Holographic Optical Tweezers (HOT) are powerful tools that can manipulate micro and nano-scale objects with high accuracy and precision. They are most commonly used for biological applications, such as cellular studies, and more recently, micro-structure assemblies. Automation has been of significant interest in the HOT field, since human-run experiments are time-consuming and require skilled operator(s). Automated HOTs, however, commonly use point traps, which focus high intensity laser light at specific spots in fluid media to attract and move micro-objects. In this paper, we develop a novel automated system of tweezing multiple micro-objects more efficiently using multiplexed optical traps. Multiplexed traps enable the simultaneous trapping of multiple beads in various alternate multiplexing formations, such as annular rings and line patterns. Our automated system is realized by augmenting the capabilities of a commercially available HOT with real-time bead detection and tracking, and wavefront-based path planning. We demonstrate the usefulness of the system by assembling two different composite micro-structures, comprising 5 $\mu m$ polystyrene beads, using both annular and line shaped traps in obstacle-rich environments.

While deep neural networks are highly effective at solving complex tasks, their computational demands can hinder their usefulness in real-time applications and with limited-resources systems. Besides, for many tasks it is known that these models are over-parametrized: neoteric works have broadly focused on reducing the width of these networks, rather than their depth. In this paper, we aim to reduce the depth of over-parametrized deep neural networks: we propose an eNtropy-basEd Pruning as a nEural Network depTH's rEducer (NEPENTHE) to alleviate deep neural networks' computational burden. Based on our theoretical finding, NEPENTHE focuses on un-structurally pruning connections in layers with low entropy to remove them entirely. We validate our approach on popular architectures such as MobileNet and Swin-T, showing that when encountering an over-parametrization regime, it can effectively linearize some layers (hence reducing the model's depth) with little to no performance loss. The code will be publicly available upon acceptance of the article.

The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation.

Virginia farmer John Boyd Jr., weighs in on a watchdog's satellite tracking methane emissions and a provision in the omnibus bill that allocates funds for electronically tracking livestock. There is one thing that distinguishes 60-year-old Vo Van Van's rice fields from a mosaic of thousands of other emerald fields across Long An province in southern Vietnam's Mekong Delta: It isn't entirely flooded. Using less water and using a drone to fertilize are new techniques that Van is trying and Vietnam hopes will help solve a paradox at the heart of growing rice: The finicky crop isn't just vulnerable to climate change but also contributes uniquely to it. Rice must be grown separately from other crops and seedlings have to be individually planted in flooded fields; backbreaking, dirty work requiring a lot of labor and water that generates a lot of methane, a potent planet-warming gas that can trap more than 80-times more heat in the atmosphere in the short term than carbon dioxide. A large drone carrying fertilizer flies over Vo Van Van's rice fields in Long An province in southern Vietnam's Mekong Delta, on Jan. 23, 2024.

Soft robots utilizing inflatable dielectric membranes can realize intricate functionalities through the application of non-mechanical fields. However, given the current limitations in simulations, including low computational efficiency and difficulty in dealing with complex external interactions, the design and control of such soft robots often require trial and error. Thus, a novel one-dimensional (1D) discrete differential geometry (DDG)-based numerical model is developed for analyzing the highly nonlinear mechanics in axisymmetric inflatable dielectric membranes. The model captures the intricate dynamics of these membranes under both inflationary pressure and electrical stimulation. Comprehensive validations using hyperelastic benchmarks demonstrate the model's accuracy and reliability. Additionally, the focus on the electro-mechanical coupling elucidates critical insights into the membrane's behavior under varying internal pressures and electrical loads. The research further translates these findings into innovative soft robotic applications, including a spherical soft actuator, a soft circular fluid pump, and a soft toroidal gripper, where the snap-through of electroelastic membrane plays a crucial role. Our analyses reveal that the functional ranges of soft robots are amplified by the snap-through of an electroelastic membrane upon electrical stimuli. This study underscores the potential of DDG-based simulations to advance the understanding of the nonlinear mechanics of electroelastic membranes and guide the design of electroelastic actuators in soft robotics applications.

Tactile and textile skin technologies have become increasingly important for enhancing human-robot interaction and allowing robots to adapt to different environments. Despite notable advancements, there are ongoing challenges in skin signal processing, particularly in achieving both accuracy and speed in dynamic touch sensing. This paper introduces a new framework that poses the touch sensing problem as an estimation problem of resistive sensory arrays. Utilizing a Regularized Least Squares objective function which estimates the resistance distribution of the skin. We enhance the touch sensing accuracy and mitigate the ghosting effects, where false or misleading touches may be registered. Furthermore, our study presents a streamlined skin design that simplifies manufacturing processes without sacrificing performance. Experimental outcomes substantiate the effectiveness of our method, showing 26.9% improvement in multi-touch force-sensing accuracy for the tactile skin.

Mycotoxins are a group of naturally occurring, toxic chemical compounds produced by certain species of moulds (fungi), during growth on various crops and foodstuffs, including cereals, nuts, spices and dairy products (The World Health Organization (WHO), 2023). The ingestion of certain mycotoxins has been linked to a range of harmful health impacts on both humans and animals, from short-term poisoning to long-term consequences such as liver cancer, and in some cases, death (Mavrommatis et al., 2021; Marroquín-Cardona et al., 2014; Liu and Wu, 2010). Mycotoxins are secondary metabolites (that is, compounds produced by an organism that are not essential for its primary life processes) and are often produced during the pre-harvest, harvest, and storage phases under favourable conditions of humidity and temperature (Marroquín-Cardona et al., 2014; Van der Fels-Klerx et al., 2022). The most prevalent mycotoxins include aflatoxins, tricothecenes, fumonisins, zearalenones, ochratoxins and patulin, and are produced by certain plant-pathogenic species of Aspergillus, Fusarium, and Penicillium (Tola and Kebede, 2016). Mycotoxin contamination in crop products has been found to vary significantly across different geographical locations and is influenced by annual weather conditions (Logrieco et al., 2021; Leggieri et al., 2020).

Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.

Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields, including drug discovery and materials science. Existing studies adopt a global alignment approach to learn the knowledge from different modalities. These global alignment approaches fail to capture fine-grained information, such as molecular fragments and their corresponding textual description, which is crucial for downstream tasks. Furthermore, it is incapable to model such information using a similar global alignment strategy due to data scarcity of paired local part annotated data from existing datasets. In this paper, we propose Atomas, a multi-modal molecular representation learning framework to jointly learn representations from SMILES string and text. We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels. Additionally, Atomas's end-to-end training framework incorporates the tasks of understanding and generating molecule, thereby supporting a wider range of downstream tasks. In the retrieval task, Atomas exhibits robust generalization ability and outperforms the baseline by 30.8% of recall@1 on average. In the generation task, Atomas achieves state-of-the-art results in both molecule captioning task and molecule generation task. Moreover, the visualization of the Hierarchical Adaptive Alignment model further confirms the chemical significance of our approach. Our codes can be found at https://anonymous.4open.science/r/Atomas-03C3.

In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is utilized for classification. The study compares data from two detectors, cerium bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy resolution and sensitivity. We test various data generation, preprocessing, and classification methods, with Maximum Likelihood Classifier (MLC) and Conditional Variational Autoencoder (CVAE) yielding the best results. The study also highlights the impact of different detector types on classification accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe performing better in longer durations. The findings suggest the importance of selecting the appropriate detector and methodology based on specific application requirements.