Maximum Lag Research Articles

An Empirical Study on the Relationship between Farmers' Income Increase and Primary Industry in Zhanjiang City

This article briefly introduces the 25 themes of the "No. 1 Document" of the CPC Central Committee. Based on the data of farmers' income, agriculture, forestry, animal husbandry and fishery, and agriculture, animal husbandry and fishery service in Zhanjiang City, Guangdong Province from 1978 to 2021, this paper conducted an empirical study on the relationship between the primary industry and farmers' income in Zhanjiang City. In the empirical analysis, first of all, the stationarity test of the data was carried out, the EG co-integration modeling was carried out, the stepwise regression method was used to eliminate multicollinearity, and the generalized difference method was used to eliminate sequence correlation, and the optimized model was obtained. The results show that the total output value of agriculture and fishery has no significant effect on the increase of farmers' income in Zhanjiang City. Forestry, animal husbandry, agriculture, forestry, animal husbandry and fishery services have a significant impact on farmers' income. The total output value of forestry, animal husbandry and agriculture and the service industry of forestry, animal husbandry and fishery increased by 1%, and the income of farmers increased by 0.429%, 0.240% and 0.619%, respectively. The impact of agriculture and fishery on per capita disposable income in Zhanjiang rural areas is lagging, with a maximum lag of 9 years. Accordingly, the corresponding policy suggestions are put forward.

Prediction of Monthly Wind Velocity Using Machine Learning

The utilization of non-renewable energy resources necessitates the power sector's adoption of alternative energy sources, including photovoltaic and wind power generation systems. This academic investigation utilizes two machine learning methodologies, in particular, the study utilizes the random forest and support vector machine algorithms. to conduct its analyses. predict the velocity of the wind in the Diyala governorate of Iraq for the subsequent time interval. This is achieved solely by utilizing historical monthly time series data as input predictors. The three performance metrics employed encompass the coefficient of assurance (R2), root cruel square mistake (RMSE), and cruel outright blunder (MAE). The findings demonstrate that utilizing a lag of 12 months in the time series data (the maximum lag duration tested) as input predictors leads to the most accurate predictions in terms of performance. However, the prediction performance of the two algorithms used was almost similar (RF's RMSE, MAE, and R2 were 0.237, 0.180, and 0.836, while for SVM were 0.223, 0.171, and 0.856). The capacity to anticipate wind speed constitutes a paramount advantage to Iraq, given its current predicament in the electric power industry, and this has the potential to enable stakeholders to forecast oversupply or undersupply and implement pre-emptive measures.

Multi-Temporal and Time-Lag Responses of Terrestrial Net Ecosystem Productivity to Extreme Climate from 1981 to 2019 in China

The escalating frequency and severity of extreme climate greatly impact the carbon dynamics of terrestrial ecosystems worldwide. To understand the multi-temporal response of net ecosystem productivity (NEP) to extreme climate, we investigated 11 temperature and precipitation extreme indices across different vegetation types in China. From 1981 to 2019, the results showed that NEP in China increased at a rate of 0.64 g·m−2·a−2. Extreme climate demonstrated a significant warming trend and a non-significant moistening trend; specifically, maximum daily minimum temperature (TNx) exhibited a significant increase at a rate of 0.34 °C/10 a, while maximum 5-day precipitation (Rx5day) showed an insignificant increase at a rate of 1.78 mm/10 a. NEP was significantly impacted by extreme temperature at the annual, seasonal, and monthly scales, but moderately impacted by extreme precipitation. Specifically, extreme temperature had the most significant effect on grassland, with minimal influence on cropland. In contrast, extreme precipitation had the most significant effect on forest, with minimal impact on cropland. Moreover, the lagged time for extreme precipitation was longer than that for extreme temperature. Extreme precipitation exhibited a corresponding lagged time of at least 2 months (p < 0.01), while extreme temperature exhibited a lagged time of at least 1 month (p < 0.01). The maximum lag time observed was 4 months (p < 0.01). Our findings provide valuable insights into the multi-temporal response of NEP to extreme climate in China and inform sustainable development practices in the region.

Residential Nitrogen Dioxide Exposure and Cause-Specific Cerebrovascular Mortality: An Individual-Level, Case-Crossover Study.

Existing studies have already shown a connection between nitrogen dioxide (NO2) exposure and cerebrovascular mortality. However, the differential effects of NO2 on cerebrovascular disease and its subtypes remain unclear and require further exploration. Daily stroke mortality data between 2013 and 2021 in Shanghai, China were collected. Residential daily air pollution data for each decedent were predicted from a satellite model. An individual-level, time-stratified, case-crossover design was applied to examine the relationship between NO2 exposure and cerebrovascular mortality. A combination of conditional logistic regression and distributed lag models with a maximum lag of 7 days was used for data analysis. A total of 219,147 cases of cerebrovascular mortality were recorded. Among them, the proportion of sequelae of cerebrovascular disease, hemorrhagic stroke and ischemic stroke was 50.7%, 17.1% and 27.5%, respectively. The monotonic increases in mortality risks of cerebrovascular diseases, sequelae of cerebrovascular disease and ischemic stroke were observed, without any discernible thresholds. Each 10 μg/m3 increase in NO2 concentration was associated with increments of 3.62% [95% confidence interval (CI): 2.56%, 4.69%] for total cerebrovascular mortality, 4.29% (95% CI: 2.81%, 5.80%) for sequelae of cerebrovascular disease mortality and 4.30% (95% CI: 2.30%, 6.33%) for ischemic stroke mortality. No positive associations between NO2 exposure and hemorrhagic stroke mortality were observed. A greater risk of NO2 was observed in the warm season, in patients with less than 9 years of education and in those with single marital status. The effects of NO2 were robust to mutual adjustment of co-pollutants. Short-term exposures to NO2 may increase the risk of cerebrovascular mortality, specifically for ischemic stroke and sequelae of cerebrovascular disease.

A label-setting algorithm for the truck driver scheduling problem in accordance with European Community social legislation

In this paper, a label-setting algorithm is proposed in order to efficiently solve the weekly Truck Driver Scheduling Problem (TDSP), which can be seen as a single-machine problem that minimizes the completion time of a fixed sequence of jobs with release and due times and break periods. A focus is made on the night working rule that has only partially been considered in the literature. In the wide literature dedicated to Vehicle Routing Problems, some published heuristics and exact algorithms include constraints related to break/rest periods. But the night working rule defined by the European Community Social Legislation is only considered in some simplified and sub-optimal versions. This constraint is of interest, since it has a direct impact on the practical applicability of routes. Moreover, from a scheduling point of view, the night working rule enforces minimum and maximum time lag constraints, which makes challenging the development of an efficient solution algorithm. The method is benchmarked with a mixed integer linear programming model. Computational experiments show the efficiency of the proposed methods and the effect of the night working constraint on the feasibility of the schedules.

Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study.

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China. HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD. HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53). Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.

Effectiveness of the different eutectic phase-change materials in cooling asphalt pavement

Choosing a phase-change material (PCM) adapted to the specific phase-change temperature (Tm) required for each temperature condition is of utmost importance in cooling pavements. Eutectic phase-change materials (EPCMs) realize the customization of the desired Tm and reduce the difficulty of matching PCMs. This work aims to investigate the effectiveness of a group of binary/ternary EPCMs with Tm ranging from 30 to 60 ℃ and melting enthalpies of around 200 J/g as thermal regulation components for different asphalt. To achieve this goal, the thermal and rheological properties of phase-change asphalt binders (PCAB) were evaluated by differential scanning calorimeter, thermogravimetric, Fourier transform infrared, and multiple stress creep and recovery tests. The results showed that PCAB with latent heat improved the specific heat capacity, which brought a maximum temperature lag of 134.5 min and a maximum temperature difference of 11 ℃. Similarly, the PCAB remained chemical stability, and its thermal stability complied with the construction temperature specifications. However, the recrystallization of EPCMs was impeded by the molten asphalt binder matrix, resulting in a significant reduction in both the Tm and enthalpy. Therefore, these reductions should be taken into consideration when choosing an EPCM. Additionally, as liquid EPCM softens the binder, the absence of elasticity in solid EPCMs renders the binder stiff, thus reducing its resistance to deformation. These impacts were particularly noticeable in Styrene-Butadiene-Styrene modified asphalt due to the polymer network being dissolved by liquefication-EPCMs. To sum up, EPCMs with a higher Tm (40–60 ℃) may decrease their negative impact on deformation resistance, such as palmitic acid-myristic acid-methyl stearate mixtures.

Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

BackgroundPrevious studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner.MethodsOur researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors.ResultsThe influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15–24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0–6 months (ERR 1.08, 95% CI 0.10–2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100–120 μg/m3, PM2.5 at the level of 60–80 μg/m3, and NO2 at the level of 60 μg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models.ConclusionsExposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.

Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-related zoonotic disease induced by hantavirus. Previous studies have identified the influence of meteorological factors on the onset of HFRS, but few studies have focused on the stratified analysis of the lagged effects and interactions of pollution and meteorological factors on HFRS. We collected meteorological, contaminant and epidemiological data on cases of HFRS in Shenyang from 2005-2019. A seasonal autoregressive integrated moving average (SARIMA) model was used to predict the incidence of HFRS and compared with Holt-Winters three-parameter exponential smoothing model. A distributed lag nonlinear model (DLNM) with a maximum lag period of 16 days was applied to assess the lag, stratification and extreme effects of pollution and meteorological factors on HFRS cases, followed by a generalized additive model (GAM) to explore the interaction of SO2 and two other meteorological factors on HFRS cases. The SARIMA monthly model has better fit and forecasting power than its own quarterly model and the Holt-Winters model, with an optimal model of (1,1,0) (2,1,0)12. Overall, environmental factors including humidity, wind speed and SO2 were correlated with the onset of HFRS and there was a non-linear exposure-lag-response association. Extremely high SO2 increased the risk of HFRS incidence, with the maximum RR values: 2.583 (95%CI:1.145,5.827). Extremely low windy and low SO2 played a significant protective role on HFRS infection, with the minimum RR values: 0.487 (95%CI:0.260,0.912) and 0.577 (95%CI:0.370,0.898), respectively. Interaction indicated that the risk of HFRS infection reached its highest when increasing daily SO2 and decreasing humidity. The SARIMA model may help to enhance the forecast of monthly HFRS incidence based on a long-range dataset. Our study had shown that environmental factors such as humidity and SO2 have a delayed effect on the occurrence of HFRS and that the effect of humidity can be influenced by SO2 and wind speed. Public health professionals should take greater care in controlling HFRS in low humidity, low windy conditions and 2-3 days after SO2 levels above 200 μg/m3.

Primary Growth Models Investigation of Pseudomonas nitroreducens Growth on Octylphenol Polyethoxylates

This study evaluated the degradation of octylphenol polyethoxylates by Pseudomonas nitrore-ducens TX1 using secondary kinetics analysis. Nonlinear kinetic regression was employed through the utilization of curve-fitting software in order to fit the digitized growth degradation data. A comprehensive analysis was conducted using various statistical metrics including root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accu-racy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC), and Hannan-Quinn information criterion (HQC). The accuracy and statistical analysis of the kinetic models used showed that only the Huang, Baranyi Roborts, modified Gompertz, Bu-chanan-3-phase, modified Richards and Von Bertalanffy model fit the data, with modified Logis-tics having the best model with low RMSE and AICc values, highest adjusted R2 values, and Bias Factor and Accuracy Factor values closest to unity. The calculated values for the modified Logis-tics constant maximum growth rate (m), maximum growth value (A) and lag period (l), were 0.179 (h-1), 2.199 and 13.015 h, respectively. Growth curve of the bacterium on varying concen-trations of this compound can then be modelled using this model and the maximum growth rate value can be utilized for secondary modelling works further revealing important parameters.

Predictive model for growth of Pseudomonas spp. on fresh duck breast as a function of temperature

This study was performed to develop a predictive growth model of Pseudomonas spp. to ensure the safety of fresh duck breast. Sterile fresh duck breasts were inoculated with Pseudomonas spp. and stored at 4°C, 10°C, 15°C, 20°C, 25°C, and 30°C to measure the microbial change. The Baranyi primary model was used to simulate the growth changes of Pseudomonas spp. at different temperatures. The square root type model and hyperbolic function as secondary models were used to model the effect of temperature on the maximum specific growth rate and lag phase duration. The results showed that the primary models fitted the growth data well (all R2 > 0.900 and RMSE were close to 0). In validation study of secondary model, R2 were 0.987 and 0.925, RMSE were 0.017 and 1.825, respectively, indicating that the parameters of primary models were accurately predicted by secondary models. The validation experiments at tested temperatures proved that the changes of Pseudomonas spp. could be predicted accurately by the developed models combining primary and secondary models both at constant and variable temperatures. The model could be applied to predict the growth of Pseudomonas spp. in logistics for avoiding microbial spoilage on fresh duck breast.

Health Benefit Evaluation for PM2.5 as Well as O3-8h Pollution Control in Chengdu, China from 2016 to 2020

Both particulate matter with aerodynamics of less than 2.5 (PM2.5) and ozone are the two main air pollutants in China, which seriously endanger human health. To estimate the adverse impacts of PM2.5 and ozone on human health during the implementation of air pollution prevention and control actions in Chengdu, both the generalized additive model and the nonlinear distribution lag model of epidemiology were adopted to explore the exposure-response relationship coefficients β of daily ozone 8h maximum concentration average (O3-8h), as well as that of PM2.5 on disease deaths in Chengdu from 2014 to 2016. On this basis, the environmental risk model and environmental value assessment model were both adopted to evaluate the health effects and health benefits in Chengdu from 2016 to 2020, respectively, with the assumption that PM2.5 and O3-8h concentration were reduced to specified air pollution control limits (35 μg·m-3 and 70 μg·m-3, respectively). The results showed 1 the annual concentration of PM2.5 presented gradually decreasing trends in Chengdu from 2016 to 2020. Specifically, ρ(PM2.5) from 63 μg·m-3 in 2016 decreased to 40.92 μg·m-3 in 2020. The average annual decline rate was approximately 9.8%. In contrast, the annual concentration of O3-8h from 155 μg·m-3 in 2016 increased to 169 μg·m-3 in 2020, and the increasing rate was approximately 2.4%. 2 Both PM2.5 and O3-8h had lag effects on three types of disease deaths. Under the maximum lag effect, the corresponding exposure-response relationship coefficients β of PM2.5were 0.0003600, 0.0005001, and 0.0009237 for all-cause, cardiovascular, and respiratory premature deaths, respectively, whereas the corresponding β of O3-8h were 0.0003103, 0.0006726, and 0.0007002, respectively. 3 If ρ(PM2.5) was reduced to the national secondary standard limit (35 μg·m-3), the corresponding number of health beneficiaries and economic benefits declined yearly. Specifically, the health beneficiary number of all-cause, cardiovascular, and respiratory disease deaths were reduced from 1128, 416, and 328 in 2016 to 229, 96, and 54 in 2020, respectively. There were a total number of 3314 avoidable premature deaths for all-cause diseases during the five years, resulting in a total health economic benefit of 7.66 billion yuan. 4 If we assume that ρ(O3-8h) was reduced to the concentration limit specified by the World Health Organization (70 μg·m-3), the corresponding number of health beneficiaries and economic benefits were increasing yearly. Specifically, the health beneficiaries' numbers of all-cause, cardiovascular, and respiratory disease deaths rose from 1919, 779, and 606 in 2016 to 2429, 1157, and 635 in 2020, respectively. The annual average growth rates of avoidable all-cause and cardiovascular mortality were 6.85% and 10.72%, respectively, which was higher than the annual average rise rate of ρ(O3-8h). There were 10790 total avoidable deaths from all-cause diseases during the five years, resulting in a total health economic benefit of 26.62 billion yuan. These findings indicate that PM2.5 pollution in Chengdu had been well controlled, whereas O3 pollution had become more severe and had become another key air pollutant threatening human health. Therefore, the synchronous control of PM2.5 and ozone should be implemented in the future.

Construction of a dynamic model to predict the growth of Staphylococcus aureus and the formation of enterotoxins during Kazak cheese maturation.

