Tootfinder

Dutch drought resumes. The season's precipitation deficit (evaporation minus rainfall) has been hovering around 200 mm for a while, a high value already. But now, it is starting to grow again, in line with the top 5% driest years in the 1906-2025 record.
#ClimateChange #drought

"Solar farms and sheep grazing show how farming and clean energy can share land"
#SolarPower #Energy #Renewables

Solar farms and sheep grazing show how farming and clean energy can share land
Solar panels now double as shade for sheep and a tool for rural energy production in Georgia, where some farmers are balancing land conservation with renewable energy development.Emily Jones reports for Grist.In short:A solar farm in Lee County, Georgia, uses sheep for vegetation control, allowing t...

Optimal Sizing and Control of a Grid-Connected Battery in a Stacked Revenue Model Including an Energy Community
Tudor Octavian Pocola, Valentin Robu, Jip Rietveld, Sonam Norbu, Benoit Couraud, Merlinda Andoni, David Flynn, H. Vincent Poor
https://arxiv.org/abs/2507.04343

Optimal Sizing and Control of a Grid-Connected Battery in a Stacked Revenue Model Including an Energy Community
Recent years have seen rapid increases in intermittent renewable generation, requiring novel battery energy storage systems (BESS) solutions. One recent trend is the emergence of large grid-connected batteries, that can be controlled to provide multiple storage and flexibility services, using a stacked revenue model. Another emerging development is renewable energy communities (REC), in which prosumers invest in their own renewable generation capacity, but also requiring battery storage for fle…

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
Jifeng Wang, Jiazhe Ju, Ying Wang
https://arxiv.org/abs/2507.04423

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (…

Study of Wormhole in $f(Q)$ gravity with some dark energy models
Tanmoy Chowdhury, Prabir Rudra, Tandrima Chowdhury, Farook Rahaman
https://arxiv.org/abs/2507.03939

Study of Wormhole in $f(Q)$ gravity with some dark energy models
This study discusses the development of some particular static wormhole models in the background of an extended $f(Q)$ gravity theory. Wormhole solutions are derived by considering the radial pressure to admit an equation of state corresponding to Chaplygin gas. The Chaplygin gas equation of state is taken into consideration in two different forms: $p_{r}=-\frac{Bb(r)^{u}}{ρ^{a}}$, $p_{r}=-\frac{B}{ρ^{a}}$. Wormhole models are also generated assuming that a variable barotropic fluid may expla…

Unstable periodic orbits galore and quantum hyperscarring in highly frustrated magnets
Andrea Pizzi, Claudio Castelnovo, Johannes Knolle
https://arxiv.org/abs/2508.04763 https:/…

Unstable periodic orbits galore and quantum hyperscarring in highly frustrated magnets
Highly frustrated magnets, with their macroscopically-degenerate classical ground states and massively-entangled quantum spin liquid phases, have been pivotal to the development of modern condensed matter concepts such as emergent symmetries, topological order, and fractionalisation. The effects of frustration and massive degeneracies at high energy, where the many-body dynamics becomes chaotic, have hitherto been far less explored. Here, we identify a high-energy dynamical analog of highly-fru…

Quantum Neural Network applications to Protein Binding Affinity Predictions
Erico Souza Teixeira, Lucas Barros Fernandes, Yara Rodrigues In\'acio
https://arxiv.org/abs/2508.03446

Quantum Neural Network applications to Protein Binding Affinity Predictions
Binding energy is a fundamental thermodynamic property that governs molecular interactions, playing a crucial role in fields such as healthcare and the natural sciences. It is particularly relevant in drug development, vaccine design, and other biomedical applications. Over the years, various methods have been developed to estimate protein binding energy, ranging from experimental techniques to computational approaches, with machine learning making significant contributions to this field. Altho…

A Novel Hierarchical Co-Optimization Framework for Coordinated Task Scheduling and Power Dispatch in Computing Power Networks
Haoxiang Luo, Kun Yang, Qi Huang, Schahram Dustdar
https://arxiv.org/abs/2508.04015

A Novel Hierarchical Co-Optimization Framework for Coordinated Task Scheduling and Power Dispatch in Computing Power Networks
The proliferation of large-scale artificial intelligence and data-intensive applications has spurred the development of Computing Power Networks (CPNs), which promise to deliver ubiquitous and on-demand computational resources. However, the immense energy consumption of these networks poses a significant sustainability challenge. Simultaneously, power grids are grappling with the instability introduced by the high penetration of intermittent renewable energy sources (RES). This paper addresses …

Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies
Marc Damie, Mihai Pop, Merijn Posthuma
https://arxiv.org/abs/2508.04583 https://

Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies
Privacy-enhancing technologies (PETs) have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data protection. At the same time, the information and communication technology (ICT) sector faces growing pressure to reduce its environmental footprint, particularly its carbon emissions. While numerous studies have assessed the energy footprint of various ICT applications, the environmental footprint of cryptographic PETs …

Integrated Experimental and Numerical Investigations on the Thermo-Hydro-Mechanical Behavior of Clays and Argillaceous Rocks: A Perspective
Saeed Tourchi
https://arxiv.org/abs/2506.05818

Integrated Experimental and Numerical Investigations on the Thermo-Hydro-Mechanical Behavior of Clays and Argillaceous Rocks: A Perspective
This paper synthesizes nearly a decade of research on the coupled thermo-hydro-mechanical (THM) behavior of clays and argillaceous rocks. Drawing from experimental observations, numerical model development, and field-scale simulations, it presents a consolidated view of soil-structure interaction under thermal loading, desiccation cracking, and long-term excavation impacts. Key findings are drawn from constitutive modeling, in situ tests, and energy geostructure applications, offering a practic…

This https://arxiv.org/abs/2505.24137 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

Energy cascades in rotating and stratified turbulence in anisotropic domains
Alexandros Alexakis, Raffaele Marino, Pablo D. Mininni
https://arxiv.org/abs/2508.00340 https://

Energy cascades in rotating and stratified turbulence in anisotropic domains
The concept of inverse energy cascades has played a central role in the development of turbulence theory, with applications in two-dimensional and quasi-two-dimensional flows. We examine the presence or absence of inverse energy cascades in rotating stably stratified flows constrained to anisotropic yet fully three-dimensional domains, in a range of parameters that are relevant for planetary atmospheres. In particular, we focus on regimes with aspect ratios, Rossby, and Froude numbers similar t…

SERS Raman detection of the CO$_2$ Moisture Swing
Javier Mendez-Lozoya, Estrella Solis Mata, J. Jesus Velazquez Salazar, Alondra Hernandez Cedillo, Miguel Jose Yacaman, Jennifer L. Wade
https://arxiv.org/abs/2508.04893

SERS Raman detection of the CO$_2$ Moisture Swing
The development of scalable, energy-efficient carbon dioxide capture technologies is critical for achieving net-zero emissions. Moisture swing sorbents offer a promising alternative to traditional thermal regeneration methods by enabling reversible CO$_2$ binding through humidity-driven ion hydrolysis. In this study, we investigate the anion speciation dynamics in two classes of MS materials, an anion-exchange resin with bicarbonate anion and activated carbon impregnated with potassium bicarbon…

⛽ Novel method to convert carbon dioxide into methanol, paving the way for green energy solutions
#fuel

Novel method to convert carbon dioxide into methanol, paving the way for green energy solutions
A research team affiliated with UNIST has made a significant breakthrough in sustainable technology through the development of a method to convert carbon dioxide (CO₂) into methanol—a process that could play a vital role in reducing greenhouse gas emissions and producing environmentally friendly fuels.

Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation
Aniket Johri, Divyanshi Dwivedi, Mayukha Pal
https://arxiv.org/abs/2508.04170

Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation
The increasing vulnerability of electrical distribution systems to extreme weather events and cyber threats necessitates the development of economically viable frameworks for resilience enhancement. While existing approaches focus primarily on technical resilience metrics and enhancement strategies, there remains a significant gap in establishing market-driven mechanisms that can effectively commercialize resilience features while optimizing their deployment through intelligent decision-making.…

EU president throws her full weight behind the Netanyahu & Trump attacks on Iran, while negotiations were ongoing.
Reminders:
1) Iran restarted uranium enrichment after Trump withdrew from the agreement in 2018.
2) US intelligence recently stated that Iran did not pursue nuclear weapon development.

