Parents of children with health conditions are more likely to be concerned about their child adjusting to a new school or teacher, dealing with conflict, staying healthy, keeping up with homework and fitting in or making friends, according to a new national poll.

The emerging interfacial polarization strategy exhibits applicative potential in piezoelectric enhancement. However, there is an ongoing effort to address the inherent limitations arising from charge bridging phenomena and stochastic interface disorder that plague the improvement of piezoelectric performance. Here, we report a dual structure reinforced MXene/PVDF-TrFE piezoelectric composite, whose piezoelectricity is enhanced under the coupling effect of interfacial polarization and structural design. Synergistically, molecular dynamics simulations, density functional theory calculations and experimental validation revealed the details of interfacial interactions, which promotes the net spontaneous polarization of PVDF-TrFE from the 0.56 to 31.41 Debye. The oriented MXene distribution and porous structure not only tripled the piezoelectric response but also achieved an eightfold increase in sensitivity within the low-pressure region, along with demonstrating cyclic stability exceeding 20,000 cycles. The properties reinforcement originating from dual structure is elucidated through the finite element simulation and experimental validation. Attributed to the excellent piezoelectric response and deep learning algorithm, the sensor can effectively recognize the signals of artery pulse and finger flexion. Finally, a 3 × 3 sensor array is fabricated to monitor the pressure distribution wirelessly. This study provides an innovative methodology for reinforcing interfacial polarized piezoelectric materials and insight into structural designs.

The study, titled "Machine Learning Technique for Carbon Sequestration Estimation of Mango Orchards Area Using Sentinel-2 Data," is led by Prof. Sittichai Choosumrong from the Department of Natural Resources and Environment at Naresuan University in Phitsanulok Province, Thailand, and Prof. Tatsuya Nemoto from the Graduate School of Science at Osaka Metropolitan University in Osaka, Japan. This research offers a detailed analysis of how machine learning can enhance our understanding of carbon sequestration in agricultural landscapes.

Biochar, a carbon-rich material made from organic waste, is gaining attention for its ability to improve soils, clean water, and capture carbon. A new review in Biochar X highlights how machine learning (ML) is reshaping biochar research, making it faster, smarter, and more effective in tackling climate change.

Researchers have successfully used artificial intelligence (AI) to predict which patients need treatment to stabilise their corneas and preserve their eyesight, in a study presented at the 43^rd Congress of the European Society of Cataract and Refractive Surgeons.