Quantifying Transboundary Plastic Pollution in Marine Protected Areas Across the Mediterranean Sea

Micro- and macro-plastics pollution is a growing threat for marine biodiversity, ecosystem functioning, and consequently human wellbeing. Numerical models that consider main sources of plastics and simulate their dispersal characteristics are unique tools for exploring plastic pollution in marine protected areas (MPAs). Here, we used a Lagrangian plastic drift model, taking into account various sizes/types of plastic litter, originating from major land-based sources (coastal cities and rivers), to predict plastic accumulation zones in protected areas of the Mediterranean Sea (i.e., nationally designated MPAs, Natura 2000 sites, and Cetacean Critical Habitats). The model predicted that the size of plastic litters plays a key role in their dispersion and ultimate destination (i.e., larger litter travel longer distances). Most of the studied Mediterranean countries (13 out of 15) had at least one national MPA with over 55% of macroplastics originating from sources beyond their borders. Consequently, in many cases, local efforts to reduce plastic pollution in protected areas would be insufficient, especially for macroplastics management. Transboundary collaboration among Mediterranean countries is critical for implementing successful management plans against plastic pollution in their territorial waters and specifically in MPAs.

Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

Prediction of divertor heat flux width for ITER Pre-Fusion Power Operation using BOUT++ transport code

Prediction of divertor heat flux width is performed for the first and the second Pre-Fusion Power Operation (PFPO) phases specified in the new ITER Research Plan using BOUT++ transport code [Li N.M. et al 2018 Comput. Phys. Commun. 228 69–82]. The initial plasma profiles inside the separatrix are taken from CORSICA scenario studies. Transport coefficients in transport code are calculated by inverting the plasma profiles inside the separatrix and are assumed to be constants in the scrape-off-layer (SOL). An anomalous thermal diffusivity scan is performed with E×B and magnetic drifts. The results in two scenarios identify two distinct regimes: a drift dominant regime when diffusivity is smaller than the respective critical diffusivity χc and a turbulence dominant regime when diffusivity is larger than it. The Goldston heuristic drift model and the ITPA multi-machine experimental scaling yield a lower limit of the width λq. From transport simulations, we obtain the critical diffusivity χc = 0.5 m2⁄ s in 5MA/1.77T PFPO-1 scenario and χc = 0.3 m2⁄ s in 7.5MA/2.65T PFPO-2 scenario. Separatrix temperature and collisionality also have a significant impact on the heat flux width in the drift dominant regime. The investigation clearly yields a scaling for critical thermal diffusivity χc ∝ A½ ⁄ ((Z(1+Z)½ Bp 2)) using ITER scenarios with fixed safety factor q95, major radius R, aspect ratio R/a, and the separatrix temperature T, as well as established the connection with CFETR and C-Mod discharges. This scaling implies that for a given tokamak device with q95, R, R/a, and T fixed, a reduction of poloidal magnetic field by a factor of 3 leads to a 9 times higher critical value of thermal diffusivity χc, possibly yielding a transition from turbulence to drift dominant regime.

Fluid turbulence simulations of divertor heat load for ITER hybrid scenario using BOUT++

The BOUT++ six-field turbulence code is used to simulate the ITER 11.5MA hybrid scenario and a brief comparison is made among ITER baseline, hybrid and steady-state operation (SSO) scenarios. Peeling-ballooning instabilities with different toroidal mode numbers dominate in different scenarios and consequently yield different types of ELMs. The energy loss fractions (ΔWped/Wped) caused by unmitigated ELMs in the baseline and hybrid scenarios are large (~2%) while the one in the SSO scenario is dramatically smaller (~1%), which are consistent with the features of type-I ELMs and grassy ELMs respectively. The intra ELM divertor heat flux width in the three scenarios given by the simulations is larger than the estimations for inter ELM phase based on Goldston’s heuristic drift model. The toroidal gap edge melting limit of tungsten monoblocks of divertor targets imposes constraints on ELM energy loss, giving that the ELM energy loss fraction should be smaller than 0.4%, 1.0%, and 1.2% for ITER baseline, hybrid and SSO scenarios, correspondingly. The simulation shows that only the SSO scenario with grassy ELMs may satisfy the constraint.

The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide

Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomass are essential for effective marine management. In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed. Based on the tracking, replication, and extinction of Lagrangian particles, FMGDM is capable of determining the dynamic growth and drift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronously between the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process is controlled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-Volume Community Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around the Yellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensing observation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial and temporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for 7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms in other regions.