This https://arxiv.org/abs/2505.00616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Catastrophic Liability: Managing Systemic Risks in Frontier AI Development
As artificial intelligence systems grow more capable and autonomous, frontier AI development poses potential systemic risks that could affect society at a massive scale. Current practices at many AI labs developing these systems lack sufficient transparency around safety measures, testing procedures, and governance structures. This opacity makes it challenging to verify safety claims or establish appropriate liability when harm occurs. Drawing on liability frameworks from nuclear energy, aviati…

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
Leo Guo, Hirak Kansara, Siamak F. Khosroshahi, GuoQi Zhang, Wei Tan
https://arxiv.org/abs/2507.22079

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
Finite element (FE) simulations of structures and materials are getting increasingly more accurate, but also more computationally expensive as a collateral result. This development happens in parallel with a growing demand of data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. In parallel, the mesh width of an FE model can be exploited…

Design of an Active Thomson Parabola for the detection of ions accelerated by laser: Numerical simulations and characterization of different solutions
A. Huber, D. Raffestin, F. Hannachi, M. Huault, J. Jouve, A. Maitrallain, P. Nicola\"i, B. Zielbauer, M. Tarisien
https://arxiv.org/abs/2506.05220

Design of an Active Thomson Parabola for the detection of ions accelerated by laser: Numerical simulations and characterization of different solutions
This article describes the development of an active Thomson Parabola ion spectrometer designed to measure the energy spectra of different multi-MeV ion species generated in laser-plasmas interactions. To do so, GEANT4 optical simulations were carried out in order to design and build a spectrometer based on scintillator detectors capable of operating at rates comparable with the highest achievable at any existing or upcoming high intensity laser facilities. Details on the different configuration…

Dose-Escalation Trial Protocols that Extend Naturally to Admit Titration
David C. Norris
https://arxiv.org/abs/2507.01370 https://arx…

Dose-Escalation Trial Protocols that Extend Naturally to Admit Titration
Dose-escalation trials in oncology drug development still today typically aim to identify 1-size-fits-all dose recommendations, as arbitrary quantiles of the toxicity thresholds evident in patient samples. In the late 1990s efforts to individualize dosing emerged fleetingly in the oncology trial methods literature, but these have gained little traction due to a nexus of conceptual, technical, commercial, and regulatory barriers. To reduce the activation energy needed for transforming current 1-…

A tidal energy turbine has generated for 6 years and this is a record that shows the technology is commercially viable.
https://apnews.com/article/tidal-energy-turbine-marine-meygen-scotland-ffff3a7082205b33b612a1417e1ec6d6

Underwater turbine spinning for 6 years off Scotland's coast is a breakthrough for tidal energy
Submerged in about 40 meters of water off Scotland’s coast, a turbine has been spinning for more than six years to harness the power of ocean tides and generate electricity. The trade association Ocean Energy Europe says keeping such a large turbine operating in the harsh sea environment that long is a record that demonstrates the technology's commercial viability. Tidal energy technologies are still in the early days of their commercial development, but their potential for generating clean e…

This https://arxiv.org/abs/2502.12839 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Reservoir-assisted quantum battery charging at finite temperatures
Quantum batteries, as highly efficient energy storage devices, have garnered significant research interest. A key challenge in their development is to maximize the extractable energy (ergotropy) when operating within a finite-temperature reservoir. To address this, we applied quantum feedback control to the charger and investigated the effects of fermionic and bosonic thermal reservoirs on the performance of quantum batteries, including stored energy, ergotropy, and charging efficiency, in an o…

End-to-end image compression and reconstruction with ultrahigh speed and ultralow energy enabled by opto-electronic computing processor
Yuhang Wang, Ang Li, Yihang Shao, Qiang Li, Yang Zhao, Shilong Pan
https://arxiv.org/abs/2507.22374

End-to-end image compression and reconstruction with ultrahigh speed and ultralow energy enabled by opto-electronic computing processor
The rapid development of AR/VR, remote sensing, satellite radar, and medical equipment has created an imperative demand for ultra efficient image compression and reconstruction that exceed the capabilities of electronic processors. For the first time, we demonstrate an end to end image compression and reconstruction approach using an optoelectronic computing processor,achieving orders of magnitude higher speed and lower energy consumption than electronic counterparts. At its core is a 32X32 sil…

Dutch caretaker-minister of defense joins in with German chancellor Merz, saying "Israel is doing our dirty work". That'd be by attacking Iran while negotiations were ongoing, and after US intelligence concluded that Iran was not actively pursuing nuclear weapons development.
ht…

Kabinet met spoed bijeen vanwege situatie in Iran
In die raad bespreken ministers internationale ontwikkelingen die de Nederlandse veiligheid raken. Nu zal het gaan over hoe om te gaan met de Amerikaanse bombardementen op Iran.

The results of the development of the SPHERE-3 detector for studying the PCR mass composition in the 1-1000 PeV energy range. The status of 2025
D. V. Chernov, E. A. Bonvech, O. V. Cherkesova, E. L. Entina, V. I. Galkin, V. A. Ivanov, T. A. Kolodkin, N. O. Ovcharenko, D. A. Podgrudkov, T. M. Roganova, M. D. Ziva
https://arxiv.or…

The results of the development of the SPHERE-3 detector for studying the PCR mass composition in the 1-1000 PeV energy range. The status of 2025
The SPHERE-3 setup is destined to register the EAS Cherenkov light, both the direct and the reflected from the snow surface. A set of methods and new approaches in the measurement technique enable substantial progress in the study of the primary cosmic ray composition in the energy range 1--1000 PeV. The present work reveals the current status of the project development and detector performance modelling.

Controlling energy delivery with bistable nanostructures
Andreas Ehrmann, Carl P. Goodrich
https://arxiv.org/abs/2506.14266 https://a…

Controlling energy delivery with bistable nanostructures
Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other {\it de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time? In this paper, we present a purely physical mechanism that so…

Small modular reactors are gaining global momentum, but will they actually be built? The nuclear energy industry is exploring SMRs as a cost-effective and flexible alternative to traditional reactors. #climatechange #climatesolutions

Small modular reactors are gaining steam globally. Will any get built?
Nuclear experts say 74 SMR designs are under active development worldwide. Proponents see the tech as the key to nuclear energy’s future.

Energy-Oriented Computing Architecture Simulator for SNN Training
Yunhao Ma (Pengcheng Laboratory, Southern University of Science and Technology), Wanyi Jia (Pengcheng Laboratory, Southern University of Science and Technology), Yanyu Lin (Pengcheng Laboratory), Wenjie Lin (Pengcheng Laboratory), Xueke Zhu (Pengcheng Laboratory), Huihui Zhou (Pengcheng Laboratory), Fengwei An (Southern University of Science and Technology)

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

This https://arxiv.org/abs/2501.08524 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Deep inference of simulated strong lenses in ground-based surveys
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing syste…

Two types of compressible isotropic neo-Hookean material models
Sergey N. Korobeynikov, Alexey Yu. Larichkin, Patrizio Neff
https://arxiv.org/abs/2506.22244

Two types of compressible isotropic neo-Hookean material models
In this book, we present a systematic study of the performance of two known types of compressible generalization of the incompressible neo-Hookean material model. The first type of generalization is based on the development of \emph{vol-iso} neo-Hookean models and involves the additive decomposition of the elastic energy into volumetric and isochoric parts. The second simpler type of generalization is based on the development of \emph{mixed} neo-Hookean models that do not use this decomposition…

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Enrique Barba Roque, Luis Cruz
https://arxiv.org/abs/2506.09599 https://

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary explora…

This https://arxiv.org/abs/2305.03556 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Energy-Latency Aware Intelligent Reflecting Surface Aided Multi-cell Mobile Edge Computing
The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighbor…

Non-Gaussian enhancement of precision in quantum batteries
Beatriz Polo, Federico Centrone
https://arxiv.org/abs/2505.24604 https://a…

Non-Gaussian enhancement of precision in quantum batteries
We investigate the fundamental limits of converting light into useful work, with a focus on the role of quantum resources in energy harvesting processes. Specifically, we analyze how quantum coherence, non-Gaussianity, and entanglement affect the fluctuations in the energy output of bosonic quantum batteries. Our findings reveal potential pathways to enhance the efficiency and stability of energy extraction from quantum batteries, with implications for the development of quantum thermal machine…

Graph neural network model for the era of large atomistic models
Duo Zhang, Anyang Peng, Chun Cai, Wentao Li, Yuanchang Zhou, Jinzhe Zeng, Mingyu Guo, Chengqian Zhang, Bowen Li, Hong Jiang, Tong Zhu, Weile Jia, Linfeng Zhang, Han Wang
https://arxiv.org/abs/2506.01686

Graph neural network model for the era of large atomistic models
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network f…

Graphene Frontiers: Recent Advancements in Energy and Electronics Applications
Abdallah M. Abdeldaiem, Abdulrhman M. Alaraj, Ahmed K. Abozaid, Habiba E. Elsayegh, Mohamed A. Khamis, Mohamed M. Kedra, Mahmoud A. Elqassas, Ahmed M. Dowidar, Aya A. Esmaeil, Nora H. El mowafy, Fayza R. Ramadan, Walid J. Hamouda, Sara R. Ghazal, Haneen A. Saad, Naglaa M. Zian, Fatma Sameh, Alshimaa M. Rizk, Mena K. Selema, Aml F. Dawood, Ebrahem H. Abdelaal, Walid Ismail, Mahmoud Abdelfatah, Swellam W. Shar…

