Multimodal Analgesic Effectiveness on Acute Postoperative Pain Management After Adult Cardiac Surgery

Background Many patients report moderate to severe pain in the acute postoperative period. Enhanced recovery protocols recommend multimodal analgesics, but the optimal combination of these is unknown. Purpose The aim of this study was to synthesize the best available evidence about effectiveness of multimodal analgesics on pain after adult cardiac surgery. Methods A systematic review to determine the effect of multimodal postoperative analgesics is proposed (International Prospective Register of Systematic Reviews Registration CRD42022355834). Multiple databases including the Cochrane Library, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, American Psychological Association, the Education Resources Information Centre, the Excerpta Medica database, the Medical Literature Analysis and Retrieval System Online, Scopus, Web of Science, and clinical trials databases will be searched. Screening in Covidence and quality assessment will be conducted by 2 authors. A grading of recommendations, assessment, development, and evaluation summary of findings will be presented if meta-analysis is possible

the prevention of chronic diseases through ehealth a practical overview

Disease prevention is an umbrella term embracing individual-based or population-based interventions aimed at preventing the manifestation of diseases (primary prevention), reducing the impact of a disease that has arisen (secondary prevention), or mitigating the impact of an ongoing illness (tertiary prevention). Digital health has the potential to improve prevention of chronic diseases. Its application ranges from effective mHealth weight-loss intervention to prevent or delay the onset of diabetes in overweight adults to the cost-effective intervention on the provision of mental-health care via mobile-based or Internet-based programs to reduce the incidence or the severity of anxiety. The present contribution focuses on the effectiveness of eHealth preventive interventions and on the role of digital health in improving health promotion and disease prevention. We also give a practical overview on how eHealth interventions have been effectively implemented, developed, and delivered for the primary, secondary, and tertiary prevention of chronic diseases

Avoided costs associated with cogeneration: a case study of Con Ed

The potential impact of cogeneration in office and apartment buildings in New York City on the Consolidated Edison Company (Con Ed) has been investigated using a method of utility cost and fuel use analysis developed at Brookhaven National Laboratory. This method computes a utility's long run marginal costs and long run marginal fuel consumption associated with load modifications due to the introduction of on-site energy producing technologies. The principal findings of this study show that Con Ed's long run average cost is more likely to go down than up due to cogeneration in office and apartment building; the utility's avoided costs (i.e., its long run marginal savings) associated with the gross power output of the cogeneration systems are 10.5 cents/KWh for the office building and 6.4 cents/KWh for the apartment buildings; the utility's marginal savings include a component for avoided capacity costs; and there are net savings in the use of oil due to cogeneration (assuming the building used oil for its boilers before it switched and diesel fuel in its cogenerators afterwards)

Classification of Man-Made targets via Invariant Coherency Matrix Eigenvector Decomposition of Polarimetric SAR/ISAR Images

In this paper, the problem of classifying nonhomogeneous man-made targets is investigated by performing a macroscopic and detailed target analysis. The Cloude-Pottier H/ αML decomposition is used as a starting point in order to find orientation-invariant feature vectors that are able to represent the average polarimetric structure of complex targets. A novel supervised classification scheme based on nearest neighbor decision rule is then designed, which makes use of the feature space. A validation process is performed by analyzing experimental data of simple targets collected in an anechoic chamber and airborne EMISAR images of eight ships. Three classification robustness performance indicators have been evaluated for each feature vector by performing the leaves-one-out-method described by Mitchell and Westerkamp. The robustness of the classifier has been tested with respect to the ability to reject unknown targets and to correctly identify known targets