To Have and to Hold: Questions about a Therapeutic Service for Children

Abstract

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

The effectiveness, acceptability and cost-effectiveness of psychosocial interventions for maltreated children and adolescents: an evidence synthesis

“…The remainder were in Canada (four studies 170,[658][659][660][661] ), the UK (14 studies 145,169,481,511,538,[662][663][664][665][666][667][668][669][670] ) and elsewhere in Europe: the Netherlands (three studies 497,671,672 ), Norway (three studies [673][674][675] ) and Sweden (one study 676 ), with one multisite review of services 145,146,640,[665][666][667][668][669][670] across Italy, the Netherlands and the UK. A small number of studies were based outside North America and Europe: Australia (two studies 506,677 ), Brazil (one study 678 ), India (one study 679 ) and the Philippines (one study 680 ).…”

“…621,629 ) to a large national sample of n = 1085. 634 The majority of sample sizes were < 50 cases (40 studies 169,175,497,504,506,510,511,526,533,538,621,629,[636][637][638][639][641][642][643]645,646,648,651,652,658,[661][662][663]665,666,[668][669][670]675,677,678,680 ), between 50 and 100 cases (15 studies 107,170,176,481,482,492,505,644,647,…”

“…The studies by Cross et al 634 and Osofsky et al 515 were set in the USA, whereas the studies by Sudbery et al 663 and Powell and Cheshire 662 were undertaken in the UK.…”

“…The Sudbery et al 663 study is the only study of the three that presents children's views of the intervention. As well as a focus group, semistructured interviews and survey methodology, case file analysis and organisational documentation were used, and two members of the research team were embedded within the organisation as participant observers.…”

“…Osofsky et al 515 recruited 75 mother-child dyads: 25 from each of three sites. The sample size in the Sudbury et al 663 study is difficult to ascertain, but the authors appear to have scrutinised the files of 113 children, conducted a focus group of eight young people and interviewed a further four. Powell and Cheshire 662 conducted semistructured interviews with four mothers and one grandmother.…”