Machine Learning to Predict Warhead Fragmentation In-Flight Behavior from Static Data

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to predict fragmentation characteristics using data from this imaging technique and simulation data combined. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired fragmentation characteristics. Using Monte Carlo simulations, the K-NN regression is shown to predict the distributions for the total number of fragments intersecting a given surface and the total fragment velocity and mass associated with that surface. An ability to predict fragment fly-out characteristics accurately and quickly would provide information which can then be used to evaluate the collateral damage and lethality of a given weapon

Factor Utilisation and Productivity Estimates for the United Kingdom

This paper derives series for capital utilisation, labour effort and total factor productivity from a DGE model with variable utilisation and labour adjustment costs. Capital utilisation tracks survey-based measures closely, while movements in total hours worked drive our labour effort series. TFP is less cyclical than the traditional Solow residual, though a weighted average of capital utilisation and labour effort - aggregate factor utilisation - and the Solow residual are not closely related. Rather, aggregate factor utilisation is correlated with detrended labour productivity, providing more evidence that differences in average and marginal labour productivity may be linked to factor hoarding.

Using Machine Learning to Predict Hypervelocity Fragment Propagation of Space Debris Collisions

The future of spaceflight is threatened by the increasing amount of space debris, especially in the near-Earth environment. To continue operations, accurate characterization of hypervelocity fragment propagation following collisions and explosions is imperative. While large debris particles can be tracked by current methods, small particles are often missed. This paper presents a method to estimate fragment fly-out properties, such as fragment, velocity, and mass distributions, using machine learning. Previous work was performed on terrestrial data and associated simulations representing space debris collisions. The fragmentation of high-velocity fragmentation can be modeled by terrestrial fragmentation tests, such as static detonations. Recently, stereoscopic imaging techniques have become an addition to static arena testing. Collecting data with this method provides position vector and mass information faster and more accurately than previous manual-collection methods. Additionally, there is limited space debris data of similar accuracy on individual fragments. Therefore, this imaging technique was used as the primary collection method for the previous research data. Now, two-line element (TLE) sets for Iridium 33 are also used. Machine learning methodologies are leveraged to predict fragmentation fly-out from the collision event with Cosmos 2251. First, gaussian mixture models (GMMs) are used to model the probability distribution of the particles for a given desired characteristic at Julian dates following the event. Once this training data is generated, regression techniques can be used to predict these characteristics. K-nearest neighbor (K-NN) regressors are used to estimate the spatial distribution of the satellite fragments. Monte Carlo simulations are then used to validate the results, finding that this technique accurately estimates the total number of fragments expected to intersect a region of interest at a given time. Following this work, the same technique can be used to estimate the velocity and mass distributions of the debris. This information can then be used to estimate the kinetic energy of the particle and classify it to avoid future debris collisions

Predicting Dynamic Fragmentation Characteristics from High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data and Machine Learning

To continue space operations with the increasing space debris, accurate characterization of fragment fly-out properties from hypervelocity impacts is essential. However, with limited realistic experimentation and the need for data, available static arena test data, collected utilizing a novel stereoscopic imaging technique, is the primary dataset for this paper. This research leverages machine learning methodologies to predict fragmentation characteristics using combined data from this imaging technique and simulations, produced considering dynamic impact conditions. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired characteristics. Using Monte Carlo simulations, the K-NN regression is shown to predict the distributions for both the total number of fragments intersecting a given surface, as well as the expected total fragment velocity and mass associated with that surface. This information can then be used to estimate the kinetic energy of the particle to classify the particle and avoid debris collisions

Binary Contamination in the SEGUE sample: Effects on SSPP Determinations of Stellar Atmospheric Parameters

