Data driven modeling of self-similar dynamics

Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-similarity is prevalent in complex systems, hinting that large-scale complex systems can be modeled at a reduced cost. In this paper, we introduce a multiscale neural network framework that incorporates self-similarity as prior knowledge, facilitating the modeling of self-similar dynamical systems. For deterministic dynamics, our framework can discern whether the dynamics are self-similar. For uncertain dynamics, it can compare and determine which parameter set is closer to self-similarity. The framework allows us to extract scale-invariant kernels from the dynamics for modeling at any scale. Moreover, our method can identify the power law exponents in self-similar systems. Preliminary tests on the Ising model yielded critical exponents consistent with theoretical expectations, providing valuable insights for addressing critical phase transitions in non-equilibrium systems.Comment: 11 pages,5 figures,1 tabl

Review or the Reviewer? Effects of Self-Congruity in Processing Online Travel Review

The self-congruity theory has often been applied in the tourism industry. Yet, it has not been used to examine consumers’ travel information processing. This study aims to explore the effects of self-congruity on consumers’ online travel review processing by integrating it with the Elaboration Likelihood Model. Results collected from a web-based survey in Singapore illustrated that self-congruity contributed significantly as a predictor of argument quality and source credibility. Particularly, reviewer-self-congruity having a strong effect on source credibility of online travel reviews. This study elucidates that Singaporean consumers are inclined to take a two-step information elaboration process: first forming self-congruence with a reviewer to achieve clearance via source credibility, before evaluating the review as useful after scrutinizing based on argument quality. This study suggests Online Travel Agents (OTAs) to provide more reviewer information or leverage on expert sources to increase confidence within consumers for positive online travel review processing

Duration of untreated bipolar disorder: A multicenter study

Little is known about the demographic and clinical differences between short and long duration of untreated bipolar disorder (DUB) in Chinese patients. This study examined the demographic and clinical features of short (≤2 years) and long DUB (\u3e2 years) in China. A consecutively recruited sample of 555 patients with bipolar disorder (BD) was examined in 7 psychiatric hospitals and general hospital psychiatric units across China. Patients’ demographic and clinical characteristics were collected using a standardized protocol and data collection procedure. The mean DUB was 3.2 ± 6.0 years; long DUB accounted for 31.0% of the sample. Multivariate analyses revealed that longer duration of illness, diagnosis of BD type II, and earlier misdiagnosis of BD for major depressive disorder or schizophrenia were independently associated with long DUB. The mean DUB in Chinese BD patients was shorter than the reported figures from Western countries. The long-term impact of DUB on the outcome of BD is warranted

Enhanced surface acceleration of fast electrons by using sub-wavelength grating targets

Surface acceleration of fast electrons in intense laser-plasma interaction is improved by using sub-wavelength grating targets. The fast electron beam emitted along the target surface was enhanced by more than three times relative to that by using planar target. The total number of the fast electrons ejected from the front side of target was also increased by about one time. The method to enhance the surface acceleration of fast electron is effective for various targets with sub-wavelength structured surface, and can be applied widely in the cone-guided fast ignition, energetic ion acceleration, plasma device, and other high energy density physics experiments.Comment: 14 pages, 4figure

Grain engineering of Sb2S3 thin films to enable efficient planar solar cells with high open-circuit voltage

Sb2S3 is a promising environmentally friendly semiconductor for high performance solar cells. But, like many other polycrystalline materials, Sb2S3 is limited by nonradiative recombination and carrier scattering by grain boundaries (GBs). This work shows how the GB density in Sb2S3 films can be significantly reduced from 1068 ± 40 to 327 ± 23 nm µm−2 by incorporating an appropriate amount of Ce3+ into the precursor solution for Sb2S3 deposition. Through extensive characterization of structural, morphological, and optoelectronic properties, complemented with computations, it is revealed that a critical factor is the formation of an ultrathin Ce2S3 layer at the CdS/Sb2S3 interface, which can reduce the interfacial energy and increase the adhesion work between Sb2S3 and the substrate to encourage heterogeneous nucleation of Sb2S3, as well as promote lateral grain growth. Through reductions in nonradiative recombination at GBs and/or the CdS/Sb2S3 heterointerface, as well as improved charge-carrier transport properties at the heterojunction, this work achieves high performance Sb2S3 solar cells with a power conversion efficiency reaching 7.66%. An impressive open-circuit voltage (VOC) of 796 mV is achieved, which is the highest reported thus far for Sb2S3 solar cells. This work provides a strategy to simultaneously regulate the nucleation and growth of Sb2S3 absorber films for enhanced device performance

Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation