Horn antenna with v-shaped corrugated surface

Corrugated shape is easily machined for millimeter wave application and is better suited for folding antenna designs. Measured performance showed ""V'' corrugations and rectangular corrugations have nearly the same pattern beamwidth, gain, and impedance. Also, ""V'' corrugations have higher relative power loss

The ischemic preconditioning effect of adenosine in patients with ischemic heart disease

<p>Abstract</p> <p>Introduction</p> <p><it>In vivo </it>and <it>in vitro </it>evidence suggests that adenosine and its agonists play key roles in the process of ischemic preconditioning. The effects of low-dose adenosine infusion on ischemic preconditioning have not been thoroughly studied in humans.</p> <p>Aims</p> <p>We hypothesised that a low-dose adenosine infusion could reduce the ischemic burden evoked by physical exercise and improve the regional left ventricular (LV) systolic function.</p> <p>Materials and methods</p> <p>We studied nine severely symptomatic male patients with severe coronary artery disease. Myocardial ischemia was induced by exercise on two separate occasions and quantified by Tissue Doppler Echocardiography. Prior to the exercise test, intravenous low-dose adenosine or placebo was infused over ten minutes according to a randomized, double blind, cross-over protocol. The LV walls were defined as ischemic if a reduction, no increment, or an increment of < 15% in peak systolic velocity (PSV) was observed during maximal exercise compared to the baseline values observed prior to placebo-infusion. Otherwise, the LV walls were defined as non-ischemic.</p> <p>Results</p> <p>PSV increased from baseline to maximal exercise in non-ischemic walls both during placebo (<it>P </it>= 0.0001) and low-dose adenosine infusion (<it>P </it>= 0.0009). However, in the ischemic walls, PSV increased only during low-dose adenosine infusion <it>(P </it>= 0.001), while no changes in PSV occurred during placebo infusion (<it>P </it>= NS).</p> <p>Conclusion</p> <p>Low-dose adenosine infusion reduced the ischemic burden and improved LV regional systolic function in the ischemic walls of patients with exercise-induced myocardial ischemia, confirming that adenosine is a potential preconditioning agent in humans.</p

Vasodilators in the treatment of acute heart failure: what we know, what we don’t

Although we have recently witnessed substantial progress in management and outcome of patients with chronic heart failure, acute heart failure (AHF) management and outcome have not changed over almost a generation. Vasodilators are one of the cornerstones of AHF management; however, to a large extent, none of those currently used has been examined by large, placebo-controlled, non-hemodynamic monitored, prospective randomized studies powered to assess the effects on outcomes, in addition to symptoms. In this article, we will discuss the role of vasodilators in AHF trying to point out which are the potentially best indications to their administration and which are the pitfalls which may be associated with their use. Unfortunately, most of this discussion is only partially evidence based due to lack of appropriate clinical trials. In general, we believe that vasodilators should be administered early to AHF patients with normal or high blood pressure (BP) at presentation. They should not be administered to patients with low BP since they may cause hypotension and hypoperfusion of vital organs, leading to renal and/or myocardial damage which may further worsen patients’ outcome. It is not clear whether vasodilators have a role in either patients with borderline BP at presentation (i.e., low-normal) or beyond the first 1–2 days from presentation. Given the limitations of the currently available clinical trial data, we cannot recommend any specific agent as first line therapy, although nitrates in different formulations are still the most widely used in clinical practice

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

Supply Chain Intelligence

This chapter provides on overall picture of business intelligence (BI) and supply chain analytics (SCA) as a means to support supply chain management (SCM) and decision-making. Based on the literature review, we clarify the needs of BI and performance measurement in the SCM sphere, and discuss its potential to enhance decision-making in strategic, tactical and operational levels. We also make a closer look in to SCA in different areas and functions of SCM. Our findings indicate that the main challenge for harnessing the full potential of SCA is the lack of holistic and integrated BI approaches that originates from the fact that each functional area is using its own IT applications without necessary integration in to the company’s overall BI system. Following this examination, we construct a holistic framework that illustrates how an integrated, managerially planned BI system can be developed. Finally, we discuss the main competency requirements, as well as the challenges still prohibiting the great majority of firms from building smart and comprehensive BI systems for SCM.fi=vertaisarvioitu|en=peerReviewed

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods

A Customer Perspective on Product Eliminations: How the Removal of Products Affects Customers and Business Relationships

Regardless of the apparent need for product eliminations, many managers hesitate to act as they fear deleterious effects on customer satisfaction and loyalty. Other managers do carry out product eliminations, but often fail to consider the consequences for customers and business relationships. Given the relevance and problems of product eliminations, research on this topic in general and on the consequences for customers and business relationships in particular is surprisingly scarce. Therefore, this empirical study explores how and to what extent the elimination of a product negatively affects customers and business relationships. Results indicate that eliminating a product may result in severe economic and psychological costs to customers, thereby seriously decreasing customer satisfaction and loyalty. This paper also shows that these costs are not exogenous in nature. Instead, depending on the characteristics of the eliminated product these costs are found to be more or less strongly driven by a company’s behavior when implementing the elimination at the customer interface

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

Data Availability Statement: The individual-level data from these studies is not publicly available to main confidentiality. Data generated by the ISARIC4C consortium is available for collaborative analysis projects through an independent data and materials access committee at isaric4c.net/sample_access. Data and samples from the COVID-Clinical Neuroscience Study are available through collaborative research by application through the NIHR bioresource at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-bioresource-data-access/. Brain injury marker and immune mediator data are present in the paper and in the source data file. Source data are provided with this paper.To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely.National Institute for Health and Care Research (NIHR) (CO-CIN-01) and jointly by NIHR and UK Research and Innovation (CV220-169, MC_PC_19059). B.D.M. is supported by the UKRI/MRC (MR/V03605X/1), the MRC/UKRI (MR/V007181/1), MRC (MR/T028750/1) and Wellcome (ISSF201902/3). C.D. is supported by MRC (MC_PC_19044). We would like to thank the University of Liverpool GCP laboratory facility team for Luminex assistance and the Liverpool University Biobank team for all their help, especially Dr. Victoria Shaw, Lara Lavelle-Langham, and Sue Holden. We would like to acknowledge the Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). We acknowledge the Liverpool Centre for Cell Imaging (CCI) for provision of imaging equipment (Dragonfly confocal microscope) and excellent technical assistance (BBSRC grant number BB/R01390X/1). Tom Solomon is supported by The Pandemic Institute and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool. D.K.M. and E.N. are supported by the NIHR Cambridge Biomedical Centre and by NIHR funding to the NIHR BioResource (RG94028 and RG85445), and by funding from Brain Research UK 201819-20. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. The authors would like to acknowledge the eDRIS team (Public Health Scotland) for their support in obtaining approvals, the provisioning and linking of data and facilitating access to the National Safe Haven. The views expressed are those of the author(s) and not necessarily those of the UKRI, NHS, the NIHR or the Department of Health and Social Care