Effects of flywheel training on strength-related variables in female populations. A systematic review

This study aimed to evaluate the effect of flywheel training on female populations, report practical recommendations for practitioners based on the currently available evidence, underline the limitations of current literature, and establish future research directions. Studies were searched through the electronic databases (PubMed, SPORTDiscus, and Web of Science) following the preferred reporting items for systematic reviews and meta-analysis statement guidelines. The methodological quality of the seven studies included in this review ranged from 10 to 19 points (good to excellent), with an average score of 14-points (good). These studies were carried out between 2004 and 2019 and comprised a total of 100 female participants. The training duration ranged from 5 weeks to 24 weeks, with volume ranging from 1 to 4 sets and 7 to 12 repetitions, and frequency ranged from 1 to 3 times a week. The contemporary literature suggests that flywheel training is a safe and time-effective strategy to enhance physical outcomes with young and elderly females. With this information, practitioners may be inclined to prescribe flywheel training as an effective countermeasure for injuries or falls and as potent stimulus for physical enhancement

Omgaan met digitale nationale beleidskaarten

In dit werkdocument worden de resultaten besproken van een casestudy die onderdeel is van het project GeO3 - Omgaan met onzekerheid binnen Ruimtelijke Ordening. De directie Platteland van het ministerie van LNV heeft bij het publiceren van het meerjarenprogramma van Agenda Vitaal Platteland geen digitale viewer gepubliceerd omdat men bang was voor verkeerde interpretatie van de digitale kaarten. In dit project is gekeken naar methoden en cartografische oplossingen om voortaan zonder angst voor misinterpretaties digitale nationale beleidskaarten te kunnen verspreiden. De oplossing is gezocht in het opstellen van een handreiking, zodat kaarten ook daadwerkelijk weergeven wat er bedoeld is door de maker

Оценка эффективности управления деятельностью предприятия

Целью исследования является оценка эффективности управления деятельностью предприятия как интегрального показателя, то есть управления совокупностью деятельностей, таких как производственная, инвестиционная, инновационная, маркетинговая и финансовая

High prevalence of peripheral neuropathy in multiple myeloma patients and the impact of vitamin D levels, a cross-sectional study

Purpose: Peripheral neuropathy (PN) is common in patients with multiple myeloma (MM). We hypothesized that the relationship between hypovitaminosis D and PN described in diabetes mellitus patients may also be present in MM patients. Methods: To study this potential association, we assessed the incidence of hypovitaminosis D (vitamin D < 75 nmol/L [= 30 ng/mL]) in smouldering and active MM patients in two Dutch hospitals. Furthermore, a validated questionnaire was used to distinguish different PN grades. Results: Of the 120 patients included between January 2017 and August 2018, 84% had an inadequate vitamin D level (median vitamin D level 49.5 nmol/L [IQR 34–65 nmol/L]; mean age: 68 years [SD ± 7.7]; males: 58%). PN was reported by 69% of patients (n = 83); however, of these 83 patients, PN was not documented in the medical records of 52%. An association was found between lower vitamin D levels and higher incidence of PN in the total population (P = 0.035), and in the active MM patients (P = 0.016). Conclusion: This multi-centre cohort study showed that PN and hypovitaminosis D are common in MM patients, and addressing low vitamin D levels in the treatment of MM patients might be beneficial in reducing the risk of PN. More attention for PN is warranted, as PN is underreported by clinicians. Further research is needed to fully understand the implications of vitamin D in the development of PN in patients with MM. Clinical trial registration: Netherland Trial Register NL5835, date of registration July 28, 2016

The rise and fall of budget support: Ownership, bargaining and donor commitment problems in foreign aid

Motivation: Budget support is the form of aid most commonly associated with recipient‐country ownership. However, a number of scholars and practitioners have criticized the approach as masking new forms of conditionality. Was budget support simply a guise for increasing donor influence in recipient countries? How can we explain the rapid shift towards budget support, as well as the rapid decline in its popularity after only a few years? Purpose: We use a bargaining framework to explain the rise and fall of budget support. Contrary to explanations that suggest that budget support was a normative decision by donors designed to increase aid effectiveness by fostering ownership, a bargaining framework emphasizes that aid policy is the result of sustained negotiations between donors and recipients. These negotiations, however, are constrained by donors' inability to deliver aid as promised. Approach: We use a Nash bargaining framework to formalize the predictions of a bargaining model. From the model, two testable predictions emerge: (1) in exchange for more credible commitments, recipient governments are willing to selectively offer donor agencies greater access to and influence over domestic policy decision-making; and (2) in exchange for such influence, donor agencies are willing to exert less pressure on recipients to be politically inclusive. We then test the implications of the model using case‐study evidence from Rwanda and Tanzania. Findings: The empirical data, based on over 80 interviews with practitioners over several periods of research in both countries, provide substantial evidence in support of the model's core assumptions and predictions. Contrary to claims that budget support increased recipient‐country ownership, interviews (identified as personal communications) suggest that, in exchange for more credible commitments, recipient governments were willing to grant donors greater access and influence. In return, donor agencies reduced demands on the recipient government regarding political inclusivity, tacitly accepting arrangements that centralized decision‐making and excluded civil society. When donor agencies could no longer provide budget support as promised, these negotiated arrangements broke down. Policy Implications: The findings challenge a common narrative that donors embraced budget support because of a normative commitment to ownership. They also demonstrate the value of a bargaining framework. To understand why particular forms of aid, like budget support, rise in popularity only to quickly fall by the wayside, we need to understand what donor agencies and recipient governments bargain over and why

Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.Comment: EvoApps 202

Inverse problems with partial data for a magnetic Schr\"odinger operator in an infinite slab and on a bounded domain

In this paper we study inverse boundary value problems with partial data for the magnetic Schr\"odinger operator. In the case of an infinite slab in $R^n$, $n\ge 3$, we establish that the magnetic field and the electric potential can be determined uniquely, when the Dirichlet and Neumann data are given either on the different boundary hyperplanes of the slab or on the same hyperplane. This is a generalization of the results of [41], obtained for the Schr\"odinger operator without magnetic potentials. In the case of a bounded domain in $R^n$, $n\ge 3$, extending the results of [2], we show the unique determination of the magnetic field and electric potential from the Dirichlet and Neumann data, given on two arbitrary open subsets of the boundary, provided that the magnetic and electric potentials are known in a neighborhood of the boundary. Generalizing the results of [31], we also obtain uniqueness results for the magnetic Schr\"odinger operator, when the Dirichlet and Neumann data are known on the same part of the boundary, assuming that the inaccessible part of the boundary is a part of a hyperplane

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest

An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Carbon monoxide production from five volatile anesthetics in dry sodalime in a patient model: halothane and sevoflurane do produce carbon monoxide; temperature is a poor predictor of carbon monoxide production

BACKGROUND: Desflurane and enflurane have been reported to produce substantial amounts of carbon monoxide (CO) in desiccated sodalime. Isoflurane is said to produce less CO and sevoflurane and halothane should produce no CO at all. The purpose of this study is to measure the maximum amounts of CO production for all modern volatile anesthetics, with completely dry sodalime. We also tried to establish a relationship between CO production and temperature increase inside the sodalime. METHODS: A patient model was simulated using a circle anesthesia system connected to an artificial lung. Completely desiccated sodalime (950 grams) was used in this system. A low flow anesthesia (500 ml/min) was maintained using nitrous oxide with desflurane, enflurane, isoflurane, halothane or sevoflurane. For immediate quantification of CO production a portable gas chromatograph was used. Temperature was measured within the sodalime container. RESULTS: Peak concentrations of CO were very high with desflurane and enflurane (14262 and 10654 ppm respectively). It was lower with isoflurane (2512 ppm). We also measured small concentrations of CO for sevoflurane and halothane. No significant temperature increases were detected with high CO productions. CONCLUSION: All modern volatile anesthetics produce CO in desiccated sodalime. Sodalime temperature increase is a poor predictor of CO production