Graphene Frontiers: Recent Advancements in Energy and Electronics Applications
This review article explores the synthesis, characterization, and potential applications of graphene, a two-dimensional material with exceptional properties. Graphene's versatility in energy and electronics applications is highlighted, with its high conductivity and huge surface area facilitating improved energy storage capabilities in supercapacitors and batteries. In electronics, graphene is revolutionizing the industry by enabling the development of flexible displays, high-speed transistors,…

Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management
Arega Getaneh Abate, Xiaobing Zhang, Xiufeng Liu, Dogan Keles
https://arxiv.org/abs/2506.20631

Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management
Integrating electric mobility (electric vehicles (EVs), electric trucks (ETs)) and renewable energy sources (RES) with the power grid is paramount for achieving decarbonization, efficiency, and stability. Given the rapid growth of decentralized technologies and their critical role in decarbonization, two critical challenges emerge: first, the development of a digital platform for operational coordination; and second, rigorous research into their cost-benefit profile. This paper addresses this b…

Sodium-Decorated P-C3N: A Porous 2D Framework for High-Capacity and Reversible Hydrogen Storage
Jose A. S. Laranjeira, Nicolas F. Martins, Kleuton A. L. Lima, Lingtao Xiao, Xihao Chen, Luiz A. Ribeiro Junior, Julio R. Sambrano
https://arxiv.org/abs/2506.02374

Sodium-Decorated P-C3N: A Porous 2D Framework for High-Capacity and Reversible Hydrogen Storage
The development of reversible hydrogen storage materials has become crucial for enabling carbon-neutral energy systems. Based on this, the present work investigates the hydrogen storage on the sodium-decorated P-C$_3$N (Na@P-C$_3$N), a porous carbon nitride monolayer recently proposed as a stable semiconductor. First-principles calculations reveal that Na atoms preferentially adsorb with an adsorption energy of -4.48~eV, effectively suppressing clusterization effects. Upon decoration, the syste…

#Copper #Mining and Vehicle Electrification | IEF Report
"To meet ... trends, 115% more copper must be mined in the next 30 years than has been mined historically until now. To electrify the global vehicle fleet requires bringing into production 55% more new mines than would otherwise be needed. On…

Copper Mining and Vehicle Electrification | IEF Report
Many studies have raised concerns that copper supply cannot meet the copper demands of both the green energy transition and equitable global development. This report addresses this issue by projecting copper supply and demand from 2018 to 2050 and placing both in the historical context of copper mine output.

The Swedish energy regulator has mapped out all network development plans for 2025-2034, including flexibility needs and implementation gaps.
https://storymaps.arcgis.com/stories/4d01ad1fa49e43c49538a50cbe22a24d

Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
Ruonan Lia, Chang Wena, Mingyu Yan, Congcong Wu, Ahmed Lotfy Elrefai, Xiaotong Zhang, Sahban Wael Saeed Alnaser
https://arxiv.org/abs/2506.15398

Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
This study focuses on the novel municipal-scale rural integrated energy system (RIES), which encompasses energy supply and application. By constructing a seven-dimensional evaluation system including energy efficiency, energy supply, low-carbon sustainability, environmental impact, energy economy, social benefits, and integrated energy system development, this research combines the improved analytic hierarchy process (IAHP) and entropy weight method (EWM) by sum of squares of deviations to bala…

traccc: GPU track reconstruction library for HEP experiments
Paul Gessinger, Heather M. Gray, Attila Krasznahorkay, Charles Leggett, Joana Niermann, Andreas Salzburger, Stephen Nicholas Swatman, Beomki Yeo
https://arxiv.org/abs/2505.22822

traccc: GPU track reconstruction library for HEP experiments
We present the current development status and progress of traccc, a GPU track reconstruction library developed in the context of the A Common Tracking Software (ACTS) project. traccc implements tracking algorithms used in high energy physics (HEP) experiments, including the Kalman filter based track finding and fitting. We benchmark the software with data simulated by Geant4 to measure the physics and computing performance. We show that the physics performance for GPU and CPU are very close. We…