Staphylococcus aureus is a common pathogen found in cheese whose Staphylococcal enterotoxins (SE) are the main pathogenic factors that cause food poisoning. The objective of this study was to construct two models to evaluate the safety of Kazak cheese products in terms of composition, changes in S. aureus inoculation amount, Aw, fermentation temperature during processing, and growth of S. aureus in the fermentation stage. A total of 66 experiments comprised of five levels of inoculation amount (2.7-4 log CFU/g), five levels of Aw (0.878-0.961), and six levels of fermentation temperature (32-44°C) were performed to confirm the growth of S. aureus and the presence of SE limit conditions. Two artificial neural networks (ANN) successfully described the relationship between the assayed conditions and the growth kinetic parameters (maximum growth rates and lag times) of the strain. The good fitting accuracy (R2 values were 0.918 and 0.976, respectively) showed that the ANN was appropriate. Experimental results showed fermentation temperature had the greatest influence on the maximum growth rate and lag time, followed by the Aw and inoculation amount. Furthermore, a probability model was built to predict the production of SE by logistic regression and neural network under the assayed conditions, which proved to be concordant in 80.8-83.8% of the cases with the observed probabilities. The maximum total number of colonies predicted by the growth model in all combinations detected with SE exceeded 5 log CFU/g. Within the range of variables, the minimum Aw for predicting SE production was 0.938, and the minimum inoculation amount for predicting SE production was 3.22 log CFU/g. Additionally, as competition between S. aureus and lactic acid bacteria (LAB) occurs in the fermentation stage, higher fermentation temperatures are conducive to the growth of LAB, which can reduce the risk of S. aureus producing SE. This study can help manufacturers to make decisions on the most appropriate production parameters for Kazak cheese products and to prevent S. aureus growth and SE production.

Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients.

Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65years) diabetic patients between 2014 and 2019 in Beijing. A time-stratified case-crossover design based on negative-control exposure was used to assess causal associations of short-term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), -0.08 (-0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), -0.04 (-0.06, -0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short-term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients.

Implementation of the Autoregressive Integrated (VARI) Vector Model for Stress-Testing Analysis of Indonesian Banking

Stress testing in the banking sector is an important tool for assessing a bank's risk level and provides a basis for assisting financial institutions and regulators in making decisions by taking into account the impact of macroeconomic changes on banking risk, one of which is credit risk, which can be seen from the level of non-performing loans (NPL). bank. NPL ratio data and macroeconomic data are periodic data presented in monthly, quarterly or semi-annual periods and are categorized as time series data. At the stationarity test stage it is known that the data is not stationary, so to overcome this a differencing process is carried out so that it uses the Vector Autoregressive Integrated (VARI) model in the stress testing analysis. Based on the Granger test, there is a causal relationship between macroeconomic variables and NPL variables, so the model can be used to predict NPL values as a stress-testing analysis. The model that meets all assumptions and has the maximum lag is the VARI (5.1) model. The results of the model analysis show that the largest and most significant contribution to changes in the NPL variable is from real GDP shocks followed by shocks from Bank Indonesia's policy variables, namely the BI Rate and Inflation.

Time Series Analysis of the Acute Effect of Atmospheric Fine Particulate Matter on Hospitalization for Heart Failure in Lanzhou, China.

The purpose of this study was to investigate the correlation between environmental PM 2.5 and heart failure (HF) hospitalization in Lanzhou, China. The correlation between atmospheric PM 2.5 and HF hospitalization was evaluated using a distributed lag nonlinear model. During the study period, 16,122 HF patients were hospitalized. The average concentration of PM 2.5 is 45.24 μg/m 3 . PM 2.5 was positively correlated with HF hospitalization with single lag (lag0) and cumulative lag (lag01, lag02, lag03). The maximum lag effect is estimated to occur in lag03, and with the PM 2.5 concentration increasing by 10 μg/m 3 , HF hospitalization risk increased by 1.023 (95% confidence interval, 1.006-1.039). The risk of hospitalization for HF in the male group, the age group 65 years or older, and the cold season was statistically significant and more positively correlated. PM 2.5 exposure increases the risk of HF admission in Lanzhou.

Numerical simulation of damping effect of ballast water system on motion response of immersed tunnel element

The OpenFOAM was extended to analyze the roll motion of an immersed tunnel element excited by outside waves and inside ballast water sloshing. The reliability of our numerical model was validated by comparing the results with related experimental tests. Then, the effect of filling depths of ballast water on damping roll motion of a 2-D tunnel cross-section excited by regular waves was analyzed, and the effect of tank width with constant filling weight on rigid-fluid coupling was also discussed. Results revealed that a crucial filling depth for best damping performance on roll motion of liquid-carrying structures exists between the dry condition and the filling depth corresponding to the most severe resonance condition. For the application of the tunnel element in this study, a dimensionless filling depth of 0.1 shows the best damping effect. Furthermore, for different tank widths with a constant filling weight, the damping effect of the ballast water is mainly dominated by the natural frequency of ballast water and has a negative correlation with the natural frequency, for small natural frequency of large tank width, the phase lag between roll motion and sloshing moment gets closer to the maximum damping lag of π/2 due to violent sloshing flow.

