Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Analyzing the spatiotemporal pattern of the decoupling degree between carbon metabolism and economic development in village and town units.

In the context of green and sustainable development and rural revitalization, analysis of the relationship between economic development and the evolution of carbon metabolism is of great significance for China's future transformation of development models. This study analyzed the spatial characteristics and spatiotemporal evolution pattern of the decoupling status between carbon metabolism and economic development of Laiwu during two periods from 2001 to 2018 at the village and town unit scales by using the Tapio decoupling model. The results showed that the growth rate of carbon metabolism from 2001 to 2009 was significantly higher than that from 2009 to 2018. The spatial heterogeneity of the decoupling states between economic development and carbon metabolism from 2009 to 2018 was significantly stronger than that from 2001 to 2009 in two units. From 2001 to 2018, the development trend gradually trended towards spatial imbalance. The decoupling status between villages and towns had a high degree of consistency from 2001 to 2009 and inconsistency from 2009 to 2018. From 2001 to 2009, the decoupling status of about 78% of villages was consistent with that of towns. Moreover, from 2009 to 2018, the consistency reduced to 32.2%, and the decoupling status of about 48% of villages was weaker than that of towns. According to the reclassification results of different decoupling state change types, from 2001 to 2018, about 52.2% of the villages had a decoupling state evolution type of eco-deteriorated economic development, which is an unsatisfactory development trend in a short time. Moreover, about 12.1% of the villages had a decoupling state evolution type of eco-improved economic development, which is a satisfactory development trend.

Correlation analysis between occupational stress and metabolic syndrome in workers of a petrochemical enterprise: based on two assessment models of occupational stress.

BACKGROUND: Occupational stress is becoming a common phenomenon around the world. Being in a high occupational stress state for a long time may destroy the metabolic balance of the body, thereby increasing the risk of metabolic diseases. There is limited evidence regarding the correlation between occupational stress and metabolic syndrome (MetS), particularly in the petrochemical workers. METHODS: A total of 1683 workers of a petrochemical enterprise in China were included in the survey by cluster sampling method. The occupational stress assessment was carried out by the Job Content Questionnaire and the Effort-Reward Imbalance Questionnaire, and the general demographic characteristics, work characteristics, occupational hazards, lifestyle and health examination data of the participants were collected. Logistic regression and multiple linear regression were used to analyze the correlations and influencing factors between occupational stress and its dimensions with MetS and its components. RESULTS: A total of 1683 questionnaires were sent out, and 1608 were effectively collected, with an effective recovery rate of 95.54%. The detection rates of occupational stress in Job Demand-Control (JDC) and Effort-Reward Imbalance (ERI) models were 28.4% and 27.2%, respectively. In this study, 257 participants (16.0%) were diagnosed with MetS. Compared with the non-MetS group, body mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG) and fasting blood-glucose (FBG) levels were significantly higher in the MetS group, and high density lipoprotein-cholesterol (HDL-C) levels were significantly lower (P < 0.001). The results of multiple linear regression showed that after adjusting for nation, marital status, education, work system, smoking and drinking, and further adjusting for occupational hazards, the D/C ratio was significantly negatively correlated with SBP in the JDC model. Social support was negatively correlated with WC. In the ERI model, there was a significant positive correlation between over-commitment and FBG. CONCLUSIONS: The detection rates of occupational stress and MetS were high in workers of a petrochemical enterprise. In the JDC model, occupational stress was negatively correlated with SBP, and social support was negatively correlated with WC. In the ERI model, there was a significantly positive correlation between over-commitment and FBG.

Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study.

Although the pediatric perioperative pain management has been improved in recent years, the valid and reliable pain assessment tool in perioperative period of children remains a challenging task. Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults, but also because there is a lack of effective and specific assessment tool for children. In addition, exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae. The short-term sequelae can induce a series of neurological, endocrine, cardiovascular system stress related to psychological trauma, while long-term sequelae may alter brain maturation process, which can lead to impair neurodevelopmental, behavioral, and cognitive function. Children's facial expressions largely reflect the degree of pain, which has led to the developing of a number of pain scoring tools that will help improve the quality of pain management in children if they are continually studied in depth. The artificial intelligence (AI) technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks, which can effectively identify and systematically analyze various subtle features of children's facial expressions. Based on the construction of a large database of images of facial expressions in children with perioperative pain, this study proposes to develop and apply automatic facial pain expression recognition software using AI technology. The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.

Ultralow-cost piezoelectric sensor constructed by thermal compression bonding for long-term biomechanical signal monitoring in chronic mental disorders.

Wearable bioelectronic devices, which circumvent issues related to the large size and high cost of clinical equipment, have emerged as powerful tools for the auxiliary diagnosis and long-term monitoring of chronic psychiatric diseases. Current devices often integrate multiple intricate and expensive devices to ensure accurate diagnosis. However, their high cost and complexity hinder widespread clinical application and long-term user compliance. Herein, we developed an ultralow-cost poly(vinylidene fluoride)/zinc oxide nanofiber film-based piezoelectric sensor in a thermal compression bonding process. Our piezoelectric sensor exhibits remarkable sensitivity (13.4 mV N-1), rapid response (8 ms), and exceptional stability over 2000 compression/release cycles, all at a negligibly low fabrication cost. We demonstrate that pulse wave, blink, and speech signals can be acquired by the sensor, proposing a single biomechanical modality to monitor multiple physiological traits associated with bipolar disorder. This ultralow-cost and mass-producible piezoelectric sensor paves the way for extensive long-term monitoring and immediate feedback for bipolar disorder management.

Research on the dynamic changes of China's agro-processing industry agglomeration and spatial impact of production factors on agglomeration.

Agglomeration is an important phenomenon that accompanies with a large concentration of various production factors. Since the agro-processing industry has become a vital bridge connecting the primary and secondary industries, agglomeration and distribution within this industry are arousing wide concern. Based on provincial panel data of China from 2001 to 2020, this study described the dynamic changes of China's agro-processing industry agglomeration using spatial Gini coefficient and average concentration ratio. A theoretical analysis framework was established and a dynamic spatial Durbin model was used to quantitatively explore the spatial effects of production factors on the agro-processing industry agglomeration, results showed that: first, the agglomerations of agro-processing industry and its sub-industries all have exhibited a fluctuating trend of "up-down-up-down", meanwhile the agro-processing industry shifted from the Eastern coastal region to the central and western regions. Second, in the short term, the capital, labor, and technology factors respectively have remarkably promoted the agro-processing industries agglomeration both in local and neighboring areas. Third, in the long term, these three production factors all had a crowding effect on industrial agglomeration. Therefore, it is necessary to continue optimizing the agro-processing industry spatial layout through adjusting production factor inputs to promote high-quality development of the agro-processing industry.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses.

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

The impact of green finance on the optimization of industrial structure: Evidence from China.

Green finance promotes the optimization of industrial structure and continuous improvement of ecological environment by supporting the development of green industries. Based on the panel data of 30 provinces in China from 2012 to 2020, this paper uses the entropy weight TOPSIS method to measure the development level of green finance and the level of industrial structure optimization in China, and constructs a panel data model to empirically test the impact of green finance on the upgrading of China's industrial structure. The study finds that there is still an imbalance and insufficiency in the development of green finance and industrial structure optimization in China. From 2012 to 2020, the development level of green finance and the level of industrial structure optimization in China have been continuously rising, but there is obvious heterogeneity, showing an eastern>central>western spatial pattern. Empirical analysis results show that at the significance level of 1‰, the development of green finance has a significant promoting effect on the rationalization and upgrading of the industrial structure. However, there is significant heterogeneity in the impact of green finance on industrial structure optimization. In terms of regional heterogeneity, at the significance level of 1‰, the role of green finance in promoting the optimization of industrial structure in central and western China is higher than that in eastern China, and the impact of green finance on China's industrial structure shows a spatial pattern of western>central>eastern China. In terms of industry heterogeneity, at the significance level of 1‰, green finance has a significant promoting effect on the development of green industries, and a significant inhibiting effect on the development of high-energy-consuming industries. Specifically, in the green industry, green finance has the greatest promoting effect on the communication and other electronic equipment manufacturing industry; in the high-energy-consuming industry, green finance has the greatest inhibiting effect on the black metal smelting and rolling processing industry, and the smallest impact on the petroleum, coal and other fuel processing industry. Finally, based on this, policy suggestions for green finance to support the optimization of industrial structure are proposed from two dimensions: government and financial institutions.