Using numerical modeling and a grid of synthetic spectra, we examine the effects that unresolved binaries have on the determination of various stellar atmospheric parameters for SEGUE targets measured using the SEGUE Stellar Parameter Pipeline (SSPP). To model undetected binaries that may be in the SEGUE sample, we use a variety of mass distributions for the primary and secondary stars in conjunction with empirically determined relationships for orbital parameters to determine the fraction of G-K dwarf stars, as defined by SDSS color cuts, that will be blended with a secondary companion. We focus on the G-K dwarf sample in SEGUE as it records the history of chemical enrichment in our galaxy. To determine the effect of the secondary on the spectroscopic parameters, we synthesize a grid of model spectra from 3275 to 7850 K (~0.1 to 1.0 \msun) and [Fe/H]=-0.5 to -2.5 from MARCS model atmospheres using TurboSpectrum. We analyze both "infinite" signal-to-noise ratio (S/N) models and degraded versions, at median S/N of 50, 25 and 10. By running individual and combined spectra (representing the binaries) through the SSPP, we determine that ~10% of the blended G-K dwarf pairs with S/N>=25 will have their atmospheric parameter determinations, in particular temperature and metallicity, noticeably affected by the presence of an undetected secondary. To account for the additional uncertainty from binary contamination at a S/N~10, uncertainties of ~140 K and ~0.17 dex in [Fe/H] must be added in quadrature to the published uncertainties of the SSPP. (Abridged)Comment: 68 pages, 20 figures, 9 table

Decay resistance variability of European wood species thermally modified by industrial process

Thermal modification is now considered as a new ecofriendly industrial wood modification process improving mainly the material decay resistance and its dimensional stability. Most industrial thermal treatment processes use convection heat transfer which induces sometimes heterogeneous treatment temperature propagation within the oven and lead to the heterogeneity in treatment efficiency. Thus, it is common that treatment is not completely effective on several stack boards, in a same batch. The aim of this paper was to study the decay resistance variability of various European wood species thermally modified. Thermal modifications were performed around 240°C during 4h, on about 10 m3 of 27 x 152 x 2000 mm3 wood planks placed in an industrial oven having a volume of 20 m3, on the following wood species: spruce, ash, beech and poplar. All of the tests concerning the decay resistance were carried out in the laboratory using untreated beech and pine woods as reference materials. An agar block test was used to determine the resistance of thermally modified woods, leached beforehand according to EN 84 standard or not, to brown-rot and white-rot fungi, according to XP CEN/TS 15083-1. A large selection of treated wood samples was tested in order to estimate the variability of treatment efficiency. Thermal treatment increased the biological durability of all leached and un-leached modified wood samples, compared with native wood species. The treatment temperature of 240°C used in this study is sufficient to reach durability classes ''durable'' or ''very durable'' for the four wood species. However, the dispersion of weight loss values, due to the fungal attacks was very important and showed a large variability of the durability of wood which has been treated in a single batch. These results showed that there is a substantial need to develop process control and² indicator in order to insure that the quality of treated timber is properly evaluated with a view to putting this modified timber on the market under a chain of custody. (Résumé d'auteur

Guidance for the Conduct and Reporting of Clinical Trials of Breast Milk Substitutes

