Green Energy

Paradise Energy Solutions and sister company Upward Broadband have started construction on a new shared corporate headquarters in Paradise, Pennsylvania. “There is clear synergy between our companies,” said Marcus Beiler, co-owner of Paradise Energy Solutions and Upward Broadband. “Bringing our teams together in one location creates opportunities for collaboration and efficiency that will be a… The post PA solar contractor Paradise Energy Solutions to install 850 kW at new headquarters appeared first on Solar Power World.

The company already uses U.S.-made batteries. But now it's investing in sourcing American lithium and cathode active materials, too.

The United States has reached a trade agreement with India that should reduce some of the tariffs placed on Indian imports. Few details were actually provided, but news reports say that the United States is lowering India’s export tariff to 18%. It was previously as high as 50%. It is welcome news for Indian solar… The post US-India trade agreement reduces general tariff amount on Indian solar panels appeared first on Solar Power World.

Amazon has signed a Power Purchase Agreement (PPA) for 110 MW from Nordseecluster B […]

New report confirms that the federal home battery rebate has put a rocket under national demand. But has rooftop solar reached critical mass? The post Rebates “strapped a rocket” to home battery demand, but has rooftop solar peaked? appeared first on Renew Economy.

Read the latest wind industry & renewable energy companies, policy, wind farm projects & technology news, analysis on Windpower Monthly

arXiv:2602.01681v1 Announce Type: cross Abstract: Current deep learning models for Multispectral and Hyperspectral Image Fusion (MS/HS fusion) are typically designed for fixed spectral bands and spatial scales, which limits their transferability across diverse sensors. To address this, we propose SSA, a universal framework for MS/HS fusion with spectral-band and fusion-scale agnosticism. Specifically, we introduce Matryoshka Kernel (MK), a novel operator that enables a single model to adapt to arbitrary numbers of spectral channels. Meanwhile, we build SSA upon an Implicit Neural Representation (INR) backbone that models the HS signal as a continuous function, enabling reconstruction at arbitrary spatial resolutions. Together, these two forms of agnosticism enable a single MS/HS fusion model that generalizes effectively to unseen sensors and spatial scales. Extensive experiments demonstrate that our single model achieves state-of-the-art performance while generalizing well to unseen

arXiv:2602.00037v1 Announce Type: cross Abstract: In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19\%. The proposed method greatly improves upon individual model performance, as well

arXiv:2602.02196v1 Announce Type: new Abstract: Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory for task completion. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), an agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is primarily constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments across diverse

arXiv:2602.01760v1 Announce Type: new Abstract: This paper focuses on a highly practical scenario: how to continue benefiting from the advantages of multi-modal image fusion under harsh conditions when only visible imaging sensors are available. To achieve this goal, we propose a novel concept of single-image fusion, which extends conventional data-level fusion to the knowledge level. Specifically, we develop MagicFuse, a novel single image fusion framework capable of deriving a comprehensive cross-spectral scene representation from a single low-quality visible image. MagicFuse first introduces an intra-spectral knowledge reinforcement branch and a cross-spectral knowledge generation branch based on the diffusion models. They mine scene information obscured in the visible spectrum and learn thermal radiation distribution patterns transferred to the infrared spectrum, respectively. Building on them, we design a multi-domain knowledge fusion branch that integrates the probabilistic

arXiv:2602.01696v1 Announce Type: new Abstract: Transmission line defect detection remains challenging for automated UAV inspection due to the dominance of small-scale defects, complex backgrounds, and illumination variations. Existing RGB-based detectors, despite recent progress, struggle to distinguish geometrically subtle defects from visually similar background structures under limited chromatic contrast. This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled purify-then-fuse paradigm. CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification via a learned codebook to suppress modality-specific noise while preserving defect-discriminative information, and a Contextual Semantic Integration Framework that captures global spatial dependencies using partial-channel attention to enhance structural semantic reasoning. Position-wise normalization within

arXiv:2602.01588v1 Announce Type: new Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively combine temporal numerical patterns with the context embedded in other modalities, such as text. While most existing methods align textual features with time-series patterns one step at a time, they neglect the multiscale temporal influences of contextual information such as time-series cycles and dynamic shifts. This mismatch between local alignment and global textual context can be addressed by spectral decomposition, which separates time series into frequency components capturing both short-term changes and long-term trends. In this paper, we propose SpecTF, a simple yet effective framework that integrates the effect of textual data on time series in the frequency domain. Our method extracts textual embeddings, projects them into the frequency domain, and

arXiv:2602.01447v1 Announce Type: new Abstract: Sentiment analysis models exhibit complementary strengths, yet existing approaches lack a unified framework for effective integration. We present SentiFuse, a flexible and model-agnostic framework that integrates heterogeneous sentiment models through a standardization layer and multiple fusion strategies. Our approach supports decision-level fusion, feature-level fusion, and adaptive fusion, enabling systematic combination of diverse models. We conduct experiments on three large-scale social-media datasets: Crowdflower, GoEmotions, and Sentiment140. These experiments show that SentiFuse consistently outperforms individual models and naive ensembles. Feature-level fusion achieves the strongest overall effectiveness, yielding up to 4\% absolute improvement in F1 score over the best individual model and simple averaging, while adaptive fusion enhances robustness on challenging cases such as negation, mixed emotions, and complex sentiment

arXiv:2602.01060v1 Announce Type: new Abstract: Existing generative models for unsupervised anomalous sound detection are limited by their inability to fully capture the complex feature distribution of normal sounds, while the potential of powerful diffusion models in this domain remains largely unexplored. To address this challenge, we propose a novel framework, TLDiffGAN, which consists of two complementary branches. One branch incorporates a latent diffusion model into the GAN generator for adversarial training, thereby making the discriminator's task more challenging and improving the quality of generated samples. The other branch leverages pretrained audio model encoders to extract features directly from raw audio waveforms for auxiliary discrimination. This framework effectively captures feature representations of normal sounds from both raw audio and Mel spectrograms. Moreover, we introduce a TMixup spectrogram augmentation technique to enhance sensitivity to subtle and

arXiv:2602.00956v1 Announce Type: new Abstract: Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this work, we propose a novel hybrid deep learning framework that integrates Topological Data Analysis (TDA) with a DenseNet121 backbone for four-class Alzheimer's disease classification using structural MRI data from the OASIS dataset. TDA is employed to capture complementary topological characteristics of brain structures that are often overlooked by conventional neural networks, while DenseNet121 efficiently learns hierarchical spatial features from MRI slices. The extracted deep and topological features are fused to enhance class separability across the four AD stages. Extensive experiments conducted on the OASIS-1 Kaggle MRI dataset demonstrate that the proposed TDA+DenseNet121 model significantly outperforms existing state-of-the-art approaches. The model achieves an accuracy of 99.93%

The election’s result is the political responsibility of the nominal “left”, in particular the pseudo-left Broad Front (FA) and its satellite organizations, which entered a de facto alliance with the traditional parties of the local oligarchy.

The sunspot region 4366 fired off dozens of powerful solar flares in 24 hours, including the single strongest flare since 2024. Auroras are possible later this week.

The court ruled that construction can restart on a wind farm off the coast of New York State. The Trump administration had ordered work to stop in December.

The sun is experiencing a violent solar storm, releasing one of the strongest solar flares seen for 30 years

For the first time, researchers have found what seems to be a cloud of dark matter about 60 million times the mass of the sun in our galactic neighbourhood

The court ruled that construction can restart on a wind farm off the coast of New York State. The Trump administration had ordered work to stop in December.

Jaguar wanted to squeeze in a ton of range without intruding on passenger space. Here's what it came up with.

GigaWatt Inc. is making its next phase of growth available to public investors as the residential solar market faces a critical inflection point. The post GigaWatt Opens Public Investment Round to Scale DIY Solar Platform appeared first on POWER Magazine.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Inside the marketplace powering bespoke AI deepfakes of real women Civitai—an online marketplace for buying and selling AI-generated content, backed by the venture capital firm Andreessen Horowitz—is letting users buy custom instruction files…

German chancellor Friedrich Merz last week repeated claims that wind turbines are only a “transitional technology” at a summit where he committed to a massive buildout of offshore wind in the North Sea.

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Demand for electric vehicles and the batteries that power them has never been hotter. In 2025, EVs made up over a quarter of new vehicle sales globally,…

arXiv:2601.22865v1 Announce Type: new Abstract: Battery energy storage systems are increasingly deployed as fast-responding resources for grid balancing services such as frequency regulation and for mitigating renewable generation uncertainty. However, repeated charging and discharging induces cycling degradation and reduces battery lifetime. This paper studies the real-time scheduling of a heterogeneous battery fleet that collectively tracks a stochastic balancing signal subject to per-battery ramp-rate and capacity constraints, while minimizing long-term cycling degradation. Cycling degradation is fundamentally path-dependent: it is determined by charge-discharge cycles formed by the state-of-charge (SoC) trajectory and is commonly quantified via rainflow cycle counting. This non-Markovian structure makes it difficult to express degradation as an additive per-time-step cost, complicating classical dynamic programming approaches. We address this challenge by formulating the fleet

arXiv:2601.22551v1 Announce Type: new Abstract: We present a hybrid cross-device localization pipeline developed for the CroCoDL 2025 Challenge. Our approach integrates a shared retrieval encoder and two complementary localization branches: a classical geometric branch using feature fusion and PnP, and a neural feed-forward branch (MapAnything) for metric localization conditioned on geometric inputs. A neural-guided candidate pruning strategy further filters unreliable map frames based on translation consistency, while depth-conditioned localization refines metric scale and translation precision on Spot scenes. These components jointly lead to significant improvements in recall and accuracy across both HYDRO and SUCCU benchmarks. Our method achieved a final score of 92.62 (R@0.5m, 5{\deg}) during the challenge.

arXiv:2601.22406v1 Announce Type: new Abstract: The contribution describes a pedestrian navigation approach designed to improve localization accuracy in urban environments where GNSS performance is degraded, a problem that is especially critical for blind or low-vision users who depend on precise guidance such as identifying the correct side of a street. To address GNSS limitations and the impracticality of camera-based visual positioning, the work proposes a particle filter based fusion of GNSS and inertial data that incorporates spatial priors from maps, such as impassable buildings and unlikely walking areas, functioning as a probabilistic form of map matching. Inertial localization is provided by the RoNIN machine learning method, and fusion with GNSS is achieved by weighting particles based on their consistency with GNSS estimates and uncertainty. The system was evaluated on six challenging walking routes in downtown San Francisco using three metrics related to sidewalk

arXiv:2601.22403v1 Announce Type: new Abstract: The complex electrochemical behavior of lithium-ion batteries results in non-linear dynamics and appropriate modeling of this non-linear dynamical system is of interest for better management and control. In this work, we proposed a family of dynamic mode decomposition (DMD)-based data-driven models that do not require detailed knowledge of the composition of the battery materials but can essentially capture the non-linear dynamics with higher computational efficiency. Only voltage and current data obtained from hybrid pulse power characterization (HPPC) tests were utilized to form the state space matrices and subsequently used for predicting the future terminal voltage at different state of charge (SoC) and aging levels. To construct the system model, 60\% of the data from a single HPPC test was utilized to generate time-delay embedded snapshots, with embedding dimension ranging from 40 to 2000. Among these, an embedding dimension of

Tesla has unveiled its newest energy product during and the buzzy new item is, in fact, a rooftop solar panel, launched at a tumultuous moment for the EV maker. The post Tesla unveils its newest energy product – a rooftop solar panel appeared first on Renew Economy.

The battery giant says its new 5C batteries will retain 80% capacity after being fast charged for 1.1 million miles.

For the first time, scientists have used satellite data to create a 3D map of the sun's interior magnetic field, the fundamental driver of solar activity. The research, published in The Astrophysical Journal Letters, should enable more accurate predictions of solar cycles and space weather that affects satellites and power grids.

Using data collected by NASA's Parker Solar Probe during its closest approach to the sun, a University of Arizona-led research team has measured the dynamics and ever-changing "shell" of hot gas from where the solar wind originates.

After years of relative quiet, Tesla’s factory in Buffalo, New York, has been repurposed back to its original purpose: to make solar panels. The 1.2 million ft2 facility received a nearly $1 billion investment from New York State, and local organizations have been questioning Tesla’s employment figures since Day 1. Originally built for Silevo in… The post Tesla claims it’s making solar panels again at its Buffalo factory appeared first on Solar Power World.

China installed at least 37GW of new wind power capacity – more than most countries' total installations – in December alone, according to new data confirmed by climate think tank Ember. 

arXiv:2601.21724v1 Announce Type: cross Abstract: This paper summarizes and consolidates fusion power-plant costing work performed in support of ARPA-E from 2017 through 2024, and documents the evolution of the associated analysis framework from early capital-cost-focused studies to a standards-aligned, auditable costing capability. Early efforts applied ARIES-style cost-scaling relations to generate Nth-of-a-kind (NOAK) estimates and were calibrated through a pilot study with Bechtel and Decysive Systems to benchmark balance-of-plant (BOP) costs and validate plant-level reasonableness from an engineering, procurement, and construction (EPC) perspective. Subsequent work, informed by Lucid Catalyst studies of nuclear cost drivers, expanded the methodology to treat indirect costs explicitly and to evaluate cost-reduction pathways for non-fusion-island systems through design-for-cost practices, modularization, centralized manufacturing, and learning. As ARPA-E's fusion portfolio

arXiv:2601.22111v1 Announce Type: new Abstract: Accurate reconstruction of atmospheric wind fields is essential for applications such as weather forecasting, hazard prediction, and wind energy assessment, yet conventional instruments leave spatio-temporal gaps within the lower atmospheric boundary layer. Unmanned aircraft systems (UAS) provide flexible in situ measurements, but individual platforms sample wind only along their flight trajectories, limiting full wind-field recovery. This study presents a framework for reconstructing four-dimensional atmospheric wind fields using measurements obtained from a coordinated UAS swarm. A synthetic turbulence environment and high-fidelity multirotor simulation are used to generate training and evaluation data. Local wind components are estimated from UAS dynamics using a bidirectional long short-term memory network (Bi-LSTM) and assimilated into a physics-informed neural network (PINN) to reconstruct a continuous wind field in space and time.

arXiv:2601.22045v1 Announce Type: new Abstract: Neural surface reconstruction (NSR) has recently shown strong potential for urban 3D reconstruction from multi-view aerial imagery. However, existing NSR methods often suffer from geometric ambiguity and instability, particularly under sparse-view conditions. This issue is critical in large-scale urban remote sensing, where aerial image acquisition is limited by flight paths, terrain, and cost. To address this challenge, we present the first urban NSR framework that fuses 3D synthetic aperture radar (SAR) point clouds with aerial imagery for high-fidelity reconstruction under constrained, sparse-view settings. 3D SAR can efficiently capture large-scale geometry even from a single side-looking flight path, providing robust priors that complement photometric cues from images. Our framework integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable and

arXiv:2601.22036v1 Announce Type: new Abstract: Quantifying degrees of fusion and separability between data groups in representation space is a fundamental problem in representation learning, particularly under domain shift. A meaningful metric should capture fusion-altering factors like geometric displacement between representation groups, whose variations change the extent of fusion, while remaining invariant to fusion-preserving factors such as global scaling and sampling-induced layout changes, whose variations do not. Existing distributional distance metrics conflate these factors, leading to measures that are not informative of the true extent of fusion between data groups. We introduce Cross-Fusion Distance (CFD), a principled measure that isolates fusion-altering geometry while remaining robust to fusion-preserving variations, with linear computational complexity. We characterize the invariance and sensitivity properties of CFD theoretically and validate them in controlled

arXiv:2601.21675v1 Announce Type: new Abstract: Multi-modal stance detection (MSD) aims to determine an author's stance toward a given target using both textual and visual content. While recent methods leverage multi-modal fusion and prompt-based learning, most fail to distinguish between modality-specific signals and cross-modal evidence, leading to suboptimal performance. We propose DiME (Disentangled Multi-modal Experts), a novel architecture that explicitly separates stance information into textual-dominant, visual-dominant, and cross-modal shared components. DiME first uses a target-aware Chain-of-Thought prompt to generate reasoning-guided textual input. Then, dual encoders extract modality features, which are processed by three expert modules with specialized loss functions: contrastive learning for modality-specific experts and cosine alignment for shared representation learning. A gating network adaptively fuses expert outputs for final prediction. Experiments on four

arXiv:2601.21648v1 Announce Type: new Abstract: Depression is a prevalent mental health disorder that severely impairs daily functioning and quality of life. While recent deep learning approaches for depression detection have shown promise, most rely on limited feature types, overlook explicit cross-modal interactions, and employ simple concatenation or static weighting for fusion. To overcome these limitations, we propose CAF-Mamba, a novel Mamba-based cross-modal adaptive attention fusion framework. CAF-Mamba not only captures cross-modal interactions explicitly and implicitly, but also dynamically adjusts modality contributions through a modality-wise attention mechanism, enabling more effective multimodal fusion. Experiments on two in-the-wild benchmark datasets, LMVD and D-Vlog, demonstrate that CAF-Mamba consistently outperforms existing methods and achieves state-of-the-art performance.

arXiv:2601.21341v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) requires models to continuously acquire new classes without forgetting previously learned ones. A dominant paradigm involves freezing a pre-trained model and training lightweight, task-specific adapters. However, maintaining task-specific parameters hinders knowledge transfer and incurs high retrieval costs, while naive parameter fusion often leads to destructive interference and catastrophic forgetting. To address these challenges, we propose Dynamical Adapter Fusion (DAF) to construct a single robust global adapter. Grounded in the PAC-Bayes theorem, we derive a fusion mechanism that explicitly integrates three components: the optimized task-specific adapter parameters, the previous global adapter parameters, and the initialization parameters. We utilize the Taylor expansion of the loss function to derive the optimal fusion coefficients, dynamically achieving the best balance between stability and

arXiv:2601.21239v1 Announce Type: new Abstract: Although Large Language Models have advanced Automated Heuristic Design, treating algorithm evolution as a monolithic text generation task overlooks the coupling between discrete algorithmic structures and continuous numerical parameters. Consequently, existing methods often discard promising algorithms due to uncalibrated constants and suffer from premature convergence resulting from simple similarity metrics. To address these limitations, we propose TIDE, a Tuning-Integrated Dynamic Evolution framework designed to decouple structural reasoning from parameter optimization. TIDE features a nested architecture where an outer parallel island model utilizes Tree Similarity Edit Distance to drive structural diversity, while an inner loop integrates LLM-based logic generation with a differential mutation operator for parameter tuning. Additionally, a UCB-based scheduler dynamically prioritizes high-yield prompt strategies to optimize resource

All Worcester Public Schools and athletic events will be closed Friday. The post Worcester schools closed Friday for ‘hazardous’ wind chill appeared first on Boston.com.

Scientists have long relied on tree rings to learn about ancient solar storms—rare bursts of high-energy particles from the sun that can disrupt satellites, power grids, and communication systems across the planet. When these particles hit Earth's atmosphere, they create a radioactive form of carbon that trees absorb and store in their wood.

SolarCycle has begun recycling solar panels at its new facility in Cedartown, Georgia. The 255,000-ft2 recycling facility is home to SolarCycle’s proprietary next-generation advanced recycling technology, which delivers more than double the throughput of the company’s first-generation recycling lines. The new process allows for 100% landfill diversion and recovers 96% of the value from the… The post SolarCycle begins solar panel recycling in Georgia appeared first on Solar Power World.

A battery electrolyte that is solid at normal temperatures yet still conducts ions could make lithium batteries safer and longer lasting by replacing flammable liquids.

arXiv:2601.20847v1 Announce Type: new Abstract: Road surface classification (RSC) is a key enabler for environment-aware predictive maintenance systems. However, existing RSC techniques often fail to generalize beyond narrow operational conditions due to limited sensing modalities and datasets that lack environmental diversity. This work addresses these limitations by introducing a multimodal framework that fuses images and inertial measurements using a lightweight bidirectional cross-attention module followed by an adaptive gating layer that adjusts modality contributions under domain shifts. Given the limitations of current benchmarks, especially regarding lack of variability, we introduce ROAD, a new dataset composed of three complementary subsets: (i) real-world multimodal recordings with RGB-IMU streams synchronized using a gold-standard industry datalogger, captured across diverse lighting, weather, and surface conditions; (ii) a large vision-only subset designed to assess

arXiv:2601.20720v1 Announce Type: new Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving. Modular pipelines restrict information flow and can amplify upstream errors. Recent query-based, fully differentiable perception-and-prediction (PnP) models mitigate these issues, yet the complementarity of cameras and LiDAR in the query-space has not been sufficiently explored. Models often rely on fusion schemes that introduce heuristic alignment and discrete selection steps which prevent full utilization of available information and can introduce unwanted bias. We propose Li-ViP3D++, a query-based multimodal PnP framework that introduces Query-Gated Deformable Fusion (QGDF) to integrate multi-view RGB and LiDAR in query space. QGDF (i) aggregates image evidence via masked attention across cameras and feature levels, (ii) extracts LiDAR context through fully differentiable BEV sampling with learned per-query

