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Hybrid Particle Swarm Optimization for Fast and Reliable Parameter Extraction in Thermoreflectance
Bingjia Xiao, Tao Chen, Wenbin Zhang, Xin Qian, Puqing Jiang
https://arxiv.org/abs/2507.22960

Hybrid Particle Swarm Optimization for Fast and Reliable Parameter Extraction in Thermoreflectance
Frequency-domain thermoreflectance (FDTR) is a widely used technique for characterizing thermal properties of multilayer thin films. However, extracting multiple parameters from FDTR measurements presents a nonlinear inverse problem due to its high dimensionality and multimodal, non-convex solution space. This study evaluates four popular global optimization algorithms: Genetic Algorithm (GA), Quantum Genetic Algorithm (QGA), Particle Swarm Optimization (PSO), and Fireworks Algorithm (FWA), for…

What happens when bees can’t buzz right? Nature starts falling apart #nature

What happens when bees can’t buzz right? Nature starts falling apart
High heat and heavy metals dampen a bumblebee’s trademark buzz, threatening pollen release and colony chatter. Tiny sensors captured up-to-400-hertz tremors that falter under environmental stress, raising alarms for ecosystems and sparking ideas for pollination robots.

Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials
Alexander Komar, Marc-Andr\'e Heidelmann, Kristina Schaaff
https://arxiv.org/abs/2508.11662

Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials
Generative artificial intelligence (GenAI) is transforming education, redefining the role of trainers and coaches in learning environments. In our study, we explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches. Through qualitative interviews with professionals in education and corporate training, we identify the following key topics: trainers and coaches increasingly …

Comprehensive Mapping of Tracer Diffusivities Across Composition Space in Ternary NiAlTi and Quinary NiCoFeAlTi High-Entropy Alloy Using Diffusion Couple Experiments and Physics Informed Neural Network Inversion
Ismail Kamil Worke, Suman Sadhu, Saswata Bhattacharyya, Aloke Paul
https://arxiv.org/abs/2509.09486

Comprehensive Mapping of Tracer Diffusivities Across Composition Space in Ternary NiAlTi and Quinary NiCoFeAlTi High-Entropy Alloy Using Diffusion Couple Experiments and Physics Informed Neural Network Inversion
A comprehensive experimental and physics informed neural network numerical inverse diffusion analysis is conducted in technologically important NiAlTi ternary and NiCoFeAlTi quinary solid solutions for estimating and extracting composition dependent diffusion coefficients. A systematic variation of tracer, intrinsic and interdiffusion coefficients with composition could be estimated in the ternary solid solution. Following, the possibility of producing Al Ti constant PB diffusion profiles keepi…

Upstream motion of oil droplets in co-axial Ouzo flow due to Marangoni forces
Steffen Bisswanger, Duarte Rocha, Sebastian Dehe, Christian Diddens, Tobias Baier, Detlef Lohse, Steffen Hardt
https://arxiv.org/abs/2509.15716

Upstream motion of oil droplets in co-axial Ouzo flow due to Marangoni forces
To explore the physicochemical hydrodynamics of phase-separating ternary liquids (Ouzo-type), a binary oil-ethanol mixture is introduced into a co-flowing stream of water. Oil droplets nucleate at the interface between the two liquids, leading to a larger oil droplet interacting with the ethanol-rich jet. Although buoyancy forces and hydrodynamic drag forces push the droplet in downstream direction, we observe an upstream motion. Using computational fluid dynamics simulations of a simplified mo…