Stability analysis of partial differential variational inequalities in Banach spaces

In this paper, we study a class of partial differential variational inequalities. A general stability result for the partial differential variational inequality is provided in the case the perturbed parameters are involved in both the nonlinear mapping and the set of constraints. The main tools are theory of semigroups, theory of monotone operators, and variational inequality techniques

Biomass Accumulation and Carbon Sequestration in Four Different Aged Casuarina equisetifolia Coastal Shelterbelt Plantations in South China

Thousands of kilometers of shelterbelt plantations of Casuarina equisetifolia have been planted to protect the southeast coastline of China. These plantations also play an important role in the regional carbon (C) cycling. In this study, we examined plant biomass increment and C accumulation in four different aged C. equisetifolia plantations in sandy beaches in South China. The C accumulated in the C. equisetifolia plant biomass increased markedly with stand age. The annual rate of C accumulation in the C. equisetifolia plant biomass during 0-3, 3-6, 6-13 and 13-18 years stage was 2.9, 8.2, 4.2 and 1.0 Mg C ha(-1) yr(-1), respectively. Soil organic C (SOC) at the top 1 m soil layer in these plantations was 17.74, 5.14, 6.93, and 11.87 Mg C ha(-1), respectively, with SOC density decreasing with increasing soil depth. Total C storage in the plantation ecosystem averaged 26.57, 38.50, 69.78, and 79.79 Mg C ha(-1) in the 3, 6, 13 and 18-yrs plantation, with most of the C accumulated in the aboveground biomass rather than in the belowground root biomass and soil organic C. Though our results suggest that C. equisetifolia plantations have the characteristics of fast growth, high biomass accumulation, and the potential of high C sequestration despite planting in poor soil conditions, the interactive effects of soil condition, natural disturbance, and human policies on the ecosystem health of the plantation need to be further studied to fully realize the ecological and social benefits of the C equisetifolia shelterbelt forests in South China

Nonlinear Free Vibration Analysis of Axisymmetric Polar Orthotropic Circular Membranes under the Fixed Boundary Condition

This paper presents the nonlinear free vibration analysis of axisymmetric polar orthotropic circular membrane, based on the large deflection theory of membrane and the principle of virtual displacement. We have derived the governing equations of nonlinear free vibration of circular membrane and solved them by the Galerkin method and the Bessel function to obtain the generally exact formula of nonlinear vibration frequency of circular membrane with outer edges fixed. The formula could be degraded into the solution from small deflection vibration; thus, its correctness has been verified. Finally, the paper gives the computational examples and comparative analysis with the other solution. The frequency is enlarged with the increase of the initial displacement, and the larger the initial displacement is, the larger the effect on the frequency is, and vice versa. When the initial displacement approaches zero, the result is consistent with that obtained on the basis of the small deflection theory. Results obtained from this paper provide the accurate theory for the measurement of the pretension of polar orthotropic composite materials by frequency method and some theoretical basis for the research of the dynamic response of membrane structure

Case-control study on fragility fractures in coal miners: A comparison between surface and underground workers

