Neutron star cooling - a challenge to the nuclear mean field

The two recent density-dependent versions of the finite-range M3Y interaction (CDM3Y$n$ and M3Y-P$n$) have been probed against the bulk properties of asymmetric nuclear matter (NM) in the nonrelativistic Hartree Fock (HF) formalism. The same HF study has also been done with the famous Skyrme (SLy4) and Gogny (D1S and D1N) interactions which were well tested in the nuclear structure calculations. Our HF results are compared with those given by other many-body calculations like the Dirac-Brueckner Hartree-Fock approach or ab-initio variational calculation using free nucleon-nucleon interaction, and by both the nonrelativistic and relativistic mean-field studies using different model parameters. Although the two considered density-dependent versions of the M3Y interaction were proven to be quite realistic in the nuclear structure or reaction studies, they give two distinct behaviors of the NM symmetry energy at high densities, like the Asy-soft and Asy-stiff scenarios found earlier with other mean-field interactions. As a consequence, we obtain two different behaviors of the proton fraction in the $\beta$-equilibrium which in turn can imply two drastically different mechanisms for the neutron star cooling. While some preference of the Asy-stiff scenario was found based on predictions of the latest microscopic many-body calculations or empirical NM pressure and isospin diffusion data deduced from heavy-ion collisions, a consistent mean-field description of nuclear structure database is more often given by some Asy-soft type interaction like the Gogny or M3Y-P$n$ ones. Such a dilemma poses an interesting challenge to the modern mean-field approaches.Comment: Version accepted for publication in Phys. Rev.

Folding model analysis of elastic and inelastic proton scattering on Sulfur isotopes

The folding formalism for the nucleon-nucleus optical potential and inelastic form factor is applied to study elastic and inelastic proton scattering on 30-40S isotopes. A recently developed realistic density dependent M3Y interaction, well tested in the folding analysis of nucleus-nucleus elastic and inelastic scattering, is used as effective NN interaction. The nuclear ground state and transition densities (for the 2+ excitations in Sulfur isotopes) are obtained in the Hartree-Fock-BCS and QRPA approaches, respectively. The best fit ratios of transition moments Mn/Mp for the lowest 2+ states in Sulfur isotopes are compared to those obtained earlier in the DWBA analysis of the same data using the same structure model and inelastic form factors obtained with the JLM effective interaction. Our folding+DWBA analysis has shown quite a strong isovector mixing in the elastic and inelastic scattering channels for the neutron rich 38,40S nuclei. In particular, the relative strength of the isovector part of the transition potential required by the inelastic p+38S data is significantly stronger than that obtained with the corresponding QRPA transition density.Comment: 24 pages, 11 figures, accepted for publication in Nucl. Phys.

Microscopic study of the isoscalar giant resonances in 208Pb induced by inelastic alpha scattering

The energetic beam of (spin and isospin zero) $\alpha$-particles remains a very efficient probe for the nuclear isoscalar giant resonances. In the present work, a microscopic folding model study of the isoscalar giant resonances in $^{208}$Pb induced by inelastic \aPb scattering at $E_{\rm lab}=240$ and 386 MeV has been performed using the (complex) CDM3Y6 interaction and nuclear transition densities given by both the collective model and Random Phase Approximation (RPA) approach. The fractions of energy weighted sum rule around the main peaks of the isoscalar monopole, dipole and quadrupole giant resonances were probed in the Distorted Wave Born Approximation analysis of inelastic \aPb scattering using the double-folded form factors given by different choices of the nuclear transition densities. The energy distribution of the $E0, E1$ and $E2$ strengths given by the multipole decomposition {analyses} of the \aap data under study are compared with those predicted by the RPA calculation.Comment: Accepted for publication in Nuclear Physics

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the transmitter using conventional RF signals and (ii) the backscatter message transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series.

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods

Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

Dengue Dynamics in Binh Thuan Province, Southern Vietnam: Periodicity, Synchronicity and Climate Variability

Dengue has become a major international public health problem due to increasing geographic distribution and a transition from epidemic transmission with long inter-epidemic intervals to endemic transmission with seasonal fluctuation. Seasonal and multi-annual cycles in dengue incidence vary over time and space. We performed wavelet analyses on time series of monthly notified dengue cases in Binh Thuan province, southern Vietnam, from January 1994 to June 2009. We observed a continuous annual mode of oscillation with a non-stationary 2–3-year multi-annual cycle. We used phase differences to describe the spatio-temporal patterns which suggest that the seasonal wave of infection was either synchronous with all districts or moving away from Phan Thiet district, while the multi-annual wave of infection was moving towards Phan Thiet district. We also found a strong non-stationary association between ENSO indices and climate variables with dengue incidence. We provided insight in dengue population transmission dynamics over the past 14.5 years. Further studies on an extensive time series dataset are needed to test the hypothesis that epidemics emanate from larger cities in southern Vietnam

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs