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Hydrothermal liquefaction (HTL) can thermochemically transform sewage sludge into a biocrude with high energy content, high chemical complexity, and high O and N content. The development of an efficient upgrading process for such complex feedstocks necessitates detailed knowledge of the molecular composition and the specific heteroatom-containing compounds to understand and optimize the hydrotreating reactions. In this study, we present the upgrading of sewage sludge-derived HTL biocrude via a two-stage hydrotreatment process and perform advanced chemical characterization of the feedstock, intermediate, and final upgraded products with gas chromatography-mass spectrometry (GC-MS) and Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). We show that hydrotreatment significantly improves the quality of the oil, primarily succeeding in cracking the heavy molecules and removing the sulfur- and oxygen-containing components. FTICR-MS analysis shows that the HTL biocrude has a high concentration of fatty acid amides that readily lose their oxygen and nitrogen during hydrotreating and are converted into saturated hydrocarbons, whereas the aromatic O

We develop methodology and theory for a general Bayesian approach towards dynamic variable selection in high-dimensional regression models with time-varying parameters. Specifically, we propose a variational inference scheme which features dynamic sparsity-inducing properties so that different subsets of ``active'' predictors can be identified over different time periods. We compare our modeling framework against established static and dynamic variable selection methods both in simulation and within the context of two common problems in macroeconomics and finance: inflation forecasting and equity returns predictability. The results show that our approach helps to tease out more accurately the dynamic impact of different predictors over time. This translates into significant gains in terms of out-of-sample point and density forecasting accuracy. We believe our results highlight the importance of taking a dynamic approach towards variable selection for economic modeling and forecasting.

The present endeavor numerically exploits the use of a phase-field model to simulate and investigate fracture patterns, deformation mechanisms, damage, and mechanical responses in a human vertebra after the incision of pedicle screws under compressive regimes. Moreover, the proposed phase field framework can elucidate scenarios where different damage patterns, such as crack nucleation sites and crack trajectories, play a role after the spine fusion procedure, considering several simulated physiological movements of the vertebral body. A convergence analysis has been conducted for the vertebra-screws model, considering several mesh refinements, which has demonstrated good agreement with the existing literature on this topic. Consequently, by assuming different angles for the insertion of the pedicle screws and taking into account a few vertebral motion loading regimes, a plethora of numerical results characterizing the damage occurring within the vertebral model has been derived. Overall, the phase field results may shed more light on the medical community, which will be useful in enhancing clinical interventions and reducing post-surgery bone failure and screw loosening., Comment: 23 pages, 9 figures. arXiv admin note: text overlap with arXiv:2207.09362

We propose an alternative approach towards cost mitigation in volatility-managed portfolios based on smoothing the predictive density of an otherwise standard stochastic volatility model. Specifically, we develop a novel variational Bayes estimation method that flexibly encompasses different smoothness assumptions irrespective of the persistence of the underlying latent state. Using a large set of equity trading strategies, we show that smoothing volatility targeting helps to regularise the extreme leverage/turnover that results from commonly used realised variance estimates. This has important implications for both the risk-adjusted returns and the mean-variance efficiency of volatility-managed portfolios, once transaction costs are factored in. An extensive simulation study shows that our variational inference scheme compares favourably against existing state-of-the-art Bayesian estimation methods for stochastic volatility models.

We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple centralised exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the role of inventory risk and adverse selection for liquidity provision.

We propose a novel variational Bayes approach to estimate high-dimensional vector autoregression (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural VAR representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (1) the prior structure directly maps into posterior inference on the reduced-form transition matrix, and (2) posterior estimates are more robust to variables permutation. An extensive simulation study provides evidence that our approach compares favourably against existing linear and non-linear Markov Chain Monte Carlo and variational Bayes methods. We investigate both the statistical and economic value of the forecasts from our variational inference approach within the context of a mean-variance investor allocating her wealth in a large set of different industry portfolios. The results show that more accurate estimates translate into substantial statistical and economic out-of-sample gains. The results hold across different hierarchical shrinkage priors and model dimensions.

This paper introduces a shoebox room simulator able to systematically generate synthetic datasets of binaural room impulse responses (BRIRs) given an arbitrary set of head-related transfer functions (HRTFs). The evaluation of machine hearing algorithms frequently requires BRIR datasets in order to simulate the acoustics of any environment. However, currently available solutions typically consider only HRTFs measured on dummy heads, which poorly characterize the high variability in spatial sound perception. Our solution allows to integrate a room impulse response (RIR) simulator with different HRTF sets represented in Spatially Oriented Format for Acoustics (SOFA). The source code and the compiled binaries for different operating systems allow to both advanced and non-expert users to benefit from our toolbox, see https://github.com/spatialaudiotools/sofamyroom/ ., 18 pages,4 figures, accompanying paper for an acoustic simulator description

We investigate the performance of funds that specialise in cryptocurrency markets and contribute to a growing literature that aims to understand the value of digital assets as investments. The main empirical results support the idea that cryptocurrency funds generate significantly alphas compared to passive benchmarks or conventional risk factors. We compare the actual fund alphas against the simulated values from a panel semi-parametric bootstrap approach. The analysis shows that the extreme outperformance is unlikely to be explained by the luck of fund managers. However, the significance of the alphas becomes statistically weaker after considering the cross-sectional correlation in fund returns.

International audience; The trend towards smaller satellites and mega-constellations has enormously changed the space sector and its utilisation in the last decades, allowing new players to enter the market and introducing stringent requirements to enable a variety of novel applications. Alongside, also the launcher market is undergoing a transformation epoch: the development, manufacturing, and integration of launcher systems is being shifted from the hands of governmental institutions to commercial industry. Moreover, nations like Unites States, China, India, and New Zealand are increasing the competition and pressure on Europe, urging the goal to ensure European autonomy in accessing and using space in a safe and secure environment. Europe does not only need innovations, but primarily a new generation of engineers, capable of understanding the full complexity of launcher development and trained to create and realise the necessary innovations. In this context, ASCenSIon is a multidisciplinary training programme involving 15 Early Stage Researchers (ESRs) from anywhere in the world, focused on several specific areas of cutting-edge space access research, particularly on launcher systems that are (partially) reusable and capable of injecting multiple payloads into multiple orbits. The network aims to identify and advance critical technologies to prove a feasibility of these concepts, and to advance the State of the Art in the field. ASCenSIon, whose acronym stands for “Advancing Space Access Capabilities –Reusability and Multiple Satellite Injection”, is a consortium of 11 beneficiaries and 17 partners across Europe, eager to contribute to the establishment of an ecologically and economically sustainable space access for Europe, oriented towards user needs. Unlike other single-aspect research projects, the core objective of ASCenSIon is not only to train 15 PhD students to become excellent specialists in their respective field, but also to provide them a thorough understanding of the complexity, multidisciplinary, and internationality of launcher development, in order to become leaders in the European effort of utilising space. This will be achieved through secondments, events, and lessons from experts, but mostly through strong interconnections among the ESRs, who will work on Individual Research Projects with a multi-disciplinal and multi-sectoral approach. This paper aims to provide an overview of ASCenSIon programme. Its values and core objectives will be introduced, together with the innovative aspects and content structure. An overview of the research methodology and recruitment strategy will be given, with a particular focus on the contributions and synergies of all participating organisations, core of such a novel training approach.

Assessment of the global budget of the greenhouse gas nitrous oxide ([Formula: see text]O) is limited by poor knowledge of the oceanic [Formula: see text]O flux to the atmosphere, of which the magnitude, spatial distribution, and temporal variability remain highly uncertain. Here, we reconstruct climatological [Formula: see text]O emissions from the ocean by training a supervised learning algorithm with over 158,000 [Formula: see text]O measurements from the surface ocean-the largest synthesis to date. The reconstruction captures observed latitudinal gradients and coastal hot spots of [Formula: see text]O flux and reveals a vigorous global seasonal cycle. We estimate an annual mean [Formula: see text]O flux of 4.2 ± 1.0 Tg N[Formula: see text], 64% of which occurs in the tropics, and 20% in coastal upwelling systems that occupy less than 3% of the ocean area. This [Formula: see text]O flux ranges from a low of 3.3 ± 1.3 Tg N[Formula: see text] in the boreal spring to a high of 5.5 ± 2.0 Tg N[Formula: see text] in the boreal summer. Much of the seasonal variations in global [Formula: see text]O emissions can be traced to seasonal upwelling in the tropical ocean and winter mixing in the Southern Ocean. The dominant contribution to seasonality by productive, low-oxygen tropical upwelling systems (>75%) suggests a sensitivity of the global [Formula: see text]O flux to El Niño-Southern Oscillation and anthropogenic stratification of the low latitude ocean. This ocean flux estimate is consistent with the range adopted by the Intergovernmental Panel on Climate Change, but reduces its uncertainty by more than fivefold, enabling more precise determination of other terms in the atmospheric [Formula: see text]O budget.

The biological productivity and diversity of the California Current System (CCS) is at the leading edge of major emerging climate trends, including hypoxia and acidification. We present results from a hindcast simulation (reanalysis) of an eddy-resolving oceanic physical-biogeochemical model of the CCS, to characterize its mean state and its patterns and drivers of variability in marine biogeochemical and ecosystem processes from 1995-2010. This is a companion paper to a physical analysis in Renault et al. (2021). The model reproduces long-term mean distributions of key ecosystem metrics, including surface nutrients and productivity and subsurface O2 and carbonate undersaturation. The spatial patterns of Net Primary Productivity (NPP) are broadly consistent with measured and remotely sensed rates, and they reflect a predominant limitation by nitrogen, with seasonal and episodic limitation by Fe nearshore in the central CCS, and in the open ocean northern CCS. The vertical distribution of NPP is governed by the trade-off between nutrient and light limitation, a balance that reproduces and explains the observed spatial variations in the depth of the deep Chl maximum. The seasonal to interannual variability of biogeochemical properties and rates is also well captured by model simulations. Because of the prevailing nutrient limitation, fluctuations in the depth of the pycnocline and associated nutricline are the leading single factor explaining interannual variability in the interior biogeochemical state, and the relationships between density and biogeochemical rates and tracers are consistent between model and observations. The magnitude and relationship between density structure and biogeochemical processes is illustrated by the 1997-98 El Niño event, which faithfully reproduces the single largest deviation from the mean state in the simulated period. A slower decadal shoaling of the pycnocline also accounts for the concomitant trends in hypoxic and corrosive conditions on the shelf. The resulting variability is key to understanding the vulnerability of marine species to oceanic change, and to the detection of such changes, soon projected to exceed the range of conditions in the past century.

The recession of an ablative heat shield could significantly modify the nominal outer shape of a re-entry capsule and affect its aerodynamic behavior during the flight through the atmosphere. To quantify this effect, an experimental activity was carried out in the Mach-6 hypersonic wind tunnel H3 at the von Karman Institute for Fluid Dynamics. Subscale models of the European Space Agency hypervelocity entry demonstrator Phoebus, equipped with a heat shield made of a low-temperature ablator to promote the surface recession in the low-enthalpy conditions, were tested to record simultaneously the heat-shield recession and the aerodynamic loads. This paper describes the theoretical conception of this activity, details the preparation of the experimental test campaign and presents a first analysis of the experimental results.

We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.

The paper is intended to present both experimental and numerical approaches for estimating the heat exchange per thermal radiation towards the walls of lab-scale paraffin-based thrust chambers. Two firing tests of a lab-scale gaseous-oxygen/paraffin-wax hybrid rocket engine have been performed to apply such methods. In particular, the radiative wall heat flux has been evaluated by both spectroscopic measurements and discrete transfer method computations. Details of such approaches are given together with results achieved and suggestions for future improvements.

