Dynamic Change Trend Research Articles

Prevalence of nosocomial infections and their influencing factors in a tertiary general hospital in Qingdao.

The goals of this study were to analyze the situation and dynamic change trend of the prevalence of nosocomial infections among inpatients, to investigate the use of antibiotics, and to provide evidence-based support for the development of targeted prevention and control measures for nosocomial infections and rational use of antibiotics. The nosocomial infection surveillance system was utilized to conduct a cross-sectional survey of nosocomial infections of all inpatients, followed by a statistical evaluation of the 5-year survey results. The average prevalence of nosocomial infections over the 5-year period (2017 to 2021) was 2.28%, and the incidence rate per case was 2.42%. The lower respiratory tract was the primary site of infection, followed by the urinary tract. The most prevalent pathogens of nosocomial infections were Gram-negative bacteria (GNB), including Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, and Escherichia coli; and the main strains of Gram-positive bacteria (GPB) included Staphylococcus aureus and Enterococcus faecium. The use of antibiotics accounted for 41.31% on average; and the proportion of antibiotics used for treatment was 84.99% on average. The average rate of pathogen examination was 51.09%, indicating an increasing trend (χ2 = 44.196, p < 0.001). The average prevalence of nosocomial infections over the past five years was 2.28% in this tertiary general hospital. Although the prevalence rate of nosocomial infections in the hospital was relatively low, it is necessary to strengthen the measures for prevention and control of infections of the lower respiratory tract, urinary tract, and surgical incision.

The epidemiology of employee injuries in a monitoring sentinel unit of a coastal area in China:A nine-year retrospective analysis of clinical data

The epidemiology of injury among subgroups of minors, older adults, students, and athletes has previously been investigated; however, studies investigating employee-related injuries are limited. We aimed to retrospectively analyze the epidemiological characteristics and dynamic change trends of injury among employees over a nine-year period in a coastal area in China to provide a reference for formulating injury prevention and control measures among employees. All 14,168 employee injury cases registered in a hospital injury monitoring system were analyzed from January 2013 to December 2021. The male-to-female sex ratio of the employee injury cases was 3.52:1. The floating-to-registered residence population ratio was 2.05:1. March, May, July, and September–October were peak months for employee injuries. Within the day, the injury cases of employees reached five peaks at 0800, 1000, 1500, 1800, and 2000 h. The highest five causes of injury were falling, blunt injuries, motor vehicle accidents, sharps injuries, and non-motor vehicle accidents. The highest five injury types were fracture; concussion or contusion of the brain; injury from a sharp instrument, bite, or open wound; contusion or abrasion; and sprain or strain. The main locations of the injuries were roads and streets, industrial and building sites, and homes. Vulnerable body regions included the upper limbs, lower limbs, head, trunk, and multiple regions. The independent predictors of all outcomes were census register classification; age; injury causes, locations and types; vulnerable body regions; and injury severity; on multivariate logistic regression analysis (P < 0.05). The average durations of missed work in the different injury outcome groups due to injuries among employees were 50.21, 42.57, 44.57, and 38.20 days, respectively. The average number of missed work days due to injuries was 49.77 days, with an increasing annual trend (F = 79.872, P < 0.01). The average hospitalization cost for employee injuries was ¥16250.37, with a decreasing annual trend (F = 4.621, P < 0.01). The average length of hospitalization was 15.22 days, with a decreasing annual trend (F = 76.657, P < 0.01), and the average number of days of missed work due to injuries was 49.77 days, with an increasing annual trend (F = 79.872, P < 0.01). The correlation coefficients showed a significant positive correlation between the average length of hospitalization and average hospitalization expenses, with an increasing trend from 2014 to 2021 (P < 0.05). Most employee injuries occurred in the male and floating populations. Targeted intervention measures should be implemented according to the epidemiological characteristics of injuries in relation to different populations, sexes, and ages to prevent and control injuries.

Evolutionary game and system dynamics for analysis on stakeholder strategies of regional high-speed rail project in investment decision stage

This paper studies the strategic choices under the conflict among stakeholders of regional high-speed rail project at the investment decision stage in the context of the ‘high-speed rail controversy’. This study develops a tripartite evolutionary game model among the central government, the local government and China Railway, analyzes the evolutionary stability strategy, discusses the influence of multiple factors on the choice of tripartite strategy, and further calculates and analyzes the stability of the equilibrium point in the tripartite evolutionary game system. Then the dynamic change trend of strategy choice brought by the change of each factor is investigated through system dynamics simulation. The results show that the lower the intervention cost and reward to the other two parties, and the increase of the opportunity cost of non-intervention and punishment to the other two parties, the faster the convergence of the central government's choice of intervention strategy. The increase of the rewards and punishments, and the increase of negotiation profits, non-negotiation costs, and loss of reputation for non-negotiation, and the decrease in the negotiation costs and non-negotiation profits all contribute to the shortening of the convergence time of the local government's choice of negotiation strategy. The increase of the rewards and punishments, the increase of support profits, the loss of reputation for non-support, and the decrease of the profits for non-support and the extra cost for support will promote the convergence of China Railway's choice of the support strategy. Finally, the simulation results are discussed so as to target feasible coordination strategies, which provides a theory basis for the practice of conflict coordination among stakeholders of regional high-speed rail project at the investment decision stage.

An improved dynamic analytical model and the parameter sensitivity analysis of spherical stacking latent thermal energy storage devices

Thermal energy storage technology plays an important role on improving energy usage flexibility for the end users. The spherical stacking latent thermal energy storage devices inherits the advantages of direct thermal energy usage efficiency and simplified practical applications that received widely attentions. However, due to dispersed and multi-layer structural arrangement, the non-ignorable dynamic heat transfer process poses difficulties in the device design and operation control. In this study, an improved analytical model for predicting the transient heat transfer process of spherical stacking devices is developed. After verification, the parameter sensitivity analysis of the device is conducted by using the model. The results show that the dynamic change trend of thermal resistance is distinct for the melting and the solidification process. The developed model can dynamically predict the outlet temperature of heat transfer fluid and the heat transfer efficiency in a good agreement, and the prediction error of outlet temperature during solidification can reach −2.49 %–4.70 %. The correlation between sphere radius and heat transfer rate is negative and reaches a kind of high correlation above 0.8 for solidification process. The inlet temperature and the mass flow rate of heat transfer fluid are the most influential parameters for melting process.

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Prediction of cutterhead torque change trend of shield machine based on partial state visible HMM and LSTM

Cutterhead torque is a key parameter to determine the normal operation of shield machine. Accurately predicting the change trend of cutterhead torque can provide decision support for shield operators to control operating parameters. Therefore, a prediction method of cutterhead torque change trend based on partial state visible Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) network is proposed in this paper. First, the Spearman correlation coefficient is introduced to analyze the correlation of the tunneling parameters, and the historical tunneling data of the cutter torque, pitch angle and related parameters are obtained to establish a database. Secondly, the pitch angle prediction model is established based on LSTM network, and the predicted pitch angle is used as the observation sequence, and the implicit relationship between cutterhead torque and pitch angle is mined through HMM to obtain the probability matrix. Finally, the cutterhead torque prediction model is established, and the pitch angle sequence is used as input to output the corresponding cutterhead torque change trend. The results show that the method can accurately predict the dynamic change trend of the cutterhead torque in the case of less known parameter states, and the effectiveness of the method are verified in this paper.

Genomic characterization of WRKY transcription factors related to secoiridoid biosynthesis in Gentiana macrophylla

Gentiana macrophylla is one of Chinese herbal medicines in which 4 kinds of iridoids or secoiridoids, such as loganic acid, sweroside, swertiamarin, and gentiopicroside, are identified as the dominant medicinal secondary metabolites. WRKY, as a large family of transcription factors (TFs), plays an important role in the synthesis of secondary metabolites in plants. Therefore, WRKY genes involved in the biosynthesis of secoiridoids in G. macrophylla were systematically studied. First, a comprehensive genome-wide analysis was performed, and 42 GmWRKY genes were identified, which were unevenly distributed in 12 chromosomes. Accordingly, gene structure, collinearity, sequence alignment, phylogenetic, conserved motif and promoter analyses were performed, and the GmWRKY proteins were divided into three subfamilies based on phylogenetic and multiple sequence alignment analyses. Moreover, the enzyme-encoding genes of the secoiridoid biosynthesis pathway and their promoters were then analysed, and the contents of the four secoiridoids were determined in different tissues. Accordingly, correlation analysis was performed using Pearson′s correlation coefficient to construct WRKY gene-enzyme-encoding genes and WRKY gene–metabolite networks. Meanwhile, G. macrophylla seedlings were treated with methyl jasmonate (MeJA) to detect the dynamic change trend of GmWRKYs, biosynthetic genes, and medicinal ingredient accumulation. Thus, a total of 12 GmWRKYs were identified to be involved in the biosynthesis of secoiridoids, of which 8 (GmWRKY1, 6, 12, 17, 33, 34, 38 and 39) were found to regulate the synthesis of gentiopicroside, and 4 (GmWRKY7, 14, 26 and 41) were found to regulate the synthesis of loganic acid. Taken together, this study systematically identified WRKY transcription factors related to the biosynthesis of secoiridoids in G. macrophylla, which could be used as a cue for further investigation of WRKY gene functions in secondary metabolite accumulation.

Dynamic assessment of community resilience in China: empirical surveys from three provinces.

Strengthening the construction of community resilience and reducing disaster impacts are on the agenda of the Chinese government. The COVID-19 pandemic could alter the existing community resilience. This study aims to explore the dynamic change trends of community resilience in China and analyze the primary influencing factors of community resilience in the context of COVID-19, as well as construct Community Resilience Governance System Framework in China. A community advancing resilience toolkit (CART) was used to conduct surveys in Guangdong, Sichuan, and Heilongjiang provinces in China in 2015 and 2022, with community resilience data and information on disaster risk awareness and disaster risk reduction behaviors of residents collected. The qualitative (in-depth interview) data from staffs of government agencies and communities (n = 15) were pooled to explore Community Resilience Governance System Framework in China. Descriptive statistics analysis and t-tests were used to investigate the dynamic development of community resilience in China. Hierarchical regression analysis was performed to explore the main influencing factors of residential community resilience with such socio-demographic characteristics as gender and age being controlled. The results indicate that community resilience in China has improved significantly, presenting differences with statistical significance (p < 0.05). In 2015, connection and caring achieved the highest score, while disaster management achieved the highest score in 2022, with resources and transformative potential ranking the lowest in their scores in both years. Generally, residents presented a high awareness of disaster risks. However, only a small proportion of residents that were surveyed had participated in any "community-organized epidemic prevention and control voluntary services" (34.9%). Analysis shows that core influencing factors of community resilience include: High sensitivity towards major epidemic-related information, particular attention to various kinds of epidemic prevention and control warning messages, participation in epidemic prevention and control voluntary services, and formulation of epidemic response plans. In this study, we have constructed Community Resilience Governance System Framework in China, which included community resilience risk awareness, community resilience governance bodies, community resilience mechanisms and systems. During the pandemic, community resilience in China underwent significant changes. However, community capital was, is, and will be a weak link to community resilience. It is suggested that multi-stages assessments of dynamic change trends of community resilience should be further performed to analyze acting points and core influencing factors of community resilience establishment at different stages.

Natural resource dependence and renewable energy development: Does government policy support matter?

Factors influencing the development of renewable energy have emerged as a hot topic of policy discussion in recent years. However, the direct impact of natural resource dependence on renewable energy has been overlooked, especially in emerging economies. Using Chinese province data from 2005 to 2019, this study employs a nonlinear panel model and a Quantile Regression with Nonadditive Fixed Effects for Panel Data (QRPD) model to examine the impact of natural resource dependence on renewable energy development and explores the great importance of government policy support. The study indicates that the influence of natural resource dependence on renewable energy development supports the “conditional resource curse” hypothesis, showing a U-shaped nonlinear relationship. The effect of natural resource dependence and government policy support on the development of renewable energy shows a dynamic change trend. In the early stage of renewable energy development, policy support is crucial, while during the growing and mature stages, natural resource dependence exerts a stronger driving force. The results of the mechanism test indicate that natural resource dependence can boost renewable energy development through green technology innovation and environmental regulation channels. This paper's conclusion provides a theoretical foundation for fostering the sustainable development of the renewable energy industry and is vital to China's efforts to reach peak carbon and carbon neutrality.

A framework for generating anomaly analysis comments in DHI interpretation report

Efficient and high-quality writing of DHI interpretation reports is critical to realize the instructive value of time-series DHI data, in which anomaly analysis of DHI indicators is the basis of diagnosing problems and giving suggestions for dairy farms. However, this work is labour intensive and knowledge-driven, which is easy to cause inadequate even incorrect analysis due to the lack of DHI specialists. Aiming to the subtasks of automatically generating comments of static anomaly and dynamic change trend of DHI indicators, we proposed a framework to tackle them separately. Firstly, we employed a series of anomaly degree matrixes to deal with the first subtask. Secondly, we proposed an improved end-to-end data-to-text model which incorporates with three pre-processing methods, attention mechanism and contrastive penalty to deal with the second subtask. Experimental results show that our proposed model outperforms the baselines and related works both in terms of fluency and correctness of generated comments, which is suitable for the preliminary description of DHI indicators.

