New research following US adolescents ages 11–12 shows that problematic use of mobile phones, social media, and video games was associated with higher risks of mental health problems, sleep disturbance, and suicidal behaviors one year later. The study in the American Journal of Preventive Medicine, published by Elsevier, reveals that the links between problematic screen use and mental health are stronger than those previously reported for overall screen time and highlights the risks of addictive use.

With the rapid development of electric vehicles and energy storage systems (ESSs), accurate state-of-health (SOH) estimation for lithium-ion batteries has become crucial for ensuring safety and optimizing performance. However, SOH estimation under dynamic operating conditions remains challenging, as non-monotonic voltage profiles and irregular current patterns reduce the effectiveness of conventional measurement methods. This paper proposes a comprehensive approach that combines health feature extraction with a parallel deep learning architecture for robust SOH estimation. First, the method extracts four highly correlated health features (K, b, σΔQ, and σδΔQ) from dynamic measurement data collected by sensors, with correlation coefficients between these features and the actual SOH exceeding 0.95. These extracted features are then processed through a novel parallel Temporal Convolutional Networks (TCN)-Transformer hybrid architecture: the TCN captures multi-scale local temporal patterns, while the Transformer models global dependencies. An attention-gated fusion module dynamically integrates complementary feature representations from the two branches and adaptively weights different paths based on degradation features. Experimental validation on three standardized battery datasets (MIT, CALCE, Oxford) shows that the method achieves an estimation accuracy with a root mean square error (RMSE) below 1% under all operating conditions, representing an 8%–70% improvement over conventional methods. Attention weight analysis reveals correlations with aging mechanisms, providing interpretability for model decisions. The proposed method enables practical real-time battery health assessment in dynamic environments.

New research led by Flinders University and international experts has finally uncovered how a rare blood clotting condition can occur after some COVID19 adenovirus-based vaccines or after a natural adenovirus infection. The new research published in the eminent New England Journal of Medicine represents the culmination of years of international scientific detective work and has broad implications for future vaccine developments.

Shriners Children’s is leading a multi-center study that they hope will improve standards of care for burn patients everywhere and could potentially lead to insurance companies changing their coverage policies for certain types of burn procedures.

 

Researchers have found that most biochar fed to dairy cows survives the digestive process and remains highly stable, meaning it could be applied to fields through manure and act as a long-term carbon storage solution. The findings provide new evidence that livestock systems could play a role in climate change mitigation.