Communications engineering最新文献

One of the main concerns affecting the uptake of battery packs is safety, particularly with respect to fires caused by cell faults. Mitigating possible risks from faults requires advances in battery management systems and an understanding of the dynamics of large packs. To address this, a machine learning classifier based upon a support vector machine was developed that detects cell faults within large packs using a limited number of current sensors. To train the classifier, a modelling framework for parallel-connected packs is introduced and shown to generalise to Doyle-Fuller-Newman electrochemical models. The fault classification performance was found to be satisfactory, with an accuracy of 83% using current information from only 27% of the cells. Validation on experimental pack data is also shown. These results highlight the potential to combine mathematical modelling and machine learning to improve battery management systems and deal with the complexities of large packs.

Fuelbreak placement is an important consideration in fire management. Historically, strategies for placing fuelbreaks have fallen on the experience of fire managers such as by following ridgelines, and recent searches for a formal placement strategy have struggled to scale to large areas. Here we present a basic strategy utilizing equal graph partitioning and quantum computing to efficiently determine placements. By posing partitioning as a quadratic constrained binary optimization problem, D-Wave's hybrid quantum optimization tool could complete the task in seconds. Results for the examined area show two alternatives to the ridgeline method in a so-called worst-case fire scenario: one with 2.9% improvement in land separation equality while clearing 76 less acres, and another with a 12.4% improvement by clearing 19 more acres. In a selected subsection, D-Wave's hybrid solver performed faster than the SCIP solver but slower than the CPLEX solver, with the prospect for increased speed-up on larger problems. These findings demonstrate the effectiveness of equal graph partitioning for fuelbreak placement and the potential of D-Wave's hybrid solvers.

Accurately predicting long-term degradation in chaotic systems remains a fundamental challenge due to their sensitive dependence on initial conditions and non-periodic dynamics. Conventional numerical models, which rely on fine time-step integration, are computationally demanding and prone to cumulative errors. Here we present a phase-space random walk framework for degradation modeling in chaotic systems. The approach characterizes local degradation velocity distributions through short-time averaging and reconstructs the long-term evolution as stochastic transitions across phase-space regions. Validation on chaotic electronic and mechanical systems demonstrates that the method improves computational efficiency by over two orders of magnitude while maintaining prediction errors below five percent. The analysis further reveals that chaotic systems experience transitions among dynamic regimes with varying degrees of chaos during degradation. This framework provides an efficient and generalizable way to modeling complex degradation processes, offering a other insights into the reliability design of electronic, mechanical, and mechatronic systems.

Magnetic particle imaging (MPI) is a tomographic imaging technique which determines the spatial distribution of magnetic nanoparticles (MNPs). Multi-contrast MPI provides the ability to detect environmental conditions of MNPs, such as temperature or viscosity. One parameter that has not been investigated but shows high potential for medical diagnosis is the pH value, as it is an indicator of inflamed or tumorous tissue. In this work, we present an approach to resolve the pH value using multi-contrast MPI. Our proof-of-concept is based on a stimuli-responsive, magnetic hydrogel that exhibits reversible swelling in response to a pH change. The pH contrast is generated indirectly via the pH-responsive hydrogel swelling modulating the signal of embedded MNPs. Magnetic particle spectrometry measurements show that the hydrogels' magnetic response correlates with the pH value, which could provide a new way of contactless pH monitoring. Finally, the feasibility of resolving different pH values in a multi-contrast MPI image is demonstrated.

Computational image enhancement for microscopy facilitates cutting-edge biological discovery. While promising, the commonly used deep learning methods are computationally expensive owing to the use of general-purpose architectures, which are inefficient for microscopy data. Here, we propose a sparsity-efficient neural network for image enhancement as a deep representation learning solution to inverse problems in imaging. To maximize accessibility, we developed a framework named DeBCR, consisting of a modular Python library and a user-friendly point-and-click DeBCR plugin for Napari, a popular bioimage analysis tool. We provide a detailed protocol for using the DeBCR as a library and a plugin, including data preparation, training, and inference. We compare the image restoration performance of DeBCR to ten current state-of-the-art models over four publicly available datasets spanning crucial modalities in advanced light microscopy. DeBCR demonstrates more robust performance in denoising and deconvolution tasks across all assessed microscopy modalities while requiring notably fewer parameters than existing models.