arXiv:2601.20369v1 Announce Type: new Abstract: Crowd counting remains challenging in variable-density scenes due to scale variations, occlusions, and the high computational cost of existing models. To address these issues, we propose RepSFNet (Reparameterized Single Fusion Network), a lightweight architecture designed for accurate and real-time crowd estimation. RepSFNet leverages a RepLK-ViT backbone with large reparameterized kernels for efficient multi-scale feature extraction. It further integrates a Feature Fusion module combining Atrous Spatial Pyramid Pooling (ASPP) and Context-Aware Network (CAN) to achieve robust, density-adaptive context modeling. A Concatenate Fusion module is employed to preserve spatial resolution and generate high-quality density maps. By avoiding attention mechanisms and multi-branch designs, RepSFNet significantly reduces parameters and computational complexity. The training objective combines Mean Squared Error and Optimal Transport loss to improve

arXiv:2601.20260v1 Announce Type: new Abstract: Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is expected to preserve fine details and maintain high visual fidelity. While diffusion models have demonstrated impressive generative capabilities in image generation, they often suffer from detail loss when applied to image fusion tasks. This issue arises from the accumulation of noise errors inherent in the Markov process, leading to inconsistency and degradation in the fused results. However, incorporating explicit supervision into end-to-end training of diffusion-based image fusion introduces challenges related to computational efficiency. To address these limitations, we propose the Reversible Efficient Diffusion (RED) model - an explicitly supervised training framework that inherits the powerful generative capability of diffusion models while avoiding the distribution estimation.

arXiv:2601.20206v1 Announce Type: new Abstract: As an important part of urbanization, the development monitoring of newly constructed parks is of great significance for evaluating the effect of urban planning and optimizing resource allocation. However, traditional change detection methods based on remote sensing imagery have obvious limitations in high-level and intelligent analysis, and thus are difficult to meet the requirements of current urban planning and management. In face of the growing demand for complex multi-modal data analysis in urban park development monitoring, these methods often fail to provide flexible analysis capabilities for diverse application scenarios. This study proposes a multi-modal LLM agent framework, which aims to make full use of the semantic understanding and reasoning capabilities of LLM to meet the challenges in urban park development monitoring. In this framework, a general horizontal and vertical data alignment mechanism is designed to ensure the

arXiv:2601.20104v1 Announce Type: new Abstract: Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various applications in downstream tasks. While several machine learning and deep learning approaches have been proposed for nuclei instance segmentation, most research in this field focuses on developing new segmentation algorithms and benchmarking them on a limited number of arbitrarily selected public datasets. In this work, rather than focusing on model development, we focused on the datasets used for this task. Based on an extensive literature review, we identified manually annotated, publicly available datasets of H&E-stained images for nuclei instance segmentation and standardized them into a unified input and annotation format. Using two state-of-the-art segmentation models, one based on convolutional neural networks (CNNs) and one based on a hybrid CNN and vision transformer

Luminace has announced the acquisition of a 9.3-MWdc portfolio of community solar projects from Renewable Properties (RP), a leading U.S. developer and investor in small-scale utility, community solar, energy storage, and electric vehicle (EV) infrastructure projects. The post Luminace, Renewable Properties Partner on Community Solar Portfolio appeared first on POWER Magazine.

Scientists have unveiled a new approach to powering quantum computers using quantum batteries—a breakthrough that could make future computers faster, more reliable, and more energy efficient.

No country has affected global solar panel supply chains more than the United States. Since the boom of solar in the early 2010s, the U.S. government has chased Chinese firms across the world, initiating tariffs on products as they’re “dumped” into the U.S. market at uncompetitive prices. When the Dept. of Commerce first placed antidumping… The post Exclusive photos: ELITE Solar opens Egypt cell and panel manufacturing for US supply appeared first on Solar Power World.

Among the companies working on advanced geothermal tech is Rodatherm Energy Corp., a privately held company with a primary focus on the Great Basin region in the Western U.S. The Utah-based company, which also has operations in Calgary, Alberta in Canada, is known for its pioneering Advanced Geothermal System (AGS). The post The POWER Interview: A Path Forward for Geothermal Energy appeared first on POWER Magazine.

It's a reasonable amount, but I wouldn't be planning any solar-powered road trips.

Pivot Energy, a national renewable energy provider headquartered in Denver, and the University of Denver (DU), have completed the state’s first off-site net-metered solar project. This project contributes to DU’s goal to offset 100% of its electricity with renewable energy. The 3.28-MWDC project, located in Johnstown, started producing renewable energy earlier this month. Under an… The post Pivot Energy, University of Denver complete Colorado’s 1st virtual net-metered solar project appeared first on Solar Power World.

Denmark and Germany reached an agreement on Bornholm Energy Island in the Baltic Sea, […]

An innovative approach to battery materials could bring sodium-ion energy density and charging speeds far closer to those of lithium-ion, scientists say.

Read the latest wind industry & renewable energy companies, policy, wind farm projects & technology news, analysis on Windpower Monthly

A reflection-based solar cell design inspired by periscopes achieves near-complete visible transparency and record light utilization efficiency, outperforming all previous transparent photovoltaic devices.

The car company has issued three different recalls affecting over 45,000 ID.4 electric crossovers sold in the United States.

At U.S. District Court in Boston, Judge Brian Murphy halted the administration's stop work order for Vineyard Wind, citing the potential economic losses from the delays and the developers' likelihood of success on their claims. The post Judge rules Massachusetts offshore wind project halted by Trump administration can continue appeared first on Boston.com.

The project, known as Vineyard Wind, was already 95 percent complete when the Trump administration ordered construction to stop.

TurningPoint Energy (TPE) and Standard Solar will collaborate on 11.2 MW of community solar built across two projects in Kent and Sussex counties in Delaware. In 2022, TPE committed to investing more than $100 million in projects across the state. Located in Harrington and Bridgeville, each of the projects are 5.6-MWDC single-axis tracker systems and… The post Standard Solar, TurningPoint Energy spearhead Delaware community solar portfolio appeared first on Solar Power World.

PureSky Energy has reached commercial operations of its Heath Brook and Sand Brook community solar projects in Corinth, New York. The two solar farms – which together total approximately 12.92 MWDC (about 5 MWAC each) – are now delivering clean, renewable power to the grid. Combined, Heath Brook and Sand Brook will generate around 18.8 million kWh… The post PureSky Energy completes community solar projects serving LMI customers appeared first on Solar Power World.

Author(s): Mano Grunwald and Claudia E. BrunnerWe experimentally investigate the impact of different inflow conditions on the breakdown of wind turbine tip vortices in a high Reynolds number wind tunnel. The data in this paper is obtained through hot wire spectral analysis. While downstream evolution of the spectra exhibits a complex scale dependent behavior, here we focus on the decay of the signature of the tip vortices for which we identify three distinct regimes. These regimes are linked to an initial advection phase, vortex breakdown, and turbulence decay. Variations in the tip speed ratio have a significant impact on the breakdown rate in the second regime, while effects of mean shear and turbulence intensity are less pronounced. [Phys. Rev. Fluids 11, 014608] Published Tue Jan 27, 2026

Gold nanosphere supraballs absorb a bout 90% of sunlight across all wavelengths, nearly doubling energy capture and boosting power output 2.4x when applied to solar cells.

Sunbeams contain a lot of energy. But current technology for harvesting solar power doesn't capture as much as it could. Now, in ACS Applied Materials & Interfaces, researchers report that gold nanospheres, named supraballs, can absorb nearly all wavelengths in sunlight—including some that traditional photovoltaic materials miss. Applying a layer of supraballs onto a commercially available electricity converter demonstrated that the technology nearly doubled solar energy absorption compared to traditional materials.

SMA America has expanded its partnership with long-standing collaborator CEP for the domestic integration of SMA’s Medium Voltage Power Station (MVPS) solutions in the United States. The initiative establishes new U.S.-based capabilities for utility-scale solar and storage projects. “This expansion builds on nearly two decades of partnership and reflects our commitment to meeting U.S. customers… The post SMA and CEP collaborate on medium-voltage power stations for utility solar appeared first on Solar Power World.

GM wants to introduce low-cost LMR and LFP batteries. But that's not the only way it's making EVs cheaper.

Maryland Gov. Wes Moore announced the Lower Bills and Local Power Act (LBLPA) as part of the Moore-Miller administration’s 2026 legislative agenda. The legislation introduces measures to secure financing for local clean energy projects, modernize the electric grid and provide additional direct energy bill rebates to Maryland families. “Energy policy is about more than megawatts… The post Maryland introduces legislation to curb energy costs, spur solar development appeared first on Solar Power World.

Solar cells face significant challenges when deployed in outer space, where extremes in the environment decrease the efficiency and longevity they enjoy back on Earth. University of Toledo physicists are taking on these challenges at the Wright Center for Photovoltaics Innovation and Commercialization, in line with a large-scale research project supported by the Air Force Research Laboratory.

arXiv:2601.18677v1 Announce Type: cross Abstract: We investigate the detection of weak complex-valued signals immersed in non-Gaussian, range-varying interference, with emphasis on maritime radar scenarios. The proposed methodology exploits a Complex-valued Variational AutoEncoder (CVAE) trained exclusively on clutter-plus-noise to perform Out-Of-Distribution detection. By operating directly on in-phase / quadrature samples, the CVAE preserves phase and Doppler structure and is assessed in two configurations: (i) using unprocessed range profiles and (ii) after local whitening, where per-range covariance estimates are obtained from neighboring profiles. Using extensive simulations together with real sea-clutter data from the CSIR maritime dataset, we benchmark performance against classical and adaptive detectors (MF, NMF, AMF-SCM, ANMF-SCM, ANMF-Tyler). In both configurations, the CVAE yields a higher detection probability Pd at matched false-alarm rate Pfa, with the most notable

arXiv:2601.17085v1 Announce Type: cross Abstract: Discrete speech tokens offer significant advantages for storage and language model integration, but their application in speech emotion recognition (SER) is limited by paralinguistic information loss during quantization. This paper presents a comprehensive investigation of discrete tokens for SER. Using a fine-tuned WavLM-Large model, we systematically quantify performance degradation across different layer configurations and k-means quantization granularities. To recover the information loss, we propose two key strategies: (1) attention-based multi-layer fusion to recapture complementary information from different layers, and (2) integration of openSMILE features to explicitly reintroduce paralinguistic cues. We also compare mainstream neural codec tokenizers (SpeechTokenizer, DAC, EnCodec) and analyze their behaviors when fused with acoustic features. Our findings demonstrate that through multi-layer fusion and acoustic feature

arXiv:2601.18739v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is a fundamental requirement for the reliable deployment of artificial intelligence applications in open-world environments. However, addressing the heterogeneous nature of OOD data, ranging from low-level corruption to semantic shifts, remains a complex challenge that single-stage detectors often fail to resolve. To address this issue, we propose SeNeDiF-OOD, a novel methodology based on Semantic Nested Dichotomy Fusion. This framework decomposes the detection task into a hierarchical structure of binary fusion nodes, where each layer is designed to integrate decision boundaries aligned with specific levels of semantic abstraction. To validate the proposed framework, we present a comprehensive case study using MonuMAI, a real-world architectural style recognition system exposed to an open environment. This application faces a diverse range of inputs, including non-monument images, unknown

arXiv:2601.18589v1 Announce Type: new Abstract: In this paper, we introduce an Adaptive Graph Signal Processing with Dynamic Semantic Alignment (AGSP DSA) framework to perform robust multimodal data fusion over heterogeneous sources, including text, audio, and images. The requested approach uses a dual-graph construction to learn both intra-modal and inter-modal relations, spectral graph filtering to boost the informative signals, and effective node embedding with Multi-scale Graph Convolutional Networks (GCNs). Semantic aware attention mechanism: each modality may dynamically contribute to the context with respect to contextual relevance. The experimental outcomes on three benchmark datasets, including CMU-MOSEI, AVE, and MM-IMDB, show that AGSP-DSA performs as the state of the art. More precisely, it achieves 95.3% accuracy, 0.936 F1-score, and 0.924 mAP on CMU-MOSEI, improving MM-GNN by 2.6 percent in accuracy. It gets 93.4% accuracy and 0.911 F1-score on AVE and 91.8% accuracy and

arXiv:2601.18579v1 Announce Type: new Abstract: Existing Graph RAG methods aiming for insightful retrieval on corpus graphs typically rely on time-intensive processes that interleave Large Language Model (LLM) reasoning. To enable time-efficient insightful retrieval, we propose FastInsight. We first introduce a graph retrieval taxonomy that categorizes existing methods into three fundamental operations: vector search, graph search, and model-based search. Through this taxonomy, we identify two critical limitations in current approaches: the topology-blindness of model-based search and the semantics-blindness of graph search. FastInsight overcomes these limitations by interleaving two novel fusion operators: the Graph-based Reranker (GRanker), which functions as a graph model-based search, and Semantic-Topological eXpansion (STeX), which operates as a vector-graph search. Extensive experiments on broad retrieval and generation datasets demonstrate that FastInsight significantly

arXiv:2601.18424v1 Announce Type: new Abstract: Dry-electrode Motor Imagery Electroencephalography (MI-EEG) enables fast, comfortable, real-world Brain Computer Interface by eliminating gels and shortening setup for at-home and wearable use.However, dry recordings pose three main issues: lower Signal-to-Noise Ratio with more baseline drift and sudden transients; weaker and noisier data with poor phase alignment across trials; and bigger variances between sessions. These drawbacks lead to larger data distribution shift, making features less stable for MI-EEG tasks.To address these problems, we introduce STGMFM, a tri-branch framework tailored for dry-electrode MI-EEG, which models complementary spatio-temporal dependencies via dual graph orders, and captures robust envelope dynamics with a multi-scale frequency mixing branch, motivated by the observation that amplitude envelopes are less sensitive to contact variability than instantaneous waveforms. Physiologically meaningful

arXiv:2601.18326v1 Announce Type: new Abstract: We propose a drone signal out-of-distribution detection (OODD) algorithm based on the cognitive fusion of Zadoff-Chu (ZC) sequences and time-frequency images (TFI). ZC sequences are identified by analyzing the communication protocols of DJI drones, while TFI capture the time-frequency characteristics of drone signals with unknown or non-standard communication protocols. Both modalities are used jointly to enable OODD in the drone remote identification (RID) task. Specifically, ZC sequence features and TFI features are generated from the received radio frequency signals, which are then processed through dedicated feature extraction module to enhance and align them. The resultant multi-modal features undergo multi-modal feature interaction, single-modal feature fusion, and multi-modal feature fusion to produce features that integrate and complement information across modalities. Discrimination scores are computed from the fused features

arXiv:2601.18008v1 Announce Type: new Abstract: Pedestrian detection is a critical task in robot perception. Multispectral modalities (visible light and thermal) can boost pedestrian detection performance by providing complementary visual information. Several gaps remain with multispectral pedestrian detection methods. First, existing approaches primarily focus on spatial fusion and often neglect temporal information. Second, RGB and thermal image pairs in multispectral benchmarks may not always be perfectly aligned. Pedestrians are also challenging to detect due to varying lighting conditions, occlusion, etc. This work proposes Strip-Fusion, a spatial-temporal fusion network that is robust to misalignment in input images, as well as varying lighting conditions and heavy occlusions. The Strip-Fusion pipeline integrates temporally adaptive convolutions to dynamically weigh spatial-temporal features, enabling our model to better capture pedestrian motion and context over time. A novel

arXiv:2601.17983v1 Announce Type: new Abstract: Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident

arXiv:2601.17978v1 Announce Type: new Abstract: Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident

arXiv:2601.17809v1 Announce Type: new Abstract: 6G system is evolving toward full-spectrum coverage,ultra-wide bandwidth, and high mobility, resulting in increasingly complex propagation environments. The deep integration of communication and sensing is widely recognized as a core 6G vision, underscoring the importance of comprehensive environment awareness. Accurate channel modeling forms the foundation of 6G system design and optimization, and channel sounders provide the essential empirical basis. However, existing channel sounders, although supporting wide bandwidth and large antenna arrays in selected bands, generally lack cross-band capability, struggle in dynamic scenarios, and provide limited environmental awareness. The absence of detailed environmental information restricts the development of environment-aware channel models. To address this gap, we propose a multi-modal sensing and channel sounding fusion platform that enables temporally and spatially synchronized

arXiv:2601.17660v1 Announce Type: new Abstract: This work presents a self powered water leak sensor that eliminates both batteries and local gateways. The design integrates a dual compartment electrochemical harvester, a low input boost converter with supercapacitor storage, and a comparator gated LTE-M radio built on the Nordic Thingy:91 platform. Laboratory tests confirm that the system can be awakened from a dormant state in the presence of water, harvest sufficient energy, and issue repeated cloud beacons using the water exposure as the power source. Beyond conventional LTE-M deployments, the system's compatibility with 3GPP standard cellular protocols paves the way for future connectivity via non terrestrial 5G networks, enabling coverage in infrastructure scarce regions.