IgG4-related disease (IgG4-RD) is a nosological entity defined as a chronic immune-mediated fibro-inflammatory condition characterized by a tendency to form tumefactive, tissue-destructive lesions or by organ failure. Urologic involvement in IgG4-RD has been described in some short series of patients and in isolated case reports, most often involving the kidneys in so-called IgG4-related kidney disease (IgG4-RKD). The disease can occasionally mimic malignancies and is at risk of being misdiagnosed due to its rarity. We report the case of a 56-year-old man presenting with a right renal mass suspected of being malignant. Laboratory tests showed normal creatinine levels, a high erythrocyte sedimentation rate, and high levels of C-reactive protein and microalbuminuria. The patient underwent radical right nephroureterectomy and histopathologic examination revealed features proving IgG4-RKD. He was therefore referred to immunologists. Typical clinical presentation of IgG4-RKD includes altered renal function with inconstant or no radiologic findings. Conversely, in the case we presented, a single nodule was detected upon imaging evaluation, thus mimicking malignancy. This raises the issue of a proper differential diagnosis. A multidisciplinary approach can be useful, although in clinical practice the selection of patients suspected of having IgG4-RKD is critical in the cases presenting with a renal mass that mimics malignancy.

Numerical simulations of the flowfield in a hybrid rocket engine are carried out with a Reynolds-averaged Navier-Stokes solver integrated with a customized gas-surface interaction wall boundary condition and loosely coupled with a radiation code based on the discrete transfer method. The fuel grain wall boundary condition is based on mass, species and energy conservation equations coupled with thermal radiation exchange and finite-rate Arrhenius kinetics for fuel pyrolysis modeling. Fuel pyrolysis is governed by the heat flux reaching the surface due to convection and radiation and by the energy required for the grain material to heat up and pyrolyze. Attention is focused here on a set of static firings performed with a lab-scale GOX/HDPE motor. A sensitivity analysis has been carried out on the literature pyrolysis models for HDPE, to evaluate the possible role of the uncertainty of such models on the actual prediction of regression rate. A reasonable agreement between measured and computed averaged regression rate and chamber pressure is obtained, with a noticeable improvement with respect to solutions without including radiative energy exchange.

Conventional wisdom posits that all the relevant investors' information lies at the highest possible frequency of observation, so that long-run expected returns can be mechanically inferred by a forward aggregation of short-run estimates. We reverse such logic and propose a novel framework to model and extract the dynamics of latent short-term expected returns by coherently combining the lower-frequency information embedded in multiple predictors. We show that the information cascade from low- to high-frequency levels allows to identify long-lasting effects on expected returns that cannot be captured by standard persistent ARMA processes. The empirical analysis demonstrates that the ability of the model to capture simultaneously medium- to long-term fluctuations in the dynamics of expected returns, has first order implications for forecasting and investment decisions.

The burial of organic carbon in marine sediments removes carbon dioxide from the ocean–atmosphere pool, provides energy to the deep biosphere, and on geological timescales drives the oxygenation of the atmosphere. Here we quantify natural variations in the burial of organic carbon in deep-sea sediments over the last glacial cycle. Using a new data compilation of hundreds of sediment cores, we show that the accumulation rate of organic carbon in the deep sea was consistently higher (50%) during glacial maxima than during interglacials. The spatial pattern and temporal progression of the changes suggest that enhanced nutrient supply to parts of the surface ocean contributed to the glacial burial pulses, with likely additional contributions from more efficient transfer of organic matter to the deep sea and better preservation of organic matter due to reduced oxygen exposure. These results demonstrate a pronounced climate sensitivity for this global carbon cycle sink., While numerous studies have indicated that carbon export to the deep ocean was greater during glacial periods, quantification is lacking. Here, via analysis of hundreds of sediment cores, the authors show carbon accumulation rate was 50% higher during glacial maxima than during interglacials.

