Topological materials occupy a frontier in condensed-matter physics thanks to their remarkable electronic and quantum properties, yet their cross-scale design remains bottlenecked by inefficient discovery workflows. Here, we introduce TopoMAS (Topological materials Multi-Agent System), an interactive human-AI framework that seamlessly orchestrates the entire materials-discovery pipeline: from user-defined queries and multi-source data retrieval, through theoretical inference and crystal-structure generation, to first-principles validation. Crucially, TopoMAS closes the loop by autonomously integrating computational outcomes into a dynamic knowledge graph, enabling continuous knowledge refinement. In collaboration with human experts, it has already guided the identification of novel topological phases SrSbO3, confirmed by first-principles calculations. Comprehensive benchmarks demonstrate robust adaptability across base Large Language Model, with the lightweight Qwen2.5-72B model achieving 94.55% accuracy while consuming only 74.3-78.4% of tokens required by Qwen3-235B and 83.0% of DeepSeek-V3's usage--delivering responses twice as fast as Qwen3-235B. This efficiency establishes TopoMAS as an accelerator for computation-driven discovery pipelines. By harmonizing rational agent orchestration with a self-evolving knowledge graph, our framework not only delivers immediate advances in topological materials but also establishes a transferable, extensible paradigm for materials-science domain.

Devices operating in Internet of Things (IoT) networks may be deployed across vast geographical areas and interconnected via multi-hop communications. Further, they may be unguarded. This makes them vulnerable to attacks and motivates operators to check on devices frequently. To this end, we propose and study an Unmanned Aerial Vehicle (UAV)-aided attestation framework for use in IoT networks with a charging station powered by solar. A key challenge is optimizing the trajectory of the UAV to ensure it attests as many devices as possible. A trade-off here is that devices being checked by the UAV are offline, which affects the amount of data delivered to a gateway. Another challenge is that the charging station experiences time-varying energy arrivals, which in turn affect the flight duration and charging schedule of the UAV. To address these challenges, we employ a Deep Reinforcement Learning (DRL) solution to optimize the UAV's charging schedule and the selection of devices to be attested during each flight. The simulation results show that our solution reduces the average age of trust by 88% and throughput loss due to attestation by 30%.

In this study, we investigate for the first time the low-rank properties of a tensorized large-scale spatio-temporal dynamic atmospheric variable. We focus on the Sentinel-5P tropospheric NO2 product (S5P-TN) over a four-year period in an area that encompasses the contiguous United States (CONUS). Here, it is demonstrated that a low-rank approximation of such a dynamic variable is feasible. We apply the low-rank properties of the S5P-TN data to inpaint gaps in the Sentinel-5P product by adopting a low-rank tensor model (LRTM) based on the CANDECOMP / PARAFAC (CP) decomposition and alternating least squares (ALS). Furthermore, we evaluate the LRTM's results by comparing them with spatial interpolation using geostatistics, and conduct a comprehensive spatial statistical and temporal analysis of the S5P-TN product. The results of this study demonstrated that the tensor completion successfully reconstructs the missing values in the S5P-TN product, particularly in the presence of extended cloud obscuration, predicting outliers and identifying hotspots, when the data is tensorized over extended spatial and temporal scales.

--Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transformative potential for testing, control, and monitoring. However, efficiently inferring the inherent hybrid continuous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. This letter proposes a neural substitute solver (NSS) approach, which is a neural-network-based framework ai med at rapid accurate inference with significantly reduced computational costs. Specifically, NSS leverages lightweight neural networks to substitute time-consuming matrix operation and high-order numerical integration steps in traditional solvers, which transforms sequential bottlenecks in to highly parallel operation suitable for edge hardware. Experimental validation on a multi-stage DC-DC converter demonstrates that NSS achieves 23x speedup and 60% hardware resource reduction compared to traditional so lvers, paving the way for deploying edge inference of high-fidelity PES dynamics.

Accurate, timely, and farm-level crop type information is paramount for national food security, agricultural policy formulation, and economic planning, particularly in agriculturally significant nations like India. While remote sensing and machine learning have become vital tools for crop monitoring, existing approaches often grapple with challenges such as limited geographical scalability, restricted crop type coverage, the complexities of mixed-pixel and heterogeneous landscapes, and crucially, the robust in-season identification essential for proactive decision-making. We present a framework designed to address the critical data gaps for targeted data driven decision making which generates farm-level, in-season, multi-crop identification at national scale (India) using deep learning. Our methodology leverages the strengths of Sentinel-1 and Sentinel-2 satellite imagery, integrated with national-scale farm boundary data. The model successfully identifies 12 major crops (which collectively account for nearly 90% of India's total cultivated area showing an agreement with national crop census 2023-24 of 94% in winter, and 75% in monsoon season). Our approach incorporates an automated season detection algorithm, which estimates crop sowing and harvest periods. This allows for reliable crop identification as early as two months into the growing season and facilitates rigorous in-season performance evaluation. Furthermore, we have engineered a highly scalable inference pipeline, culminating in what is, to our knowledge, the first pan-India, in-season, farm-level crop type data product. The system's effectiveness and scalability are demonstrated through robust validation against national agricultural statistics, showcasing its potential to deliver actionable, data-driven insights for transformative agricultural monitoring and management across India.

