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Background Previous studies have indicated that high temperatures are associated with excess mortality among individuals with mental disorders, but comprehensive evaluations of the association between high and low temperatures, extreme temperature events and mortality in patients with severe mental disorders are limited. Methods A time-stratified case-crossover study was conducted using mortality data from 22,342 deaths among community-based patients with severe mental disorders in western China between 2006 and 2018 (11,235 during hot seasons and 11,107 during cold seasons). Individual-level exposure to high temperatures, heat waves, low temperatures, and cold spells was assessed, and the associations between these ambient temperatures and mortality were estimated using conditional logistic regression models. Results High temperatures during the hot seasons were associated with a 23.66% increased risk of all-cause mortality (95% CI, 15.17%–31.66%), with effects diminishing as the lag period increased. Low temperatures during the cold seasons showed a significant association with mortality at lag day 4, peaking at 15.25% (95% CI, 3.92%–25.72%) at lag day 6. Heat waves were associated with increased mortality risk, particularly with prolonged exposure and higher temperature thresholds. Cold spells did not show a similar pattern. Conclusions Both heat and cold related exposures are associated with higher mortality risk in community-based patients with severe mental disorders, but their temporal patterns differ—heat has an immediate effect, whereas cold acts with a delay. Our findings suggest that these patients should be prioritized in weather-related health policies, including heat–health and cold–weather warning systems, proactive follow-up by community mental health services, and tailored protection measures during forecasted extreme temperature events. Clinical trial number Not applicable.

Background Despite the importance of medication adherence in treatment effectiveness, little is known about the association between medication non-adherence and self-inflicted violence behaviors. We aimed to assess whether medication non-adherence increased the risk of self-inflicted violence behaviors among schizophrenics in communities (hypothesis 1) and whether the dose–response relationship existed (hypothesis 2). Methods This 12-year cohort study in western China recruited 292,667 community-dwelling schizophrenics. The proportion of regular medication (PRM) was calculated by dividing the time of “regular adherence” by the total time of antipsychotic treatment during follow-up period as an indicator of medication adherence. For hypothesis 1, medication adherence was designated as a binary variable with a threshold of 0.8 (PRM); for hypothesis 2, medication adherence was specified as five-category and continuous variables, respectively. Inverse probability weighting and mixed effects Cox proportional hazards models were conducted for confounders control and survival analyses. Results One hundred eighty-five thousand eight hundred participants were eligible for the final analyses, with a mean age of 47.49 years (SD 14.55 years), of whom 53.6% were female. For hypothesis 1, the medication non-adherence group (PRM < 0.8) had a lower risk of suicide (HR, 0.527, 95% CI, 0.447–0.620), an increased risk of NSSI (HR, 1.229, 95% CI, 1.088–1.388), and non-significant risk of attempted suicide compared with adherence group (PRM ≥ 0.8). For hypothesis 2, the lowest medication adherence (PRM < 0.2) was associated with increased risks of suicide attempt (HR, 1.614, 95% CI, 1.412–1.845), NSSI (HR, 1.873, 95% CI, 1.649–2.126), and a decreased risk of suicide (HR, 0.593, 95% CI, 0.490–0.719). The other non-adherence groups had lower risks for all three self-inflicted violence behaviors. The associations between medication adherence in continuous-variable and three outcomes were consistent with the categorical medication adherence results. Conclusions Almost no medication taken as prescribed was associated with an increased risk of suicide attempt and NSSI. However, medication adherence did not appear to prevent completed suicide. Besides, patients with moderate adherence had a lower incidence of suicide attempt and NSSI. These findings highlight the need for a more detailed portrayal of medication adherence and the need to be vigilant for suicide intent in schizophrenics with good medication adherence who may be overlooked previously.

The association between negative wealth shocks and depression among middle-aged and older individuals remains unclear. Our study aimed to assess the association between negative wealth shocks and depression and its trajectories, and to explore cross-national differences in these associations. Our sample included 21 999 participants, of which 9519 were from the Health and Retirement Study (2012-2020), 4936 from the English Longitudinal Study of Ageing (2012-2020), 2520 from the China Health and Retirement Longitudinal Study (2011-2020), and 5024 from the Mexican Health and Aging Study (2012-2021). We used latent class trajectory models to identify depressive trajectories, alongside mixed-model logistic regression and multinomial logistic regression to evaluate associations. In the USA (OR 1.73, 95% CI 1.40-2.16), England (OR 1.71, 95% CI 1.09-2.70), and China (OR 1.38, 95% CI 1.09-1.75), negative wealth shocks were associated with subsequent depressive symptoms, but not in Mexico (OR 1.06, 95% CI 0.86-1.29). Additionally, negative wealth shocks were associated with several depressive trajectories in the USA and China. This association occurred only in increasing-decreasing trajectory in England, while no significant association was found across any trajectory in Mexico. Negative wealth shocks were associated with subsequent depressive symptoms, with significant associations observed in some specific depressive trajectories. These associations exhibited cross-national differences, underscoring the importance of considering country-specific contexts when addressing the mental health impacts of wealth shocks.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne zoonotic disease with a case fatality rate ranging from 1% to 15%. Long-term evidence regarding its association with local hydrometeorological conditions remain limited. This study aimed to assess the non-linear and lagged effects of extreme hydrometeorological conditions on HFRS risk and examine the modifying effects of regional characteristics in Shandong Province, China. Data from January 1, 2005 to December 31, 2019 across 136 counties in Shandong Province were collected. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated from temperature, precipitation and evaporation, was used to represent local hydrometeorological conditions (dry and wet). A spatiotemporal Bayesian hierarchical model combined with distributed lag non-linear model was applied to explore the association between climate indicators and HFRS risk over a 6-month lag. Modification effects were quantified using linear interaction terms. Over the 15-year period, annual HFRS incidence declined from 2.62/100,000 to 0.72/100,000, with two minor peaks observed. The cumulative association between SPEI and HFRS over 6-month lag appeared U-shaped. The relative risk (RR) of HFRS under extreme wet conditions increased at 4-6 months lag, peaking at the 6-month lag (RR = 1.49, 95% confidence interval (CI): 1.37-1.63). Extreme dry conditions had a persistent impact, also peaking at the 6-month lag (RR = 1.05, 95% CI: 1.01-1.09). Areas with low per capita Gross Domestic Product, Normalized difference vegetation index, Total power of agricultural machinery and annual temperature, as well as high elevation, exhibited higher risks of HFRS under extreme wet conditions. The modification effects under extreme dry conditions were similar but weaker. Both extreme wet and dry conditions increase the risk of HFRS, with county characteristics further modifying these associations. These findings provide a scientific foundation for policymakers to develop targeted and effective HFRS prevention and control strategies, particularly in high-risk regions, while considering hydrometeorological conditions.

Background While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated. Methods Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015–2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups. Results GBDT identified absolute humidity (AH), atmospheric pressure (AP), sulfur dioxide (SO 2 ), and ozone (O 3 ) as key factors, along with TCN as a key variable of interest. DLNM results revealed J-shaped and bimodal exposure–response relationships for TCN and AH, respectively, with increased relative risks (RRs) under low AH. SO 2 was positively associated with influenza risk. The highest RRs were 1.190 (95% confidence interval (CI): 1.070–1.322) observed at 1012 hPa for AP and 3.373 (95% CI: 2.650–4.294) at 125 g/m 3 for O 3 . EDM results indicated that long-lag AP and SO 2 may have a potential driving effect on influenza incidence. Positive effects on influenza were observed when TCN > 5 °C, AP > 1100 hPa, and across the full SO 2 range. Children aged 0–4 were more sensitive to AP and SO 2 , while the aged 5–59 were more affected by TCN, AH, and O 3 . Conclusion This study demonstrated that both the DLNM and EDM methods consistently revealed the complex and nonlinear effects of specific environmental factors on influenza infection. Specifically, TCN > 5 ℃, AP > 1100 hPa, and SO 2  > 60 g/m 3 were associated with increased risk of influenza infection. Children aged 0–4 years and individuals aged 5–59 years exhibit different susceptibility patterns to environmental factors. These findings can inform public health strategies for influenza prevention, particularly in the context of increasing air pollution and climate variability.

