Sebocytes are lipid - secreting cells whose differentiation is marked by the accumulation of intracellular lipid droplets, making their quantification a key readout in sebocyte biology. Manual counting is labor - intensive and subjective, motivating automated solutions. Here, we introduce a simple attention - based multiple instance learning (MIL) framework for sebocyte image analysis. Nile Red - stained sebocyte images were annotated into 14 classes according to droplet counts, expanded via data augmentation to ab out 50,000 cells. Two models were benchmarked: a baseline multi - layer perceptron (MLP) trained on aggregated patch - level counts, and an attention - based MIL model leveraging precomputed ResNet - 50 feature embeddings with trainable instance weighting. Experiments using five - fold cross - validation showed that the baseline MLP achieved more stable performance (mean MAE = 5.6) compared with the attention - based MIL, which was less consistent (mean MAE = 10.7) but occasionally superior in specific folds. The se findings indicate that simple bag - level aggregation provides a robust baseline for slide - level droplet counting, while attention - based MIL requires task - aligned pooling and regularization to fully realize its potential in sebocyte image analysis.

Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence of herbicide resistant weeds. To address these challenges, we developed a vision guided, AI-driven variable rate sprayer system capable of detecting weed presence, estimating canopy size, and dynamically adjusting nozzle activation in real time. The system integrates lightweight YOLO11n and YOLO11n-seg deep learning models, deployed on an NVIDIA Jetson Orin Nano for onboard inference, and uses an Arduino Uno-based relay interface to control solenoid actuated nozzles based on canopy segmentation results. Indoor trials were conducted using 15 potted Hibiscus rosa sinensis plants of varying canopy sizes to simulate a range of weed patch scenarios. The YOLO11n model achieved a mean average precision (mAP@50) of 0.98, with a precision of 0.99 and a recall close to 1.0. The YOLO11n-seg segmentation model achieved a mAP@50 of 0.48, precision of 0.55, and recall of 0.52. System performance was validated using water sensitive paper, which showed an average spray coverage of 24.22% in zones where canopy was present. An upward trend in mean spray coverage from 16.22% for small canopies to 21.46% and 21.65% for medium and large canopies, respectively, demonstrated the system's capability to adjust spray output based on canopy size in real time. These results highlight the potential of combining real time deep learning with low-cost embedded hardware for selective herbicide application. Future work will focus on expanding the detection capabilities to include three common weed species in South Dakota: water hemp (Amaranthus tuberculatus), kochia (Bassia scoparia), and foxtail (Setaria spp.), followed by further validation in both indoor and field trials within soybean and corn production systems.

A zettabyte is a trillion gigabytes. That's a lot--but, according to one estimate, humanity will produce a hundred and eighty zettabytes of digital data this year. It all adds up: PowerPoints and selfies; video captured by cameras; electronic health records; data retrieved from smart devices or collected by telescopes and particle accelerators; backups, and backups of the backups. Where should it all go, and how much of it should be kept, and for how long? These questions vex the computer scientists who manage the world's storage. For them, the cloud isn't nebulous but a physical system that must be built, paid for, and maintained.

Sheets of metal just two atoms thick can be produced by squashing molten droplets at great pressure between two sapphires. The researchers who developed the process say the unusual materials could have applications in industrial chemistry, optics and computers. Last year, scientists created a gold sheet that was a single atom thick, which they dubbed "goldene" after graphene, a material made of a single layer of carbon atoms. Such materials have been described as two-dimensional, as they are as thin as chemically possible. But making other 2D metals hadn't been possible until now. The new technique, developed by Luojun Du at the Chinese Academy of Sciences and his colleagues, can create 2D sheets of bismuth, gallium, indium, tin and lead that are as thin as their atomic bonds allow.

Accurate molecular quantification is essential for advancing research and diagnostics in fields such as infectious diseases, cancer biology, and genetic disorders. Droplet digital PCR (ddPCR) has emerged as a gold standard for achieving absolute quantification. While computational ddPCR technologies have advanced significantly, achieving automatic interpretation and consistent adaptability across diverse operational environments remains a challenge. To address these limitations, we introduce the intelligent interpretable droplet digital PCR (I2ddPCR) assay, a comprehensive framework integrating front-end predictive models (for droplet segmentation and classification) with GPT-4o multimodal large language model (MLLM, for context-aware explanations and recommendations) to automate and enhance ddPCR image analysis. This approach surpasses the state-of-the-art models, affording 99.05% accuracy in processing complex ddPCR images containing over 300 droplets per image with varying signal-to-noise ratios (SNRs). By combining specialized neural networks and large language models, the I2ddPCR assay offers a robust and adaptable solution for absolute molecular quantification, achieving a sensitivity capable of detecting low-abundance targets as low as 90.32 copies/{\mu}L. Furthermore, it improves model's transparency through detailed explanation and troubleshooting guidance, empowering users to make informed decisions. This innovative framework has the potential to benefit molecular diagnostics, disease research, and clinical applications, especially in resource-constrained settings.

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, illsuited to either classical control methods or machine learning. Here we introduce a rewarddriven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decisionmaking logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM. 2 Introduction Scanning probe microscopy (SPM) has revolutionized our understanding of the nanoworld, providing unprecedented insights into the structure and properties of materials at the nanoscale. This powerful technique allows for structural imaging in diverse environments, including ambient conditions, liquids, and vacuum, making it versatile for various applications [1-3]. Over the years, SPM has evolved significantly, building upon the initial contact and noncontact modes [4, 5] to yield a broad array of advanced imaging modes.

Jellyfish-shaped robots made of magnetic ferrofluid can be controlled by light through an underwater obstacle course. Swarms of these soft robots could be useful for delivering chemicals throughout a liquid mixture or moving fluids through a lab-on-a-chip. Ferrofluid droplets are made of magnetic nanoparticles suspended in oil, and they can move across flat surfaces or change shape when coaxed in different directions by magnets. By immersing these droplets in water and exposing them to light, Mengmeng Sun at the Max Planck Institute for Intelligent Systems in Germany and his colleagues have now made them defy gravity. When ferrofluids absorb light – they are particularly good at that because they are dark – they heat up and any tiny bubbles within them expand.

Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and extend neural PDE solvers as a tool to aid in scaling simulations for two-phase flow problems, and simulations of oil expulsion from a pore specifically. We extend existing numerical methods for this problem to a more complex setting involving varying geometries of the domain to generate a challenging dataset. Further, we investigate three prominent neural PDE solver methods, namely the UNet, DRN, and U-FNO, and extend them for characteristics of the oil-expulsion problem: (1) spatial conditioning on the geometry; (2) periodicity in the boundary; (3) approximate mass conservation. We scale all methods and benchmark their speed-accuracy trade-off, evaluate qualitative properties, and perform an ablation study. We find that the investigated methods can accurately model the droplet dynamics with up to three orders of magnitude speed-up, that our extensions improve performance over the baselines, and that the introduced varying geometries constitute a significantly more challenging setting over the previously considered oil expulsion problem.

Knowing the printer model used to print a given document may provide a crucial lead towards identifying counterfeits or conversely verifying the validity of a real document. Inkjet printers produce probabilistic droplet patterns that appear to be distinct for each printer model and as such we investigate the utilization of droplet characteristics including frequency domain features extracted from printed document scans for the classification of the underlying printer model. We collect and publish a dataset of high resolution document scans and show that our extracted features are informative enough to enable a neural network to distinguish not only the printer manufacturer, but also individual printer models.

Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical substrates, including atomic switches, photonics, volatile memristors, and, recently, memcapacitors, to achieve efficient high-dimensional mapping. Traditional PRCs often consist of homogeneous device arrays, which rely on input encoding methods and large stochastic device-to-device variations for increased nonlinearity and high-dimensional mapping. These approaches incur high pre-processing costs and restrict real-time deployment. Here, we introduce a novel heterogeneous memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and non-monotonic input-state correlations crucial for efficient high-dimensional transformations. We demonstrate our approach's efficacy by predicting a second-order nonlinear dynamical system with an extremely low prediction error (0.00018). Additionally, we predict a chaotic H\'enon map, achieving a low normalized root mean square error (0.080). Unlike previous PRCs, such errors are achieved without input encoding methods, underscoring the power of distinct input-state correlations. Most importantly, we generalize our approach to other neuromorphic devices that lack inherent voltage offsets using externally applied offsets to realize various input-state correlations. Our approach and unprecedented performance are a major milestone towards high-performance full in-materia PRCs.