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Autism spectrum disorder (ASD) interventions increasingly harness the capabilities of wearable technology, utilizing predictive analytics of physiological signals and personalized sensory feedback. Herein, we review the key developments in the multifaceted application of wearable technology for ASD and highlight their potential value, covering both the state-of-the-art devices and requirements for ASD interventions. Wearable technology can be classified into two pivotal functional systems: signal acquisition and interactive feedback. The signal acquisition system employs an array of sensors for real-time physiological data monitoring in individuals with ASD, furnishing essential insights for the tailoring of customized therapeutic interventions. Additively, the interactive feedback system endeavors to ameliorate psychological and behavioral challenges, address sensory processing disorders, and enhance social and cognitive proficiencies. By facilitating diverse sensory stimulations and feedback mechanisms, wearable technology demonstrates a versatile approach to ASD intervention. Crucially, the principles and diverse applications of wearable technology in ASD are expounded upon, underscoring the emerging role of flexible electronics in this domain. Finally, the challenges and opportunities of wearable technologies are discussed, as well as the immense potential to revolutionize human-centered intervention solutions for ASD. The continued exploration of this dynamic field not only augments the quality of life and well-being of individuals with ASD but also presents an avenue for innovation at the intersection of technology and healthcare.

Characterization of the chemical composition and bacterial community of corrosion scales was studied in real drinking water distribution systems from eight cities. The results of X-ray powder diffraction (XRD) indicated that α-FeOOH and Fe3O4were present in all corrosion scales, and green rust was only found in corrosion scales of pipes transporting surface water. The corrosion scales with a higher Fe3O4/α-FeOOH ratio showed less iron release than those with a lower ratio. Moreover, the results of 454 pyrosequencing revealed that Proteobacteria(46.5–84.3%) was the main bacterial phylum in all corrosion scales; however, the bacterial genera were very different in the pipes from eight cities. Nitrate-reducing bacteria (10.9–36.0%) were the main potential corrosive bacteria, and denitrifying genes including nirS, nirKand nosZwere all found in the corrosion scales. The results of most probable number (MPN) tests indicated that nitrate-reducing bacteria, Fe(iii)-reducing bacteria and nitrate-dependent Fe(ii)-oxidizing bacteria in corrosion scales were indeed active and had the function of Fe(ii) oxidation and Fe(iii) reduction. All the results suggested that there was an apparent relationship between the relevant biochemical functions (e.g., iron redox cycling) of the bacterial community and the formation of α-FeOOH and Fe3O4in corrosion scales. Furthermore, the relative abundance of Desulfovibrioalso correlated very well with the content of green rust in corrosion scales. These results will be very helpful for future control of iron release in water distribution systems.

With the rapid development of the complexity and diversification of robot services, large number of data processing and analysis problems occurred during the robot working process. In this paper, we designed a collaborative system consisted multi-user and multi-robot based on cloud platform. By pre-setting the rights and restrictions owned by each user in different scenarios, the server determines the scene in which the robot is located by the picture taken by the robot, thereby giving each user different permissions. Enable the robot to listen to the user's instructions to protect privacy. The integration of cloud technology and multi-robot systems made it possible to improve energy efficiency, real-time performance and reduce memory cost.