Objective Scrub typhus, a natural epidemic disease that seriously impacts the health of the population, has imposed a substantial disease burden in Shandong Province. This study aimed to determine the periodicity of the scrub typhus incidence and identify the environmental risk factors affecting scrub typhus to help prevent and control its occurrence in Shandong Province. Methods Monthly cases of scrub typhus, mean air temperature, relative humidity, cumulative precipitation, and Normalized Difference Vegetation Index (NDVI) data in Shandong Province from 2006 to 2021 were collected. Wavelet analysis was used to determine the incidence period of scrub typhus and to explore the relationships between environmental factors and the incidence of scrub typhus. Additionally, partial wavelet coherence (PWC) was employed to identify whether meteorological factors affect the association between NDVI and scrub typhus incidence. Results Our results showed that scrub typhus incidence has a predominantly one-year period, followed by a less powerful six-month period. The wavelet coherence results revealed positive correlations between scrub typhus incidence and temperature, precipitation, relative humidity, and NDVI. Meteorological factors had a lagged effect of approximately 1–2 months (The phase angles of temperature, precipitation, and relative humidity were 59.15°, 56.57°, and 47.17° respectively) on scrub typhus incidence, whereas NDVI showed a lagged effect of approximately 1–2 weeks (The phase angle of NDVI was 18.11°). On the basis of partial wavelet analysis, we found that temperature and precipitation affected the association between NDVI and scrub typhus incidence. Conclusion Meteorological factors and NDVI play important roles in the occurrence of scrub typhus in Shandong Province. Moreover, temperature and precipitation can affect the role of NDVI. This study provides valuable recommendations and resources for the timely detection, mitigation, and management of scrub typhus in Shandong Province.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne zoonotic disease with a case fatality rate ranging from 1% to 15%. Long-term evidence regarding its association with local hydrometeorological conditions remain limited. This study aimed to assess the non-linear and lagged effects of extreme hydrometeorological conditions on HFRS risk and examine the modifying effects of regional characteristics in Shandong Province, China.MethodsData from January 1, 2005 to December 31, 2019 across 136 counties in Shandong Province were collected. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated from temperature, precipitation and evaporation, was used to represent local hydrometeorological conditions (dry and wet). A spatiotemporal Bayesian hierarchical model combined with distributed lag non-linear model was applied to explore the association between climate indicators and HFRS risk over a 6-month lag. Modification effects were quantified using linear interaction terms.ResultsOver the 15-year period, annual HFRS incidence declined from 2.62/100,000 to 0.72/100,000, with two minor peaks observed. The cumulative association between SPEI and HFRS over 6-month lag appeared U-shaped. The relative risk (RR) of HFRS under extreme wet conditions increased at 4-6 months lag, peaking at the 6-month lag (RR = 1.49, 95% confidence interval (CI): 1.37-1.63). Extreme dry conditions had a persistent impact, also peaking at the 6-month lag (RR = 1.05, 95% CI: 1.01-1.09). Areas with low per capita Gross Domestic Product, Normalized difference vegetation index, Total power of agricultural machinery and annual temperature, as well as high elevation, exhibited higher risks of HFRS under extreme wet conditions. The modification effects under extreme dry conditions were similar but weaker.ConclusionsBoth extreme wet and dry conditions increase the risk of HFRS, with county characteristics further modifying these associations. These findings provide a scientific foundation for policymakers to develop targeted and effective HFRS prevention and control strategies, particularly in high-risk regions, while considering hydrometeorological conditions.

High demand for batteries with a wide operating temperature range is on the rise with the development of wearable electronic devices, especially electric vehicles used in cold regions. Al–air batteries for electric vehicles have triggered worldwide interest due to their excellent theoretical energy density and safety. In this study, the low-temperature performance of Al–air batteries is tested for the first time. The effects of temperature and electrolyte concentrations on the discharge performance are then studied in detail. The discharge voltage is significantly influenced by the temperature. The low temperature could significantly depress the hydrogen evolution reaction of Al anodes. The Al–air batteries reached an extraordinary capacity of 2480 mAh/g, with 31 wt% KOH electrolyte at −15 °C. Moreover, the Al–air batteries at 0 °C exhibited higher discharge voltage and power densities than those at 15 and −15 °C. This study provides an important reference for future studies to improve low-temperature performance of Al–air batteries.

The severe corrosion accompanied with hydrogen evolution reaction has become the main obstacle restricting the utilization of zinc as an electrode in alkaline batteries. Al2O3 coating helps control the corrosion of zinc in alkaline solution. Herein, a stable Al2O3 coating is fabricated through facile electrospinning from Al(NO3)3 as an efficient anti-corrosion film on zinc. The electrospinning technique facilitates uniform dispersion of Al2O3 particles, therefore the corrosion inhibition efficiency could be up to 88.5% in this work. The Al2O3 coating prevents direct contact between zinc and the alkaline solution and minimize hydrogen evolution. Further, the effects of the thickness of Al2O3 coating on corrosion behavior of zinc are investigated through hydrogen evolution reaction, Tafel polarization, and impedance test. The results show that the thicker Al2O3 coating possessed better corrosion inhibition efficiency due to the higher corrosion resistance and lower porosity. The 18 μm Al2O3 coating on zinc provides corrosion current density of 60.6 mA/cm2, while the bare zinc substrate delivers as much as 526.3 mA/cm2.This study presents a promising approach for fabricating Al2O3 coating for corrosion-resistant applications.

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.