Time-clustering analysis of the 1978–2008 sub-crustal seismicity of Vrancea region

The analysis of time-clustering behaviour of the sub-crustal seismicity (depth larger than 60 km) of the Vrancea region has been performed. The time span of the analyzed catalogue is from 1978 to 2008, and only the events with a magnitude of <i>M</i><sub>w</sub> ≥ 3 have been considered. The analysis, carried out on the full and aftershock-depleted catalogues, was performed using the Allan Factor (AF) that allows the identificatiion and quantification of correlated temporal structures in temporal point processes. Our results, whose significance was analysed by means of two methods of generation of surrogate series, reveal the presence of time-clustering behaviour in the temporal distribution of seismicity data of the full catalogue. The analysis performed on the aftershock-depleted catalogue indicates that the time-clustering is associated mainly to the aftershocks generated by the two largest events occurred on 30 August 1986 (<i>M</i><sub>w</sub> = 7.1) and 30 May 1990 (<i>M</i><sub>w</sub> = 6.9)

Scaling and correlations in the dynamics of forest-fire occurrence

Forest-fire waiting times, defined as the time between successive events above a certain size in a given region, are calculated for Italy. The probability densities of the waiting times are found to verify a scaling law, despite that fact that the distribution of fire sizes is not a power law. The meaning of such behavior in terms of the possible self-similarity of the process in a nonstationary system is discussed. We find that the scaling law arises as a consequence of the stationarity of fire sizes and the existence of a non-trivial ``instantaneous'' scaling law, sustained by the correlations of the process.Comment: Not a long paper, but many figures (but no large size in kb

A stacked ensemble learning and non-homogeneous hidden Markov model for daily precipitation downscaling and projection

Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact-related models at local scales. The nonhomogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central-eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with around 26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%– 48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario

High power neon seeded JET discharges: Experiments and simulations

A series of neon seeded JET ELMy H-mode pulses is considered from the modeling as well as from the experimental point of view. For two different Ne seeding rates and two different D puffing gas levels the heating power, P heat , is in the range 22–29.5 MW. The main focus is on the numerical reconstruction of the total radiated power (which mostly depends on the W concentration) and its distribution between core and divertor and of Z eff(which mostly depends on the Ne concentration). To model self-consistently the core and the SOL two input parameters had to be adjusted case by case: the SOL diffusivity, D SOL , and the core impurity inward pinch, v pinch . D SOL had to be increased with increasing Ne and the level of v pinch had to be changed, for any given Ne , according to the level of P heat : it decreases with increasing P heat . Since the ELM frequency, f ELM , is experimentally correlated with P heat , (it increases with P heat ) the impurity inward pinch can be seen as to depend on f ELM . Therefore, to maintain a low v pinch level (i.e. high f ELM ) Ne / P heat should not exceed a certain threshold, which slightly increases with the D puffing rate. This might lead to a limitation in the viability of reducing the target heat load by Ne seeding at moderate D , while keeping Z effat acceptably low level.EURATOM 63305

Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea

: Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context

Density-functional study of the evolution of the electronic structure of oligomers of thiophene:Towards a model Hamiltonian

We present density-functional and time-dependent density-functional studies of the ground, ionic, and excited states of a series of oligomers of thiophene. We show that, for the physical properties, the most relevant highest occupied and lowest unoccupied molecular orbitals develop gradually from monomer molecular orbitals into occupied and unoccupied broad bands in the large length limit. We show that band gap and ionization potentials decrease with size, as found experimentally and from empirical calculations. This gives credence to a simple tight-binding model Hamiltonian approach to these systems. We demonstrate that the length dependence of the experimental excitation spectra for both singlet and triplet excitations can be very well explained with an extended Hubbard-like Hamiltonian, with a monomer on-site Coulomb and exchange interaction and a nearest-neighbor Coulomb interaction. We also study the ground and excited-state electronic structures as functions of the torsion angle between the units in a dimer, and find almost equal stabilities for the transoid and cisoid isomers, with a transition energy barrier for isomerization of only 4.3 kcal/mol. Fluctuations in the torsion angle turn out to be very low in energy, and therefore of great importance in describing even the room-temperature properties. At a torsion angle of 90° the hopping integral is switched off for the highest occupied molecular orbital levels because of symmetry, allowing a first-principles estimate of the on-site interaction minus the next-neighbor Coulomb interaction as it enters in a Hubbard-like model Hamiltonian

Anomalous diffusion in the dynamics of complex processes

Anomalous diffusion, process in which the mean-squared displacement of system states is a non-linear function of time, is usually identified in real stochastic processes by comparing experimental and theoretical displacements at relatively small time intervals. This paper proposes an interpolation expression for the identification of anomalous diffusion in complex signals for the cases when the dynamics of the system under study reaches a steady state (large time intervals). This interpolation expression uses the chaotic difference moment (transient structural function) of the second order as an average characteristic of displacements. A general procedure for identifying anomalous diffusion and calculating its parameters in real stochastic signals, which includes the removal of the regular (low-frequency) components from the source signal and the fitting of the chaotic part of the experimental difference moment of the second order to the interpolation expression, is presented. The procedure was applied to the analysis of the dynamics of magnetoencephalograms, blinking fluorescence of quantum dots, and X-ray emission from accreting objects. For all three applications, the interpolation was able to adequately describe the chaotic part of the experimental difference moment, which implies that anomalous diffusion manifests itself in these natural signals. The results of this study make it possible to broaden the range of complex natural processes in which anomalous diffusion can be identified. The relation between the interpolation expression and a diffusion model, which is derived in the paper, allows one to simulate the chaotic processes in the open complex systems with anomalous diffusion.Comment: 47 pages, 15 figures; Submitted to Physical Review

T1 bladder cancer: comparison of the prognostic impact of two substaging systems on disease recurrence and progression and suggestion of a novel nomogram

Background: The T1 substaging of bladder cancer (BCa) potentially impacts disease progression. The objective of the study was to compare the prognostic accuracy of two substaging systems on the recurrence and progression of primary pathologic T1 (pT1) BCa and to test a nomogram based on pT1 substaging for predicting recurrence-free survival (RFS) and progression-free survival (PFS). Methods: The medical records of 204 patients affected by pT1 BCa were retrospectively reviewed. Substaging was defined according to the depth of lamina propria invasion in T1a-c and the extension of the lamina propria invasion to T1-microinvasive (T1m) or T1-extensive (T1e). Uni- and multivariable Cox regression models evaluated the independent variables correlated with recurrence and progression. The predictive accuracies of the two substaging systems were compared by Harrell's C index. Multivariate Cox regression models for the RFS and PFS were also depicted by a nomogram. Results: The 5-year RFS was 47.5% with a significant difference between T1c and T1a (p = 0.02) and between T1e and T1m (p < 0.001). The 5-year PFS was 75.9% with a significant difference between T1c and T1a (p = 0.011) and between T1e and T1m (p < 0.001). Model T1m-e showed a higher predictive power than T1a-c for predicting RFS and PFS. In the univariate and multivariate model subcategory T1e, the diameter, location, and number of tumors were confirmed as factors influencing recurrence and progression after adjusting for the other variables. The nomogram incorporating the T1m-e model showed a satisfactory agreement between model predictions at 5 years and actual observations. Conclusions: Substaging is significantly associated with RFS and PFS for patients affected by T1 BCa and should be included in innovative prognostic nomograms

Ileal neuroendocrine tumor in a patient with sclerosing mesenteritis: Which came first?

Objective: Unusual clinical courseBackground: Jejunoileal neuroendocrine tumors (JI-NETs) are rare tumors that can be associated with mesenteric fibrosis. This case report is of an incidental finding of a JI-NET in a patient who was previously misdiagnosed with sclerosing mesenteritis.Case Report: A 42-year-old man was admitted to our institution with diffuse abdominal pain and clinical and radiographic signs of bowel obstruction. He had a previous diagnosis of sclerosing mesenteritis, which had been histologically diagnosed after an exploratory laparoscopy performed in 2009 for recurrent acute abdominal pain. He was also annually monitored through computed tomography scans for an incidentally discovered, gradually enlarging mesenteric mass for which a "wait and watch" management approach was adopted. After a period of fasting and observation, the patient underwent an urgent exploratory laparotomy because of his worsening condition. Intraoperatively, an ileocecal resection was performed, along with excision of the known mesenteric mass. The pathology report revealed an ileal NET with nodal metastases within the mesentery and mesenteric tumor deposits (pT3N1).Conclusions: JI-NETs are rare entities, which are usually encountered as incidental findings or in patients with unspecific abdominal pain. Our case represents a probable delayed diagnosis of JI-NET in the context of sclerosing mesenteritis; therefore, a possible association between these 2 conditions should be investigated