Optimal energy-harvesting cycles for load-driven dielectric generators in plane strain

The performances of energy harvesting generators based on dielectric elastomers are investigated. The configuration is of a thin dielectric film coated by stretchable electrodes at both sides. The film is first stretched, then charged and subsequently, afterwards it is released, and finally the charge is harvested at a higher electric potential. The amount of energy extracted by this cycle is bounded by the electric breakdown and the ultimate stretch ratio of the film as well as by structural instabilities due to loss of tension. To identify the optimal cycle that complies with these limits we formulate a constraint optimization problem and solve it with a dedicated solver for two typical classes of elastic dielectrics. As anticipated, we find that the performance of the generator depends critically on the ultimate stretch ratio of the film. However, more surprising is our finding of a universal limit on the dielectric strength of the film beyond which the optimal cycle is independent of this parameter. Thus, we reveal that, regardless of how large the dielectric strength of the material is, there is an upper bound on the amount of harvested energy that depends only on the ultimate stretch ratio. We conclude the work with detailed calculations of the optimal cycles for two commercially available elastic dielectrics

A soft robot structure with limbless resonant, stick and slip locomotion

We present a smart robot structure that exploits anisotropic friction to achieve stick-slip locomotion. The robot is made out of three components: a plastic beam, a planar dielectric elastomer actuator and four bristle pads with asymmetric rigid metallic bristles. We show that when the robot is electronically activated at increasing frequency, its structure exploits the resonance condition to reach the maximum locomotion speed. The fundamental frequency of the structure is estimated both analytically and numerically, allowing the range of frequencies in which the top locomotion speed was observed during the experiments to be identified. The locomotion speed of the robot as a function of the actuation frequency is estimated with a frequency response analysis performed on a discretised model of the structure, revealing good agreement with the experimental evidence

On the universality of the frequency spectrum and band-gap optimisation of quasicrystalline-generated structured rods

The dynamical properties of periodic two-component phononic rods, whose elementary cells are generated adopting the Fibonacci substitution rules, are studied through the recently introduced method of the toroidal manifold. The method allows all band gaps and pass bands featuring the frequency spectrum to be represented in a compact form with a frequency-dependent flow line on the surface describing their ordered sequence. The flow lines on the torus can be either closed or open: in the former case, (i) the frequency spectrum is periodic and the elementary cell corresponds to a canonical configuration, (ii) the band gap density depends on the lengths of the two phases; in the latter, the flow lines cover ergodically the torus and the band gap density is independent of those lengths. It is then shown how the proposed compact description of the spectrum can be exploited (i) to find the widest band gap for a given configuration and (ii) to optimize the layout of the elementary cell in order to maximize the low-frequency band gap. The scaling property of the frequency spectrum, that is a distinctive feature of quasicrystalline-generated phononic media, is also confirmed by inspecting band-gap/pass-band regions on the torus for the elementary cells of different Fibonacci orders

Nutrient addition effects on tropical dry forests: a mini-review from microbial to ecosystem scales.

Humans have more than doubled inputs of reactive nitrogen globally and greatly accelerated the biogeochemical cycles of phosphorus and metals. However, the impacts of increased element mobility on tropical ecosystems remain poorly quantified, particularly for the vast tropical dry forest biome. Tropical dry forests are characterized by marked seasonality, relatively little precipitation, and high heterogeneity in plant functional diversity and soil chemistry. For these reasons, increased nutrient deposition may affect tropical dry forests differently than wet tropical or temperate forests. Here, we review studies that investigated how nutrient availability affects ecosystem and community processes from the microsite to ecosystem scales in tropical dry forests. The effects of N and P addition on ecosystem carbon cycling and plant and microbial dynamics depend on forest successional stage, soil parent material, and rainfall regime. Responses may depend on whether overall productivity is N- vs. P-limited, although data to test this hypothesis are limited. These results highlight the many important gaps in our understanding of tropical dry forest responses to global change. Large-scale experiments are required to resolve these uncertainties

Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes?

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are already limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales

Soil biogeochemistry across Central and South American tropical dry forests

The availability of nitrogen (N) and phosphorus (P) controls the flow of carbon (C) among plants, soils, and the atmosphere, thereby shaping terrestrial ecosystem responses to global change. Soil C, N, and P cycles are linked by drivers operating at multiple spatial and temporal scales: landscape-level variation in macroclimate and soil geochemistry, stand-scale heterogeneity in forest composition, and microbial community dynamics at the soil pore scale. Yet in many biomes, we do not know at which scales most of the biogeochemical variation emerges, nor which processes drive cross-scale feedbacks. Here, we examined the drivers and spatial/temporal scales of variation in soil biogeochemistry across four tropical dry forests spanning steep environmental gradients. To do so, we quantified soil C, N, and P pools, extracellular enzyme activities, and microbial community structure across wet and dry seasons in 16 plots located in Colombia, Costa Rica, Mexico, and Puerto Rico. Soil biogeochemistry exhibited marked heterogeneity across the 16 plots, with total organic C, N, and P pools varying fourfold, and inorganic nutrient pools by an order of magnitude. Most soil characteristics changed more across space (i.e., among sites and plots) than over time (between dry and wet season samplings). We observed stoichiometric decoupling among C, N, and P cycles, which may reflect their divergent biogeochemical drivers. Organic C and N pool sizes were positively correlated with the relative abundance of ectomycorrhizal trees and legumes. By contrast, the distribution of soil P pools was driven by soil geochemistry, with larger inorganic P pools in soils with P-rich parent material. Most earth system models assume that soils within a texture class operate similarly, and ignore subgrid cell variation in soil properties. Here we reveal that soil nutrient pools and fluxes exhibit as much variation among four Neotropical dry forests as is observed across terrestrial ecosystems at the global scale. Soil biogeochemical patterns are driven not only by regional differences in soil parent material and climate, but also by local-scale variation in plant and microbial communities. Thus, the biogeochemical patterns we observed across the Neotropical dry forest biome challenge representation of soil processes in ecosystem models

Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.

Importance: Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. Objective: To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. Design, Setting, and Participants: This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. Main Outcomes and Measures: In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. Results: The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). Conclusions and Relevance: In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes

Leaders’ Impact on Public Spending Priorities: The Case of the German Laender

We examine determinants of the composition of public expenditure in the German Laender (states) over the period 1993-2008, as the Laender exhibit a high degree of institutional and political homogeneity and are endowed with extensive fiscal competences. Our prime contribution is an investigation into how political leaders' socioeconomic background influences public spending priorities. Applying sociological theory, we link preferences for the composition of public spending to social status. In contrast to approaches relying on political budget cycles or partisan theory, we find strong and theory-consistent evidence that prime ministers tend to favour fiscal policies supporting the social class in which they are socialised. Governments led by prime ministers from a poor socioeconomic background spend significantly more on social security, education, health, infrastructure, and public safety

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367

Daily use of high-potency cannabis is associated with more positive symptoms in first-episode psychosis patients: the EU-GEI case-control study.

BACKGROUND: Daily use of high-potency cannabis has been reported to carry a high risk for developing a psychotic disorder. However, the evidence is mixed on whether any pattern of cannabis use is associated with a particular symptomatology in first-episode psychosis (FEP) patients. METHOD: We analysed data from 901 FEP patients and 1235 controls recruited across six countries, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study. We used item response modelling to estimate two bifactor models, which included general and specific dimensions of psychotic symptoms in patients and psychotic experiences in controls. The associations between these dimensions and cannabis use were evaluated using linear mixed-effects models analyses. RESULTS: In patients, there was a linear relationship between the positive symptom dimension and the extent of lifetime exposure to cannabis, with daily users of high-potency cannabis having the highest score (B = 0.35; 95% CI 0.14-0.56). Moreover, negative symptoms were more common among patients who never used cannabis compared with those with any pattern of use (B = -0.22; 95% CI -0.37 to -0.07). In controls, psychotic experiences were associated with current use of cannabis but not with the extent of lifetime use. Neither patients nor controls presented differences in depressive dimension related to cannabis use. CONCLUSIONS: Our findings provide the first large-scale evidence that FEP patients with a history of daily use of high-potency cannabis present with more positive and less negative symptoms, compared with those who never used cannabis or used low-potency types.The work was supported by: Clinician Scientist Medical Research Council fellowship (project reference MR/M008436/1) to MDF; the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King's College Hospital NHS Foundation Trust to DQ; DFG Heisenberg professorship (no. 389624707) to UR. National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The EU-GEI Project is funded by the European Community’s Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (Project EU-GEI). The Brazilian study was funded by the São Paulo Research Foundation under grant number 2012/0417-0