Green Energy

Honda Joins the Energy Storage Rush Honda this week began production of batteries destined for energy storage systems,

Its LFP pack had 90% health at 26,000 miles. That’s not a disaster, but it’s worse than the chemistry’s reputation suggests.

Researchers from Rice University and North Carolina State University have created a nontoxic, stretchable battery that operates by

As we scour and scorch the Earth for deeper wells of energy, investors and government agencies are pouring

Southwest Research Institute (SwRI) scientists are using a solar wind forecasting method combined with analytic and numerical heliosphere models to find out where the first plasma boundary of the outer heliosphere lies as NASA’s New Horizons spacecraft hurtles toward this mysterious region of space.

arXiv:2607.02091v1 Announce Type: new Abstract: Breast fibroadenoma (FA) and phyllodes tumor (PT) are fibroepithelial breast lesions with highly overlapping appearances on B-mode ultrasound, making benign and borderline PT prone to being misclassified as FA and complicating preoperative decision-making. Existing computer-aided diagnosis methods commonly rely on single-modal imaging features and insufficiently exploit complementary clinical and textual information. To address this limitation, we construct the FAPT-M Dataset, a pathology-confirmed multimodal dataset comprising 910 patients with strictly reviewed ultrasound images, structured clinical attributes, and ultrasound diagnostic descriptions. Based on this dataset, we propose a clinically guided multimodal framework that integrates DenseNet-based visual encoding, CLIP-inspired text encoding, and lightweight clinical encoding, and further introduces clinical-conditioned adaptive modulation, cross-modal Transformer fusion, and

arXiv:2607.01992v1 Announce Type: new Abstract: Large-scale battery energy storage systems (BESSs) require O&M decisions that combine alarms, cell-level measurements, device topology, diagnostic tables, historical cases, and maintenance documents. Monitoring platforms can flag threshold violations, but they often cannot explain whether voltage inconsistency, resistance drift, short-circuit risk, capacity divergence, or thermal abnormality needs intervention. This digest presents a traceable BESS fault-diagnosis assistant that uses retrieval-augmented multi-agent reasoning to connect operational data, domain knowledge, visual evidence, and report generation. Reliability is improved through BESS-specific task routing, schema-constrained natural-language database access, hybrid text-image retrieval, and evidence-based answer synthesis. Preliminary internal evaluation is reported for routing, database access, and diagnostic reasoning.

arXiv:2607.01772v1 Announce Type: new Abstract: Vision-radar fusion is central to robust autonomous driving, combining dense visual semantics with precise range and velocity measurements from radar. However, real-world fusion quality is fundamentally challenged by dynamically varying input quality, stemming from occlusion, adverse weather, and channel noise. To address this, we re-frame the problem from static data fusion to channel-aware semantic reasoning and propose a Large Language Model-centric Semantic-layer Channel-aware Integrated Perception (LM-SCIP) framework. It places a Large Language Model (LLM) as a central reasoning core to fuse a local visual stream with a quality-varying external radar stream used to cover perception-blind spots. Concretely, LM-SCIP couples a hierarchical radar-vision encoder with a Channel-Adaptive Semantic Module (CASM) that maps link indicators into a "Channel Prompt" to dynamically gate external radar features. A parameter-efficient, LoRA-tuned

arXiv:2607.01675v1 Announce Type: new Abstract: Segmentation of biomarkers in medical images is frequently viewed as a first step towards medical image analysis in any bioinformatics or biomedical application. Despite progress, existing methods still struggle to capture information at multiple scales and to perform upsampling effectively across different datasets. These shortcomings often result in suboptimal generalization capabilities. Recently, architectures belonging to the Nested-UNet family excel in capturing multiscale contextual information and upsample them effectively. In this work, We propose a novel Nested-UNet architecture that effectively captures multi-scale contextual information. It includes inner and outer attention units to enhance focus during upsampling, along with channel-wise feature recalibration using squeeze-and-excitation modules, leading to improved segmentation performance. Additionally, the architecture integrates an edge-aware loss to emphasize boundary

arXiv:2607.01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation. It is primarily driven by future meteorological conditions while retaining temporal dependencies in power generation. In practice, observed wind-farm power often entangles physically available power with local environmental effects and latent operational states, such as shutdowns and curtailment. Existing physical models provide useful constraints but adapt poorly across wind farms, whereas data-driven models can capture rich correlations but often conflate meteorological effects with state-induced deviations. In this study, we propose UniWind, a wind power forecasting model based on physics-informed state routing. UniWind first employs a Physical Prior Estimator to construct a site-calibrated physical prior by combining site-conditioned monotonic warping with a shared physical power curve. It further applies a physical upper-bound constraint to

arXiv:2607.01621v1 Announce Type: new Abstract: Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatial supports: gauges measure points, microwave links measure paths, and radar/satellite products measure gridded areas. These differences in measurement support impose geometrically distinct constraints on the rainfall field, yet existing heterogeneous graph approaches reconcile such sources in feature space, giving each its own embedding while discarding the geometry of its support. We propose a geometry-aware multi-support heterogeneous graph neural network that represents each observation according to its support type (0D point, 1D line, or 2D grid) as a distinct node layer, and fuses them through cross-support message passing into a point-support prediction layer from which the field is reconstructed. An inductive masked-node formulation decouples prediction resolution from sensing

arXiv:2607.01597v1 Announce Type: new Abstract: Modern LLM coding agents are commonly evaluated using pass@k, but developers typically apply a single final patch in real-world settings. This pass@k-to-pass@1 gap is a post-generation problem: a candidate patch pool may contain a correct patch, but the system must decide which one to suggest to developers. Existing post-generation approaches mainly rank whole candidates, filter them with tests, or query an LLM judge, but none deterministically reuse shared edit-atom evidence to both select and construct the final patch. Thus, we propose PatchFusion, a deterministic atomic evidence fusion approach for candidate patches that consults no test outcome at decision time. PatchFusion first fuses whole-diff agreement into a repair neighborhood, selects an auditable representative, and then applies evidence-constrained fusion (ECF) to retain repeated edit atoms and prune unsupported parts. To evaluate this setting, we build PatchFuseBench, a

