Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows.

Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach to autonomy, but are known to not generalize well to `out-of-distribution' environments not encountered during training. In this work, we train a normalizing flow-based prior over the environment, which provides a measure of how far out-of-distribution the quadrotor is at any given time. We use this measure as a runtime monitor, allowing us to switch between a learning-based controller and a safe controller when we are sufficiently out-of-distribution. Our methods are benchmarked on a point-to-point navigation task in a simulated 3D cave environment based on real-world point cloud data from the DARPA Subterranean Challenge Final Event Dataset. Our experimental results show that our combined controller simultaneously possesses the liveness of the learning-based controller (completing the task quickly) and the safety of the safety controller (avoiding collision).

Breakthroughs, discoveries, and DIY tips sent every weekday. Sweat stains are a white t-shirt's worst enemy. Unfortunately, that notorious fabric yellowing is often unavoidable due to the combination of oleic acid, squalene, and other organic compounds found in your skin oil and sweat. Factor in a chance encounter with natural food pigments like the carotene and lycopene found in tomatoes and oranges, and it's probably only a matter of time before you'll need to break out the bleach or hydrogen peroxide. Even then, the results are often unsatisfactory for your (once) vibrant white shirts.

Sequential decision-making is central to sustainable agricultural management and precision agriculture, where resource inputs must be optimized under uncertainty and over time. However, such decisions must often be made with limited observations, whereas classical bandit and reinforcement learning approaches typically rely on either linear or black-box reward models that may misrepresent domain knowledge or require large amounts of data. We propose a family of nonlinear, model-based bandit algorithms that embed domain-specific response curves directly into the exploration-exploitation loop. By coupling (i) principled uncertainty quantification with (ii) closed-form or rapidly computable profit optima, these algorithms achieve sublinear regret and near-optimal sample complexity while preserving interpretability. Theoretical analysis establishes regret and sample complexity bounds, and extensive simulations emulating real-world fertilizer-rate decisions show consistent improvements over both linear and nonparametric baselines (such as linear UCB and $k$-NN UCB) in the low-sample regime, under both well-specified and shape-compatible misspecified models. Because our approach leverages mechanistic insight rather than large data volumes, it is especially suited to resource-constrained settings, supporting sustainable, inclusive, and transparent sequential decision-making across agriculture, environmental management, and allied applications. This methodology directly contributes to SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by enabling data-driven, less wasteful agricultural practices.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful and widely used tools for molecular structure elucidation in organic chemistry. However, the interpretation of NMR spectra to determine unknown molecular structures remains a labor-intensive and expertise-dependent process, particularly for complex or novel compounds. Although recent methods have been proposed for molecular structure elucidation, they often underperform in real-world applications due to inherent algorithmic limitations and limited high-quality data. Here, we present NMR-Solver, a practical and interpretable framework for the automated determination of small organic molecule structures from $^1$H and $^{13}$C NMR spectra. Our method introduces an automated framework for molecular structure elucidation, integrating large-scale spectral matching with physics-guided fragment-based optimization that exploits atomic-level structure-spectrum relationships in NMR. We evaluate NMR-Solver on simulated benchmarks, curated experimental data from the literature, and real-world experiments, demonstrating its strong generalization, robustness, and practical utility in challenging, real-life scenarios. NMR-Solver unifies computational NMR analysis, deep learning, and interpretable chemical reasoning into a coherent system. By incorporating the physical principles of NMR into molecular optimization, it enables scalable, automated, and chemically meaningful molecular identification, establishing a generalizable paradigm for solving inverse problems in molecular science.

--ConceptBot is a modular robotic planning framework that combines Large Language Models and Knowledge Graphs to generate feasible and risk-aware plans despite ambiguities in natural language instructions and correctly analyzing the objects present in the environment--challenges that typically arise from a lack of commonsense reasoning. T o do that, ConceptBot integrates (i) an Object Property Extraction (OPE) module that enriches scene understanding with semantic concepts from ConceptNet, (ii) a User Request Processing (URP) module that disambiguates and structures instructions, and (iii) a Planner that generates context-aware, feasible pick-and-place policies. In comparative evaluations against Google SayCan, ConceptBot achieved 100% success on explicit tasks, maintained 87% accuracy on implicit tasks (versus 31% for SayCan), reached 76% on risk-aware tasks (versus 15%), and outperformed SayCan in application-specific scenarios, including material classification (70% vs. 20%) and toxicity detection (86% vs. 36%). On SafeAgentBench, ConceptBot achieved an overall score of 80% (versus 46% for the next-best baseline). These results, validated in both simulation and laboratory experiments, demonstrate ConceptBot's ability to generalize without domain-specific training and to significantly improve the reliability of robotic policies in unstructured environments. Advances in recent decades in robotic core capabilities, i.e., perception, control, and manipulation, have increased demand for autonomous systems in fields ranging from manufacturing to healthcare, logistics to home care, etc. These capabilities are deeply interconnected with the planning phase [1], as successful planning depends on a robot's ability to perceive its environment accurately, execute precise control, and perform effective manipulation. Despite significant progress, planning in robotic systems continues to face challenges, particularly in unstructured environments [2]. A key element in achieving effective planning is task decomposition [3], which involves breaking complex objectives into smaller, manageable actions. This process is essential for simplifying execution and ensuring flexibility in diverse environments. Traditional task decomposition approaches, however, often rely on rigid, pre-programmed templates or static models, which struggle to adapt to unfamiliar or dynamic conditions [4]-[7]. Recently, advancements in Large Language Models (LLMs) have introduced a more dynamic alternative. LLMs enable robots to process natural language instructions, understand contextual nuances, and dynamically decompose tasks into actionable steps [8]-[10]. However, directly employing pre-trained LLMs often leads to non-executable or ineffective plans, as these models struggle to account for domain-specific constraints and real-world feasibility [11]- [13].

Accurate determination of total oxidant concentration ([Ox]_{tot}) in non-thermal plasma (NTP)-treated aqueous systems remains a critical challenge due to the transient nature of reactive oxygen and nitrogen species and the subjectivity of conventional titration methods used for [Ox]_{tot} determination. This study introduces a novel, color-based computer analysis (CBCA) method that integrates advanced image processing with machine learning (ML) to quantify colorimetric shifts in potassium iodide (KI) solutions during oxidation. First, a custom-built visual data acquisition system captured high-resolution video of the color transitions in a KI solution during oxidation with an NTP system. The change in [Ox]_{tot} during the experiments was monitored with a standard titrimetric method. Second, the captured frames were processed using a robust image processing pipeline to extract RGB, HSV, and Lab color features. The extracted features were statistically evaluated, and the results revealed strong linear correlations with the measured [Ox]_{tot} values, particularly in the saturation (HSV), a and b (Lab), and blue (RGB) channels. Subsequently, the [Ox]_{tot} measurements and the extracted color features were used to train and validate five ML models. Among them, linear regression and gradient boosting models achieved the highest predictive accuracy (R^2 > 0.990). It was also found that reducing the feature set from nine to four resulted in comparable performance with improved prediction efficiency, especially for gradient boosting. Finally, comparison of the model predictions with real titration measurements revealed that the CBCA system successfully predicts the [Ox]_{tot} in KI solution with high accuracy (R^2 > 0.998) even with a reduced number of features.

As organizations face increasing pressure to understand their corporate and products' carbon footprints, artificial intelligence (AI)-assisted calculation systems for footprinting are proliferating, but with widely varying levels of rigor and transparency. Standards and guidance have not kept pace with the technology; evaluation datasets are nascent; and statistical approaches to uncertainty analysis are not yet practical to apply to scaled systems. We present a set of criteria to validate AI-assisted systems that calculate greenhouse gas (GHG) emissions for products and materials. We implement a three-step approach: (1) Identification of needs and constraints, (2) Draft criteria development and (3) Refinements through pilots. The process identifies three use cases of AI applications: Case 1 focuses on AI-assisted mapping to existing datasets for corporate GHG accounting and product hotspotting, automating repetitive manual tasks while maintaining mapping quality. Case 2 addresses AI systems that generate complete product models for corporate decision-making, which require comprehensive validation of both component tasks and end-to-end performance. We discuss the outlook for Case 3 applications, systems that generate standards-compliant models. We find that credible AI systems can be built and that they should be validated using system-level evaluations rather than line-item review, with metrics such as benchmark performance, indications of data quality and uncertainty, and transparent documentation. This approach may be used as a foundation for practitioners, auditors, and standards bodies to evaluate AI-assisted environmental assessment tools. By establishing evaluation criteria that balance scalability with credibility requirements, our approach contributes to the field's efforts to develop appropriate standards for AI-assisted carbon footprinting systems.

Rebar tying is a repetitive but critical task in reinforced concrete construction, typically performed manually at considerable ergonomic risk. Recent advances in robotic manipulation hold the potential to automate the tying process, yet face challenges in accurately estimating tying poses in congested rebar nodes. In this paper, we introduce a hybrid perception and motion planning approach that integrates geometry-based perception with Equivariant Denoising Diffusion on SE(3) (Diffusion-EDFs) to enable robust multi-node rebar tying with minimal training data. Our perception module utilizes density-based clustering (DBSCAN), geometry-based node feature extraction, and principal component analysis (PCA) to segment rebar bars, identify rebar nodes, and estimate orientation vectors for sequential ranking, even in complex, unstructured environments. The motion planner, based on Diffusion-EDFs, is trained on as few as 5-10 demonstrations to generate sequential end-effector poses that optimize collision avoidance and tying efficiency. The proposed system is validated on various rebar meshes, including single-layer, multi-layer, and cluttered configurations, demonstrating high success rates in node detection and accurate sequential tying. Compared with conventional approaches that rely on large datasets or extensive manual parameter tuning, our method achieves robust, efficient, and adaptable multi-node tying while significantly reducing data requirements. This result underscores the potential of hybrid perception and diffusion-driven planning to enhance automation in on-site construction tasks, improving both safety and labor efficiency.

Robotic practices on the construction site emerge as an attention-attracting manner owing to their capability of tackle complex challenges, especially in the rebar-involved scenarios. Most of existing products and research are mainly focused on flat rebar setting with model training demands. To fulfill this gap, we propose OpenTie, a 3D training-free rebar tying framework utilizing a RGB-to-point-cloud generation and an open-vocabulary detection. We implements the OpenTie via a robotic arm with a binocular camera and guarantees a high accuracy by applying the prompt-based object detection method on the image filtered by our propose post-processing procedure based a image to point cloud generation framework. The system is flexible for horizontal and vertical rebar tying tasks and the experiments on the real-world rebar setting verifies that the effectiveness of the system in practice.