Improving pretraining data quality and size is known to boost downstream performance, but the role of text complexity--how hard a text is to read--remains less explored. We reduce surface-level complexity (shorter sentences, simpler words, simpler structure) while keeping core content approximately constant and ask: (i) How does complexity affect language modeling across model sizes? (ii) Can useful representations be learned from simpler text alone? (iii) How does pretraining text complexity influence downstream language understanding? We simplify human-written texts using a large language model, pretrain causal models (28M-500M) from scratch on original vs. simplified data, and evaluate them in fine-tuning and zero-shot setups. We find that perplexity is sensitive to the interaction between model capacity and text complexity--smaller models degrade far less on simpler texts--while text complexity has little impact on fine-tuning evaluations, with zero-shot evaluations indicating that simpler texts benefit performance on linguistic knowledge tasks, whereas more complex texts favor tasks requiring world knowledge and entity tracking. Our findings suggest that different types of data diversity affect transfer and zero-shot performance differently, providing insight into tailoring data curation to specific goals.

Accurate detection and localization of solar photovoltaic (PV) panels in satellite imagery is essential for optimizing microgrids and active distribution networks (ADNs), which are critical components of renewable energy systems. Existing methods lack transparency regarding their underlying algorithms or training datasets, rely on large, high-quality PV training data, and struggle to generalize to new geographic regions or varied environmental conditions without extensive re-training. These limitations lead to inconsistent detection outcomes, hindering large-scale deployment and data-driven grid optimization. In this paper, we investigate how large language models (LLMs) can be leveraged to overcome these challenges. Despite their promise, LLMs face several challenges in solar panel detection, including difficulties with multi-step logical processes, inconsistent output formatting, frequent misclassification of visually similar objects (e.g., shadows, parking lots), and low accuracy in complex tasks such as spatial localization and quantification. To overcome these issues, we propose the PV Assessment with LLMs (PVAL) framework, which incorporates task decomposition for more efficient workflows, output standardization for consistent and scalable formatting, few-shot prompting to enhance classification accuracy, and fine-tuning using curated PV datasets with detailed annotations. PVAL ensures transparency, scalability, and adaptability across heterogeneous datasets while minimizing computational overhead. By combining open-source accessibility with robust methodologies, PVAL establishes an automated and reproducible pipeline for solar panel detection, paving the way for large-scale renewable energy integration and optimized grid management.

West Virginia Attorney General JB McCuskey joins 15-state investigation into Amazon, Google, Meta and Microsoft over alleged greenwashing of renewable energy claims.

A few EV makers in India went belly up after the government abruptly scaled back incentives and cracked down on the misuse of subsidies. Ather survived the storm and sales are increasing. More than 70% of the 200 million registered vehicles in India are two-wheelers. Ather Energy builds e-scooters for the rising middle class that could help commuters ditch highly-polluting, gas-guzzling models. While sales of Tesla or BYD cars drove electric vehicle adoption elsewhere in the world, two-wheelers have led the green energy transition in India. As one of the earliest "pure play" e-scooter makers, Ather Energy has helped drive micromobility EV penetration throughout India and boosted the shift away from carbon-emitting vehicles.

The startup is using waste materials and alternative fuels to make cement, slashing greenhouse gas emissions in a polluting industry. Cement is one of the most used materials on the planet, and the industry emits billions of tons of greenhouse gasses annually. Cemvision wants to use waste materials and alternative fuels to help reduce climate pollution from cement production. Today, making cement requires crushing limestone and heating it to super high temperatures, usually by burning fossil fuels. The chemical reactions also release carbon dioxide pollution. Swedish startup Cemvision made a few key production changes to reduce both emissions and the need to mine new materials.

And what distinguishes the firms that made the 2025 edition of our annual list of Climate Tech Companies to Watch. 's reporters and editors faced a dilemma as we began to mull nominees for this year's list of Climate Tech Companies to Watch. How do you pick companies poised to succeed in a moment of such deep uncertainty, at a time when the new Trump administration is downplaying the dangers of climate change, unraveling supportive policies for clean technologies, and enacting tariffs that will boost costs and disrupt supply chains for numerous industries? We as a publication are focused more on identifying companies developing technologies that can address the escalating threats of climate change, than on businesses positioned purely for market success. But we still don't want to lead our readers astray by highlighting a startup that winds up filing for bankruptcy six months later, even if its demise is due to a policy whiplash outside of its control. So we had to shift our thinking some.

This work introduces the application of the Orthogonal Procrustes problem to the generation of synthetic data. The proposed methodology ensures that the resulting synthetic data preserves important statistical relationships among features, specifically the Pearson correlation. An empirical illustration using a large, real-world, tabular dataset of energy consumption demonstrates the effectiveness of the approach and highlights its potential for application in practical synthetic data generation. Our approach is not meant to replace existing generative models, but rather as a lightweight post-processing step that enforces exact Pearson correlation to an already generated synthetic dataset.

In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing.

As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity markets. We develop a joint bidding strategy that combines participation in the primary frequency reserve market with continuous trading in the intraday market, addressing a gap in the extant literature which typically considers these markets in isolation or simplifies the continuous nature of intraday trading. Our approach utilizes a mixed integer linear programming implementation of the rolling intrinsic algorithm for intraday decisions and state of charge recovery, alongside a learned classifier strategy (LCS) that determines optimal capacity allocation between markets. A comprehensive out-of-sample backtest over more than one year of historical German market data validates our approach: The LCS increases overall profits by over 4% compared to the best-performing static strategy and by more than 3% over a naive dynamic benchmark. Crucially, our method closes the gap to a theoretical perfect foresight strategy to just 4%, demonstrating the effectiveness of dynamic, learning-based allocation in a complex, multi-market environment.

In recent years, with the rapid development of manufacturing industries, unmanned systems have found widespread applications across various fields. Among them, quadro-tors have been increasingly utilized in industrial applications such as aerial photography and surveying [1]. As electricity consumption continues to rise, the frequency of power grid maintenance has also increased. Given the high risks and costs associated with manual inspections, the importance of utilizing unmanned systems for autonomous power grid inspections has become increasingly evident [2], as shown in Fig 1. Substations, as critical components of the power grid system, play an essential role in ensuring seamless inspection across modules within the same facility or between different facilities. The units scheduled for inspection can be abstracted as a series of access points, with drones acting as agents tasked with visiting these points.