Distribution of H$$^\upbeta$$ Hyperfine Couplings in a Tyrosyl Radical Revealed by 263 GHz ENDOR Spectroscopy

Abstract$$^1$$ 1 H ENDOR spectra of tyrosyl radicals (Y$$^\bullet$$ ∙ ) have been the subject of numerous EPR spectroscopic studies due to their importance in biology. Nevertheless, assignment of all internal $$^1$$ 1 H hyperfine couplings has been challenging because of substantial spectral overlap. Recently, using 263 GHz ENDOR in conjunction with statistical analysis, we could identify the signature of the H$$^{\upbeta _2}$$ β 2 coupling in the essential Y$$_{122}$$ 122 radical of Escherichia coli ribonucleotide reductase, and modeled it with a distribution of radical conformations. Here, we demonstrate that this analysis can be extended to the full-width $$^1$$ 1 H ENDOR spectra that contain the larger H$$^{\upbeta _1}$$ β 1 coupling. The H$$^{\upbeta _2}$$ β 2 and H$$^{\upbeta _1}$$ β 1 couplings are related to each other through the ring dihedral and report on the amino acid conformation. The 263 GHz ENDOR data, acquired in batches instead of averaging, and data processing by a new “drift model” allow reconstructing the ENDOR spectra with statistically meaningful confidence intervals and separating them from baseline distortions. Spectral simulations using a distribution of ring dihedral angles confirm the presence of a conformational distribution, consistent with the previous analysis of the H$$^{\upbeta _2}$$ β 2 coupling. The analysis was corroborated by 94 GHz $$^2$$ 2 H ENDOR of deuterated Y$$_{122}^\bullet$$ 122 ∙ . These studies provide a starting point to investigate low populated states of tyrosyl radicals in greater detail.

Comparison of the Performance of the Memristor Models in 2D Cellular Nonlinear Network

Many charge controlled models of memristor have been proposed for various applications. First, the original linear dopant drift model suffers discontinuities close to the memristor layer boundaries. Then, the nonlinear dopant drift model improves the memristor behavior near these boundaries but lacks physical meaning and fails for some initial conditions. Finally, we present a new model to correct these defects. We compare these three models in specific situations: (1) when a sine input voltage is applied to the memristor, (2) when a constant voltage is applied to it, and (3) how a memristor transfers charges in a circuit point of view involving resistance-capacitance network. In the later case, we show that our model allows for study of the memristor behavior with phase portraits for any initial conditions and without boundary limitations.

Development of Iceberg Profiling Technology

Iceberg management on the Grand Banks of Newfoundland, Canada is currently carried out without knowledge of the underwater shape of the iceberg. An iceberg profiling system is being developed to integrate the rapid generation of 3D iceberg shape data with a collection of tools that utilize the data to provide recommendations, intended to improve iceberg management effectiveness. The intent is for the system to be operated by vessel crew with minimal training. The system utilizes a LiDAR and a pole mounted multibeam sonar to profile the iceberg sail and keel, respectively. A vessel equipped with the profiling system circles an iceberg twice to collect a profile, a process that on average requires approximately 15–30 minutes. The data is collected in the form of a point cloud, which must be de-noised and corrected for both drift and rotation of the iceberg. Tools have been developed to assess the stability of the iceberg, and to consider the shape of the iceberg relative to towing net dimensions, to provide guidance to the operator regarding the recommended towing direction to avoid iceberg rolling or net slippage events. Other applications of the profile data include an impact loads analysis tool that determines the distribution of potential iceberg loads in the event of a collision with a given platform, and an operational iceberg drift model that uses the iceberg shape to improve iceberg drift forecasts. Large-scale field programs were carried out in both 2018 and 2019 as part of the development process for the system. Data collected has shown that iceberg characteristics have changed significantly when compared to iceberg profile data collected in the 1980s. For a given iceberg waterline length, the more recent data shows significantly reduced drafts. The 1980s iceberg dataset currently dominates the data used as the basis for assessing iceberg loads on surface facilities and iceberg risk to subsea assets. Reduced iceberg drafts will result in reduced risk to subsea facilities and pipelines. These results and observations demonstrate the usefulness of the iceberg profiling system as an environmental monitoring tool, and the data collected has design and operational applications. The development and capabilities of the system are presented, as well as the comparison of the 1980’s and newer iceberg datasets and implications for iceberg risk to facilities on the Grand Banks and surrounding regions.

Drift Indices Confirm That Rapid Larval Displacement Is Essential for Recruitment Success in High-Latitude Oceans

Larval drift is a key process for successful fish recruitment. We used Norwegian spring-spawning herring (Clupea harengus) as model species to investigate the relationship between larval drift and recruitment. Larval drift indices were derived from simulations based on survey observations between 1993 and 2016. We show that forward simulated larval drift indices have an important positive relation to recruitment success. The relationship demonstrates elevated recruitment when larvae relocate rapidly northwards toward the Barents Sea. Negative or low larval drift indices coincide with only weak recruitment emphasizing limited survival in years with enhanced larval retention. Hence, with this work we combine drift model outcomes refined with survey data indicating that more extensive larval drift is an important component in population dynamics for high-latitude small pelagic fishes. However, larval displacement alone represents only one among many controlling factors but may offer possible predictions of the probability of higher or lower recruitment in the short term. The applicability of the drift indices is adaptable in all world oceans and all marine organisms that occupy planktonic life stages exposed to dynamic ocean currents. The study demonstrates how larval drift indices help to identify larval transport or retention to be crucial for population replenishment.