Awareness of dementia risk reduction among current and future healthcare professionals: A survey study

Background: The total number of people affected by dementia worldwide is increasing rapidly. Recent studies provided evidence for the contribution of modifiable risk and protective factors to dementia risk. Although healthcare professionals could play an essential role in informing the general public about the relationship between lifestyle and dementia, it is unclear what they know about this relationship. Therefore, this study assesses the awareness of dementia risk reduction among current and future healthcare professionals.Design and methods: An online survey was carried out among 182 healthcare students from Maastricht University and 20 general practitioners (GPs) and practice nurses in Limburg, The Netherlands. The survey assessed the knowledge about risk and protective factors of dementia and identified needs, wishes and barriers concerning dementia risk reduction strategies.Results: The majority of current (75.0%) and future (81.9%) healthcare professionals indicated that dementia risk reduction is possible. Among students, awareness of cardiovascular risk factors of dementia (e.g., coronary heart disease (44.5%), hypertension (53.8%)) was low. Most participants (>70.0%) would like to receive more information about dementia risk reduction.Conclusions: The majority of current and future healthcare professionals were aware of the relationship between lifestyle and dementia risk. However, there are still substantial gaps in knowledge regarding individual dementia risk factors. Given the essential role of healthcare professionals in providing lifestyle advice, there is a need to increase awareness by providing educational programs focused on dementia risk reduction

A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing

Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time-varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success

Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: enabling large feedforward tasks on a position-dependent printer

Iterative learning control (ILC) enables high performance for systems that execute repeating tasks. Norm-optimal ILC based on lifted system representations provides an analytic expression for the optimal feedforward signal. However, for large tasks the computational load increases rapidly for increasing task lengths, compared to the low computational load associated with so-called frequency domain ILC designs. The aim of this paper is to solve norm-optimal ILC through a Riccati-based approach for a general performance criterion. The approach leads to exactly the same solution as found through lifted ILC, but at a much smaller computational load (O(N) vs O(N^3)) for both linear time-invariant (LTI) and linear time-varying (LTV) systems. Interestingly, the approach involves solving a two-point boundary value problem (TPBVP). This is shown to have close connections to stable inversion techniques, which are central in typical frequency domain ILC designs. The proposed approach is implemented on an industrial flatbed printer with large tasks which cannot be implemented using traditional lifted ILC solutions. The proposed methodologies and results are applicable to both ILC and rational feedforward techniques by applying them to suitable closed-loop or open-loop system representations. In addition, they are applied to a position-dependent system, revealing necessity of addressing position-dependent dynamics and confirming the potential of LTV approaches in this situation

EMDR: Eye movements superior to beeps in taxing working memory and reducing vividness of recollections

AbstractPosttraumatic stress disorder (PTSD) is effectively treated with eye movement desensitization and reprocessing (EMDR) with patients making eye movements during recall of traumatic memories. Many therapists have replaced eye movements with bilateral beeps, but there are no data on the effects of beeps. Experimental studies suggest that eye movements may be beneficial because they tax working memory, especially the central executive component, but the presence/degree of taxation has not been assessed directly. Using discrimination Reaction Time (RT) tasks, we found that eye movements slow down RTs to auditive cues (experiment I), but binaural beeps do not slow down RTs to visual cues (experiment II). In an arguably more sensitive “Random Interval Repetition” task using tactile stimulation, working memory taxation of beeps and eye movements were directly compared. RTs slowed down during beeps, but the effects were much stronger for eye movements (experiment III). The same pattern was observed in a memory experiment with healthy volunteers (experiment IV): vividness of negative memories was reduced after both beeps and eye movements, but effects were larger for eye movements. Findings support a working memory account of EMDR and suggest that effects of beeps on negative memories are inferior to those of eye movements