Study on anxiety, depression, and subjective wellbeing of patients with bladder cancer in their different chemotherapy stages.

This study aims to explore the changes in anxiety, depression, and subjective wellbeing in patients with bladder perfusion during different stages of their chemotherapy, and analyze the correlation among their anxiety, depression, and subjective wellbeing. A total of 174 patients with bladder cancer who received bladder perfusion chemotherapy were selected. The questionnaire survey was conducted with SAS, SDS, and GWB. The patients were surveyed and the data were analyzed. The results of patients' anxiety, depression, and subjective wellbeing show a dynamic change trend. The change is most obvious after 1 month of chemotherapy and tends to be flat after 3 months of chemotherapy. The scores of anxiety, depression, and subjective wellbeing in patients with bladder perfusion at different stages are statistically significant (P < 0.05). There is a negative correlation among anxiety, depression, and subjective wellbeing in patients (r = -0.605, 0.601). Patients' emotions change obviously in the first 3 months of their chemotherapy. Clinical workers can take active intervention measures in this period, guide patients to relieve their anxiety and depression, increase their subjective wellbeing level, and improve their quality of life, which is helpful to ensure the successful completion of chemotherapy.

Heterogeneity in Adolescents' Non-Suicidal Self-Injury Behaviour Trajectories Based on the Group-Based Trajectory Model and a Decision Tree Analysis of Family-Related Determinants.

Non-suicidal self-injury (NSSI) is a global public health problem. While some studies have noted the importance of behavioural development patterns in NSSI, most NSSI research is based on cross-sectional survey data. Few studies explore the time-series heterogeneity of trajectories of NSSI symptoms and family-related influencing factors of NSSI. The purpose of the study was to identify the heterogeneity in trajectories of adolescents' NSSI behaviour and their family-related influencing factors. The group-based trajectory model (GBTM) was used to identify the heterogeneity in the NSSI behaviour trajectories of 208 adolescents in China. Next, the decision tree model (DTM) was used to analyse which family related factors influence the trajectory type. The GBTM revealed two heterogeneous trajectories of NSSI behaviour: the high-risk and low-risk NSSI behaviour groups. Next, DTM's average accuracy was 83.2%. A total of seven independent variables were used for the DTM: gender, number of NSSIs in the past month, and family economic, family structure, family conflict, parental psychological control, parental behavior control and family intimacy risks. Family conflict risk was located at the root node and was the most important factor. Heterogeneity within the population should be considered in the management of adolescents' NSSI behaviours. Further, from the perspective of family system theory and cumulative risk, focusing on the adverse effect of multiple risk factors on adolescents' NSSI addiction is more meaningful rather than the impact of single risk factors. Studies that use time series data should focus on the trend of dynamic changes in NSSI addiction characteristics over time.

Alteration of Cognitive Function in Aging and Alzheimer's Disease Mice Is Related to Dysfunction of the Neuroimmune System.

Both Alzheimer's disease (AD) and aging have aging-related cognitive dysfunction with a high incidence. These neurological diseases cause serious cognitive problems in patients' daily life. But the cognitive dysfunction mechanism in-depth of aging is far less known than that of AD. To reveal the different mechanisms of AD and aging-related cognitive dysfunction, we compared the mechanisms of aging and AD through analysis of differentially expressed genes. Mice were divided into four groups (3-month C57BL, 16-month C57BL, 3-month 3xTg AD mice, and 16-month 3xTg AD mice) according to genotype and age. The Morris water maze was employed to investigate the spatial cognition of mice. Differential expressions of genes of AD and aging were analyzed through RNA sequencing and GO, KEGG, Reactome analysis, and the dynamic change trend analysis. Microglia was stained with immunofluorescence and its numbers were counted for analysis. The cognitive function of elderly mice were worse through testing with the Morris water maze. The cognitive function of 16-month 3xTg AD mice were worse than 16-month C57BL mice. The alteration tendencies of DE genes were uncovered, and microglia numbers increased during aging and AD progression through immunofluorescence. These results suggest that immune-related pathways might play a critical role in aging and AD-related cognitive dysfunction. Our research will help to provide some new potential targets for treating cognitive dysfunction in aging and AD.

Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik, produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik. The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques.