arXiv:2607.01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control. This work illustrates a two-stage learning pipeline that first estimates the local wind from onboard kinematics and dynamics and then exploits that estimate inside a reinforcement learning (RL) flight controller. The wind estimator, an attention-augmented gated recurrent network trained on thousands of simulated flights through von Karman turbulence with power-law shear and veer, recovers the horizontal wind vector with a per-flight root-mean-square error of 0.40 m/s and a direction error of 3.2 degrees on unseen wind regimes, an accuracy near the floor imposed by unresolved turbulence, and generalizes to vertical ascent profiles with a skill score of 0.861 over a constant-wind reference. A proximal policy

arXiv:2607.01462v1 Announce Type: new Abstract: Networked battery systems arise in industrial automation, distributed energy applications, and multi-agent systems, where terminals consume energy locally and recharge only when connected to a source. Resource constraints often limit the number of simultaneous connections, requiring networks to be dynamically reconfigured to maintain system functionality. Managing such networks in dynamic environments is challenging, particularly when low-energy terminals must be prioritized for timely replenishment. This paper presents a battery-aware topology optimization algorithm that extends the GeoSteiner framework with a tailored Mixed-Integer Linear Program (MILP) formulation for Full Steiner Tree (FST) aggregation. The formulation minimizes network length while prioritizing low-battery terminals through a weighted objective subject to a global budget constraint, enabling partial network formation under realistic resource limits. An

arXiv:2607.01286v1 Announce Type: new Abstract: Public lithium-ion battery datasets are increasingly used for state-of-health estimation, remaining-useful-life prediction, anomaly detection, electrochemical diagnostics, second-life analytics, and battery safety research. However, these datasets vary substantially in chemistry, modality, scale, label quality, sequence structure, access status, and preprocessing complexity. These differences directly affect whether a dataset is feasible for near-term hybrid quantum-classical machine-learning workflows. This paper presents IonSense-QKG, a quantum-readiness metadata framework for lithium-ion battery dataset discovery. Starting from the EV-Battery-IonSense index, the proposed framework enriches public battery dataset records with quantum-relevant metadata, including task type, sensing modality, chemistry, label availability, sequence type, preprocessing requirements, candidate quantum encodings, estimated qubit range, and NISQ

Some households will use batteries, EV charging and behaviour change to make very good use of free solar offer. But none will be able to avoid the rise in daily fixed charges. The post Solar Sharer free power offer is being undermined by higher network charges and complex tariffs appeared first on Renew Economy.

State government quietly reboots its paused solar battery rebate and expands the scheme to offer significant discounts on storage for apartments, businesses, and on C&I batteries sized up to 30 MWh. The post Solar battery rebate expanded to apartments and big energy users for up to 30 MWh in game-changing move appeared first on Renew Economy.

Innovative energy trading using Australian software is "going gangbusters" in Europe – and making our newly launched Solar Sharer Offer look like a blunt instrument. The post Australian software allows Italian homes to get free power from solar panels in Spain. Why not here? appeared first on Renew Economy.

The once-in-seven-years wind drought that hit South Australia in the last week of June has not stopped the renewables-rich state from setting a new monthly record for wind generation. The post Even with the “dunkelflaute,” South Australia has set a new monthly record for wind generation appeared first on Renew Economy.

Astronomers have revealed new details about the makeup and age of a visiting comet that was born around a distant star. They conclude that the composition of 3I/Atlas is strikingly different from any object found in our solar system.

The Massachusetts Senate passed a bill yesterday that its authors are referring to as “an act to save people money, repair the climate and grow the economy.” One way legislators intend to do this is by reforming and streamlining residential solar permitting processes statewide. The state’s energy omnibus bill was passed by the House in… The post Solar plays big role in Massachusetts Senate’s energy savings bill appeared first on Solar Power World.

Chinese oil major CNOOC has deployed the country’s largest tension leg platform (TLP) fitted with a floating wind turbine at the port of Zhuhai, Guangdong province. It will help to power an oil field in the South China Sea.

rPlus Energies announced the start of operations for the Green River Energy Center, a 400 MW solar and 1,600 MWh battery project in Emery County, Utah.

A new approach to battery recycling could turn today’s electric vehicle waste into the building blocks of tomorrow’s

arXiv:2604.16627v2 Announce Type: replace Abstract: Porous electrode theory (PET) provides essential insights into electrochemical states, but its computational complexity hinders real-time control and obscures scaling relations. To bridge the gap between high-fidelity simulations and reduced-order models, we present a framework of scaling analysis and analytical approximations. By assuming high-performance electrodes minimize transport limitations and overpotentials, we derive a simplified "lean model" governed by four dimensionless numbers: (i) a traditional Damk\"ohler number, $Da$, scaling the characteristic reaction rate to the diffusion rate in the electrolyte-filled pores; (ii) the "process Damk\"ohler number," $Da_p$, scaling the reaction rate to the applied capacity utilization rate (C-rate); (iii) the "wiring Damk\"ohler number," $Da_w$, scaling the reaction rate to an effective electromigration rate for ions in the pores in series with electrons in the conducting matrix;

arXiv:2607.01008v1 Announce Type: cross Abstract: Short-horizon prediction is essential for electro-optical UAV tracking, especially when the target is small, maneuvering, or intermittently observed. Image center, line-of-sight, and range measurements provide direct constraints on target position, but their constraints on acceleration are weak. As a result, prediction can lag during aggressive maneuvers. This paper proposes an image-domain tilt constrained distributed fusion method for maneuvering UAV tracking. The method uses the apparent roll and pitch of a rotorcraft target in the image as low-level maneuver cues. A weak-prior auto-labeling pipeline first generates oriented bounding box and image-domain tilt labels from synchronized video, gimbal IMU, and UAV IMU data. A YOLO-OBB detector is then trained to provide online target position and tilt measurements. The front-end Python implementation is publicly available at github.com/ShineMinxing/PythonYOLO. In the fusion stage,