Assessment of the prognostic value of preoperative high-sensitive troponin T for myocardial injury and long-term mortality for groups at high risk for cardiovascular events following noncardiac surgery: a retrospective cohort study.

Background: Few studies explored the association between high-sensitive cardiac troponin T (hs-cTnT) and long-term mortality for patients after surgery. This study was conducted to assess the association of hs-cTnT with long-term mortality and to investigate the extent to which this association is mediated via myocardial injury after noncardiac surgery (MINS). Methods: This retrospective cohort study included all patients with hs-cTnT measurements who underwent non-cardiac surgery at Sichuan University West China Hospital. Data were collected from February 2018 and November 2020, with follow-up through February 2022. The primary outcome was all-cause mortality within 1 year. As secondary outcomes, MINS, length of hospital stay (LOS), and ICU admission were analyzed. Results: The cohort included 7,156 patients (4,299 [60.1%] men; 61.0 [49.0-71.0] years). Among 7,156 patients, there were 2,151 (30.05%) with elevated hs-cTnT(>14 ng/L). After more than 1 year of follow-up, more than 91.8% of mortality information was available. During one-year follow-up after surgery, there were 308 deaths (14.8%) with a preoperative hs-cTnT >14 ng/L, compared with 192 deaths (3.9%) with a preoperative hs-cTnT <=14 ng/L(adjusted hazard ratio [aHR] 1.93, 95% CI 1.58-2.36; p < 0.001). Elevated preoperative hs-cTnT was also associated with several other adverse outcomes (MINS: adjusted odds ratio [aOR] 3.01; 95% CI, 2.46-3.69; p < 0.001; LOS: aOR 1.48, 95%CI 1.34-1.641; p < 0.001; ICU admission: aOR 1.52, 95%CI 1.31-1.76; p < 0.001). MINS explained approximately 33.6% of the variance in mortality due to preoperative hs-cTnT levels. Conclusion: Preoperative elevated hs-cTnT concentrations have a significant association with long-term mortality after noncardiac surgery, one-third of which may by accounted for by MINS.

Pricing decisions of risk averse logistic companies with carbon cap and trade under Stackelberg game.

With the implementation of the double carbon plan, this paper considers the delivery fees of two risk averse logistics companies under carbon cap and trade mechanism. We establish logistics company Stackelberg (MS) model and retailer Stackelberg (RS) model under mean variance (MV) framework, respectively. We obtain the optimal delivery fees and retail prices. We find out that the higher degree of risk aversion can lead to a lower delivery fee. We also show that a higher carbon trading price or a higher cross price sensitivity will increase delivery fees. Moreover, we indicate that the performances of logistics companies under MS scenario are higher than that RS scenario. In addition, we suggest that under the carbon cap and trade rules, in order to obtain higher profits, logistics companies should use fuel vehicle for transportation under certain conditions, and use electric vehicle in other cases.

Prognostic Value of Tumor Multifocality in Pediatric Papillary Thyroid Carcinoma: A Real-Life Multicentric Study.

OBJECTIVE: To investigate the association of multifocality with clinical outcomes in pediatric papillary thyroid cancer. STUDY DESIGN: Multicenter retrospective study of prospectively collected data. SETTING: Tertiary referral center. METHODS: This study included patients 18 years or younger who underwent total thyroidectomy and radioiodine ablation for papillary thyroid carcinoma (PTC) between 2005 and 2020 at 3 tertiary adult and pediatric hospitals in China. For disease-free survival (DFS), events were defined as persistent and/or recurrent diseases. The primary outcome was the association of tumor multifocality and DFS, assessed using Cox proportional hazards regression models. RESULTS: One hundred and seventy-three patients (median age 16 years [range, 5-18 years]) were recruited. Multifocal diseases were seen in 59 patients (34.1%). After a median follow-up of 57 (range, 12-193 months) months, 63 (36.4%) patients had persistent diseases. There was a significant association between tumor multifocality and decreased DFS on univariable analysis (hazard ratio [HR] = 1.90, p = .01), yet it was nonsignificant after multivariate adjustment (HR = 1.20, p = .55). In a subgroup analysis of 132 pediatric patients with clinically M0 PTC, neither unadjusted HR (2.21, p = .06) nor adjusted HR (1.70, p = .27) of multifocal PTC was significantly higher in comparison to unifocal PTC. CONCLUSION: In this highly selective surgical pediatric patient cohort with PTC, tumor multifocality was not an independent risk factor for decreased DFS.

Assessment of prognostic value of preoperative neutrophil-to-lymphocyte ratio for postoperative mortality and morbidity.

Background: The preoperative elevated neutrophil-to-lymphocyte ratio (NLR) was reported to be associated with poorer outcomes after cancer and cardiovascular surgeries. It is unclear, however, if the predictive value is particular or if it may be applied to other types of surgery. We aimed to assess the prognostic value of preoperative NLR levels for morbidity and mortality after various surgery and determine an optimal threshold for NLR. Methods: We conducted a cohort analysis on patients receiving surgery at Sichuan University West China Hospital between 2018 and 2020. Multivariable piecewise regression analysis were used to determine the optimal cutoff value of NLR. Subgroup analysis were performed to verify the correlation. Sensitivity analysis was used to explore the effect of different thresholds. Results: We obtained data from 136,347 patients. The optimal cutoff of NLR was determined as 3.6 [95% CI (3.0, 4.1)] by piecewise regression method. After multivariable adjustment, preoperative high NLR remained significantly associated with increased in-hospital mortality (aOR, 2.19; 95% CI, 1.90-2.52; p < 0.001) and ICU admission after surgery (aOR, 1.69; 95% CI, 1.59-1.79; p < 0.001). Subgroup analyses confirmed the predictive value of high NLR in multiple surgical subgroups, including general, orthopedic, neurosurgical, and thoracic surgery subgroups, otorhinolaryngology, head and neck surgery, and burn plastic surgery. A NLR threshold of 3.6 gave excellent predictive value, whether employed alone or added in an extended model. Conclusions: In conclusion, the association of elevated NLR with higher mortality and ICU admission can be extended to a wider range of procedures. NLR threshold of 3.6 could provide good prognostic value for the prognostic model.

Automated measurement of leg length discrepancy from infancy to adolescence based on cascaded LLDNet and comprehensive assessment.

Background: Deep learning (DL) has been suggested for the automated measurement of leg length discrepancy (LLD) on radiographs, which could free up time for pediatric radiologists to focus on value-adding duties. The purpose of our study was to develop a unified solution using DL for both automated LLD measurements and comprehensive assessments in a large and comprehensive radiographic dataset covering children at all stages, from infancy to adolescence, and with a wide range of diagnoses. Methods: The bilateral femurs and tibias were segmented by a cascaded convolutional neural network (CNN), referred to as LLDNet. Each LLDNet was conducted through use of residual blocks to learn more abundant features, a residual convolutional block attention module (Res-CBAM) to integrate both spatial and channel attention mechanisms, and an attention gate structure to alleviate the semantic gap. The leg length was calculated by localizing anatomical landmarks and computing the distances between them. A comprehensive assessment based on 9 indices (5 similarity indices and 4 stability indices) and the paired Wilcoxon signed-rank test was undertaken to demonstrate the superiority of the cascaded LLDNet for segmenting pediatric legs through comparison with alternative DL models, including ResUNet, TransUNet, and the single LLDNet. Furthermore, the consistency between the ground truth and the DL-calculated measurements of leg length was also comprehensively evaluated, based on 5 indices and a Bland-Altman analysis. The sensitivity and specificity of LLD >5 mm were also calculated. Results: A total of 976 children were identified (0-19 years old; male/female 522/454; 520 children between 0 and 2 years, 456 children older than 2 years, 4 children excluded). Experiments demonstrated that the proposed cascaded LLDNet achieved the best pediatric leg segmentation in both similarity indices (0.5-1% increase; P<0.05) and stability indices (13-47% percentage decrease; P<0.05) compared with the alternative DL methods. A high consistency of LLD measurements between DL and the ground truth was also observed using Bland-Altman analysis [Pearson correlation coefficient (PCC) =0.94; mean bias =0.003 cm]. The sensitivity and specificity established for LLD >5 mm were 0.792 and 0.962, respectively, while those for LLD >10 mm were 0.938 and 0.992, respectively. Conclusions: The cascaded LLDNet was able to achieve promising pediatric leg segmentation and LLD measurement on radiography. A comprehensive assessment in terms of similarity, stability, and measurement consistency is essential in computer-aided LLD measurement of pediatric patients.

The association between chronic disease and depression in middle-aged and elderly people: The moderating effect of health insurance and health service quality.

Background: Depression in chronic disease patients was an important public health problem. However, limited work has been done on how to alleviate the depression of chronic disease patients. This paper attempted to explore the alleviating effect of health insurance and health service quality. Methods: A total of 11,500 middle-aged and elderly people were drawn from four waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study (CHARLS). We analyzed the effect of chronic disease on depression in middle-aged and elderly people in China, and explored the mechanism of action from health insurance and health service quality. Results: After adjusting for sociodemographic factors, any chronic disease (coefficient 1.471, p < 0.01) and multiple chronic diseases (coefficient 1.733, p < 0.01) could significantly increase the depression score. Any chronic disease increased the depression score (165.3 and 147.4% in non-health insurance group and health insurance group, respectively), the multiple chronic diseases increased the depression score (190.6 and 173.5% in non-health insurance group and health insurance group, respectively). Any chronic disease increased the depression score (161.3 and 139.5% in lower health service quality group and higher health service quality group, respectively), the multiple chronic diseases increased the depression score (228.4 and 162.9% in lower health service quality group and higher health service quality group, respectively). And similar results were obtained after using depression status instead of depression score. Conclusions: Chronic disease and multiple chronic diseases were important determinants of depression in middle-aged and elderly people. Health insurance and health service quality were the key factors in relieving the depression of chronic disease patients. Several strategies were urgently needed: paying attention to the mental health of chronic disease patients, increasing the participation rate of health insurance, further improving the quality of health service, and alleviating the psychological harm caused by chronic disease.

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR.

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

Regional green total factor performance analysis of China's construction industry based on a unified framework combining static and dynamic indexes.

It has an important meaning to improve green total factor performance in the construction industry for China's green and high-quality development. In addition to the traditional desirable economic output and undesirable CO2 emission output, the indicators representing social welfare of China's construction industry were incorporated into the total factor framework. On this basis, a global non-radial DEA approach was employed to explore the sources and the key factors leading to changes in regional green total factor performance of China's construction industry from both static and dynamic perspectives. The results were concluded as follows: (1) The lower energy efficiency and CO2 emission efficiency were the main reasons for low level of green total factor efficiency. During the sample period, energy efficiency increased rapidly while capital efficiency remained almost unchanged. (2) The green total factor productivity demonstrated an increasing trend, which was driven by technological progress rather than technical efficiency. An in-depth decomposition revealed that the deterioration of technical efficiency was mainly caused by the negative effect of CO2 emissions. (3) Regional green total factor performance of China's construction industry demonstrated obvious spatial heterogeneity. Each province should comprehensively consider its own characteristics of green development in the construction industry to formulate the corresponding optimization strategy. These findings can help decision makers design specific policies on regional-oriented green development of China's construction industry.

Financial Crisis in Management Stress: From the Perspective of Crisis Anxiety of Others.

The crisis anxiety of others is a phenomenon that goes hand in hand with the spread of the occupational health pandemic. It is becoming increasingly important to better understand its emergence process, especially in the era of greater uncertainty. This study aims to examine the impact of the external financial crisis on managerial stress among financial employees. The sample consists of 347 senior managers and financial employees from companies in China. The empirical analysis shows that external financial crises have significant effects on anxiety levels, especially external corporate crisis, debt crisis and growth crisis both have mediating effect on the relationship between anxiety level and pressure management and the relationship between external financial crisis and pressure management. This study explores the rules for the emergence of anxiety among corporate managers and expands the scope of environmental factors that need to be discussed in the study of corporate financial management. This study provides theoretical implications for the psychological study of Financial Management and practical implications for corporate financial management.

Three-dimensional radiographic assessment of bone changes around posterior dental implants at native bone site in Gansu Province, Northwest of China: A retrospective cohort study.

PURPOSE: The aim of this study was to assess bone density and thickness changed following dental implant placement in the maxillary and mandibular jaws. Also, observe the form of bone loss around the implant and the relationship between preoperative bone density and bone thickness with bone loss around dental implants. METHODS: 65 patients, including 102 dental implants, were assessed in this study. CBCT was utilized to determine the bone condition (bone thickness and density at three levels (sub-crestal bone at 3 mm (CB3), 6 mm (CB6), and 9 mm (CB9)) before implant placement, and 2 to 3 years after placement, also determine the bone loss pattern. RESULTS: The difference in bone thickness was 0.32 ± 0.50 mm at CB3, 0.18 ± 0.40 mm at CB6, and 0.14 ± 0.07 mm at CB9. The change buccal bone density at CB3, CB6, and CB9 were 344.5 ± 278.9, 260.5 ± 276, and 138.9 ± 313.9 HU, respectively, and the change in lingual bone density was 252.7 ± 247, 179.9 ± 244.1, and 281 ± 4063 HU, respectively. Only the CB3 level showed a significant decrease in bone thickness (p < 0.001), and a change in bone density was observed at the three levels (p < 0.001). The means of vertical and horizontal bone loss were 0.19 ± 0.23 mm and 0.18 ± 0.22 mm, respectively. Splinted or adjacent dental implants have more horizontal bone loss, with statistically significant (p < 0.001). Age, gender, and implant position were not statistically related to the outcome variables. There was a negative correlation between the preoperative status of the bone condition and pattern bone loss, as indicated by Pearson's correlation coefficient. CONCLUSION: CBCT detected a significant bone thickness decrease was found only at the crestal third. A significant bone density increase was found at three levels around dental implants. Implant areas with higher bone thickness and density had less bone loss.

Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency.

Multi-principal element alloys (MPEAs) with remarkable performances possess great potential as structural, functional, and smart materials. However, their efficient performance-orientated design in a wide range of compositions and types is an extremely challenging issue, because of properties strongly dependent upon the composition and composition-dominated microstructure. Here, we propose a multistage-design approach integrating machine learning, physical laws and a mathematical model for developing the desired-property MPEAs in a very time-efficient way. Compared to the existing physical model- or machine-learning-assisted material development, the forward-and-inverse problems, including identifying the target property and unearthing the optimal composition, can be tackled with better efficiency and higher accuracy using our proposed avenue, which defeats the one-step component-performance design strategy by multistage-design coupling constraints. Furthermore, we developed a new multi-phase MPEA at the minimal time and cost, whose high strength-ductility synergy exceeded those of its system and subsystem reported so far by searching for the optimal combination of phase fraction and composition. The present work suggests that the property-guided composition and microstructure are precisely tailored through the newly built approach with significant reductions of the development period and cost, which is readily extendable to other multi-principal element materials.

Short-term effects and economic burden assessment of ambient air pollution on hospitalizations for schizophrenia.

The evidence on the health and economic impacts of air pollution with schizophrenia is scarce, especially in developing countries. In this study, we aimed to systemically examine the short-term effects of PM2.5 (particulate matter ≤ 2.5 µm in diameter), PM10 (≤ 10 µm in diameter), NO2 (nitrogen dioxide), SO2 (sulfur dioxide), CO (carbon monoxide), and O3 (ozone) on hospital admissions for schizophrenia in a Chinese coastal city (Qingdao) and to further assess the corresponding attributable risk and economic burden. A generalized additive model (GAM) was applied to model the impact of air pollution on schizophrenia, and the corresponding economic burden including the direct costs (medical expenses) and indirect costs (productivity loss). Stratified analyses were also performed by age, gender, and season (warm or cold). Our results showed that for a 10 µg/m3 increase in the concentrations of PM2.5, PM10, SO2, and CO at lag5, the corresponding relative risks (RRs) were 1.0160 (95% CI: 1.0038-1.0282), 1.0097 (1.0018-1.0177), 1.0738 (1.0222-1.01280), and 1.0013 (1.0001-1.0026), respectively. However, no significant effect of NO2 or O3 on schizophrenia admissions was found. The stratified analysis indicated that females and younger individuals (< 45 years old) appeared to be more vulnerable, but no significant difference was found between seasons. Furthermore, 12.41% of schizophrenia hospitalizations were attributable to exposure to air pollution exceeding the World Health Organization (WHO) air quality standard, with a total economic burden of 89.67 million RMB during the study period. At the individual level, excessive air pollution exposure resulted in an economic burden of 8232.08 RMB per hospitalization. Our study found that short-term exposure to air pollutants increased the risk of hospital admissions for schizophrenia and resulted in a substantial economic burden. Considerable health benefits can be achieved by further reducing air pollution.