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In recent years, the correlation mechanisms between geopolitical risks and financial markets have drawn considerable attention from both academic circles and investment communities. However, their multiscale, nonlinear interactive characteristics still require further investigation. To address this, this paper proposes a dynamic nonlinear causal information network combined with a wavelet transform model and the transfer entropy method. We select the geopolitical risk index, the US dollar index, Brent and WTI crude oil prices, COMEX gold futures, and London gold prices time series as the research objects. The results suggest that the network’s structure changes with time at different time scales. On the one hand, COMEX gold (London gold) acts as the major causal information transmitter (receiver) at all scales; both of their highest values appear at the mid-scale. The US dollar index plays a bridging role in information transmission, and this mediating ability decreases with increasing time scales. On the other hand, the fastest speed of causal information transmission is at the short scale, and the slowest speed is at the mid-scale. The complexity and systematic risk of causal network decrease with increasing time scales. Importantly, at the short-scale (D1), the information transmission speed slowed during the Russian–Ukrainian conflict and further decreased after the start of the Israel–Hamas conflict. Systematic risk has increased annually since 2018. This study provides a multiscale perspective to study the nonlinear causal relationship between geopolitical risk and financial markets and serves as a reference for policy-makers and investors.

Regime switching in a time series is an important and challenging issue in complex financial system analysis. Existing regime models have focused on the features of fluctuations at a single point in financial time series, often neglecting time series nonlinearity and uncertainties from a dynamic perspective. This study proposes a heteroskedastic network combined with a Hidden Markov Model, the ARMA-GARCH model, and a machine learning algorithm to characterize the dynamic process of a fluctuation in a time series which can uncover the hidden structure of a nonlinear time series with uncertainty. The network community structure can be used to detect regime switching and its early warning signals. We select the S&P 500 time series as our sample data. Our findings indicate that the critical switches between regimes can be detected across various typical periods, and we analyze them from the perspective of the fundamentals and trader expectations in financial markets. The evolution features of regime switching and its early warning signals are also analyzed over the entire sample period. In particular, the critical features of early warning signals can be extracted. This study not only expands regime switching research in time series analysis but also provides a strong theoretical basis for early warning of risk in financial markets for policy-makers and market investors.

[This corrects the article DOI: 10.1371/journal.pone.0333734.].

Recently, the economy in Guangdong province has ranked first in the country, maintaining a good growth momentum. The prediction of Gross Domestic Product (GDP) for Guangdong province is an important issue. Through predicting the GDP, it is possible to analyze whether the economy in Guangdong province can maintain high-quality growth. Hence, to accurately forecast the economy in Guangdong, this paper proposed an Elman neural network combining with wavelet function. The wavelet function not only stimulates the forecast ability of Elman neural network, but also improves the convergence speed of Elman neural network. Experimental results indicate that our model has good forecast ability of regional economy, and the forecast accuracy reach 0.971. In terms of forecast precision and errors, our model defeats the competitors. Moreover, our model gains advanced forecast results to both individual economic indicator and multiple economic indicators. This means that our model is independently of specific scenarios in regional economic forecast. We also find that the investment in education has a major positive impact on regional economic development in Guangdong province, and the both surges positive correlation. Experimental results also show that our model does not exhibit exponential training time with the augmenting of data volume. Consequently, we propose that our model is suitable for the prediction of large-scale datasets. Additionally, we demonstrate that using wavelet function gains more profits than using complex network architectures in forecast accuracy and training cost. Moreover, using wavelet function can simplify the designs of complexity network architectures, reducing the training parameter of neural networks.

Background Sub-threshold depression is a prevalent psychological adaptation issue among university students. Although prior research has explored the potential relationship between social media use and depressive symptoms, the findings have been inconsistent, and the potential mediating and moderating mechanisms remain unclear. This study aims to examine the relationship between passive social network site use and sub-threshold depression in college students from the perspective of the differential susceptibility to media effects model, investigating the mediating role of fear of missing out (FOMO) and the moderating role of coping styles. Methods A total of 738 students from five universities were assessed using the Passive Social Network Site Use Scale, the Center for Epidemiologic Studies Depression Scale, the Fear of Missing Out Scale, and the Simplified Coping Style Questionnaire. Descriptive statistics and correlation analysis were conducted using SPSS 26.0, and a moderated mediation model was established using Mplus 8.3. Results The findings revealed that: (1) After controlling for gender and age, passive social network site use was a significant positive predictor of sub-threshold depression; (2) FOMO mediated the relationship between passive social network site use and sub-threshold depression; (3) Negative coping styles moderated the relationship between FOMO and sub-threshold depression, such that higher levels of negative coping enhanced the predictive effect of FOMO on sub-threshold depression. Conclusion The results contribute to understanding the mechanisms through which passive social network site use influences sub-threshold depression in university students. The study suggests that reducing FOMO and enhancing students’ coping styles may help mitigate sub-threshold depression, thereby improving their psychological well-being.

As the global consensus on carbon reduction targets continues to deepen, the relationship between carbon prices and new energy stock indices has become increasingly close. Revealing the evolutionary characteristics of the short-term correlation between carbon prices and new energy stock indices is particularly important. This study takes the Guangdong carbon price and the CSI Mainland New Energy Theme Index as research samples and adopts Pearson correlation analysis combined with the sliding window method to portray the correlation between the two. Next, we construct a complex network and research the evolution between the two under the influence of seasonal factors. The research results show that the correlation is complex and time-varying. Five key correlation modes govern how the correlation evolves, and their conversions exhibit self-stabilizing characteristics. The modes with high media capabilities in the network control the transitions between modes and provide early warning information for policymakers and enterprises. In addition, under the effect of different seasonal climates and performance cycles, the correlations reflect seasonal evolutionary characteristics. According to the regularities of correlation evolution between carbon prices and new energy stock indices, policymakers can adjust short-term policies flexibly, and enterprises can adapt their production and energy conversion strategies. This article explores the dynamic correlation evolution between China's carbon price and the new energy stock index, which holds significant reference value for Chinese enterprises and investors and provides inspiration for the development of the global carbon market. Moreover, by introducing the perspective of seasonal factors, it provides a new research perspective for the dynamic correlation between carbon prices and the new energy stock index.

