The Norwegian Labour and Welfare Administration (NAV, originally an abbreviation of Nye arbeids- og velferdsetaten) is the current Norwegian public welfare agency, which consists of the state Labour and Welfare Service as well as municipal welfare agencies. It is responsible for a third of the state budget of Norway, administering programs such as unemployment benefits, pensions, child benefits and more. The agency has 19,000 employees (14,000 in the state service). Its head is the Labour and Welfare Director, currently Hans Christian Holte, who is appointed by the government..
This paper presents an Artificial Intelligence (AI) and Internet of Things (IoT)‑driven system for intelligent poultry farm management, environmental monitoring, and disease detection. The proposed framework integrates IoT‑enabled sensors and actuators to automate critical operations such as feed distribution, water supply, temperature, and humidity regulation, ensuring optimal living conditions for poultry. A real‑time web‑based dashboard provides monitoring, decision support, and reporting features for multiple stakeholders including farm owners, workers, customers, and supply chain managers. Advanced deep learning‑based computer vision models are incorporated for poultry disease detection, egg counting, and chicken tracking, enabling early interventions and reducing losses from outbreaks. The system also introduces predictive analytics for mortality tracking, crisis response, and productivity forecasting, enhancing decision‑making capabilities. Furthermore, ERP‑inspired modules such as inventory management, financial records, and e‑commerce integration digitalize the poultry supply chain, creating a scalable and sustainable solution for precision agriculture. Built on ESP32 microcontrollers, cloud services via Firebase, and a Python Flask‑based backend, the Smart Poultry Farm demonstrates how the convergence of IoT and AI can significantly enhance automation, operational efficiency, profitability, and animal welfare in the poultry industry.
A novel decentralized model of pre-eclampsia risk assessment during maternal care is presented in this research that combines edge intelligence, safe aggregation, and a trust model. Provided that the proposed system, in contrast to traditional cloud-based solutions, is based on the on-device TinyML to operate risk assessment in real-time and on the blockchain-based federated learning, to update models safely and without disclosing personal information. Architecture based on Hyperledger Fabric, with Practical Byzantine Fault Tolerance (PBFT) consensus, does not require a central server, which provides resilience against a single point of failure. The system was validated through a four-week pilot test involving 350 participants in rural India, reporting a predictive accuracy of 94.2
The aim of the study was to determine the effects on health care of an adapted Norwegian version of a Canadian model of collaborative care, involving general practitioners (GPs) and mental health specialists working together co-located in GP practices. In previous papers, we have shown that the adapted model was successfully implemented and found to be beneficial by participating GPs, improving their detection of anxiety in young people, and with a reduction in long term sickness benefits. The current study examines whether collaborative care was associated with changes in (a) the number of referrals from GPs to mental health services, (b) the number of GP patients provided outpatient visits in mental health services, (c) GPs’ recognition of common mental disorders, and (d) GPs’ prescription of various types of psychotropic medication. The study was a cluster-randomised controlled trial of the collaborative care model in three GP practices (intervention group) compared with usual health care in three other practices (control group) in Oslo, Norway. A clinical psychologist and a psychiatrist from a community mental health centre worked half time and two hours per week, respectively, in each intervention practice for 18 months. They were available for case discussions and provided assessments and brief therapies. Structured data were extracted retrospectively from the electronic patient records of both GPs and mental health services for 12 months before and during the implementation of collaborative care for patients 16–65 years old. Data were analysed with generalized linear mixed models. There were no significant differences in referrals to mental health services (the primary outcome) and in the use of outpatient specialised mental health services. The GPs in the intervention practices diagnosed significantly more patients with common mental disorders (anxiety, depression), and these changes were significantly associated with a reduction in unexplained physical symptoms. Significant changes in prescribing patterns of psychotropic medication were consistent with the increased recognition of mental disorders, and their use was possibly more appropriate. Collaborative care with co-located mental health specialists in GP practices led to an increased recognition of common mental disorders by those GPs. Due to a lack of structured clinical measurements in the electronic patient records, the clinical outcomes of the intervention were unknown.
Honeycomb sandwich structures have widespread use in structural engineering due to their high specific strength and high specific stiffness. Honeycomb core that reacts transverse shear load in sandwich structures can fail either under static or fatigue loading. This paper examines how to predict static and fatigue loading under multiaxial loading and how to determine the critical angle for loading of the core. This is supported by testing performed on specimens manufactured with three different aramid fabric cores dipped in phenolic resins. The specimens include two hexagonal cores of different densities and one over expanded core. The results of the testing used to substantiate the conclusions are presented. This paper describes the results of tests and analysis performed to determine failure criteria for representative phenolic coated paper cores used in aircraft structures. These tests involve both static and fatigue tests carried out in the material L and W direction as well as at intermediate angles. Static and fatigue testing was performed using the ASTM C273 test protocol. Static testing was performed at room temperature, at -100°F and on saturated specimens at 120°F. This testing was performed in the ribbon and normal to the ribbon (L and W) directions and points in between. Fatigue testing was performed on representative core at room temperature. The results of this testing are presented and failure criteria for the representative cores under biaxial loading presented. Using the results from these tests, a failure criterion is proposed for these cores. It is shown that the minimum shear strength is lower than either the design L or W directions. The minimum shear strength is determined for the cores examined. One explanation for the reported results is given, based on FE (finite element) analysis performed by the European Space Agency and recommendations made for additional research.
