Cosmic Millicharge Background and Reheating Probes

We demonstrate that the searches for dark sector particles can provide probes of reheating scenarios, focusing on the cosmic millicharge background produced in the early universe. We discuss two types of millicharge particles (mCPs): either with, or without, an accompanying dark photon. These two types of mCPs have distinct theoretical motivations and cosmological signatures. We discuss constraints from the overproduction and mCP-baryon interactions of the mCP without an accompanying dark photon, with different reheating temperatures. We also consider the $\Delta N_{\rm eff}$ constraints on the mCPs from kinetic mixing, varying the reheating temperature. The regions of interest in which the accelerator and other experiments can probe the reheating scenarios are identified in this paper for both scenarios. These probes can potentially allow us to set an upper bound on the reheating temperature down to $\sim 10$ MeV, much lower than the previously considered upper bound from inflationary cosmology at around $\sim 10^{16}$ GeV. In addition, we find parameter regions in which the two mCP scenarios may be differentiated by cosmological considerations. Finally, we discuss the implications of dedicated mCP searches and future CMB-S4 observations.Comment: 10 pages plus references, 5 figure

H2S gas sensing performance and mechanisms using CuO-Al2O3 composite films based on both surface acoustic wave and chemiresistor techniques

Surface acoustic wave and chemiresistor based gas sensors integrated with a sensing layer of sol-gel CuO-Al2O3 composite film were fabricated and their performance and mechanisms for H2S sensing were characterized and compared. In the composite film, CuO nanoparticles provide active sites for adsorption and reaction of H2S molecules while Al2O3 nanoparticles help to form a uniform and mesoporous film structure, both of which enhance the sensitivity of the sensors by providing numerous active CuO surfaces. Through the comparative studies, the SAW based H2S sensor operated at room temperature showed a lower detection limit, higher sensitivity, better linearity and good selectivity to H2S gas with its concentration ranging from 5 ppb to 100 ppm, compared with those of the chemiresistor sensor, which are mainly attributed to the effective mass sensing properties of the SAW sensor, because a minor change in the mass of the film caused by adsorbed H2S molecules would lead to a significant and monotonous change of the resonant frequency of the SAW devices

Activation network improves spatiotemporal modelling of human brain communication processes

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated ''ground truth'' validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics