Fiscal space and the procyclicality of fiscal policy: the case for making hay while the sun shines

Utilizing data from 133 countries over the period 1950-2014, we identify fiscal space - the ability to pursue active fiscal policy without undermining fiscal sustainability - as a key factor underlying the cyclicality of fiscal policies. We find that less fiscal space induces greater fiscal procyclicality; and the reduction in fiscal space in high income countries in the post-global financial crisis period prevented these economies from adopting countercyclical fiscal policies. We also show that this relationship is non-linear such that countries in the bottom tail of the fiscal space distribution need to make significant improvements before they are able to perform countercyclical policy. Taken together with the increasingly dominant role of fiscal action in downturns, as is highlighted in the context of the responses to the Covid-19 crisis, these findings clearly indicate the importance of building fiscal space in good times to provide capacity for countercyclical policy in bad times

DNA amplified fingerprinting, a useful tool for determination of genetic origin and diversity analysis in Citrus

We used three short repetitive nucleotide sequences [(GTG)5, (TAC)5, and (GACA)4] either as radiolabeled probes for hybridization with restricted Citrus DNA or as single primers in polymerase chain reaction amplification experiments with total genomic DNA. We tested the ability of the sequences to discriminate between seedlings of zygotic or nuclear origin in the progeny of a Volkamer lemon #Citrus volkameriana# Ten. & Pasq.) tree. The genetic variability within two species [#Citrus sinensis# (L.) Osbeck (sweet oranges) and #Citrus reticulata# Blanco and relatives (mandarins)] was evaluated. DNA amplified figerprinting with single primers was the more successful technique for discriminating between nucellular and zygotic seedlings. Although we were not able to distinguish among 10 cultivars of #C. sinensis#, all 10 #C. reticulata# cultivars tested were distinguishable. However, it still is difficult to identify the putative parents of a hybrid plant when the two parental genomes are closely related. (Résumé d'auteur

Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations

Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim is to forecast future anomalies by assimilating observed anomalies on an estimate of the model climate. The large variety of experimental setups, models and observational networks adopted worldwide make it difficult to draw firm conclusions on the respective advantages and drawbacks of FFI and AI, or to identify distinctive lines for improvement. The lack of a unified mathematical framework adds an additional difficulty toward the design of adequate initialization strategies that fit the desired forecast horizon, observational network and model at hand. Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and use the notation and concepts of data assimilation theory to highlight their error scaling properties. This analysis suggests better performances using FFI when a good observational network is available and reveals the direct relation of its skill with the observational accuracy. The skill of AI appears, however, mostly related to the model quality and clear increases of skill can only be expected in coincidence with model upgrades. We have compared FFI and AI in experiments in which either the full system or the atmosphere and ocean were independently initialized. In the former case FFI shows better and longer-lasting improvements, with skillful predictions until month 30. In the initialization of single compartments, the best performance is obtained when the stabler component of the model (the ocean) is initialized, but with FFI it is possible to have some predictive skill even when the most unstable compartment (the extratropical atmosphere) is observed. Two advanced formulations, least-square initialization (LSI) and exploring parameter uncertainty (EPU), are introduced. Using LSI the initialization makes use of model statistics to propagate information from observation locations to the entire model domain. Numerical results show that LSI improves the performance of FFI in all the situations when only a portion of the system's state is observed. EPU is an online drift correction method in which the drift caused by the parametric error is estimated using a short-time evolution law and is then removed during the forecast run. Its implementation in conjunction with FFI allows us to improve the prediction skill within the first forecast year. Finally, the application of these results in the context of realistic climate models is discussed

Towards System Modelling to Support Diseases Data Extraction from the Electronic Health Records for Physicians Research Activities

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.Comment: 15 pages, 18 figures and 12 table

A Prospective Evaluation of Opioid Utilization After Upper-Extremity Surgical Procedures: Identifying Consumption Patterns and Determining Prescribing Guidelines.

BACKGROUND: Although adequate management of postoperative pain with oral analgesics is an important aspect of surgical procedures, inadvertent overprescribing can lead to excess availability of opioids in the community for potential diversion. The purpose of our study was to prospectively evaluate opioid consumption following outpatient upper-extremity surgical procedures to determine opioid utilization patterns and to develop prescribing guidelines. METHODS: All patients undergoing outpatient upper-extremity surgical procedures over a consecutive 6-month period had the following prospective data collected: patient demographic characteristics, surgical details, anesthesia type, and opioid prescription and consumption patterns. Analysis of variance and post hoc comparisons were performed using t tests, with the p value for multiple pairwise tests adjusted by the Bonferroni correction. RESULTS: A total of 1,416 patients with a mean age of 56 years (range, 18 to 93 years) were included in the study. Surgeons prescribed a mean total of 24 pills, and patients reported consuming a mean total of 8.1 pills, resulting in a utilization rate of 34%. Patients undergoing soft-tissue procedures reported requiring fewer opioids (5.1 pills for 2.2 days) compared with fracture surgical procedures (13.0 pills for 4.5 days) or joint procedures (14.5 pills for 5.0 days) (p \u3c 0.001). Patients who underwent wrist surgical procedures required a mean number of 7.5 pills for 3.1 days and those who underwent hand surgical procedures required a mean number of 7.7 pills for 2.9 days, compared with patients who underwent forearm or elbow surgical procedures (11.1 pills) and those who underwent upper arm or shoulder surgical procedures (22.0 pills) (p \u3c 0.01). Procedure type, anatomic location, anesthesia type, age, and type of insurance were also all significantly associated with reported opioid consumption (p \u3c 0.001). CONCLUSIONS: In this large, prospective evaluation of postoperative opioid consumption, we found that patients are being prescribed approximately 3 times greater opioid medications than needed following upper-extremity surgical procedures. We have provided general prescribing guidelines, and we recommend that surgeons carefully examine their patients\u27 opioid utilization and consider customizing their opioid prescriptions on the basis of anatomic location and procedure type to prescribe the optimal amount of opioids while avoiding dissemination of excess opioids

High-count Multi-Core Fibers for Space-Division Multiplexing with Propagation-Direction Interleaving

By introducing a square lattice structure for bidirectional core assignments in multi-core fibers, the e ectiveness of propagation-direction interleaving for crosstalk reduction can be increased, realizing a 24-core fiber with-30.6 dB crosstalk over 100 km

Seeing new opportunities to help smokers quit: A UK national survey of optometrist delivered smoking cessation behavioural support interventions

Background: Smoking is a risk factor for various eye conditions. Brief smoking cessation interventions have demonstrated effectiveness when delivered by a range of healthcare professionals. Optometrists are well-placed in the community to advise otherwise healthy smokers to quit, yet remain relatively neglected in smoking cessation research and policy. In a national survey, this study investigated self-reported practices of UK optometrists for delivering brief tobacco smoking cessation interventions to patients. Methods: A randomly selected sample of 1,200 optometrists out of the 9000 optometrists registered on the UK College of Optometrists database were invited to complete a 40-item, web-based survey assessing: training related to smoking cessation; current practice [i.e. the proportion of patients to which components of very brief advice (Ask, Advise, Assist) and other evidence-based smoking cessation behaviour change techniques were delivered]; and barriers/enablers to intervention delivery. Results: In total, 408 (34%) responses were received. Most (83%) optometrists received no training in practical skills for delivering smoking cessation support. A third (34%) routinely assessed smoking status. Fewer self-reported advising smokers to quit (22%), offering assistance (via referral to dedicated services) (3%), or advice on smoking cessation medications (2%). Perceived barriers included insufficient knowledge/training (81%) and time (65%). Optometrists were more likely to assess and advise on smoking cessation if they practised in Scotland (χ²(2)=32.95,  p<0.001), an independent optometry practice (χ²(1)=4.27,  p=0.39), or had received smoking cessation training χ²(1)=13.1,  p<0.001). Conclusions: Substantial gaps exist in UK optometrists' current smoking cessation training and practice. Evidence-based training resources are needed to support the implementation of smoking cessation interventions into routine optometry practice. Implications: Optometrists are well placed in the community to delivery brief advice interventions to a large population of smokers. This survey provides a comprehensive description of current UK optometry practice related to the provision of evidence-based brief tobacco smoking cessation interventions to patients. Although optometrists perceive advising on smoking cessation as part of their role, numerous substantial gaps in current practice and training remain which need to be addressed through targeted interventions to increase implementation

An Energy conserving routing scheme for wireless body sensor nanonetwork communication

Current developments in nanotechnology make electromagnetic communication possible at the nanoscale for applications involving body sensor networks (BSNs). This specialized branch of wireless sensor networks, drawing attention from diverse fields, such as engineering, medicine, biology, physics, and computer science, has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, terahertz frequency bands with limited transmission range, and so on, in designing protocols for wireless nanosensor networks. This work proposes an improved performance scheme of nanocommunication over terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains - a new energy-efficient forwarding routine for electromagnetic communication in wireless nanonetworks consisting of hybrid clusters with centralized scheduling; a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing; and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a nano-sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communications; multisensor fusion at the fusion node enhances the link quality of the transmission