--Privacy-preserving distributed model training is crucial for modern machine learning applications, yet existing Federated Learning approaches struggle with heterogeneous data distributions and varying computational capabilities. Traditional solutions either treat all participants uniformly or require costly dynamic clustering during training, leading to reduced efficiency and delayed model specialization. We present FedCCL (Federated Clustered Continual Learning), a framework specifically designed for environments with static organizational characteristics but dynamic client availability. By combining static pre-training clustering with an adapted asynchronous FedA vg algorithm, Fed-CCL enables new clients to immediately profit from specialized models without prior exposure to their data distribution, while maintaining reduced coordination overhead and resilience to client disconnections. Our approach implements an asynchronous Federated Learning protocol with a three-tier model topology -- global, cluster-specific, and local models -- that efficiently manages knowledge sharing across heterogeneous participants. Evaluation using photovoltaic installations across central Europe demonstrates that FedCCL's location-based clustering achieves an energy prediction error of 3.93% ( 0.21%), while maintaining data privacy and showing that the framework maintains stability for population-independent deployments, with 0.14 percentage point degradation in performance for new installations. The results demonstrate that FedCCL offers an effective framework for privacy-preserving distributed learning, maintaining high accuracy and adaptability even with dynamic participant populations. The Federated Learning (FL) paradigm [1], [2] has emerged as a pivotal solution for privacy-preserving machine learning, enabling multiple participants to collaboratively train models while maintaining data privacy.

A solar-powered pool robot sounds like a perfect cleaning solution, but it turns out the sun can provide only so much juice in a day. The dream of every swimming pool owner is that some device will come along that will clean the pool without requiring much--or any--interaction. Pump-powered robots are obtrusive and unsightly thanks to their snaking cables. Battery-powered robots must be manually retrieved after a few hours, cleaned out, and recharged. The holy grail remains elusive. With its S2 Solar Vision, Wybot takes at least one baby step in the right direction, outfitting a modified version of its existing Wybot S2 robot with a solar-powered docking and charging station.

United Nations Secretary-General Antonio Guterres has condemned a Russian drone and missile attack against Ukraine this week that has been described as the largest such assault in the three-year war. In a statement on Saturday, Guterres's spokesperson said the Russian strikes "disrupted the power supply to the Zaporizhzhia Nuclear Power Plant, once again underlining the ongoing risks to nuclear safety". "The secretary-general is alarmed by this dangerous escalation and the growing number of civilian casualties," the statement read. Ukrainian officials said Moscow fired more than 500 drones and 11 missiles at the capital Kyiv overnight into Friday in an attack that killed one person, injured at least 23 others and damaged buildings across the city. The sounds of air raid sirens, kamikaze drones and booming detonations reverberated until dawn.

There's a certain peace of mind that comes from being able to see who's outside your door before you even get up from your seat. If you haven't yet made the jump, or you want an upgrade, the Ring Battery Doorbell Pro is on sale for its very best price of 150 ahead of Prime Day. The Ring Battery Doorbell Pro is usually available for 230 and, unlike other similar products, it's not constantly on sale, making this 35% discount even more enticing. The video doorbell captures head-to-toe images in 1536p, which means you'll get a full view of who's outside your door, what's on the porch, and well beyond that. There are many useful features you'll end up loving.

Large Language Models (LLMs) are increasingly being integrated into various applications and services running on billions of mobile devices. However, deploying LLMs on resource-limited mobile devices faces a significant challenge due to their high demand for computation, memory, and ultimately energy. While current LLM frameworks for mobile use three power-hungry components-CPU, GPU, and Memory-even when running primarily-GPU LLM models, optimized DVFS governors for CPU, GPU, and memory featured in modern mobile devices operate independently and are oblivious of each other. Motivated by the above observation, in this work, we first measure the energy-efficiency of a SOTA LLM framework consisting of various LLM models on mobile phones which showed the triplet mobile governors result in up to 40.4% longer prefilling and decoding latency compared to optimal combinations of CPU, GPU, and memory frequencies with the same energy consumption for sampled prefill and decode lengths. Second, we conduct an in-depth measurement study to uncover how the intricate interplay (or lack of) among the mobile governors cause the above inefficiency in LLM inference. Finally, based on these insights, we design FUSE - a unified energy-aware governor for optimizing the energy efficiency of LLM inference on mobile devices. Our evaluation using a ShareGPT dataset shows FUSE reduces the time-to-first-token and time-per-output-token latencies by 7.0%-16.9% and 25.4%-36.8% on average with the same energy-per-token for various mobile LLM models.