Carlton Stevenson (doorengine16)

findings highlight a need to develop structures and role clarity among school personnel, which can advance further development of intra-school and inter-sectoral collaboration in primary substance use prevention and mental health promotion. In the Finnish context, the successful implementation of relevant legislation, which some school representatives view as unclear or contravening, could be further supported.Key pointsViews regarding responsibilities in primary substance use prevention in the school setting have been less researched in the Nordic countriesThe importance of inter-sectoral and intra-school collaboration is emphasized among school personnel representatives, including the role of the homesPrimary prevention and mental health promotion responsibilities are viewed as less clear than secondary and tertiary prevention responsibilitiesStructural guidelines concerning e.g. confidentiality aspects and curriculum features can both support and challenge school representatives in their roles.Purpose This study evaluated the effects of a linguistic characteristic, typicality, and a processing variable, working memory on the abilities of people with aphasia (PWA) and neurologically intact adults to process semantic representations. This was accomplished using a newly developed assessment task, the Category Typicality Test, which was created for the Temple Assessment of Language and Short-Term Memory in Aphasia. Method A post hoc quasi-experimental design was used. Participants included 27 PWA and 14 neurologically intact adults who completed the picture and word versions of the Category Typicality Test, which required them to determine if two items are in the same category. selleck chemical Memory load was altered by increasing the number of items to be compared, and the typicality of items was altered to increase linguistic complexity. Results A four-way mixed analysis of covariance was conducted. There was a significant interaction between working memory load and category typicality with performance accuracy decrSupplemental Material https//doi.org/10.23641/asha.14781996.Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature. Many factors contribute to long wait times for patients on the day of their chemotherapy infusion appointments. Longer wait time leads to nonoptimal care, increased costs, and decreased patient satisfaction. We conducted a quality improvement project to reduce the infusion wait times at a Comprehensive Cancer Center. A multidisciplinary working group of physicians, infusion center nurses, pharmacists, information technology analysts, the Chief Medical Officer, and patient advocates formed a working group. Wait times were analyzed, and the contributing factors to long wait time were identified. Plan-Do-Study-Act cycles we