Uncertainty Annotations for Holistic Test Description of Cyber-physical Energy Systems
Kai Heussen, Jan S\"oren Schwarz, Eike Schulte, Zhiwang Feng, Leonard Enrique Ramos Perez, John Nikoletatos, Filip Pr\"ostl Andren
https://arxiv.org/abs/2506.23436

Uncertainty Annotations for Holistic Test Description of Cyber-physical Energy Systems
The complexity of experimental setups in the field of cyber-physical energy systems has motivated the development of the Holistic Test Description (HTD), a well-adopted approach for documenting and communicating test designs. Uncertainty, in its many flavours, is an important factor influencing the communication about experiment plans, execution of, and the reproducibility of experimental results. The work presented here focuses on supporting the structured analysis of experimental uncertainty …

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

"Geothermal energy keeps utility bills low in this Texas neighborhood"
#US #USA #America #Energy

Geothermal energy keeps utility bills low in this Texas neighborhood
The development outside Austin taps into natural heat deep underground to run heating and cooling systems for the planned 7,500 homes and other buildings.

Further steps towards next generation of covariant energy density functionals
B. Osei, A. V. Afanasjev, A. Taninah, A. Dalbah, U. C. Perera, V. A. Dzuba, V. V. Flambaum
https://arxiv.org/abs/2507.17082

Further steps towards next generation of covariant energy density functionals
The present study aims at further development of covariant energy density functionals (CEDFs) towards more accurate description of binding energies across the nuclear chart. For the first time, infinite basis corrections to binding energies in the fermionic and bosonic sectors of the covariant density functional theory have been taken into account in the fitting protocol within the covariant density functional theory. In addition, total electron binding energies have been used in the conversi…

A Physics-Informed Neural Network Framework for Simulating Creep Buckling in Growing Viscoelastic Biological Tissues
Zhongya Lin, Jinshuai Bai, Shuang Li, Xindong Chen, Bo Li, Xi-Qiao Feng
https://arxiv.org/abs/2506.18565

A Physics-Informed Neural Network Framework for Simulating Creep Buckling in Growing Viscoelastic Biological Tissues
Modeling viscoelastic behavior is crucial in engineering and biomechanics, where materials undergo time-dependent deformations, including stress relaxation, creep buckling and biological tissue development. Traditional numerical methods, like the finite element method, often require explicit meshing, artificial perturbations or embedding customised programs to capture these phenomena, adding computational complexity. In this study, we develop an energy-based physics-informed neural network (PIN…

A continuous-wave vacuum ultraviolet laser for the nuclear clock
Qi Xiao, Gleb Penyazkov, Xiangliang Li, Beichen Huang, Wenhao Bu, Juanlang Shi, Haoyu Shi, Tangyin Liao, Gaowei Yan, Haochen Tian, Yixuan Li, Jiatong Li, Bingkun Lu, Li You, Yige Lin, Yuxiang Mo, Shiqian Ding
https://arxiv.org/abs/2507.19449 https://arxiv.org/pdf/2507.19449 https://arxiv.org/html/2507.19449
arXiv:2507.19449v1 Announce Type: new
Abstract: The exceptionally low-energy isomeric transition in $^{229}$Th at around 148.4 nm offers a unique opportunity for coherent nuclear control and the realisation of a nuclear clock. Recent advances, most notably the incorporation of large ensembles of $^{229}$Th nuclei in transparent crystals and the development of pulsed vacuum-ultraviolet (VUV) lasers, have enabled initial laser spectroscopy of this transition. However, the lack of an intense, narrow-linewidth VUV laser has precluded coherent nuclear manipulation. Here we introduce and demonstrate the first continuous-wave laser at 148.4 nm, generated via four-wave mixing (FWM) in cadmium vapor. The source delivers 100 nW of power with a linewidth well below 100 Hz and supports broad wavelength tunability. This represents a five-orders-of-magnitude improvement in linewidth over all previous single-frequency lasers below 190 nm, marking a major advance in laser technology. We develop a spatially resolved homodyne technique to place a stringent upper bound on the phase noise induced by the FWM process and demonstrate sub-hertz linewidth capability. These results eliminate the final technical hurdle to a $^{229}$Th-based nuclear clock, opening new directions in quantum metrology, nuclear quantum optics and precision tests of the Standard Model. More broadly, they establish a widely tunable, ultranarrow-linewidth laser platform for applications across quantum information science, condensed matter physics, and high-resolution VUV spectroscopy.
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