Browse Articles

Yue Xiang and colleagues propose an adaptive hierarchical learning framework to address implicit uncertainties in distributed energy resource planning when third-party operators lack full grid visibility. Their method co-optimizes investment and operational decisions data-drivenly, reducing costs while maintaining voltage stability without relying on predefined scenarios or model simplifications.

The use of electric vehicles contributes to sustainability efforts by reducing our reliance on fossil fuels and improving air quality. Communications Engineering is delighted to present its first complete Research Collection on the topic of Battery Management Systems for Vehicle Electrification; you can read all the content here: https://www.nature.com/collections/hfdifjjbha.

The formation of faults in lithium-ion batteries can significantly impact pack safety and performance. Pierre Lambert and colleagues propose a modelling and machine learning framework to detect these faults from the current oscillations found in parallel connected packs.

Zhendan Lu et al present a novel framework for degradation modelling of chaotic systems through random walks in phase space. This approach effectively overcomes the computational inefficiency of time-domain iterative methods while maintaining high predictive accuracy.

Fuelbreaks are treated areas of land that mitigate the spread of wildfires. Dent and colleagues present a method for determining optimal placement using network science and partitioning through optimization with the D-Wave hybrid quantum solver.

Bruno Kluwe and colleagues propose using stimuli-responsive, magnetic hydrogels in magnetic particle imaging to resolve pH levels. Non-invasive pH measurement is promising for diagnosing inflamed or tumorous tissues.

Rui Li and colleagues propose a deep learning solution to inverse problems in imaging. Their sparsity-efficient network and software improve image restoration across advanced light microscopy modalities with fewer parameters than existing models.

CEST MRI is a unique molecular contrast that indicates specific proton exchange. Its wide clinical translation has been hindered by long scan time. Huabing Liu and colleagues developed a high performance deep-learning-based reconstruction method to enable fast CEST imaging.

Yu Zhou and colleagues propose novel designs for oscillating water column wave energy converters coupled to a parabolic coast. The designs yield higher power capture and a broader frequency bandwidth than traditional designs.

Anika Alim and colleagues micro engineered a 3D vascularized midbrain model that emulates the capillary interface of midbrain dopaminergic neurons. This approach accurately recapitulates key Parkinson’s disease pathologies, including neurodegeneration and vascular regression.

Sihan Tan and colleagues report an AI agent capable of process simulation, optimization, carbon emission accounting, and decarbonization intervention evaluation. This progress facilitates high-resolution carbon emission models of the industry.

Jia Hu and colleagues propose a human-lead truck platooning method with lane-changing capability to improve logistics efficiency and reduce labor costs. It has been deployed in large-scale port operations in Shanghai.

Jintao Li and Xinming Wu introduce memory-efficient techniques that enable accurate, full-volume 3D dense prediction without degrading model performance. These methods help large 3D models run reliably in seismic and other industrial settings.

Jing Yan and colleagues propose an innovative solution for swarm control of autonomous underwater vehicles driven by digital twin technology. The effectiveness of this approach is validated through experimental results, which demonstrate that DT-driven swarm control enhances underwater situation awareness and prediction accuracy while simultaneously reducing communication energy consumption

Jianyu Chen and colleagues propose an easily deployable method that optimizes exoskeleton assistance in just 2-min with comfort and effectiveness. This rapid optimization could make personalized assistive devices more accessible for everyday use.

Bower and colleagues demonstrate sub-diffraction 3D imaging to visualize rods and foveal cones in the living human eye. Their modular strategy can be readily applied to most existing high-resolution ophthalmic imaging systems to improve resolution.

Zhuang Yaoming and colleagues propose a diffusion model-based framework for generating underwater detection datasets. This approach requires only a small set of underwater images with corresponding annotations to produce high-quality, diverse underwater images, thereby enhancing object detection performance in real-world underwater scenarios.

Eloisa Torchia and colleagues present HYDRA, a robotic approach for fabricating uniform and planar hydrogel layers in multiwell plates. The method enables scalable, imaging-compatible drug screening on physiologically relevant substrates

Conventional body-coupled wireless communication links for wearables rely on electro-quasistatic conduction, treating the human body as a wire. Samyadip Sarkar and colleagues demonstrate leveraging fields beyond electro -quasistatic frequencies unveils the human body’s transmission-line behaviour that enhances wearable-to-wearable communication channel capacity.

Shahin Alipour Bonab and colleagues proposed and developed AI surrogate models that can predict time-dependent AC losses of superconducting motor of future hydrogen-powered cryo-electric aircraft. Benefiting of ultra-fast predictions, the model will be used in system-level model for aircraft propulsion system in the context Airbus CryoProp project.