arXiv:2601.17656v1 Announce Type: new Abstract: This paper presents a battery-free and gateway-free water leak detection system capable of direct communication over LTE-M (Cat-M1). The system operates solely on energy harvested through a hydroelectric mechanism driven by an electrochemical sensor, thereby removing the need for conventional batteries. To address the stringent startup and operational power demands of LTE-M transceivers, the architecture incorporates a compartmentalized sensing module and a dedicated power management subsystem, comprising a boost converter, supercapacitor based energy storage, and a hysteresis controlled load isolation circuit. This design enables autonomous, direct to cloud data transmission without reliance on local networking infrastructure. Experimental results demonstrate consistent LTE-M beacon transmissions triggered by water induced energy generation, underscoring the system's potential for sustainable, maintenance free, and globally scalable IoT

arXiv:2601.17468v1 Announce Type: new Abstract: Single Image Reflection Separation (SIRS) disentangles mixed images into transmission and reflection layers. Existing methods suffer from transmission-reflection confusion under nonlinear mixing, particularly in deep decoder layers, due to implicit fusion mechanisms and inadequate multi-scale coordination. We propose ReflexSplit, a dual-stream framework with three key innovations. (1) Cross-scale Gated Fusion (CrGF) adaptively aggregates semantic priors, texture details, and decoder context across hierarchical depths, stabilizing gradient flow and maintaining feature consistency. (2) Layer Fusion-Separation Blocks (LFSB) alternate between fusion for shared structure extraction and differential separation for layer-specific disentanglement. Inspired by Differential Transformer, we extend attention cancellation to dual-stream separation via cross-stream subtraction. (3) Curriculum training progressively strengthens differential separation

arXiv:2601.17336v1 Announce Type: new Abstract: Automated Kellgren--Lawrence (KL) grading from knee radiographs is challenging due to subtle structural changes, long-range anatomical dependencies, and ambiguity near grade boundaries. We propose AGE-Net, a ConvNeXt-based framework that integrates Spectral--Spatial Fusion (SSF), Anatomical Graph Reasoning (AGR), and Differential Refinement (DFR). To capture predictive uncertainty and preserve label ordinality, AGE-Net employs a Normal-Inverse-Gamma (NIG) evidential regression head and a pairwise ordinal ranking constraint. On a knee KL dataset, AGE-Net achieves a quadratic weighted kappa (QWK) of 0.9017 +/- 0.0045 and a mean squared error (MSE) of 0.2349 +/- 0.0028 over three random seeds, outperforming strong CNN baselines and showing consistent gains in ablation studies. We further outline evaluations of uncertainty quality, robustness, and explainability, with additional experimental figures to be included in the full manuscript.

arXiv:2601.17193v1 Announce Type: new Abstract: This paper studies the problem of maximizing revenue from a grid-scale battery energy storage system, accounting for uncertain future electricity prices and the effect of degradation on battery lifetime. We formulate this task as an online resource allocation problem. We propose an algorithm, based on online mirror descent, that is no-regret in the stochastic i.i.d. setting and attains finite asymptotic competitive ratio in the adversarial setting (robustness). When untrusted advice about the opportunity cost of degradation is available, we propose a learning-augmented algorithm that performs well when the advice is accurate (consistency) while still retaining robustness properties when the advice is poor.

Windpower Monthly rounds up the latest wind power technology patents filed and published in the past week.

Australians with some form of rooftop solar system at home are more likely to switch energy retailers as they seek the best solar feed-in tariff. The post Rooftop solar households more likely to switch electricity retailers in search for best feed-in tariff appeared first on Renew Economy.

When a solar storm strikes Earth, it can disrupt technology that's vital for our daily lives. Solar storms occur when magnetic fields and electrically charged particles collide with Earth's magnetic field. This type of event falls into the category known as "space weather."

Solid-state batteries are widely viewed as a key technology for the future of energy storage, particularly for electric vehicles and large-scale renewable energy systems. Unlike conventional lithium-ion batteries, which rely on flammable liquid electrolytes, solid-state batteries use solid electrolytes to transport ions. This shift offers major advantages in safety, energy density, and long-term reliability.

Solo, a proposal management platform for home energy contractors, has launched Solo Studio, a new self-serve design solution for solar and home energy professionals. Solo Studio combines Solo’s proposal engine, integrated financing marketplace and compliance tools with in-house design capabilities. The result: Contractors can create accurate, finance-ready proposals on their own timeline. “Our customers asked… The post Solo’s new proposal platform packages solar with other energy services appeared first on Solar Power World.

GameChange Solar, a global supplier of solar tracker and fixed-tilt racking solutions, has launched its Distributed Generation (DG) Solar Division, which will serve commercial, industrial and community solar projects nationwide. “Distributed generation represents a fast-growing and strategically critical segment of the renewable energy market,” said Phillip Vyhanek, CEO at GameChange Solar. “By capitalizing on our… The post GameChange Solar creates division dedicated to distributed generation market appeared first on Solar Power World.

Read the latest wind industry & renewable energy companies, policy, wind farm projects & technology news, analysis on Windpower Monthly

The Danish Energy Agency (DEA) has approved a 30-year electricity production licence to the […]