Physics-Informed Neural Networks as Fast Surrogate Models for Electrochemical Flow Reactors
This paper introduces a physics-informed neural network (PINN) to model a transient two-dimensional electrochemical flow reactor, trained without labeled concentration data by embedding governing equations and conditions into a loss function. The PINN accurately predicts concentration fields across a broad operating domain, showing strong agreement with finite-difference solutions and achieving a 5.34 times faster inference speed. This demonstrates PINNs as accurate and efficient parametric surrogates for electrochemical transport problems, foundational for low-computational-cost digital-twin modeling.
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As temperatures get hotter, pesticides are more dangerous to farmworkers
Rising temperatures are making pesticides more dangerous for farmworkers, according to new research. Heat stress increases the body's vulnerability to chemicals, while higher evaporation rates and chemical transformations can lead to greater exposure and more toxic compounds. This issue disproportionately affects vulnerable populations, with many cases of pesticide poisoning going unreported.
Characterization of Thermal Systems from Noisy and Low-resolution Measurements Using Dynamic Mode Decomposition
This chapter explores the application of Dynamic Mode Decomposition (DMD) for characterizing thermal systems using noisy and low-resolution measurement data. It focuses on preprocessing and truncation strategies to improve stability and interpretability, considering cases like forced convection and transient heat conduction. The study demonstrates that DMD can effectively recover dominant thermal behavior from sparse and degraded datasets with appropriate truncation, balancing reconstruction fidelity and noise sensitivity.
FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment
A study investigated the relationship between Floating Point Operations (FLOPs) and execution time in AI models, finding that raw FLOPs are an insufficient metric due to varying parallelization efficiencies. While validating the original study's empirical findings, the replication revealed that the proposed $\alpha$-FLOPs estimation formula underestimates execution time on newer hardware, which exhibits instabilities and discontinuities. The research emphasizes the critical need for comprehensive replication packages in hardware-dependent efficiency assessments.
Theban tomb reveals how Egyptian burial trends evolved in time
A new study on Theban tomb 209 reveals how ancient Egyptian burial practices evolved over centuries. Initially, individual burials in elaborate coffins were common, but later periods saw the reuse of existing sites, leading to increased density and the superposition of bodies. This adaptation to previous burials suggests a "funerary memory" rather than a chaotic accumulation.
An adaptive and evolvable deep reinforcement learning framework for weather prediction
A new deep reinforcement learning framework, Feitian Adaptive Ensemble Weather (FTAE-Weather), has been developed to improve weather prediction. It coordinates an open pool of pretrained forecasters by learning when and where to trust each model, significantly reducing prediction errors across various atmospheric variables and lead times. This framework transforms a fragmented inventory of specialist models into a unified system that benefits from new AI weather forecasting architectures.
DEF CON crowd suspected in fake-hotspot attack on Delta flight
Passengers on a Delta flight from Las Vegas to Atlanta allegedly created a fake Wi-Fi hotspot, potentially to phish for credentials, after attending a cybersecurity conference. Delta confirmed an unauthorized network was present but stated flight safety was not compromised. The FBI is investigating the incident.
The Stock Market Is Doing Something Observed Only Once Before. History Is Clear About What Comes Next.
Despite recent market rallies, a key valuation metric, the S&P 500 Shiller Cyclically Adjusted Price-to-Earnings (CAPE) ratio, has remained above 40 since May, a level only seen once before in history during the dot-com bubble. This suggests that stocks are unusually richly valued, potentially signaling an impending market downturn. While not a definitive predictor, historical patterns indicate that stock prices tend to fall in the years following such peaks.
AI isn’t enough to protect social media communities from AI
AI moderation tools on social media platforms are struggling to effectively manage content, sometimes leading to the erroneous removal of valuable human-generated posts. The r/AskHistorians subreddit experienced significant data loss when Reddit's AI tools deleted historical content, highlighting the limitations of AI in discerning valuable information from spam. While AI aims to increase enforcement against harmful content, it can also exacerbate issues like spam detection due to the rise of sophisticated AI-generated content.