No abstract

Internet addiction has been typically conceptualized as either a continuous construct or a dichotomous construct. Limited research has differentiated adolescents with problematic Internet use (PIU) from the Internet addiction group (IA) and/or nonproblematic Internet use group (NPIU) and examined the potential correlates. To fill this gap, based on data obtained from 956 Chinese adolescents (11-19 years, 47% male), this study examined if adolescents with PIU is a distinctive group from the IA and NPIU. This study also examined factors from different ecological levels that may differentiate among the three groups, including individual, parental, peer, and sociodemographic factors. Results indicated that IA, PIU, and NPIU differed significantly on scores of Young's Diagnostic Questionnaire (YDQ). Critical factors emerging from different ecological levels could differentiate between PIU and NPIU and between IA and NPIU. Such findings suggest that PIU may represent a distinct, intermediate group of Internet users. The potential theoretical and practical implications of identifying PIU were also discussed. (PsycINFO Database Record

The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models.This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

No abstract

A Cu‐doped, NiO‐based quantum dot light‐emitting diode (QLED) reported by Xuyong Yang and co‐workers in article number https://doi.org/10.1002/adfm.201704278 exhibits a significant operating lifetime enhancement compared with organic PEDOT:PSS‐based QLEDs. Relying on an inorganic hole transport layer, the device's quantum dots retain their brightness for an extended time period. This is in contrast to light from a PEDOT:PSS‐based QLED shown in the upper‐left corner of the image.