Large shift current via in-gap and charge-neutral exciton excitations in BN nanotubes and single BN layer

We perform {\it ab initio} many-body calculations to investigate the exciton shift current in small diameter zigzag BN nanotubes and also single BN sheet, using the GW plus Bethe-Salpeter equation (GW-BSE) method with the newly developed efficient algorithms. Our GW-BSE calculations reveal a giant in-gap peak in the shift current spectrum in all the studied BN systems due to the excitation of the A exciton. The peak value of the excitonic shift current is more than three times larger than that of the quasiparticle shift current, and is attributed to the gigantic enhancement of the optical dipole matrix element by the A exciton resonance. The effective exciton shift current conductivity is nearly ten times larger than the largest shift conductivity observed in ferroelectric semiconductors. Importantly, the direction of the shift current in the BN nanotubes is found to be independent of the tube chirality ($n,0$) (or diameter), contrary to the simple rule of $sgn(J_\text{shift})=\text{mod}(n,3)$ predicted by previous model Hamiltonian studies. Finally, our {\it ab initio} calculations also show that the exciton excitation energies decrease significantly with the decreasing diameter due to the curvature-induced orbital rehybridization in small diameter zigzag BN nanotubes.Comment: 12 pages, 8 figures and 2 table

Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a nine-year population-based study

Background: Most studies on the impact of COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of pandemic is less well-studied. In this population-based cohort study, we examined the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression. Methods: Using the territory-wide electronic medical records in Hong Kong, we identified all patients aged 10 years with new diagnoses of depression from 2014 to 2022. We performed an interrupted time-series (ITS) analysis to examine changes in incidence of medically attended depression before and during the pandemic. We then divided all patients into nine cohorts based on year of depression incidence and studied their initial and ongoing service use patterns until the end of 2022. We applied generalized linear modelling to compare the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among prevalent patients with depression. Results: We found an immediate increase in depression incidence (RR=1.21, 95% CI:1.10-1.33, p<0.001) in the population after the pandemic began with non-significant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that the increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11% fewer resources than the pre-pandemic patients in the first diagnosis year. Pre-existing depression patients also had an immediate decrease of 16% in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound over a three-year period. Conclusions: During the pandemic, service provision for depression was suboptimal in the face of increased demand generated by the increasing depression incidence during the COVID-19 pandemic. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises

Improved Fine-Tuning by Better Leveraging Pre-Training Data

As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the number of training samples is increased in some vision tasks. In this work, we revisit this phenomenon from the perspective of generalization analysis by using excess risk bound which is popular in learning theory. The result reveals that the excess risk bound may have a weak dependency on the pre-trained model. The observation inspires us to leverage pre-training data for fine-tuning, since this data is also available for fine-tuning. The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning. With the theoretical motivation, we propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task. Extensive experimental results for image classification tasks on 8 benchmark data sets verify the effectiveness of the proposed data selection based fine-tuning pipeline

Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

Antidepressant use and risk of self-harm among people aged 40 years or older: A population-based cohort and self-controlled case series study

Background: Studies on the association between antidepressants and self-harm in adults were mostly conducted over a decade ago and have inconsistent findings. We aimed to compare self-harm risks by antidepressant classes among people aged 40 years or older with depression. Methods: Individuals aged ≥40 years with depression who initiated antidepressant treatment between 2001 and 2015 were retrieved from the Hong Kong Clinical Data Analysis & Reporting system, and were followed up until December 31, 2016. We conducted self-controlled case series (SCCS) analyses to estimate the incidence rate ratio (IRR) of self-harm comparing the pre-exposure (90 days before the first antidepressant use), index exposure (the first antidepressant use), and subsequent exposure (subsequent antidepressant use) periods to nonexposed periods. We applied Cox proportional hazard regressions to estimate the hazard ratio (HR) of self-harm comparing five antidepressant classes (tricyclic and related antidepressant drugs [TCAs], selective serotonin reuptake inhibitors [SSRIs], noradrenergic and specific serotonergic antidepressants [NaSSAs], serotonin–norepinephrine reuptake inhibitors [SNRIs], and others). Findings: A total of 48,724 individuals were identified. SCCS analyses (N = 3,846) found that the increased self-harm risk occurred during the pre-exposure (IRR: 22.24; 95% CI, 20.25-24.42), index exposure (7.03; 6.34-7.80), and subsequent exposure periods (2.47; 2.18-2.79) compared to the unexposed period. Cohort analyses (N = 48,724) found an association of higher self-harm risks in short-term (one year) for NaSSAs vs. TCAs (HR, 2.13; 95% CI, 1.53-2.96), SNRIs vs. TCAs (1.64; 1.01-2.68), and NaSSAs vs. SSRIs (1.75; 1.29-2.36) in the 40-64 years group. The higher risk remained significant in long-term (> one year) for NaSSAs vs. TCAs (1.55; 1.26-1.91) and NaSSAs vs. SSRIs (1.53; 1.26-1.87). In the 65+ group, only short-term differences were observed (SSRIs vs. TCAs [1.31; 1.03-1.66], SNRIs vs. SSRIs [0.44; 0.22-0.87], and SNRIs vs. NaSSAs [0.43; 0.21-0.87]). Interpretation: Within-person comparisons did not suggest that antidepressant exposure is causally associated with an increased risk of self-harm in people with depression. Between-person comparisons revealed differences in self-harm risks between certain pairs of antidepressant classes. These findings may inform clinicians’ benefit-risk assessments when prescribing antidepressants

Chinese Social Media Reaction to the MERS-Cov and Avian Influenza A (H7N9) Outbreaks

Background: As internet and social media use have skyrocketed, epidemiologists have begun to use online data such as Google query data and Twitter trends to track the activity levels of influenza and other infectious diseases. In China, Weibo is an extremely popular microblogging site that is equivalent to Twitter. Capitalizing on the wealth of public opinion data contained in posts on Weibo, this study used Weibo as a measure of the Chinese people’s reactions to two different outbreaks: the 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak, and the 2013 outbreak of human infection of avian influenza A(H7N9) in China. Methods: Keyword searches were performed in Weibo data collected by The University of Hong Kong’s Weiboscope project. Baseline values were determined for each keyword and reaction values per million posts in the days after outbreak information was released to the public. Results: The results show that the Chinese people reacted significantly to both outbreaks online, where their social media reaction was two orders of magnitude stronger to the H7N9 influenza outbreak that happened in China than the MERS-CoV outbreak that was far away from China. Conclusions: These results demonstrate that social media could be a useful measure of public awareness and reaction to disease outbreak information released by health authorities

Low-velocity-favored transition radiation

When a charged particle penetrates through an optical interface, photon emissions emerge - a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the particle velocity v; as a result, low-energy particles are not favored in practice. Here we find that there exist situations where transition radiation from particles with extremely low velocities (e.g. v/c<0.001) exhibits comparable intensity as that from high-energy particles (e.g. v/c=0.999), where c is light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to eight orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the excitation of Ferrell-Berreman modes in epsilon-near-zero materials. Our findings may provide a promising route towards the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles.Comment: 13 pages, 4 figure