Using the AX-CPT paradigm, this study investigates the differences in cognitive control under negative emotion priming among college students with different emotional regulation abilities in reactive control mode and proactive control mode. The paradigm comprises four experimental conditions AY, BY, BX, and AX trials designed to distinguish between proactive control and reactive control modes.The experiment employed a 2 (high emotional regulation ability group vs. low emotional regulation ability group) × 4 (AY, BY, BX vs. AX) mixed experimental design, with reaction times and error rates recorded using E-prime software. The results revealed that: (1) the error rate was significantly lower in the high emotional regulation ability group compared to the low emotional regulation ability group (2) subjects displayed significantly longer reaction times on AY sequences than on BX sequences, with a significantly higher error rate on AY sequences; (3) in reactive control mode, the average reaction time for the high emotional regulation ability group was significantly shorter than that for the low emotional regulation ability group, with a higher d’-target value; in proactive control mode, the high emotional regulation ability group had a significantly higher d’-context value than the low emotional regulation ability group. These findings indicate that under negative emotion priming, college students with varying emotional regulation abilities exhibit enhanced proactive control and weakened reactive control. college students with high emotional regulation abilities exhibit superior cognitive control in both reactive and proactive control modes compared to those with low emotional regulation abilities.

PurposeTo describe the clinical outcomes of Descemet membrane endothelial keratoplasty combined with phacoemulsification/posterior chamber intraocular lens implantation (triple procedure) for treatment of corneal decompensation induced by a phakic anterior chamber intraocular lens (AC IOL) implantation.MethodsTen patients (10 eyes) with corneal decompensation due to phakic AC IOL implantation that had undergone the triple procedure were included in this study. Among the 10 eyes, 5 eyes underwent explantation of AC IOL prior to the transplantation, and then underwent the triple procedure. The remaining 5 eyes with a phakic AC IOL in situ underwent the triple procedure with concurrent explantation of AC IOL. Corrected distance visual acuity (CDVA), subjective refraction, endothelial cell density (ECD), and complications were documented.ResultsThe triple procedure was performed across all eyes without any adverse events. The average CDVA improved from 1.32 ± 0.24 preoperatively to 0.15 ± 0.05 logarithm of the minimum angle of resolution (logMAR), which represents an improvement in Snellen equivalent from 20/400 (0.05) preoperatively to 20/28 (0.71) at 12 months after surgery. At 12 months, all eyes reached a CDVA of 20/32 (0.63) or better, and 50% of eyes reached a CDVA of 20/25 (0.8) or better. The mean donor ECD±SD was 2868.7 ± 67.9 cells/mm2, which decreased to 1724.1 ± 84.6 cells/mm2 at 12 months, representing 39.9% of endothelial cell loss. Patients did not experience any severe adverse events.ConclusionThe triple procedure is a safe and effective option for corneal decompensation induced by a phakic AC IOL implantation, helping achieve a satisfactory visual rehabilitation with few complications.

Based on laser-assisted machining (LAM) and minimum quantity lubrication machining (MQLM), a processing technology of laser combined minimum quantity lubrication-assisted machining (LAM-MQL) of titanium alloy is proposed, which combines heating, lubrication, and cooling effects. Dry cutting (DC), LAM, MQLM, and LAM-MQL experiments were carried out on TC 4 titanium alloy with cemented carbide tool. With the help of metallographic microscope, scanning electron microscope and roughness measuring instrument, the tool flank wear, tool wear morphology, chip morphology, and surface roughness were detected, respectively. Meanwhile, the EDS spectrum analysis of tool wear area was carried out. The results show that compared with DC, LAM, and MQLM, the tool wear, chip shape, and surface quality of the LAM-MQL have been significantly improved. The tool flank wear has been reduced by 49.1%, 20.5%, and 12.9%, and the surface roughness of the workpiece is reduced by 33.7%, 19.9%, ​​and 12.7%, respectively. The titanium alloy chip transforms from a serrated shape to a continuous shape, and no longer has obvious adiabatic shear bands and severe plastic deformation. The failure of the tool is mainly due to the combined effects of bonding wear, oxidation wear, and abrasive wear.

Background Cisplatin resistance remains a major obstacle in the treatment of ovarian cancer (OC). Although ELK3 , an ETS transcription factor, has been implicated in chemoresistance across various cancers, its specific role and molecular mechanisms in OC progression and cisplatin resistance remain poorly understood. Methods ELK3 expression was analyzed in cisplatin-resistant OC cells using qPCR and Western blotting. Functional assays, including CCK-8, apoptosis analysis, and xenograft models, were employed to assess the impact of ELK3 . Mechanistic insights were gained through Co-IP and histone lactylation analysis. Results ELK3 was significantly upregulated in cisplatin-resistant OC tissues and cells, and its elevated expression correlated with poor patient survival according to TCGA and KM Plotter data. Knockdown of ELK3 sensitized OC cells to cisplatin by suppressing CHD4 expression and reducing histone lactylation levels. Importantly, restoration of CHD4 expression rescued cisplatin resistance in ELK3 -deficient cells. Conclusion The ELK3 - CHD4 -histone lactylation axis plays a critical role in driving cisplatin resistance in OC, highlighting its potential as a novel therapeutic target for overcoming chemoresistance.