Observing Non-Gaussian Sources in Heavy-Ion Reactions

We examine the possibility of extracting non-Gaussian sources from two-particle correlations in heavy-ion reactions. Non-Gaussian sources have been predicted in a variety of model calculations and may have been seen in various like-meson pair correlations. As a tool for this investigation, we have developed an improved imaging method that relies on a Basis spline expansion of the source functions with an improved implementation of constraints. We examine under what conditions this improved method can distinguish between Gaussian and non-Gaussian sources. Finally, we investigate pion, kaon, and proton sources from the p-Pb reaction at 450 GeV/nucleon and from the S-Pb reaction at 200 GeV/nucleon studied by the NA44 experiment. Both the pion and kaon sources from the S-Pb correlations seem to exhibit a Gaussian core with an extended, non-Gaussian halo. We also find evidence for a scaling of the source widths with particle mass in the sources from the p-Pb reaction.Comment: 16 pages, 15 figures, 5 tables, uses RevTex3.

Two-Proton Correlations near Midrapidity in p+Pb and S+Pb Collisions at the CERN SPS

Correlations of two protons emitted near midrapidity in p+Pb collisions at 450 GeV/c and S+Pb collisions at 200A GeV/c are presented, as measured by the NA44 Experiment. The correlation effect, which arises as a result of final state interactions and Fermi-Dirac statistics, is related to the space-time characteristics of proton emission. The measured source sizes are smaller than the size of the target lead nucleus but larger than the sizes of the projectiles. A dependence on the collision centrality is observed; the source size increases with decreasing impact parameter. Proton source sizes near midrapidity appear to be smaller than those of pions in the same interactions. Quantitative agreement with the results of RQMD (v1.08) simulations is found for p+Pb collisions. For S+Pb collisions the measured correlation effect is somewhat weaker than that predicted by the model simulations, implying either a larger source size or larger contribution of protons from long-lived particle decays.Comment: 10 pages (LaTeX) text, 4 (EPS) figures; accepted for publication in Phys. Lett.

What Determines Cognitive Functioning in the Oldest-Old? The EMIF-AD 90+ Study

OBJECTIVES: Determinants of cognitive functioning in individuals aged 90 years and older, the oldest-old, remain poorly understood. We aimed to establish the association of risk factors, white matter hyperintensities (WMH), hippocampal atrophy and amyloid aggregation with cognition in the oldest-old. METHODS: We included 84 individuals without cognitive impairment and 38 individuals with cognitive impairment from the EMIF-AD 90+ Study (mean age 92.4 years) and tested cross-sectional associations between risk factors (cognitive activity, physical parameters, nutritional status, inflammatory markers and cardiovascular risk factors), brain pathology biomarkers (WMH and hippocampal volume on MRI, and amyloid binding measured with PET) and cognition. Additionally, we tested whether the brain pathology biomarkers were independently associated with cognition. When applicable, we tested whether the effect of risk factors on cognition was mediated by brain pathology. RESULTS: Lower values for handgrip strength, Short Physical Performance Battery (SPPB), nutritional status, HbA1c and hippocampal volume, and higher values for WMH volume and amyloid binding were associated with worse cognition. Higher past cognitive activity and lower BMI were associated with increased amyloid binding, lower muscle mass with more WMH, and lower SPPB scores with more WMH and hippocampal atrophy. The brain pathology markers were independently associated with cognition. The association of SPPB with cognition was partially mediated by hippocampal volume. DISCUSSION: In the oldest-old, physical parameters, nutritional status, HbA1c, WMH, hippocampal atrophy and amyloid binding are associated with cognitive impairment. Physical performance may affect cognition through hippocampal atrophy. This study highlights the importance to consider multiple factors when assessing cognition in the oldest-old

Lambda-proton correlations in relativistic heavy ion collisions

The prospect of using lambda-proton correlations to extract source sizes in relativistic heavy ion collisions is investigated. It is found that the strong interaction induces a large peak in the correlation function that provides more sensitive source size measurements than two-proton correlations under some circumstances. The prospect of using lambda-proton correlations to measure the time lag between lambda and proton emissions is also studied.Comment: 4 pages, 3 figure, revtex style. Two short paragraphs are added at referees' recommendations. Phys. Rev. Lett. in pres

Baryon phase-space density in heavy-ion collisions

The baryon phase-space density at mid-rapidity from central heavy-ion collisions is estimated from proton spectra with interferometry and deuteron coalescence measurements. It is found that the mid-rapidity phase-space density of baryons is significantly lower at the SPS than the AGS, while those of total particles (pion + baryon) are comparable. Thermal and chemical equilibrium model calculations tend to over-estimate the phase-space densities at both energies.Comment: 5 pages, 2 tables, no figure. RevTeX style. Accepted for publication in Phys. Rev. C Rapid Communicatio

Strange Meson Enhancement in PbPb Collisions

The NA44 Collaboration has measured yields and differential distributions of K+, K-, pi+, pi- in transverse kinetic energy and rapidity, around the center-of-mass rapidity in 158 A GeV/c Pb+Pb collisions at the CERN SPS. A considerable enhancement of K+ production per pi is observed, as compared to p+p collisions at this energy. To illustrate the importance of secondary hadron rescattering as an enhancement mechanism, we compare strangeness production at the SPS and AGS with predictions of the transport model RQMD.Comment: 11 pages, including 4 figures, LATE

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data

Characterization of the seismic environment at the Sanford Underground Laboratory, South Dakota

An array of seismometers is being developed at the Sanford Underground Laboratory, the former Homestake mine, in South Dakota to study the properties of underground seismic fields and Newtonian noise, and to investigate the possible advantages of constructing a third-generation gravitational-wave detector underground. Seismic data were analyzed to characterize seismic noise and disturbances. External databases were used to identify sources of seismic waves: ocean-wave data to identify sources of oceanic microseisms, and surface wind-speed data to investigate correlations with seismic motion as a function of depth. In addition, sources of events contributing to the spectrum at higher frequencies are characterized by studying the variation of event rates over the course of a day. Long-term observations of spectral variations provide further insight into the nature of seismic sources. Seismic spectra at three different depths are compared, establishing the 4100-ft level as a world-class low seismic-noise environment.Comment: 29 pages, 16 figure