General practitioners' conceptions about treatment of depression and factors that may influence their practice in this area. A postal survey

BACKGROUND: The way GPs work does not appear to be adapted to the needs of depressive patients. Therefore we wanted to examine Swedish GPs' conceptions of depressive disorders and their treatment and GPs' ideas of factors that may influence their manner of work with depressive patients. METHODS: A postal questionnaire to a stratified sample of 617 Swedish GPs. RESULTS: Most respondents assumed antidepressive drugs effective and did not assume that psychotherapy can replace drugs in depression treatment though many of them looked at psychotherapy as an essential complement. Nearly all respondents thought that clinical experiences had great importance in decision situations, but patients' own preferences and official clinical guidelines were also regarded as essential. As influences on their work, almost all surveyed GPs regarded experiences from general practice very important, and a majority also emphasised experiences from private life. Courses arranged by pharmaceutical companies were seen as essential sources of knowledge. A majority thought that psychiatrists did not provide sufficient help, while most respondents perceived they were well backed up by colleagues. CONCLUSION: GPs tend to emphasize experiences, both from clinical work and private life, and overlook influences of collegial dealings and ongoing CME as well as the effects of the pharmaceutical companies' marketing activities. Many GPs appear to need more evidence based knowledge about depressive disorders. Interventions to improve depression management have to be supporting and interactive, and should be combined with organisational reforms to improve co-operation with psychiatrists

Durability of CaO–CaZrO₃ Sorbents for High-Temperature CO₂ Capture Prepared by a Wet Chemical Method

Powders of CaO sorbent modified with CaZrO have been synthesized by a wet chemical route. For carbonation and calcination conditions relevant to sorbent-enhanced steam reforming applications, a powder of composition 10 wt % CaZrO/90 wt % CaO showed an initial rise in CO uptake capacity in the first 10 carbonation-decarbonation cycles, increasing from 0.31 g of CO/g of sorbent in cycle 1 to 0.37 g of CO/g of sorbent in cycle 10 and stabilizing at this value for the remainder of the 30 cycles tested, with carbonation at 650 C in 15% CO and calcination at 800 C in air. Under more severe conditions of calcination at 950 C in 100% CO, following carbonation at 650 C in 100% CO, the best overall performance was for a sorbent with 30 wt % CaZrO/70 wt % CaO (the highest Zr ratio studied), with an initial uptake of 0.36 g of CO/g of sorbent, decreasing to 0.31 g of CO /g of sorbent at the 30th cycle. Electron microscopy revealed that CaZrO was present in the form of ≤0.5 μm cuboid and 20-80 nm particles dispersed within a porous matrix of CaO/CaCO; the nanoparticles are considered to be the principal reason for promoting multicycle durability

Reinforcement Learning Methods for Operations Research Applications: The Order Release Problem

An important goal in Manufacturing Planning and Control systems is to achieve short and predictable flow times, especially where high flexibility in meeting customer demand is required. Besides achieving short flow times, one should also maintain high output and due-date performance. One approach to address this problem is the use of an order release mechanism which collects all incoming orders in an order-pool and thereafter determines when to release the orders to the shop-floor. A major disadvantage of traditional order release mechanisms is their inability to consider the nonlinear relationship between resource utilization and flow times which is well known from practice and queuing theory. Therefore, we propose a novel adaptive order release mechanism which utilizes deep reinforcement learning to set release times of the orders and provide several techniques for challenging operations research problems with reinforcement learning. We use a simulation model of a two-stage flow-shop and show that our approach outperforms well-known order release mechanism.(VLID)3401079Accepted versio