arXiv:2607.00051v1 Announce Type: cross Abstract: Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation. Nearly all existing power curve models focus on temporal variables such as wind speed and temperature while overlooking the influence of terrain covariates, which governs inflow wind conditions and thus also affects wind power production. This paper proposes a nonparametric spatio-temporal Gaussian process model that integrates temporal environmental covariates with spatial terrain features. The model falls in the category of spatial-temporal Gaussian process models with data on a grid. The challenge to be addressed is that the spatio-temporal modeling require certain temporal alignment among the data, a property that the wind farm data does not have. Our solution strategy is to construct a shared representative temporal covariate set which not only aligns the temporal inputs but also has a size an order of magnitude smaller than the original

arXiv:2607.00850v1 Announce Type: new Abstract: Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right correspondences in approximately bilateral data such as medical images and human faces. We propose Mirror-Fusion-Augmented Self-Supervised Learning (MFASSL), a Vision Transformer framework that injects a soft reflection prior into standard SSL without redesigning the backbone. MFASSL constructs mirror-paired views aligned to an estimated symmetry axis and introduces a lightweight Mirror-Fusion Attention (MFA) module for adaptive token-level interaction between mirrored regions while preserving asymmetric cues. The base SSL objective is further coupled with reflection-consistency and mid-layer token-alignment losses. Across CheXpert, BraTS, CelebA-HQ, and WFLW, MFASSL improves downstream performance, calibration, and reflection robustness over MoCo-v3, DINO, and MAE baselines under matched

arXiv:2607.00746v1 Announce Type: new Abstract: The bird's-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achieving comprehensive 3D perception. However, the discrete grid representation of BEV leads to significant detail loss and limits feature alignment and cross-modal information interaction in multimodal fusion perception. In this work, we break from the conventional BEV paradigm and propose a new universal framework for multi-modal fusion based on 3D Gaussian representation. This approach naturally unifies multi-modal features within a shared and continuous 3D Gaussian space, effectively preserving edge and fine texture details. To achieve this, we design a novel forward-projection-based multi-modal Gaussian initialization module and a shared cross-modal Gaussian encoder that iteratively updates Gaussian properties based on an attention mechanism. GaussianFusion is inherently a task-agnostic

arXiv:2607.00296v1 Announce Type: new Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations. While facial affect potentially provides complementary behavioral cues, its practical utility and mechanistic boundaries within motion forecasting frameworks remain poorly understood. In this work, we present a systematic study investigating the utility and temporal limitations of affect-conditioned forecasting in-the-wild. We establish a rigorous multimodal pipeline combining MediaPipe body pose trajectories with HSEmotion facial affect representations, and introduce the Gated Affect Transformer (GAT) to dynamically regulate cross-modal information flow. Through extensive multi-horizon evaluations under a strict subject-wise protocol, we demonstrate that naive early cross-modal concatenation consistently degrades forecasting accuracy relative to pose-only baselines.

A researcher at The University of Alabama in Huntsville (UAH), a part of The University of Alabama System, has published a new study in The Astrophysical Journal suggesting that tiny charged dust grains near the sun may significantly influence how energy moves through the solar corona, the outer atmosphere of the sun. The discovery potentially rewrites how scientists understand why the corona is millions of degrees hotter than the surface of the sun itself.

A new study suggests creating a satellite constellation, dubbed StormWall, that could reduce the impacts of the worst solar storms by more than 50%. The novel plan, which involves dumping gas into the magnetosphere, could be the only way to directly protect ourselves from dangerous space weather, experts say.

Solar Power World, the premier media outlet for the U.S. solar market and publisher of the annual Top Solar Contractors List, is now accepting submissions for its manufacturer-focused awards program: Top Products presented by Solar Power World. Companies can submit their products through the online application portal. “As a news and media outlet dedicated to the… The post Solar Power World is now accepting submissions for the 2026 Top Products contest appeared first on Solar Power World.

By Ali Turner, Principal Writer and Content Strategist As commercial and industrial solar continues to expand, exposed-fastened metal roofs remain one of the most practical platforms for photovoltaic (PV) deployment. But as more solar is installed on exposed-fastened roof profiles, one question is becoming more important for EPCs, installers, engineers and building owners alike: What… The post Why Verified Waterproofing and Load Testing Matter for Solar Mounting on Exposed-Fastened Metal Roofs appeared first on Solar Power World.

Researchers reveal that we waste a huge amount of energy on redundant bitcoin mining operations — where different miners try to grab the same bitcoin.

Deakin University’s Andrea La Nauze on the early findings from an Australia-first trial of technology that lets households and businesses trade rooftop solar via a digital platform. The post Virtual networks and the real pursuit of energy democracy | Solar Insiders appeared first on Renew Economy.

Føn Energy Services Poland has signed a long-term, multi-million-euro agreement with the joint venture […]

A major renewable energy developer and a leading independent asset manager have joined to support a portfolio of battery energy storage systems in Poland, part of the continuing buildout of new power infrastructure across Europe. The post Battery Energy Storage, Grid Investments Surge Across Europe appeared first on POWER Magazine.

Arevon’s Eland solar-plus-storage project in California provides power for the Los Angeles region and is helping the state progress toward its goal of providing more renewable energy. The post A Model for a Clean Energy Future: Arevon’s Eland Solar-Plus-Storage Project appeared first on POWER Magazine.

A decade after Dominion Energy secured a federal lease off Virginia Beach, the 2.6-GW Coastal Virginia Offshore Wind (CVOW) project has cleared the full U.S. permitting stack, survived a federal stop-work The post Against the Wind: Inside the Completion of America’s Largest Offshore Wind Plant appeared first on POWER Magazine.

After decades of ambition and 14 years of construction, Ethiopia’s 5.15-GW Grand Ethiopian Renaissance Dam has become Africa’s largest hydropower project. The 13-unit plant gives Ethiopia a single The post GERD: How Ethiopia’s Blue Nile Vision Became Africa’s Largest Hydropower Plant appeared first on POWER Magazine.

arXiv:2606.31242v1 Announce Type: new Abstract: With the advancement of imaging technology, ultra-high-definition images have become increasingly essential in modern visual applications. However, existing multi-focus image fusion remains largely confined to low-resolution images and faces three major barriers in UHD scenarios, namely data availability, model adaptability, and deployment feasibility, which severely hinder its practical application. To shatter these barriers, first, we propose the UHD-MFF dataset, the first large-scale ultra-high-resolution multi-focus fusion dataset. Second, we propose a scale-specialized lookup-table framework tailored for ultra-high-resolution images, termed as UMF-LUT. It consists of Coarse-Region Lookup Table (C-LUT) and Detail-Edge Lookup Table (D-LUT). Specifically, C-LUT performs joint queries of multiple gradient cues and semantic cues at low-resolution scales to enable region-level decision-making. Also, D-LUT operates at high-resolution

arXiv:2606.31186v1 Announce Type: new Abstract: Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi-View Gated Graph Attention Network that transcribes audio via Automatic Speech Recognition (ASR) to construct semantic, dependency, and co-occurrence graphs, characterizing speech through a "content-structure-flow" framework. Notably, the co-occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quantify narrative logic and linguistic deviation. To address symptomatic diversity, an adaptive gated fusion mechanism dynamically integrates these views. Evaluated on the ADReSSo dataset, our model achieves 90.00% accuracy. Ablation results confirm that the PMI-based graph and heterogeneity-aware gating are essential for robust classification across diverse clinical populations. Our source code is

arXiv:2606.31096v1 Announce Type: new Abstract: Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods remain limited at extended ranges. BEV-based methods capture scene-level context but incur rapidly growing computation and often lose fine-grained object detail, while query-based methods are efficient but provide limited scene-level context. Temporal fusion further requires both multi-frame accumulation for sparse distant observations and object-level motion modeling for fast-moving objects. We propose Horizon3D, a sparse radar-camera fusion framework for long-range 3D object detection that combines Gaussian primitives with sparse BEV features. Horizon3D initializes Gaussian primitives at radar- and camera-estimated object keypoints using Keypoint-Guided Gaussian Initialization, refines them through Object-Centric Sparse Fusion, and splats them onto the BEV plane to fuse object-level detail with sparse radar

arXiv:2606.30804v1 Announce Type: new Abstract: Use of quadrotor UAVs for wind velocity estimation is gaining popularity in recent studies, leveraging their maneuverability, compact size and low cost. Among available approaches, model-based wind velocity estimation is most commonly used, since it relies only on onboard sensors. However, as the quadrotor is a highly nonlinear system, thus making this task challenging. This study evaluate the use of both discrete and continuous dynamic equations of the quadrotor UAV for wind velocity estimation on SE(3), rather than commonly adapted continuous or discretized form. Lie Group Variational Integrator, developed on discrete Lagrangian is used as the discrete model without any approximation or discritization. The study assess both the discrete and continuous form of the quadrotor dynamics on SE(3) using Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). The quadrotor UAV performance is evaluated in both MATLAB-based numerical

arXiv:2606.30647v1 Announce Type: new Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the available measurements are heterogeneous in both modality and spatial coverage. We present AtmoFuseNet, a framework that fuses multi-view sky camera imagery with millimeter-wave cloud radar and ceilometer observations to produce 4D (three spatial dimensions plus time) estimates of cloud state and wind. The method operates in three stages: a cross-modal hierarchical aggregation module that combines image feature pyramids with instrument-derived vertical profiles through layer-wise cross-attention; a conditional variational refinement module that maps the resulting volume to physically consistent microphysical fields under differentiable radar and image forward models; and a correlation-based motion estimator that recovers per-voxel 3D wind vectors from consecutive volumetric reconstructions. On

Project Ares wants to build a 1 GW data centre on a cattle station on Australia's Top End and power it with 3 GW of solar and a 16 GWh battery. It also wants to build a giga-scale gas plant. The post Plan to power off-grid data centre with solar, gas and a 16 gigawatt-hour battery seek federal green tick appeared first on Renew Economy.

Renewable energy sources across the UK generated a record share of the country’s electricity in the first quarter of 2026, driven by increased wind power generation. The post Renewables generate record share of UK electricity, as wind out-supplies gas appeared first on Renew Economy.

Solar Sharer may have been sold as an consumer equity policy, but it could have more success as a grid levelling policy. The post Solar Sharer is here, offering free power to all. Will it level the playing field, or benefit batteries most? appeared first on Renew Economy.

Thieves have made off with trailers loaded with Tesla battery packs straight from the factory nearly a dozen times since December.

US power company Duke Energy has become the latest developer to accept a Trump administration deal to voluntarily renege on a US offshore wind lease it won previously and invest its reimbursed lease fees in non-renewable energy infrastructure.

Renewable energy developer New Energy Equity along with community leaders and Harlem School District officials and students gathered in Machesney Park, Illinois, on Wednesday to celebrate the energization of a new 5.5-MW community solar project. Operated by New Energy Equity on land owned by the school district, this community solar project will generate 8.3 million… The post New Energy Equity community solar project funds scholarship program appeared first on Solar Power World.

A four-year-old Model 3 Performance scored 88% battery health in Tesla's own test—lower than its owner expected, but close to a prediction by a third-party app.

US power company Duke Energy has become the latest developer to accept a Trump administration deal to voluntarily renege on US offshore wind leases it won previously and invest its reimbursed lease fees in non-renewable energy infrastructure.

South Korea has awarded power deals to five offshore wind projects with a combined capacity of 1.8GW in its latest auction, following bids from nine projects totalling just over 3.6GW.

Two companies behind floating wind power designs are locked in a dispute over rival designs and patents filed with the European Patent Office (EPO) and in Sweden. 

arXiv:2606.21026v2 Announce Type: replace Abstract: The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems are recognized as important natural carbon sinks. In this context, this paper addresses precise seaweed classification for blue carbon quantification in Ocean Digital Twin initiatives. Conventional methods, including supervised learning (limited by data scarcity and domain gaps) and self-supervised learning (unable to assign class labels), struggle with underwater complexities and diverse seaweed species. To overcome this, we propose a novel two-stage seaweed segmentation technique. This technique first utilizes Supervised and Self-supervised Learning Model Propagation (SSL.Prop.), which leverages supervised learning for initial class information and approximate locations, guiding self-supervised learning for detailed, accurate segmentation. Subsequently, MaskFusion (MF) refines these

arXiv:2606.30581v1 Announce Type: new Abstract: In environmental monitoring as well as emergency response applications such as wildfires, wind velocity measurement is essential. Quadrotor UAVs have become popular platforms for wind velocity estimation due to their maneuverability, compact size, and cost-effectiveness. Numerous studies use the Extended Kalman Filter (EKF) to estimate the wind velocity based on the quadrotor dynamic model. However, most of them use hovering quadrotors only for wind estimation, others use a near-linear trajectory to estimate near-constant velocities. Furthermore, EKF performance is constrained by its reliance on linearized approximations of the nonlinear quadrotor dynamics around current states, limiting accuracy in highly nonlinear scenarios, including windy conditions. This study proposes the use of an Unscented Kalman Filter (UKF), a nonlinear estimator to provide accurate wind estimations while maintaining the trajectory of the quadrotor UAV. The

arXiv:2606.30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios. As a controlled benchmarking basis, we then focus on the minimal case with one wind turbine and one co-located data center. In this setting, pure Reinforcement Learning exhibits a pronounced credit-assignment problem and tends to underuse free wind energy early in the day. We therefore evaluate two complementary countermeasures: optimization-based Imitation Learning and potential-based Reward Shaping. Across multi-seed training and a 200-day test set, Proximal Policy Optimization (PPO) and a Soft Actor-Critic (SAC) variant with an additional on-policy update routine achieve

arXiv:2606.30291v1 Announce Type: new Abstract: Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks. However, existing fusion methods often treat text and structure as separate inputs in a shallow, one-way pipeline, which limits deep interaction between modalities and weakens performance under sparse connectivity or cross-graph generalisation. To address this issue, we propose PromptGNN-sim, a bi-directional structure-semantic fusion framework for collaborative GNN-LLM learning. PromptGNN-sim uses a Graph Attention Network (GAT) for semantically aware neighborhood selection by combining structural attention with textual similarity. The selected structural context is then used to generate structure-aware prompts for an LLM, including the target node summary, label categories, and representative keywords from similar neighbors. During training, bi-directional cross-modal contrastive learning and cross-attention are

arXiv:2606.30215v1 Announce Type: new Abstract: RGB-T detectors leverage the complementary strengths of visible and thermal infrared modalities, achieving robust performance under challenging conditions. Many of them resort to heavy dual backbones and exhaustive cross-modality fusion across the entire image, leading to impractically high computational costs. We observe that most image regions are smooth backgrounds (e.g., sky, ground) that can be easily handled by lightweight single-modality models. In light of this observation, we propose a sparse fusion mechanism for efficient RGB-T detection: first rapidly scanning the image to identify the proposals and then carefully examining the remaining sparse proposals via feature fusion. We propose a two-stage framework to instantiate this mechanism, which performs detection in two stages: 1) a lightweight and modality-specific detection stage that produces high-recall RoIs, and 2) a fusion-driven examination and refinement stage that

arXiv:2606.30030v1 Announce Type: new Abstract: Blind image deblurring demands the recovery of high-fidelity details and coherent structures from complex, unknown degradations. Current blind image deblurring methods struggle with real-world, spatially varying degradations, and lack the semantic awareness necessary to reliably differentiate valid textures from artifacts. To bridge this gap, we propose CogSENet, a dynamic, semantic-aligned reconstruction framework inspired by the eagle's visual system. By mimicking the eagle's active saccadic scanning, we devise a Semantic-Driven State Space Module (SDSSM) with semantic-aware token regrouping via differentiable routing, enabling prompt-conditioned long-range dependency modeling. To ensure physically interpretable recovery of textures and structures, a BiFreqFusionBlock (BFFB) mirrors functional differentiation of the eagle's retina by decomposing features into high and low frequencies using wavelet transforms. Finally, we estimate a

arXiv:2606.29900v1 Announce Type: new Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of interviewees in AVI. However, unimodel methods often suffer from information loss (e.g., ignore facial cues). In contrast, multimodal methods that employ full-face images or sparsely sampled frames can discard fine-grained temporal dynamics critical for accurate personality assessment. To overcome these limitations, we propose an LLM-based framework that semantically fuse facial action units (AUs) with textual responses of AVI. AU sequences are first converted into interpretable textual descriptions, which are then fused with participants' textual responses through an LLM. A lightweight regression head transforms the resulting embeddings into continuous personality scores without disrupting

arXiv:2606.29494v1 Announce Type: new Abstract: Visual SLAM performance often deteriorates in complex real-world applications. Semantic 3D Gaussian SLAM commonly fuses 2D semantic priors into a persistent 3D map using uniform optimization weights. However, such priors are not equally reliable in online mapping: occlusions, unsupported semantic boundaries, and ambiguous ray geometry can introduce persistent semantic artifacts into the global Gaussian map. We propose VCS-SLAM, a geometry-validated semantic evidence fusion framework for RGB-D 3D Gaussian SLAM. Instead of treating all semantic observations as uniformly valid supervision, VCS-SLAM evaluates their geometric reliability through visibility consistency, surface-supported boundary evidence, and ray-level conflict uncertainty. The resulting reliability-aware objective suppresses occluded semantic updates, reduces unsupported semantic bleeding, and delays premature label assignment in ambiguous regions. Experiments on Replica

arXiv:2606.29384v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become an important paradigm of embodied AI. However, existing VLA models typically assume well-lit and stable indoor settings, while real-world embodied manipulation may involve degraded RGB observations caused by illumination shifts, posing critical challenges for robust robotic manipulation. To address this gap, we propose \textbf{Event-VLA}, an event-enhanced VLA framework for generalizable manipulation across varying illumination conditions. We formulate VLA-based manipulation under degraded visibility as a practical robustness problem for RGB-centric policies, and introduce event streams as an illumination-robust, motion-sensitive complementary observation to improve robustness across visibility levels. Specifically, unlike conventional multimodal fusion that directly merges event features into the global semantic token space, Event-VLA injects event information through an action-query