Parkinson's Disease (PD) involves not only α-synuclein pathology in dopaminergic neurons but also vascular impairments that remain underexplored due to limitations of traditional in vitro models. Here we present a microengineered 3D neurovascular midbrain model that reconstructs the capillary interface of substantia nigra dopaminergic neurons. In our proof-of-concept demonstration, we successfully recapitulated neuronal pathology in PD, including α-synuclein aggregation, inflammatory responses, and progressive neuronal degeneration, by exposing our model to specially generated PD-associated α-synuclein preformed-fibrils. Importantly, this engineering approach also enables the investigation of progressive vascular abnormalities in PD, such as endothelial dysfunction, barrier disruption, vascular regression, and the resulting impairment of blood flow. Our PD model establishes a tractable platform for investigating the multifaceted nature of the disease and understanding the complex interplay between neurodegeneration and vascular pathology, offering a unique tool for developing innovative therapeutic strategies that address both the neuronal and vascular components of PD pathology.

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique that provides molecular-level information in vivo. To obtain this unique contrast, repeated acquisition at multiple frequency offsets is needed, resulting a long scanning time. In this study, we propose a hybrid strategy at k-space and image domain to accelerate CEST MRI to facilitate its wider application. In k-space, we developed a complementary undersampling strategy which enforces adjacent frequency offsets by acquiring different subregions of k-space. Both Cartesian and spiral k-space trajectories were applied to validate its effectiveness. In the image domain, we developed a multi-offset transformer reconstruction network that uses complementary information from adjacent frequency offsets to improve reconstruction performance. Additionally, we introduced a data consistency layer to preserve undersampled k-space and a differentiable coil combination layer to leverage multi-coil information. The proposed method was evaluated on rodent brain and multi-coil human brain CEST images from both pre-clinical and clinical 3 T MRI scanners. Compared to fully-sampled images, our method outperforms a number of state-of-the-art CEST MRI reconstruction methods in both accuracy and image fidelity. CEST maps, including amide proton transfer (APT) and relayed nuclear Overhauser enhancement (rNOE), were calculated. The results also showed close agreement with fully-sampled ones.

A parabolic coast or wall concentrates incoming waves at its focal point, creating a high‑energy zone ideal for enhanced capture. Yet, how to efficiently harvest this concentrated energy remains unclear. Here we propose designs of single- and dual-chamber Oscillating Water Column (OWC) chambers for enhancing wave energy capture. A time‑domain higher‑order boundary element method, grounded in nonlinear potential flow theory, is coupled with a nonlinear pneumatic model-calibrated via geometric scaling, dual‑chamber coupling, and focused‑wave boundary tests-to simulate OWC performance. Under parabolic focusing, a bimodal resonance yields peak power absorption up to 17 times that of an isolated device, and a leeward perforation design boosts the single‑chamber capture ratio to 25 times baseline. A dual‑chamber configuration with an added semicircular chamber further elevates total absorbed energy and widens the effective bandwidth. This work provides practical design guidance for efficient wave-energy devices operating in focused-wave environments.

Distillation is the most energy-consuming unit operation of the chemical industry, however, its decarbonization strategy necessitates laborious manual process simulation, optimization and carbon emission accounting. Here we established a reasoning agent consisting of a large language model (LLM) and an extensive tool set to automate learning material collection, process simulation, optimization and carbon emission accounting of a representative methanol and ethanol distillation case study. Then the agent automatically constructed a heat pump-assisted distillation process to save energy. The impact of three energy supply scenarios on the carbon emissions of distillation, namely, coal, natural gas and renewables, was evaluated. Combining the heat pump-assisted process and renewables could substantially reduce the carbon emission by 98% compared with the coal-based traditional distillation process. This study explored using reasoning agents to automate carbon emission and decarbonization intervention quantification, and facilitated high-resolution carbon emission models of the industry.

Truck platooning promises to enhance the efficiency of logistics, but commercial operation is hampered by safety and economic concerns. Human-lead truck platooning can mitigate these challenges by leveraging a human driver's expertise. However, existing human-lead truck platooning is limited to longitudinal control and lacks the lane-changing capability, which restricts logistical efficiency. To address this, we build upon previous research to propose a human-lead truck platooning method with lane-changing capability. The platoon leader is controlled by a skilled human driver, who is responsible for leading the following automated trucks. The human-lead platoon is enabled to cruise, lane-change, and obstacle avoidance, leveraging the driver's expertise to mitigate safety risks in long-tail scenarios. Drivers of the following trucks are not needed, reducing labor costs. The proposed method has been implemented in commercial operations at the world's largest port, Shanghai Yangshan Port, achieving an annual transport volume of 200,000 Twenty-foot Equivalent Units. It highlights a route for large-scale truck platooning implementation, potentially reshaping freight-transport operations.