Earth System Models increasingly include ocean biogeochemistry models in order to predict changes in ocean carbon storage, hypoxia, and biological productivity under climate change. However, state‐of‐the‐art ocean biogeochemical models include many advected tracers, that significantly increase the computational resources required, forcing a trade‐off with spatial resolution. Here, we compare a state‐of‐the art model with 30 prognostic tracers (TOPAZ) with two reduced‐tracer models, one with 6 tracers (BLING), and the other with 3 tracers (miniBLING). The reduced‐tracer models employ parameterized, implicit biological functions, which nonetheless capture many of the most important processes resolved by TOPAZ. All three are embedded in the same coupled climate model. Despite the large difference in tracer number, the absence of tracers for living organic matter is shown to have a minimal impact on the transport of nutrient elements, and the three models produce similar mean annual preindustrial distributions of macronutrients, oxygen, and carbon. Significant differences do exist among the models, in particular the seasonal cycle of biomass and export production, but it does not appear that these are necessary consequences of the reduced tracer number. With increasing CO2, changes in dissolved oxygen and anthropogenic carbon uptake are very similar across the different models. Thus, while the reduced‐tracer models do not explicitly resolve the diversity and internal dynamics of marine ecosystems, we demonstrate that such models are applicable to a broad suite of major biogeochemical concerns, including anthropogenic change. These results are very promising for the further development and application of reduced‐tracer biogeochemical models that incorporate “sub‐ecosystem‐scale” parameterizations., Journal of Advances in Modeling Earth Systems, 7 (4), ISSN:1942-2466

Numerical simulation of ablative-material response: code and model comparisons

We use Bayesian methods to estimate a multi-factor linear asset pricing model characterized by structural instability in factor loadings, idiosyncratic variances, and factor risk premia. We use such a framework to investigate the key differences in the pricing mechanism that applies to residential vs. non-residential (such as office space, industrial buildings, retail property) real estate investment trusts (REITs). Under the assumption that the subprime crisis has had its epicentre in the housing/residential sector, we interpret any differential dynamics as indicative of the propagation mechanism of the crisis towards business-oriented segments of the US real estate market. We find important differences in the structure as well as the dynamic evolution of risk factor exposures across residential vs. non-residential REITs. An analysis of cross-sectional mispricings reveals that only retail, residential, and mortgage-specialized REITs were over-priced over the initial part of our sample, i.e., 1999-2006. Moreover, residential-driven real estate has structural properties that make it different from non-residential assets.

Guido Colasurdo, Maria Vittoria Salvetti, Walter d'Ambrogio

Although it has long been assumed that the glacial–interglacial cycles of atmospheric CO2 occurred due to increased storage of CO2 in the ocean, with no change in the size of the “active” carbon inventory, there are signs that the geological CO2 supply rate to the active pool varied significantly. The resulting changes of the carbon inventory cannot be assessed without constraining the rate of carbon re- moval from the system, which largely occurs in marine sed- iments. The oceanic supply of alkalinity is also removed by the burial of calcium carbonate in marine sediments, which plays a major role in air–sea partitioning of the active carbon inventory. Here, we present the first global reconstruction of carbon and alkalinity burial in deep-sea sediments over the last glacial cycle. Although subject to large uncertainties, the reconstruction provides a first-order constraint on the effects of changes in deep-sea burial fluxes on global carbon and alkalinity inventories over the last glacial cycle. The results suggest that reduced burial of carbonate in the Atlantic Ocean was not entirely compensated by the increased burial in the Pacific basin during the last glacial period, which would have caused a gradual buildup of alkalinity in the ocean. We also consider the magnitude of possible changes in the larger but poorly constrained rates of burial on continental shelves, and show that these could have been significantly larger than the deep-sea burial changes. The burial-driven inventory variations are sufficiently large to have significantly altered the δ13C of the ocean–atmosphere carbon and changed the aver- age dissolved inorganic carbon (DIC) and alkalinity concentrations of the ocean by more than 100 μM, confirming that carbon burial fluxes were a dynamic, interactive component of the glacial cycles that significantly modified the size of the active carbon pool. Our results also suggest that geolog- ical sources and sinks were significantly unbalanced during the late Holocene, leading to a slow net removal flux on the order of 0.1 PgC yr−1 prior to the rapid input of carbon dur- ing the industrial period.