Spatio-temporal dynamic change mechanism analysis of traffic conflict risk based on trajectory data

Analyzing risk dynamic change mechanism under spatio-temporal effects can provide a better understanding of traffic risk, which helps reinforce the safety improvement. Traditionally, spatio-temporal studies based on crash data were mostly conducted to explore crash risk evolution mechanism from a macroscopic perspective. Dynamic change mechanism of short-term risk within a small-scale area deserves exploration, which cannot be captured in macroscopic crash-based studies. It is practical to analyze traffic conflict risk as a surrogate safety measure, which can preferably overcome the limitations of crash-based studies. This study aims to explore the spatio-temporal dynamic change mechanism of conflict risk based on trajectory data. Both conflict frequency and severity are integrated and assessed by applying fuzzy logic theory to develop the whole risk indicator. Trajectories on U.S. Highway101 from NGSIM dataset are utilized and aggregated. A two-step framework is proposed to analyze the risk dynamic change mechanism. The spatial Markov model is firstly applied to explore the transition probability of risk level, and then the panel regression approach is employed to quantify the relationship between spatio-temporal risk and traffic characteristics. Modeling results show that (1) the dynamic change trend of safety states differs under different spatial lag conditions, and it can be well depicted by the spatial Markov model; (2) dynamic spatial panel data modeling method performs better than the model that only considers temporal or spatial dependency. The novel proposed framework promotes a systematic exploration of conflict risk from a mesoscopic perspective, which contributes to assess the real-time road safety more comprehensively.

Characteristics of Spatial–Temporal Differences and Measurement of the Level of Forestry Industry Integration in China

The integration of the forestry industry can effectively resolve the conflict between ecological protection and socioeconomic development while bringing new vitality and growth to traditional forestry. In this study, the level of forestry industry integration in 31 provinces in China from 2005 to 2019 was measured using the Herfindahl index method. With ArcGIS and exploratory spatial data analysis methods, the spatial-temporal distribution characteristics, dynamic change trends, spatial correlation characteristics, and existing problems in China’s forestry industry integration development were analyzed. The results showed that the total output value of forestry integrated products and the output value of each product segment increased, but the proportion of product development was imbalanced, and it was concentrated in the understory planting and collection industry and wood processing and manufacturing industry, leaving substantial room for improvement and integration. The value of the forestry industry integration index also increased overall, but the level of integration was low or moderate. In terms of time, the integration index of most provinces trended upward but failed to break through 0.73, leaving a significant gap between it and deep integration. Spatially, the level of integration of the forestry industry varied across the northeast, central, west, and east, with the central and northeast showing a higher integration degree than the east and west. China’s forestry industry integration showed a significant positive spatial correlation, indicating that spatial factors had become an important factor affecting the development of the forestry industry in various regions. Therefore, it is necessary to strengthen the relevant mechanisms of cross-border cooperation and benefit sharing. Lastly, we identified problems with the integration development of the forestry industry, including insufficient and imbalanced integration, unreasonable structural layout of integration development, and insufficient driving capacity for integration. As a result, there were phased and regional differences in the evolution of forestry industry integration.

An Improved Emergency Blow Theoretical Model for Naval Submarine Blowing System and Experimental Verification

_ An improved emergency blow model is proposed, which is based on the traditional emergency blow model and takes into account the influence of compressed air overflows from flood holes in the later stage of blowing. In order to verify the prediction accuracy of the improved emergency blow model for tank blowing, the full-scale model experiment of tank blowing was conducted to investigate the effects of air source volume, air source initial pressure, and flood holes diameters on blowing. The process of air release from bottle and main ballast tank drainage can be accurately simulated by the improved emergency blow model, and the prediction error of tank air peak pressure is shown to be &amp;lt;10%. Additionally, it is found that the air source volume has no effect on the tank’s air peak pressure or tank drainage rate. By analyzing the dynamic characteristics of tank air pressure, it is found that the dynamic change trend of air pressure differs between flood holes with small and large diameters. In the small diameter conditions, the air pressure reaches the maximum when the compressed air just enters the tank; however, under large diameter conditions, the peak pressure comes before the accumulated air pressure is released. The experiment and simulation demonstrate that increasing the area of the flood holes has a decreasing effect on the amount of air accumulated in the tank, and that the decreasing effect becomes more pronounced as the air source initial pressure increases. Introduction To carry out military operations, submarines are deployed both underwater and close to the open surface. About 170 submarines have sunk since they were originally created as a result of an accident, such as a fire, explosion, malfunction, grounding, or collision (Park &amp; Kim 2017). Submarines run the risk of losing safety control in these critical situations. The best method of self-rescue is emergency floating to the surface to prevent bottoming or going deeper than allowed (Liu et al. 2009). A key factor in emergency rising to the water’s surface is the compressed air blowing mechanism. In such an operation, supplying air to the main ballast tank and blowing out ballast water should be used to achieve positive buoyancy or recover a positive pitching moment, which can be used to restore the safe depth of the submarine. Emergency blow is the term used to describe the process of directly supplying air to the main ballast tank without using a high-pressure valve column. The emergency blow is explored in this work because its influence is significantly greater than that of usual blowing, or traditional blowing.

Ecological response of land use change in a large opencast coal mine area of China

Land is the carrier of ecological environment, and the change of land use is bound to cause changes in many natural phenomena and ecological processes. In turn, the change of ecological environment will largely restrict the degree and direction of land use change in a certain region. With the development of current land use, the ecological response of regional land use has become a research hot spot. The study is mainly based on the improved remote sensing ecological index calculated by MATLAB software. The land use transfer matrix method is used to identify the transferred area and key land types, and spatial multiple regression analysis is used to establish the ecological response equation of key land types. The study provides a reference for land resource development and ecological environment protection in large opencast coal mining areas by quantitatively evaluating the land use change and ecological response process in the study area. The research results show that the remote sensing ecological index is mainly obtained through remote sensing technology, which has the advantages of easy parameter acquisition, large time series span, and wide evaluation range. The improved remote sensing ecological index can better quantitatively reflect the quality of the ecological environment in the study area. The calculation method is intuitive and clear, and has strong theoretical significance and reference function. The conceptual model of ecological response to land use change constructed on this basis can better reveal the dynamic process, action mechanism and changing trend of ecological response. The ecological environment quality of the study area is generally poor, and the ecological environment quality of the poor grade accounts for a large proportion. The change of land use from cultivated land to industrial and mining land is the Granger reason that leads to the decline of ecological environment quality in the study area, and the process of transformation has a strong negative correlation effect on ecological environment quality. Different land use types have different transfer processes and different losses to the quality of the ecological environment.

The dynamic change trends and internal driving factors of green development efficiency: robust evidence from resource-based Yellow River Basin cities.

Promoting the green development of resource-based cities is an essential way to achieve sustainable regional economic development. Based on 2009-2019 panel data of the Yellow River Basin cities, this study adopts the super-directional distance function model to measure the green development efficiency of these selected cities. Furthermore, based on the Malmquist-Luenberger index, this paper focuses on the dynamic change trend of green development efficiency and internal driving factors. Furthermore, the Tobit model is used to specifically explore the influencing factors affecting the green development of cities. The findings suggested that the green development efficiency of selected cities falls in the middle to high range and that the efficiency varies among all cities in the Yellow River Basin. Likewise, technical efficiency improvements and technological progress drive development efficiency, and the former contributes more to green development. However, financial development, energy structure adjustments, and environmental regulation can strongly contribute to the green development of cities, and each influencing factor has obvious temporal and regional differences. This paper proposes appropriate policy suggestions to promote the coordinated development of the economic development and environmental protection of the Yellow River Basin.

Dynamic evaluation of low-carbon development in China's power industry and the impact of carbon market policies

At the 75th session of the United Nations General Assembly, China clearly put forward the goals of "carbon peak" in 2030 and "carbon neutrality" in 2060. Achievement of carbon targets. Therefore, the goal of this paper is to analyze the low-carbon development level of China's power industry and study the impact of carbon market policies on the low-carbon development level of the power industry. Based on this, this paper first constructs the low-carbon development evaluation index system of the power industry around the connotation of low-carbon development in the power industry and uses the global principal component analysis model to measure the low-carbon development level of China's power industry. Then, the dynamic change trend and spatial distribution characteristics of the low-carbon development level of China's power industry are analyzed using kernel density estimation and the K-means clustering method. Finally, propensity score matching and difference-in-difference methods are used to analyze the impact of carbon market policies on the low-carbon development level of China's power industry. The results show that, first, the low-carbon development level of China's power industry generally shows an upward trend and a polarized development trend. Second, the low-carbon development level of China's power industry has regional effects and gradient effects. The low-carbon development level of the power industry from high to low is the eastern region, central region and western region. Third, carbon market policies can help improve the low-carbon development level of China's power industry. The research results provide some reference and guidance for the evaluation of the low-carbon development level of China's power industry and the improvement of carbon market policies.