The explosive growth of electric vehicles, renewable energy integration, and large-scale energy storage systems has placed lithium-ion batteries at the heart of the global transition to sustainable energy. Ensuring their safe, efficient, and long-lasting performance demands sophisticated battery management systems capable of continuously tracking critical states such as state of charge, state of health, aging, and potential faults. While conventional monitoring relies on temperature sensors, voltage-current profiling, ultrasonic probes, or embedded optical fibers, these methods often provide limited multidimensional insights, incur high implementation costs, or struggle with real-time applicability in operational environments. Electrochemical impedance spectroscopy stands out as a particularly powerful technique, offering rich, frequency-dependent information about internal electrochemical processes that reflect battery dynamics under varying conditions. However, traditional EIS measurements using dedicated electrochemical workstations remain confined to laboratory settings due to their expense, bulk, and lack of online capability.

The global surge in electric vehicle adoption stands as a cornerstone of efforts to combat climate change and reduce reliance on fossil fuels, with EVs poised to play a transformative role when paired with renewable energy sources. Public charging stations, as critical nodes in this ecosystem, face mounting pressure from uncoordinated large-scale charging that risks overloading local grids, driving up peak demand, and hindering the seamless incorporation of intermittent solar and wind power. While grid upgrades offer one path forward, their prohibitive costs make smarter management of existing infrastructure far more practical. The extended dwell times typical of most charging sessions—often far exceeding the actual energy transfer needed—create valuable flexibility for optimized scheduling, enabling EVs to act as distributed energy resources through Vehicle-to-Grid interactions that stabilize grids and maximize clean energy utilization.

The rapid rise of electric vehicles combined with breakthroughs in autonomous driving technology is reshaping the future of transportation toward greater sustainability. Intelligent electric vehicles, particularly plug-in hybrid electric vehicles (PHEVs), hold immense potential to slash energy consumption and curb emissions through smarter, more coordinated control of motion and powertrain operations. Yet achieving this dual goal of uncompromising safety and superior energy efficiency remains challenging. Traditional approaches often treat driving safety, eco-friendly trajectory planning, and powertrain energy management as separate tasks, leading to trade-offs that limit overall performance in complex, real-world driving scenarios.

As urban centers grapple with escalating traffic congestion and the limitations of traditional two-dimensional road networks, the concept of the split flying car has emerged as a transformative solution for future intelligent transportation. These innovative vehicles, which consist of a flight module, a passenger capsule, and an intelligent chassis, offer the flexibility to switch between aerial and ground travel. However, the transition between these modes—specifically the autonomous docking of the chassis to the aircraft bracket—presents a formidable technical hurdle. Researchers at Wuhan University of Technology have recently addressed this challenge by developing a lightweight, vision-based detection model that ensures high-precision, real-time docking even in complex environments and on hardware with limited computing power.

A review in Chinese Journal of Natural Medicines evaluates Ziziphora clinopodioides, a Lamiaceae medicinal plant rich in flavonoids, phenolic acids, and essential oils, and summarizes mechanistic evidence supporting anti-inflammatory, antioxidant, anti-apoptotic, mitochondrial, and vasodilatory actions relevant to cardiovascular diseases.

In mouse and cell-based models, tracheloside mitigated fibrotic remodeling by activating AMPK and suppressing NOX4-driven oxidative stress, implicating an AMPK/NOX4 axis in anti-fibrotic activity.

A landmark clinical study identifies Human Papillomavirus (HPV) genotype as a significant independent predictor of treatment response in patients with Recurrent Respiratory Papillomatosis (RRP), revealing that tumors associated with HPV-11 exhibit a substantially better response to intralesional bevacizumab therapy compared to those driven by HPV-6.