arXiv:2606.28951v1 Announce Type: new Abstract: Accurate localization of unmanned aerial vehicles (UAVs) is essential for applications such as structural health monitoring, especially in environments where Global Positioning System (GPS) signals are denied or unreliable, like indoor spaces, tunnels, urban canyons, or areas beneath large structures. To address this challenge, we propose Cross-Fusion, a novel method for real-time UAV localization that integrates data from a 3D Light Detection and Ranging (LiDAR) and a monocular camera. A key contribution is its cross-session fusion strategy, which integrates visual and geometric information collected from multiple agents during routine baseline surveys to improve localization consistency and map completeness. The system employs LiDAR-based odometry for motion tracking and image-based feature matching via a single red-green-blue (RGB) camera to correct drift and improve accuracy. Unlike visual-inertial systems, Cross-Fusion maintains a

arXiv:2606.28826v1 Announce Type: new Abstract: Existing digitization of buildings with reflective glass facades suffers from geometric reconstruction distortion, unrealistic view-dependent texture rendering, and difficulties in object-based semantic enhancement. Therefore, we propose RefGlass-GS, a fusion framework that enables end-to-end UAV-based photorealistic, semantic, and interactive digitization of reflective glass facades. The contributions include: (1) proposing an individual glass panel segmentation method based on maximum a posteriori estimation with structural regularities, robust to severe reflection and background interference; (2) formulating a UAV viewpoint planning optimization function that maximizes the coverage of view-dependent appearance for sufficient data capture; (3) developing an optimized Gaussian Splatting framework with a Reflection MLP, a novel deferred shading function, and two enhanced regularization terms for effective modeling of high-frequency

arXiv:2606.28604v1 Announce Type: new Abstract: Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-of-sight requirements, but existing IMU-based motion capture assumes an isolated human and ignores object contacts and dynamics. To bridge this gap, we present IMU-HOI, a novel framework that jointly recovers full-body human pose and 6-DoF object trajectory from sparse IMUs on the body and object, explicitly modeling human-object interaction. Our approach first infers probabilistic hand-object contacts directly from IMU streams and uses them as a high-level signal to route between kinematic and inertial reasoning. These contact cues drive a three-stage fusion pipeline that refines human pose and root translation, and fuses hand-based forward

arXiv:2606.28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures. These structures often represent aggregations (e.g., products to categories to regions), where forecasts must not only be accurate but also coherent, meaning that lower-level predictions sum correctly to higher-level forecasts. Traditional statistical methods, such as Bottom-Up and MinT, enforce coherence through post-processing but fail to model complex nonlinear temporal dependencies and covariate interactions. We propose Hierarchical Temporal Fusion (HTF), a novel extension of the Temporal Fusion Transformer (TFT) that integrates structured hierarchical embeddings with a coherence-aware loss function to ensure consistent forecasts across all levels of a hierarchy. Rather than applying reconciliation after forecasting, HTF embeds coherence directly into the training objective. The

arXiv:2606.28507v1 Announce Type: new Abstract: Accurate and efficient parameter estimation is essential for applying electrochemical battery models in simulation, state estimation, control, and repeated model updating. However, conventional optimization methods, such as particle swarm optimization (PSO) and genetic algorithms (GA), often require many model evaluations and show considerable run-to-run variability, limiting their use in time-sensitive calibration scenarios. This study proposes a Batch-Optimized Local-to-Global Technique (BOLT) for rapid and robust parameter estimation of electrochemical battery models. BOLT combines diversified candidate initialization, batch-parallel trust-region reflective (TRF) local refinement, JIT-accelerated model evaluation, and multi-condition consistency screening within a unified calibration workflow. Comparative experiments based on a grouped single-particle model and measured data from a commercial 18650 NMC lithium-ion cell show that BOLT

The US Department of the Interior (DOI) has reached a settlement agreement with Duke […]

Researchers are developing an AI-powered system to improve energy flow between apartment buildings, paving the way for cheap,

One of the first projects in Australia to combine grid-forming wind generation with DC-coupled battery storage has won approval to connect to the main grid. The post “Next generation” wind-battery hybrid and CIS winner wins landmark grid connection approval appeared first on Renew Economy.

A new Southwest Research Institute (SwRI) study based on data from NASA's New Horizons spacecraft has uncovered insights into why the solar wind gradually slows as it moves toward the edge of the solar system and the boundary with interstellar space. The study "The Gradual Slowing of the Solar Wind in the Outer Heliosphere" is published in The Astrophysical Journal.

It was the fourth such deal struck by the administration to get companies to forfeit their offshore wind leases.

A new study has revealed an unexpected link between solar storms and the flux of high-energy cosmic rays arriving at Earth. The findings, made using one of the world's largest cosmic ray detectors, could open up a new way to probe the magnetic structures inside solar storms—and potentially improve our ability to forecast their effects on Earth. The research has been published in Physical Review Letters.

Windpower Monthly rounds up the latest patents for wind power technology granted and applied for in the last week.

Geothermal is poised to become an important source of electricity that is both cost-competitive and emission-free, thanks to

arXiv:2606.27800v1 Announce Type: cross Abstract: Reliable monitoring of hydroelectric generators requires descriptors that capture both electrical loading and electromagnetic field behavior. This work investigates operating-regime identification in the Porjus U9 10-MW Kaplan hydrogenerator using synchronized measurements from ten stator-mounted Hall probes and six rotor-current channels. Seven steady guide-vane-opening settings are considered, and each 300s record is divided into 1s windows. The resulting windows are represented by spatial Fourier descriptors of the circumferential air-gap field, probe-wise temporal flux indicators, and channel-wise RMS rotor-current features. Correlation analysis and principal component analysis are used to examine how the feature groups vary with the operating point, and Random Forest, radial-basis-function support vector classification, and multilayer perceptron models are evaluated for supervised identification of the guide-vane-opening state.

arXiv:2606.28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models. They typically en-force conservation laws within the loss function to ensure physically consistent solutions. Tradi-tional numerical methods such as finite difference, finite volume, and finite element techniques, re-ly on discretization and can be computationally expensive for nonlinear systems. To address this challenge, PINNs offer improved scalability, particularly for reduced-order models like the single particle model with electrolyte (SPMe). The SPMe describes lithium-ion battery dynamics through coupled diffusion, transport, reaction kinetics, and voltage equations. Despite these advantages, training SPMe-based PINNs from scratch for different battery chemistries or operating conditions is demanding and often leads to slow convergence. To overcome this

arXiv:2606.28062v1 Announce Type: new Abstract: Data fusion, also known as truth discovery, is a data integration problem that aims to determine the correct value or set of values for each attribute of an object when presented with potentially conflicting values from multiple sources. Data fusion tasks belong to two main categories: single-truth scenarios, where each attribute has only one correct value, and multi-truth scenarios, where multiple values can be valid simultaneously. This paper investigates the use of Large Language Models (LLMs) in data fusion tasks for tabular data. Various prompting strategies, encompassing both single-truth and multi-truth scenarios, are investigated empirically. Domain-dependent, domain-independent, zero-shot and one-shot prompts are evaluated on three different benchmark datasets. Experimental results demonstrate that LLM-based approaches outperform traditional unsupervised truth discovery methods, such as DART and LTM, across all datasets. The

arXiv:2606.27905v1 Announce Type: new Abstract: Multi-exposure fusion (MEF) brings the dynamic range of conventional cameras closer to that of human vision, producing images with rich scene content. Given the large variability in scene luminance, exposure strategies often require different numbers of frames to capture the full radiance range faithfully. However, conventional MEF techniques are typically designed for a fixed number of inputs, forcing deployment systems to maintain separate models for different frame-count requirements, which undermines deployment efficiency. To address this limitation, we propose FreeMEF, the first flexible-frame transformer for MEF that seamlessly accommodates varying numbers of input exposures without retraining or architectural changes. The proposed approach consists of two key modules. First, we introduce a recurrent state space module (RSSM) that sequentially fuses features from arbitrary sequences via adaptive alignment and state-space recurrent

arXiv:2606.27886v1 Announce Type: new Abstract: Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts. Modalities can include IMUs, RGB cameras, audio signals, and others. One important aspect of multi-modal deep learning is the sensor fusion approach we apply. Over recent years, multiple fusion paradigms have been proposed for multi-modal HAR. However, to the best of our knowledge, no head-to-head comparison of these paradigms exists on a common multi-modal HAR benchmark dataset. To address this research gap, we systematically compare seven state-of-the-art sensor fusion methods on the recently released HARMES dataset, which comprises 61 hours of fully labeled IMU, audio, and ambient humidity data. The chosen dataset focuses on 15 household and personal hygiene activities of daily living (ADLs). By applying the seven different fusion techniques to a

arXiv:2606.27881v1 Announce Type: new Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time. While language models (LMs) have made progress in various NLP tasks, their ability to reason about temporality, especially in diachronic contexts, remains limited or at least, questionable. In this paper, we systematically study how temporal metadata can be structurally embedded into NER models using a range of lightweight fusion strategies. We experiment with both absolute and relative temporal representations, injected into Transformer-based architectures via early or late fusion mechanisms such as cross-attention, adapters, and concatenation. Our evaluations on French and German historical datasets reveal that late fusion strategies yield more robust and temporally generalisable performance, particularly in early and noisy periods.

arXiv:2606.27750v1 Announce Type: new Abstract: Multi-vehicle collaborative perception (MvCP) is considered as a key technology to facilitate automated driving (AD), where real-time MvCP under limited resources is significant for reliable AD. In this paper, we formulate a lightweight acceleration scheme for intermediate-fusion (IF) MvCP, which can adapt to both situations of limited computation and communication resources. We provide a relaxed definition conditional additivity and analyze the conditional additivity for various DNN linear layers. On this basis, we focus on the IF-MvCP based on additive feature fusion, and derive the MvCP precision consistency of the forward and backward feature fusion position (FP) adjustments among linear layers. Through experiments, we further validate the precision consistency of the FP adjustment method. Moreover, we propose an FP adjustment among linear layers (FALL) scheme for MvCP acceleration without precision loss theoretically. Simulation

arXiv:2606.27637v1 Announce Type: new Abstract: Recent advancements in synthetic data technology have opened a new era where images of remarkable quality are generated, blurring the lines between real-life images and those produced by Artificial Intelligence (AI). This evolution poses a significant challenge to ensuring the reliability and authenticity of data, underscoring the need for robust detection methods. In this paper, we present a robust approach aimed at addressing these pressing concerns. Our methodology revolves around leveraging fusion strategies, combining the strengths of multiple detection methods for identifying AI-generated images. Through extensive experimentation on the CIFAKE dataset, our model showcases remarkable performance, achieving an impressive accuracy rate of 97.32%. This accomplishment underscores the efficacy of our approach in accurately distinguishing between AI-generated images and real-life images, thus contributing to the advancement of data

arXiv:2606.27556v1 Announce Type: new Abstract: Radar camera fusion is widely used in Automatic Emergency Braking AEB systems because radar provides reliable range and velocity measurements while cameras provide a proper visual confirmation of the objects . Most of the deployed systems perform this confirmation using computationally intensive object detectors. However, if the radar has already localized a target, the camera may only need to verify the obstacles presence rather than solving a full problem by identifying the object. Our work proposes a radar scoped edge density gate that performs obstacle verification within radar guided image regions of interest. This method requires no training data, model weights, or GPU acceleration and was integrated into a complete radar camera fusion AEB system with brake by wire actuation. Evaluated on a real instrumented vehicle across 72 driving sessions and 131,603 camera frames, the proposed approach reduced the camera search space by up to

It feels like every few months we get to report on another academic paper singing the praises of the Solar Gravitational Lens (SGL). Partly, this is due to Dr. Slava Turyshev's astounding productivity in pumping out academic articles, but partly because such a groundbreaking mission has lots of positive aspects—as well as challenges that need to be addressed. A new paper, posted to the arXiv preprint server from Dr. Turyshev, stresses an often overlooked feature of the SGL: how useful it can be for imaging things other than faraway exoplanets.

One year ago, on July 1, 2025, astronomers discovered a fascinating new object moving through the solar system. Detected by the Asteroid Terrestrial-impact Last Alert System (ATLAS), the object was quickly recognized as something special.

