Critical heat flux (CHF) marks the onset of boiling crisis in light-water reactors, defining safe thermal-hydraulic operating limits. To support Phase II of the OECD/NEA AI/ML CHF benchmark, which introduces spatially varying power profiles, this work compiles and digitizes a broad CHF dataset covering both uniform and non-uniform axial heating conditions. Heating profiles were extracted from technical reports, interpolated onto a consistent axial mesh, validated via energy-balance checks, and encoded in machine-readable formats for benchmark compatibility. Classical CHF correlations exhibit substantial errors under uniform heating and degrade markedly when applied to non-uniform profiles, while modern tabular methods offer improved but still imperfect predictions. A neural network trained solely on uniform data performs well in that regime but fails to generalize to spatially varying scenarios, underscoring the need for models that explicitly incorporate axial power distributions. By providing these curated datasets and baseline modeling results, this study lays the groundwork for advanced transfer-learning strategies, rigorous uncertainty quantification, and design-optimization efforts in the next phase of the CHF benchmark.

The extraction and standardization of pharmacokinetic (PK) information from scientific literature remain significant challenges in computational pharmacology, which limits the reliability of data-driven models in drug development. Large language models (LLMs) have achieved remarkable progress in text understanding and reasoning, yet their adaptation to structured biomedical data, such as PK tables, remains constrained by heterogeneity, noise, and domain shift. To address these limitations, we propose HySim-LLM, a unified mathematical and computational framework that integrates embedding-weighted fine-tuning and manifold-aware denoising to enhance the robustness and interpretability of LLMs. We establish two theoretical results: (1) a similarity-weighted generalization bound that quantifies adaptation performance under embedding divergence, and (2) a manifold-based denoising guarantee that bounds loss contributions from noisy or off-manifold samples. These theorems provide a principled foundation for fine-tuning LLMs in structured biomedical settings. The framework offers a mathematically grounded pathway toward reliable and interpretable LLM adaptation for biomedical and data-intensive scientific domains.

The safe use of pharmaceuticals in food-producing animals is vital to protect animal welfare and human food safety. Adverse events (AEs) may signal unexpected pharmacokinetic or toxicokinetic effects, increasing the risk of violative residues in the food chain. This study introduces a predictive framework for classifying outcomes (Death vs. Recovery) using ~1.28 million reports (1987-2025 Q1) from the U.S. FDA's OpenFDA Center for Veterinary Medicine. A preprocessing pipeline merged relational tables and standardized AEs through VeDDRA ontologies. Data were normalized, missing values imputed, and high-cardinality features reduced; physicochemical drug properties were integrated to capture chemical-residue links. We evaluated supervised models, including Random Forest, CatBoost, XGBoost, ExcelFormer, and large language models (Gemma 3-27B, Phi 3-12B). Class imbalance was addressed, such as undersampling and oversampling, with a focus on prioritizing recall for fatal outcomes. Ensemble methods(Voting, Stacking) and CatBoost performed best, achieving precision, recall, and F1-scores of 0.95. Incorporating Average Uncertainty Margin (AUM)-based pseudo-labeling of uncertain cases improved minority-class detection, particularly in ExcelFormer and XGBoost. Interpretability via SHAP identified biologically plausible predictors, including lung, heart, and bronchial disorders, animal demographics, and drug physicochemical properties. These features were strongly linked to fatal outcomes. Overall, the framework shows that combining rigorous data engineering, advanced machine learning, and explainable AI enables accurate, interpretable predictions of veterinary safety outcomes. The approach supports FARAD's mission by enabling early detection of high-risk drug-event profiles, strengthening residue risk assessment, and informing regulatory and clinical decision-making.

Congressional speeches made by US politicians have become simpler since the 1970s and only require the reading age of a 13-year-old to be followed, study found. Computer scientists from Kansas State University analysed two million congressional speeches from Republican and Democrat politicians made between 1873 and 2010. Text analysis algorithms were used to examine how congressional speeches changed in terms of complexity, emotion and divisiveness over 138 years. More recent speeches use a smaller vocabulary, simpler language and talk about'the other party' more than speeches made even a decade ago, the authors found. Researchers put the drop in the reading level down to the rise of broadcast media in congress that started in the mid-1970s - with politicians'playing to the camera'.

Kansas State University Polytechnic Campus partners with Kansas Department of Wildlife, Parks and Tourism to send some of their employees and law enforcement officers to attend UAS Commercial Remote Pilot Training and be eligible to sit for the FAA exam to become certified remote pilots. "We're extremely proud to be selected as the UAS training provider for the Kansas Department of Wildlife, Parks and Tourism," says Kurt Carraway, executive director of Kansas State Polytechnic's Applied Aviation Research Centre. "The UAS training will help our law enforcement officers ensure safety across the state by being able to conduct search-and-recovery efforts more efficiently with UAS," says Susan Steffen, fisheries biologists for the Kansas Department of Wildlife, Parks and Tourism.

The entry from Kansas State University used minimal hardware sensors but used a conversation utility with a limited database to engage in conversation with users. Both tasks required moving carefully among The entry from Kansas State University (figure people, politely offering them information or 1) was developed by three exchange students hors d'oeuvres, recognizing when the people from the Czech Republic. Their entry consisted are making a request, and answering the request. of a The robot had sonar Celebrating the sixth year for the Robot Host sensors to provide obstacle avoidance and an competition, a new task, the robot information infrared sensor to sense the presence of people kiosk, was added. Three entries took on the by their temperature. Navigation was random challenge of creating host robots who can both and limited by xy bounds.

Serving hors d'oeuvres is not as easy as it might For the fifth five entries took on the challenge of devices were connected to both robots. Mannequins creating service robots who can offer hors were mounted on top of each robot to d'oeuvres to attendees of the robot exhibition. The robots communicated area, find and stop at people to offer food and with each other through a local area network interact with them, detect when more food is on wireless network cards on their laptop computers. For example, Ron Nucci from expected responses. The robot had voice-recognition guest, and serves him/her.

Second, team's software was able to find, recognize, Because the camera and the arm are on about 200 pounds. An edge-detection algorithm equipped with 2 rings of 16 sonar sensors. The camera was calibrated system is used on board the robot. Positioned to Pick Up every time it was moved. When the robot was trying edge in 3D space relative to the robot.

In real life, such functions range of objects along with the perceptual might be useful for in-home care of the capabilities required to support it. The rules specified a fixed course and a fixed set This event was extremely difficult because it of objects that would populate it. The course forced teams to implement both manipulation consisted of typical household furniture and (the grasping and moving of objects) and visual Lexan partitions arranged to produce a simplified object recognition. The objects were typical required teams to implement them for a wide household objects, such as a television remote, range of objects. It therefore eliminated a a pill bottle, and fruits and vegetables.

The team's robot software was nicknamed Their project was to develop software on the Nomad 200 robot for tasks such as maze following, office delivery, and office navigation. In both the second for the competition's Office Navigation and the final rounds, the software achieved event. The perfectly complete the task. It is equipped with 2 sonar route from the director's office to the conference rings of 16 sonars each and with 2 charge-coupled rooms, directed the robot to each of the device (CCD) cameras. The robot has a two conference rooms, correctly determined 486 processor on board with a hard drive and which conference room was not occupied, 16 megabytes of memory. The behaviors at the bottom level needed to worry about low-level responsibilities, such as avoiding obstacles and not hitting walls but did not need to know about the overall strategy for solving the task.