A biomechanical analysis of the effect of hydroxyapatite augmentation for trochanteric femoral fractures.

Hydroxyapatite (HA) augments are used to treat trochanteric femoral fractures. However, the efficacy of HA augmentation has not been fully described in trochanteric femoral fracture surgery. In total, 85 patients were enrolled in the present study; all had trochanteric femoral fractures between January 2016 and October 2020, 45 with HA (HA group) and 40 without HA (N group). The intraoperative lag screw insertion torque was directly measured and the amount of lag screw telescoping with and without HA augmentation after surgery was analyzed. Maximum lag screw insertion torque (max-torque), bone mineral density in the opposite femoral neck (n-BMD), tip apex distance (TAD) of the lag screw, radiographic findings including fracture union, the amounts of lag screw telescoping and occurrence of complications were evaluated. A total of 12 patients were excluded if they were aged under 60 years old, had ipsilateral surgery and disorders in the hip joint, TAD of the lag screw ≥26 mm on postoperative radiographs and had measurement errors. A total of 73 fractures could be analyzed: HA group (n=36) and N group (n=37). Max-torque/n-BMD ratios were higher in the HA group compared with in the N group (7.23±2.71 vs. 5.93±1.91 g/cm2·N·m; P=0.04). The amounts of lag screw telescoping in the HA group were smaller compared with the N group (1.41±2.00 vs. 2.58±2.34; P=0.05). Evaluation of screw insertion torque showed maximum screw insertion torque correlated well with n-BMD in both groups, HA (R=0.57; P<0.01) and N group (R=0.64; P<0.01). No correlation was found between maximum screw insertion torque and TAD in both groups, HA (R=-0.10; P=0.62) and N group (R=0.02; P=0.93). All fractures were radiographically united without any complications. These results support the effectiveness of HA augmentation, indicating higher resistance against rotational instability and reduced lag screw telescoping in trochanteric femoral fracture treatment.

Design and Numerical Energetic Analysis of a Novel Semi-Automated Biomass-Powered Multipurpose Dryer

This work presents a new all-inclusive mathematical model that combines both processes and an energy analysis of a semi-automated biomass-powered multipurpose dryer. A mathematical model was developed and a wood sample was used to simulate the model. Energy interaction between the system and sample was established. Most importantly, the incorporation of a sensor control system ensures that, once there is an increase in thermal energy from the combustion of the biomass, a signal is passed to the temperature sensor module that controls the system’s temperature and hence shuts down the heat supply at a predetermined temperature; in this case, at 67 °C. The results of the system’s modification show that the peak temperature of the drying space and the sample was 67 °C and 56 °C, respectively, and that the maximum temperature lag witnessed by the two regimes was 10 °C. The peak temperature removal rate of the sample was 0.0066 kg/h, while the sample attained 0.4 (40%) moisture concentration of its initial value; 90% mass content removal (10% remaining mass content) of the initial mass of the sample was achieved at the end, with a simulation time of 240 s.

Comparative effect of conductive and dielectric materials on methanogenesis from highly concentrated volatile fatty acids

Various conductive materials and their dielectric counterparts were used to get deeper insights into contribution of direct interspecies electron transfer (DIET) in improving methanogenesis from highly concentrated volatile fatty acids (12.5 g/L). Potential CH4 yield, maximum CH4 production rate and lag phase were significantly (up to 1.4, 3.9 and 2.0 times, respectively) improved with addition of stainless-steel mesh (SM) and carbon felt (CF) compared to both control and dielectric counterparts (p < 0.05). kapp increased by 82% for SM and 63% for CF compared to control (p < 0.05). Short thick pili-like structures up to 150 nm in width were formed only in CF and SM biofilms, however, were more abundant for SM. Ureibacillus and Limnochordia specific for SM biofilms, and Coprothermobacter and Ca. Caldatribacterium for CF biofilms, were considered electrogenic. Promotion of DIET by conductive materials is governed by many factors, including specificity of electrogenic groups to material surface.