategies. The measure is upper bounded, and the extreme properties of objects with high values of electromagnetic chirality make them useful in both near- and far-field applications. We analyze the consequences that different possible symmetries of the array have on its electromagnetic chirality. We use the measure to study four different arrays. The results indicate the suitability of helices for building arrays of high electromagnetic chirality, and the low effectiveness of a substrate for breaking the transverse mirror symmetry.

Bacteria in aquatic environments often interact with particulate matter. A key example is bacterial degradation of marine snow responsible for carbon export from the upper ocean in the biological pump. The ecological interaction between bacteria and sinking particles is regulated by their encounter rate, which is therefore important to predict accurately in models of bacteria-particle interactions. Models available to date cover the diffusive encounter regime, valid for sinking particles larger than the typical run length of a bacterium. The majority of sinking particles, however, are small, and the encounter process is then ballistic rather than diffusive. In the ballistic regime, the shear generated by the particle's motion can be important in reorienting bacteria and thus determining the encounter rate, yet the effect of shear is not captured in current encounter rate models. Here, we combine analytical and numerical calculations to quantify the encounter rate between sinking particles and non-motile or motile microorganisms in the ballistic regime, explicitly accounting for the hydrodynamic shear created by the particle and its coupling with microorganism shape. We complement results with selected experiments on non-motile diatoms. We find that the shape-shear coupling has a considerable effect on the encounter rate and encounter location through the mechanisms of hydrodynamic focusing and screening, whereby elongated microorganisms preferentially orient normally to the particle surface downstream of the particle (focusing) and tangentially to the particle surface upstream of the particle (screening). We study these mechanisms as a function of the key dimensionless parameters: the ratio of particle sinking speed to microorganism swimming speed, the ratio of particle radius to microorganism length, and the microorganism's aspect ratio.

Dispersants aid the breakup of crude oil masses and increase the available interfacial surface area for bacteria to degrade insoluble hydrocarbons in the marine environment. However, this common view neglects key aspects of the microscale interactions between bacteria and oil droplets, particularly the encounters between these elements that are required for degradation to occur. This chapter discusses a biophysical model for hydrocarbon consumption of suspended oil droplets under conditions of rapid dilution that occur in natural environments. Based on the model, which includes typical biological growth parameters, dilution is found to produce an effective delay in the onset of biodegradation by approximately a week. The steady and rapid reduction in oil concertation, due to dilution, is found to outpace the production of oil-degrading bacteria that result from colonization of degrading oil droplets, maintaining the process in an encounter-limited state. This mechanistic model provides a baseline for better understanding of microscale biodegradation in dilute oil environments and can help inform the design of mitigation strategies in marine systems.