A Nanoconfinement Strategy to Construct Co@CNTs for Lightweight and Ultra-Broadband Microwave Absorption.

The construction of stable and efficient nanocomposites with low addition and light weight has always been the goal pursued in the field of electromagnetic wave (EMW) absorption. In this study, the Co@CNTs nanocomposites with Co nanoparticles (13 nm) nanoconfined in the carbon nanotube (CNT) are successfully synthesized by a simple hydrothermal method and phenolic assisted pyrolysis method. The degree of graphitization of CNTs and the microstructure of Co nanoparticles can be effectively regulated by controlling the calcination temperature. The sample calcined at 700 °C can obtain excellent absorption performance at a low filling capacity of 10 wt.%: the minimum reflection loss (RL) is -41.2 dB and the effective absorption bandwidth (EAB) reaches a maximum width of 14.2 GHz. When the sample thickness is only 2.2 mm, the EAB of <-20 dB reaches 8.3 GHz, which is the maximum EAB of most current Co-based absorbers. In particular, the polarization and ferromagnetic coupling behaviors are elucidated in depth with the aid of electromagnetic field simulations using the High-Frequency Structure Simulator (HFSS). This work provides a new nanoconfinement strategy for constructing the Co@CNTs nanocomposites as lightweight and ultra-broadband absorbing materials for EMW protection and EMW pollution control.

State of the art and prospects of Fe3O4/carbon microwave absorbing composites from the dimension and structure perspective.

At present, to solve the threat of electromagnetic wave (EMW) radiation pollution to human health, intelligent control and information security, tremendous efforts have been made to manufacture EMW absorbing materials. For ideal microwave absorption materials (MAMs), it is generally necessary not only to pursue strong microwave absorption (MA) over wide effective absorption bandwidth (EAB), but also to take into account the requirements of light weight, thin matching thickness and chemical stability characteristics. It has been found that magnetite (Fe3O4) is the most promising MAM to absorb and dissipate EMW among various absorbers, because of its good mechanical and chemical stability, controllable morphology, high Curie temperature, easy preparation, economy and excellent magnetic properties. However, the application performance of Fe3O4 absorber with single composition is limited by its easy agglomeration, eddy current, high density, and impedance mismatch. In addition, achieving efficient MA metrics with low absorber loading remains a huge challenge. To overcome these limitations, conjugation with dielectric carbon-based materials and special structural designs have been extensively explored as viable solutions to optimize the microwave absorption performance (MAP) of Fe3O4. This paper reviews the recent research progress of Fe3O4/carbon MAMs, and then the influence of dimensions and structures regulations on the MAPs are introduced in detail. Finally, the current existing problems and future development direction of Fe3O4/carbon composites in the field of MA are also presented.

Implementing the special action plan to comprehensively strengthen and improve students mental health work / 中国学校卫生

Abstract@#In recent years, mental health issues have gradually expanded from adults and professional groups to the primary and secondary school students, and are showing a trend of younger age. The article analyzes the "Special Action Plan for Comprehensively Strengthening and Improving the Mental Health Work of Students in the New Era (2023-2025)" jointly issued by 17 departments including the Ministry of Education. It explores the current situation, overall concept, key tasks, and other aspects to further improve the "four in one" student mental health work system of health education, monitoring and warning, consulting services, intervention disposal, and improve students mental health literacy.

Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets.

Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy ands parse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxi cabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques.