Search

The structure of 71Zn was investigated by one-neutron transfer and heavy-ion induced complex (deep-inelastic) reactions using the GRETINA-CHICO2 and the Gammasphere setups, respectively. The observed inversion between the 9/2+ and 1/2â states is explained in terms of the role of neutron pairing correlations. Non-collective sequences of levels were delineated above the 9/2+ isomeric state. These are interpreted as being associated with a modest oblate deformation in the framework of Monte-Carlo shell-model calculations carried out with the A3DA-m Hamiltonian in the pfg9/2d5/2 valence space. Similarities with the structure of Ni402868 were observed and the shape-coexistence mechanism in the N=40 region of neutron-rich nuclei is discussed in terms of the so-called Type-II shell evolution, with an emphasis on protonâneutron correlations between valence nucleons, especially those involving the shape-driving g9/2 neutron orbital.

A lifetime measurement of the 19/2− state in 207Pb has been performed using the Recoil Distance Doppler-Shift (RDDS) method. The nuclei of interest were produced in multi-nucleon transfer reactions induced by a 208Pb beam impinging on a 100Mo enriched target. The beam-like nuclei were detected and identified in terms of their atomic mass number in the VAMOS++ spectrometer while the prompt γ rays were detected by the AGATA tracking array. The measured large reduced transition probability B(E3,19/2−→13/2+)=40(8) W.u. is the first indication of the octupole phonon at high spin in 207Pb. An analysis in terms of a particle-octupole-vibration coupling model indicates that the measured B(E3) value in 207Pb is compatible with the contributions from single-phonon and single particle E3 as well as E3 strength arising from the double-octupole-phonon 6+ state, all adding coherently. A crucial aspect of the coupling model, namely the strong mixing between single-hole and the phonon-hole states, is confirmed in a realistic shell-model calculation. Keywords: AGATA spectrometer, γ-Ray tracking, VAMOS++ spectrometer, Plunger device, Nuclear deformation, Octupole phonon

Abstract Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years. The AI-based protocol focused on regions of interest delineated between the segmented dermal-epidermal junction and the superficial dermis, exploiting complex patterns within the collagen network for age prediction. The model achieved a mean absolute error of 4.2 years and exhibited a Pearson correlation coefficient of 0.937 with actual ages. Furthermore, there was a notable correlation (r = 0.87) between quantified clinical scoring, encompassing parameters such as firmness, elasticity, density, and wrinkle appearance, and the ages predicted by deep learning model. This strong correlation underscores how integrating emerging imaging technologies with deep learning can accelerate aging research and deepen our understanding of how alterations in skin microstructure are related to visible signs of aging.

The quadrupole collectivity of low-lying states and the anomalous behavior of the 02+ and 23+ levels in 72Ge are investigated via projectile multi-step Coulomb excitation with GRETINA and CHICO-2. A total of forty six E2 and M1 matrix elements connecting fourteen low-lying levels were determined using the least-squares search code, gosia. Evidence for triaxiality and shape coexistence, based on the model-independent shape invariants deduced from the Kumar–Cline sum rule, is presented. These are interpreted using a simple two-state mixing model as well as multi-state mixing calculations carried out within the framework of the triaxial rotor model. The results represent a significant milestone towards the understanding of the unusual structure of this nucleus.

The α decay of the neutron-deficient At190 isotope was observed following the Rh103(Zr90,3n)At190 reaction at Argonne National Laboratory. The reaction products were separated from the beam using the Argonne Gas-Filled Analyzer and implanted into a double-sided Si strip detector. The spatial and temporal correlations between implanted nuclei and subsequent α decays towards the known daughter isotope Bi186 were used to identify and characterize At190 nuclei. Two possible decay scenarios are proposed for the At190→Bi186 decay.

A ground-state rotational band in the fissile nucleus Rf254 was observed for the first time. Levels up to spin 14 and excitation energy of 1.56 MeV were observed. The Rf254 nuclei were produced using the Pb206(Ti50,2n) fusion-evaporation reaction. It is the weakest reaction channel ever studied using in-beam γ-ray spectroscopic methods. The reaction products were separated from the beam in the Argonne gas-filled analyzer (AGFA). The Rf254 nuclei were implanted into a double-sided Si strip detector at the AGFA focal plane and tagged with subsequent ground-state spontaneous fission decays using temporal and spatial correlations. Prompt γ rays in coincidence with the Rf254 recoils were detected in the Gammasphere array of Ge detectors. In order to identify the ground-state rotational band in Rf254, a method for identifying rotational bands in low statistics γ-ray spectra was developed. The deduced Rf254 kinematic moment of inertia is smaller compared to neighboring even-even nuclei. This is most likely associated with a slightly lower quadrupole deformation and stronger pairing correlations in Rf254. The behavior of the moment of inertia as a function of rotational frequency is similar to that of the lighter N=150 isotones Fm250 and No252.

The products of the Tl203,205(Ti50,2n) fusion-evaporation reactions were studied using the recently commissioned Argonne Gas-Filled Analyzer at Argonne National Laboratory. Two α-decay activities with energies of 9210(19) and 9246(19) keV and half-lives of 42-14+42 and 24.4-4.5+7.0 ms were observed which were followed by the known α decays of Md247 and Es243. They are interpreted as originating from the 1/2-[521] and 7/2-[514] single-proton Nilsson states in the hitherto unknown isotope Lr251. From the measured Qα values the 1/2- level was placed 117(27) keV above the 7/2- level in Lr251 in contrast to Lr255 where the 1/2- level is the lowest. Also, the α decay of Lr253 was studied in more detail and a new α line at 8660(20) keV was found and a new half-life value of 2.46(32) s for an isomeric state in Lr253 was measured. The Lr251,253,255Qα values were compared with predictions of various mass models. The relative energies of the 1/2-[521] and 7/2-[514] single-proton Nilsson states in Lr251,253,255 isotopes were compared with results of the cranking shell model with pairing treated using the particle-number-conserving method. The level separation and, in particular, the level order change between Lr251 and Lr255 was reproduced only when the hexacontetrapole deformation 6 was included in the calculations.

The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored. This paper aims to fill this gap by investigating the robustness of ATNs in adversarial contexts. We propose a high-performing attention-based deep learning model specifically designed for classifying Motor Imagery (MI) brain signals extracted from EEG data. Subsequently, we conduct a thorough series of experiments to assess various attack strategies targeting ATNs employed in EEG-based BCI tasks. Our analysis utilizes the widely recognized BCI Competition 2a dataset to demonstrate the effectiveness of attention mechanisms in BCI endeavors. Despite achieving commendable classification results in terms of accuracy (87.15%) and kappa score (0.8287), our findings reveal the vulnerability of attention-based models to adversarial manipulations (accuracy: 9.07%, kappa score: -0.21), highlighting the imperative for bolstering the robustness of attention architectures for EEG classification tasks.

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.

A theoretical model based on the integral formalism approach for laminar external natural convection in the vicinity of a vertical wall is used to be extended to nanofluids. Two kinds of thermal boundary conditions including uniform wall temperature (UWT) and uniform heat flux (UHF) are used for this modeling. Two different nanofluids are tested, namely, Cu/water and CuO/water nanofluids for which both viscosity and thermal conductivity were determined using Brownian motion-based models. A close attention is focused on the influence due to increasing the volume fraction of nanoparticles on both the heat transfer and dynamic parameters. Results are presented only for particle volume fractions up to 4% to ensure a Newtonian behavior of the mixture. It has been found that natural convection heat transfer increases with the volume fraction for a fixed Grashof number, whatever the nanofluid is. Nevertheless, the enhancement of heat transfer is more pronounced in the case of Cu/water than for the CuO/water nanofluid. Moreover, this trend is also confirmed regarding the dynamical parameters such as the maximum velocity value within the dynamical boundary layer and the corresponding boundary layer thickness.

In this paper, we investigate stochastic optimal intervention control of mean-field nonlinear random Poisson-jump-system with related noisy process. We derive the necessary conditions of optimality for partially observed optimal intervention control problems of mean-field type. The coefficients depend on the state of the solution process as well as of its probability distribution and the control variable. The proof of our main result is obtained by applying L-derivatives in the sense of Lions. In our control model, there are two models of jumps for the state process, the inaccessible ones which come from the random Poission process and the predictable ones which come from the intervention control. Finally, we apply our result to study conditional mean-variance portfolio selection problem with interventions, where the foreign exchange interventions are intended to contain excessive fluctuations in foreign exchange rates and to stabilize them.

Abstract Classical novae are thermonuclear explosions in stellar binary systems, and important sources of 26Al and 22Na. While γ rays from the decay of the former radioisotope have been observed throughout the Galaxy, 22Na remains untraceable. Its half-life (2.6 yr) would allow the observation of its 1.275 MeV γ-ray line from a cosmic source. However, the prediction of such an observation requires good knowledge of its nucleosynthesis. The 22Na(p, γ)23Mg reaction remains the only source of large uncertainty about the amount of 22Na ejected. Its rate is dominated by a single resonance on the short-lived state at 7785.0(7) keV in 23Mg. Here, we propose a combined analysis of particle-particle correlations and velocity-difference profiles to measure femtosecond nuclear lifetimes. The application of this method to the study of the 23Mg states, places strong limits on the amount of 22Na produced in novae and constrains its detectability with future space-borne observatories.

This work presents a comparison of two-dimensional numerical solutions of unsteady free surface flow. This is a simulation of the dam-break wave with different configurations using based-mesh finite volume method and meshless smoothed particle hydrodynamics (SPH). Two well-known approaches, widely used in the computational fluid dynamics (CFD). These techniques have proven their robustness in the numerical treatment of such conservation laws. The main goal is to check the ability of the SPH method and the first order finite volume HLLC solver to reproduce the numerical solutions of the 2D shallow water equations. Based on many benchmark tests, one investigates the effect of the topographic variation along the x and y directions on behavior of the numerical solutions namely at the wet-dry front. The comparison between the simulated results, the analytical solutions and the experimental measurements shows a good correlation, although the finite volume approach remains more advantageous in terms of accuracy and the CPU time.