Question What is the best way to ensure the validity of clinical trials of breast milk substitutes while protecting trial participants? Findings Through a Delphi consensus project, guidance was developed to address issues specific to trials of breast milk substitutes assessing growth and tolerance, as well as trials of breast milk substitutes with other objectives. This consensus guidance summarizes best practice for the design, conduct, analysis, and reporting of trials of breast milk substitutes. Meaning Use of this guidance, in conjunction with existing clinical trial regulations, should enhance the quality and validity of trials of breast milk substitutes, protect trial participants, and support the evidence base for infant nutrition recommendations. This consensus guidance summarizes best practice for the design, conduct, analysis, and reporting of trials of breast milk substitutes. Importance Breast milk substitutes (BMS) are important nutritional products evaluated in clinical trials. Concerns have been raised about the risk of bias in BMS trials, the reliability of claims that arise from such trials, and the potential for BMS trials to undermine breastfeeding in trial participants. Existing clinical trial guidance does not fully address issues specific to BMS trials. Objectives To establish new methodological criteria to guide the design, conduct, analysis, and reporting of BMS trials and to support clinical trialists designing and undertaking BMS trials, editors and peer reviewers assessing trial reports for publication, and regulators evaluating the safety, nutritional adequacy, and efficacy of BMS products. Design, Setting, and Participants A modified Delphi method was conducted, involving 3 rounds of anonymous questionnaires and a face-to-face consensus meeting between January 1 and October 24, 2018. Participants were 23 experts in BMS trials, BMS regulation, trial methods, breastfeeding support, infant feeding research, and medical publishing, and were affiliated with institutions across Europe, North America, and Australasia. Guidance development was supported by an industry consultation, analysis of methodological issues in a sample of published BMS trials, and consultations with BMS trial participants and a research ethics committee. Results An initial 73 criteria, derived from the literature, were sent to the experts. The final consensus guidance contains 54 essential criteria and 4 recommended criteria. An 18-point checklist summarizes the criteria that are specific to BMS trials. Key themes emphasized in the guidance are research integrity and transparency of reporting, supporting breastfeeding in trial participants, accurate description of trial interventions, and use of valid and meaningful outcome measures. Conclusions and Relevance Implementation of this guidance should enhance the quality and validity of BMS trials, protect BMS trial participants, and better inform the infant nutrition community about BMS products.Peer reviewe

Diet during pregnancy and infancy, and risk of allergic or autoimmune disease: a systematic review and meta-analysis

Background: There is uncertainty about the influence of diet during pregnancy and infancy on a child’s immune development. We assessed whether variations in maternal or infant diet can influence risk of allergic or autoimmune disease. Methods and findings: Two authors selected studies, extracted data, and assessed risk of bias. Grading of Recommendations Assessment, Development and Evaluation (GRADE) was used to assess certainty of findings. We searched Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), Web of Science, Central Register of Controlled Trials (CENTRAL), and Literatura Latino Americana em Ciências da Saúde (LILACS) between January 1946 and July 2013 for observational studies and until December 2017 for intervention studies that evaluated the relationship between diet during pregnancy, lactation, or the first year of life and future risk of allergic or autoimmune disease. We identified 260 original studies (964,143 participants) of milk feeding, including 1 intervention trial of breastfeeding promotion, and 173 original studies (542,672 participants) of other maternal or infant dietary exposures, including 80 trials of maternal (n = 26), infant (n = 32), or combined (n = 22) interventions. Risk of bias was high in 125 (48%) milk feeding studies and 44 (25%) studies of other dietary exposures. Evidence from 19 intervention trials suggests that oral supplementation with nonpathogenic micro-organisms (probiotics) during late pregnancy and lactation may reduce risk of eczema (Risk Ratio [RR] 0.78; 95% CI 0.68–0.90; I2 = 61%; Absolute Risk Reduction 44 cases per 1,000; 95% CI 20–64), and 6 trials suggest that fish oil supplementation during pregnancy and lactation may reduce risk of allergic sensitisation to egg (RR 0.69, 95% CI 0.53–0.90; I2 = 15%; Absolute Risk Reduction 31 cases per 1,000; 95% CI 10–47). GRADE certainty of these findings was moderate. We found weaker support for the hypotheses that breastfeeding promotion reduces risk of eczema during infancy (1 intervention trial), that longer exclusive breastfeeding is associated with reduced type 1 diabetes mellitus (28 observational studies), and that probiotics reduce risk of allergic sensitisation to cow’s milk (9 intervention trials), where GRADE certainty of findings was low. We did not find that other dietary exposures—including prebiotic supplements, maternal allergenic food avoidance, and vitamin, mineral, fruit, and vegetable intake—influence risk of allergic or autoimmune disease. For many dietary exposures, data were inconclusive or inconsistent, such that we were unable to exclude the possibility of important beneficial or harmful effects. In this comprehensive systematic review, we were not able to include more recent observational studies or verify data via direct contact with authors, and we did not evaluate measures of food diversity during infancy. Conclusions: Our findings support a relationship between maternal diet and risk of immune-mediated diseases in the child. Maternal probiotic and fish oil supplementation may reduce risk of eczema and allergic sensitisation to food, respectively

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London