Author(s): Michael SchirberA solar storm hitting Earth appears to have reduced the amount of incoming high-energy cosmic rays, suggesting a new way of measuring solar activity. [Physics 19, 92] Published Fri Jun 26, 2026

Author(s): R. Mathis, S. Cazin, J. Methel, F. Charru, J. Magnaudet, F. Moisy, and M. RabaudThe growth of wind-generated waves may depend on free-stream turbulence, a parameter that is generally neglected in existing models. Here, we investigate this effect experimentally using grid-generated turbulence blowing over a viscous fluid. Our results show that free-stream turbulence enhances the amplitude of three-dimensional wrinkles and lowers the critical wind velocity for the onset of regular two-dimensional waves, while the wrinkle–wave transition remains associated with an approximately constant friction velocity. A qualitative model explains why the observed decrease of the friction velocity with the fetch results in a non-monotonic variation of the wave amplitude. [Phys. Rev. Fluids 11, 064804] Published Fri Jun 26, 2026

For decades, scientists have searched for a clear link between the Sun’s explosive storms and the weather that occurs on Earth. A breakthrough study from the University of New Hampshire reveals that in the hours and days following a solar storm, parts of North America can see sharp changes in the weather — such as declines in precipitation — and the more powerful the storm, the more dramatic the shift. However, the exact mechanism behind the effects is still waiting for an explanation.

In May 2024, auroras were observed at unusually low latitudes across the globe, lighting up skies that rarely see such displays. Inside Earth's magnetosphere, the region of space surrounding our planet and dominated by its intrinsic magnetic field, something significant was finally observed.

After years of operating on the fringe of Democratic values, pro-Palestinian activists felt validated after the primary wins by several candidates who oppose Israel’s actions.

arXiv:2606.27055v1 Announce Type: cross Abstract: This paper presents solarsystem, a validated lightweight and dependency-free Python package for planetary positions and solar-lunar event calculations. The package provides heliocentric and geocentric positions for the major planets, selected dwarf planets, the Centaur Chiron, and the Moon, together with sunrise, sunset, moonrise, moonset, and lunar illumination calculations. Additional functionality includes coordinate transformations between commonly used astronomical reference systems. The implemented algorithms employ analytical models that avoid reliance on external ephemeris datasets, resulting in a portable and computationally efficient solution suitable for a broad range of astronomical applications. An optional precession correction model is included, enabling calculations either in a precession-corrected reference frame or in a fixed epoch framework, depending on user requirements. The numerical performance of solarsystem

arXiv:2606.26712v1 Announce Type: cross Abstract: Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair and shadows. Recently, diffusion models have shown strong performance in medical image segmentation thanks to their progressive denoising and distribution modeling capabilities. Nevertheless, existing diffusion-based methods still suffer from limited cross-level feature interaction and insufficient boundary detail recovery. To address these issues, we propose MLFFM-SegDiff, a multi-level feature fusion diffusion model for skin lesion segmentation. Built on a diffusion framework, the method introduces a dual-path U-Net encoder, a Multi-Level Feature Fusion Module (MLFFM), and a boundary-sensitive loss function. The dual-path encoder enhances

arXiv:2606.26932v1 Announce Type: new Abstract: Hot-weather electric-vehicle thermal management is no longer a separate cabin and battery problem. A single climate system must cool the traction battery, maintain passenger comfort, and admit outdoor air for cabin air quality, while high ambient temperature and solar load derate the compressor serving all three demands. We identify fresh-air ventilation as a hidden battery-safety load: on a derated shared cooling loop, the compliant fresh-air floor consumes finite cabin-side cooling capacity and removes residual cooling reserve from the battery. In a 40 $^\circ$C, 800 W m$^{-2}$, 150 kW event, raising the fresh-air floor from 0.30 to 0.43 lowers peak cabin CO$_2$ from 1219 to 978 ppm, but raises peak battery temperature from 39.96 to 40.02 $^\circ$C and reduces the battery cooling bus from 575 to 529 W. We develop a reserve-aware predictive controller combining a physics-guided scientific-machine-learning surrogate, grid-connected

arXiv:2606.26920v1 Announce Type: new Abstract: Cold-climate transit agencies are electrifying fixed-timetable fleets, but winter exposes a block-level failure mode hidden by seasonal energy margins: cabin heating can deplete batteries faster than layovers recharge them, causing later trips to start undercharged and making one cold day cascade into timetable infeasibility. We present WeatherRobustBus, an open-data framework that converts this risk into block-level failure probability by injecting real hourly weather into real transit duties and propagating cold-weather energy uncertainty. The framework couples a transparent traction and cabin-thermal backbone with a bounded monotone residual ensemble, and validates cabin heating against an independent EnergyPlus bus-cabin simulation driven by the same Toronto weather record. Against this first-principles reference, it achieves the lowest all-year error (0.213 kWh RMSE over 8760 hours) and remains reliable in the out-of-support cold

arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing environmental hotspots into actionable strategic pathways under technological, social, and policy uncertainty. To overcome this limitation, this study introduces a perspective-conditioned retrieval-augmented generation framework for LCA interpretation, where a multi-perspective retrieval and controlled synthesis is incorporated in the artificial intelligence (AI)-assisted LCA. To operationalise large language models in LCA interpretation, a perspective fusion RAG architecture was developed, covering academic, industry, public discourse, and European union (EU) funding datasets. Our approach comprises three steps: (1) a scenario anchor defining system boundaries and decarbonization targets, (2) a set of perspective-specific micro-queries with constrained retrieval, and (3) a neutral synthesis

arXiv:2606.26849v1 Announce Type: new Abstract: General Salient Object Detection (SOD) aims to identify and segment visually interesting objects from uni-modality or multi-modality scenes, recently advanced by cutting-edge State Space Models (SSMs). However, a critical limitation of current approaches is their neglect of the inherent spectral biases exhibited by different neural network paradigms. By digging to the dataset-level spectral analysis of Convolutional Neural Networks (CNNs) and SSMs, their semantic representations are inherently complementary based on their complementary frequency preferences. Inspired by this, we harmonize heterogeneous representations from SSMs and CNNs to bridge their spectral biases for general salient object detection. To this end, inspired by the dynamic information propagation of Liquid Neural Networks (LNNs), we introduce a liquid fusion to dynamically integrates features from two backbones, including VMamba and ConvNeXt, referred to Liquid Fusion