BackgroundThe prevalence of osteoporosis and osteopenia is higher among underground coal miners than surface workers. The special underground work environment and unhealthy habits such as smoking, drinking, and a high-salt diet may lead to changes in bone metabolism, increasing the risk of fragility fractures and placing a heavy economic burden on individuals and society. ObjectiveTo identify potential factors influencing fragility fractures among coal miners in different working environments and to provide a basis for targeted preventive measures to reduce the occurrence of fragility fractures.MethodsMale participants who attended at least one of the physical examinations in Kailuan Group between June 2006 and December 2020 were included in the study. The participants were divided into two groups based on their working environment: surface or underground. A case-control study was conducted, where patients with new fragility fractures served as the case group and participants without fragility fractures served as the control group. The two groups were matched with a case:control ratio of 1:4 by age (±1 year) and the same year of physical examination. The matching process was repeated twice, once for the surface working population and once for the underground working population. The analysis of risk factors was conducted using conditional logistic regression models.ResultsAmong a total of 113138 employees in Kailuan Group, 82631 surface workers and 30507 underground workers were included, respectively. The number of individuals who suffered fragility fractures was 1375, accounting for 1.22% of the total population. The incidence of fragility fractures in underground workers was significantly higher than that in surface workers (1.63%>1.07%, P<0.001). The results of conditional logistic regression model showed that current smoking (OR=1.26, 95%CI: 1.05, 1.51), manual labor (OR=1.37, 95%CI: 1.06, 1.78), diabetes (OR=1.26, 95%CI: 1.04, 1.54), sinus tachycardia (OR=1.81, 95%CI: 1.23, 2.66), history of stroke (OR=1.51, 95%CI: 1.09, 2.09), education at college and above (OR=0.65, 95%CI: 0.45, 0.95), high income level (OR=0.69, 95%CI: 0.54, 0.90), elevated hemoglobin (OR=0.91, 95%CI: 0.85, 0.98), and elevated total cholesterol (OR=0.90, 95%CI: 0.82, 0.99) were associated with fragility fractures in the surface working population of coal mines; current smoking (OR=1.48, 95%CI: 1.17, 1.87), current drinking (OR=1.26, 95%CI: 1.01, 1.56), manual labor (OR=2.64, 95%CI: 1.41, 4.94), history of dust exposure (OR=1.28, 95%CI: 1.03, 1.58), and obesity (OR=0.72, 95%CI: 0.52, 0.96) were associated with fragility fractures in the underground working population of coal mines.ConclusionIn preventing fragility fractures, special attention should be paid to the bone health of underground workers engaged in manual labor or having a history of dust exposure. It is important to correct their unhealthy behaviors in a timely manner, such as smoking and drinking, and to appropriately increase body weight to prevent fragility fractures. For surface workers, particular attention should be given to the high-risk group for fragility fractures, such as low family income per capita, manual labor, and having a history of stroke or diabetes; in addition, close monitoring of their resting heart rate, hemoglobin levels, and total cholesterol levels may help prevent fragility fractures

Cumulative exposure to remnant cholesterol and the risk of fragility fractures: a longitudinal cohort study

ObjectiveTo investigate the association between cumulative remnant cholesterol (cumRC) and the risk of new-onset fragility fractures.MethodsThis study included individuals who participated in the 2006, 2008, and 2010 Kailuan health examinations. Baseline characteristics were compared between groups according to cumRC quartiles. The incidence density was calculated, and the log-rank test was used to compare the cumulative incidence. Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI), and restricted cubic spline was used to examine the possibly non-linear relation between cumRC and the risk of fragility fractures. Additional analyses were performed with stratification by age (≥ or &lt;65 years).ResultsA total of 43,839 individuals were included in this study. During the median follow-up period of 10.97 years, a total of 489 fragility fractures occurred. Multivariable Cox proportional hazards regression model 3 showed that the Q1 and Q4 groups versus the Q2 group were associated with a higher HR of fragility fracture (HR 1.61, 95% CI: 1.23–2.11; HR 1.38, 95% CI: 1.06–1.81), and restricted cubic spline regression analysis showed a non-linear relationship between cumRC level and the risk of fragility fractures (POverall association &lt; 0.001, PNon-linear association = 0.001). The association was significant in the age group &lt;65 years but not in the age group ≥65 years. The sensitivity analyses were consistent with the main results.ConclusionsBoth too high and too low cumRC levels were associated with a greater risk of fragility fractures, and this association was more significant in young and middle-aged people

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation. This study presents a machine learning approach based on the C5.0 decision tree (DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data (70% landslide pixels) and validation data (30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model. Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC (area under the receiver operating characteristic (ROC) curve) of the proposed model was the highest, reaching 0.88, compared with traditional models (support vector machine (SVM) = 0.85, Bayesian network (BN) = 0.81, frequency ratio (FR) = 0.75, weight of evidence (WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km2 and 0.88/km2, respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area. Our results indicate that the distribution of high susceptibility zones was more focused without containing more “stable” pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China

Preparation and Characterization of Binary Organogels via Some Azobenzene Amino Derivatives and Different Fatty Acids: Self-Assembly and Nanostructures

In present work the gelation behaviors of binary organogels composed of azobenzene amino derivatives and fatty acids with different alkyl chains in various organic solvents were designed and investigated. Their gelation behaviors in 20 solvents were tested as new binary organic gelators. It showed that the length of alkyl substituent chains and azobenzene segment have played a crucial role in the gelation behavior of all gelator mixtures in various organic solvents. Longer alkyl chains in molecular skeletons in present gelators are favorable for the gelation of organic solvents. Morphological studies revealed that the gelator molecules self-assemble into different aggregates from lamella, wrinkle, to belt with change of solvents. Spectral studies indicated that there existed different H-bond formation and hydrophobic force, depending on different substituent chains in molecular skeletons. The present work may also give new perspectives for designing new binary organogelators and soft materials